6 Skills to Prepare Your Gen Z Employees for Success at Work

The makeup of the workforce is changing, with younger generations taking up an increasing percentage of workers. According to Robert Half, in 2020, 80% of the workforce will be post-boomer and more than 20% will be Gen Z. But who exactly are these newest entrants to the workforce and how can companies prepare for them?

Members of Gen Z are those born roughly between 1995 to 2010. According to McKinsey, they are “true digital natives: from earliest youth, they have been exposed to the internet, to social networks, and to mobile systems. That context has produced a hypercognitive generation very comfortable with collecting and cross-referencing many sources of information and with integrating virtual and offline experiences.” Check out how L&D leaders at Culture Amp and other Silicon Valley startups are training their Gen Z workforce to be leaders of tomorrow.

Other factors that define Gen Z according to McKinsey’s research include a demand for personalization and a desire to understand what’s going on in the world around them. Given their lifelong immersion in the digital world, it’s not surprising to hear that members of Gen Z are self-learners who feel more comfortable absorbing information online rather than in traditional styles of learning.

Give your Gen Z employees the learning experience they crave. Learn how an online Udemy for Business subscription can support self-paced learning on the job.

In this post, the first in our series on Gen Z in the workplace, we’ll explore some of the characteristics of Gen Z that are likely to influence their behavior at work and how to best support them as they enter the workforce. Stay tuned for a future post that will explore Gen Z’s learning preferences and what this means for your L&D offerings.

The top skills to help Gen Z employees succeed on the job

1. Communication 

Despite the fact that they’ve grown up in a digitally connected world, the majority of Gen Z employees actually prefer to communicate at work via face-to-face conversations according to the research by Robert Half cited earlier. Members of Gen Z tend to thrive on genuine relationships with authority figures, so it’s important for managers to prioritize communicating in an authentic and meaningful way with their youngest employees. 

Growing up with the instant feedback loop of media has led Gen Z to expect regular feedback on their performance. According to a study by Future Workplace, 25% of Generation Z desire regular feedback while only 3% want annual performance reviews. This means that managers will need to prepare to engage their Gen Z employees with regular check-ins about their performance.

Recommended Udemy course: Brush up your communication and feedback skills in Cross-Cultural Communication: How to Flex Your Style and Feedback is Fuel.

2. Fostering diversity & inclusion

According to Deloitte’s research, Gen Z is the most diverse generation in the nation’s history. The report explains that “Diversity matters to them through many dimensions, not just isolated to race and gender, but also related to identity and orientation.” Additionally, Deloitte finds that inclusion and diversity are critical factors Gen Z considers when considering a job offer.

Consider how you can promote diversity and inclusion to your candidates and employees — are your job descriptions and careers site designed to be inclusive? Is your interview panel comprised of people from different backgrounds? How does your company foster inclusion among employees?

Unconscious bias can affect many workplace decisions such as who to hire and promote and even the language that’s used in performance evaluations. By making your managers aware of their biases, you can help limit their effects and promote a more diverse and inclusive workplace. 

Recommended Udemy courses: Learn more about fostering diversity and inclusion in Unconscious Bias: Fuel Diversity and Become a Better You and Diversity, Equity, and Inclusion: A Beginner’s Guide.

3. Coaching & mentoring

Mentoring ability is the second most popular trait Gen Z employees value in leaders, according to Robert Half’s research. While Gen Z employees are independent and have entrepreneurial inclinations, they still appreciate having support to guide them along the way. 

When it comes to professional development, Deloitte finds that “Gen Z’s preferred career development is to have diverse and entrepreneurial opportunities with the safety of stable employment, and they may offer more loyalty to companies that can offer this.”

While taking a coaching or mentoring approach may not come naturally to all managers, it is a skill that can be developed. Udemy instructor and leadership coach JeanAnn Nichols explains that managers can develop a “mentor mindset” by cultivating curiosity and a growth mindset during interactions with their direct reports.

Recommended Udemy course: Help your employees improve their mentorship skills in Be a Great Mentor: A Practical Guide to Mentoring.

4. Career exploration

For most people, the early years of their career are a time for exploring their options and discovering their strengths and interests. And with the fluidity of skills coming and going as we undergo a digital transformation, we are moving toward a “role-less” future of work. Career paths will be defined by constant reskilling and movement into new kinds of roles, so instead of job-hopping to new companies, employees will be constantly “role-hopping” within their company. According to the 2019 Global Millennial Survey from Deloitte, 25% of Gen Z believe employers should be responsible for preparing workers for technological changes.

This means employers have two big responsibilities to their Gen Z employees: to help them explore the career opportunities that are available to them and to help them ensure their skills are up to date so they can adapt to technological developments.

Recommended Udemy course: Help your employees explore their career options in Career Navigator: A Manager’s Guide to Career Development.

5. Business writing

Gen Z employees have grown up writing texts and social media posts, but they may not be familiar with the conventions of writing in a business setting. Abbreviations, emoji, and a lack of punctuation may be commonplace in social media, but they can come across as unprofessional in workplace communications. For example, Udemy’s 2019 Workplace Boundaries Report found that 37% of employees believe their coworkers are too informal on chat and messaging.

Employers may find that offering training on written business communication helps Gen Z employees adapt their writing style to the work environment.

Recommended Udemy course: Support your employees’ professional writing development with Master Business Writing Skills.

6. Focus & productivity

Gen Z employees have grown up with the internet and social media — the answers to questions are just a quick search away and their phones are constantly available as sources of information, entertainment, and companionship. But phones are also a source of distraction. The Udemy 2018 Workplace Distraction Report found that 69% of younger workers say checking their personal device interferes with their concentration. Millennials and Gen Z are also the most likely generations to describe themselves as distracted at work: 74% say they’re distracted, 46% say it makes them feel unmotivated, and 41% say it stresses them out. 

Employers have an opportunity to help Gen Z employees by providing training in how to limit distractions and improve productivity. In fact, the Udemy Workplace Distraction Report found that 70% of employees believe that training can help people learn to block out distractions and become more productive.

Recommended Udemy course: Help your employees boost productivity in Do More Stress Less.

The workforce is already transforming with the influx of Gen Z employees. By understanding their unique needs, you can help prepare your managers for the new generation and create a company where Gen Z employees can thrive. Check out how L&D leaders at Culture Amp and other Silicon Valley startups are training their Gen Z workforce to be leaders of tomorrow.

5 AI Trends to Watch in 2020

What AI trends should you keep an eye on? As Udemy instructors and the founders of SuperDataScience, a common refrain we hear from students and companies is that there are too many artificial intelligence trends to keep up with — how do you know which one matters and will still be in use in five years? If you train your team of data scientists in machine learning, will it have a lasting impact on the business? What other businesses are using this technology, and is it working for them?

We recently hosted a webinar on Udemy for Business that cuts through the AI hype and focuses on which technologies companies and individuals should consider adopting in the coming decade. As AI becomes ubiquitous, it can also be challenging to know which buzzword is worth the investment. Here are 5 AI trends that we’re telling students and businesses to follow in 2020 and beyond. 

Find out how you can train your team on the latest AI skills with a Udemy for Business subscription.

5 AI Trends to Watch in 2020

1. Robotic Process Automation (RPA)

Robotic Process Automation (RPA) is a simple AI technology, but also one of the most disruptive. Imagine your job requires you to perform a high-volume, repetitive task on the computer. Maybe it’s related to invoicing a client. This requires you to open an email attachment, copy data from the attachment into a CRM database, then grab related data from a different database, and send that new data in an email reply. The same task is done multiple times per day and prevents you from working on projects that you’re more interested in.

Robotic Process Automation is a type of software robot that can take on these manual repetitive tasks. Using the example above, an RPA tool would read the email, open the attachment, copy data into a CRM, get data from a different database, and even send the email reply. If there were an escalation requiring human intervention, the RPA would notify the employee to step in. In a nutshell, RPA removes mundane tasks and frees up people for more exciting work.

Key RPA applications: Invoicing, billing, payroll processing, data extraction and aggregation, shipment scheduling and tracking.

RPA case study: Financial services company Vanguard has $5.6 trillion in global assets under management. It uses RPA to perform certain straightforward trading tasks, “when x happens, do y,” etc. The RPA tools have not diminished the need for human traders. Rather, the combination of the two allows humans to work on more complex jobs, thereby creating a better overall service for Vanguard clients. 

Suggested course on Udemy: Artificial Intelligence for Business

2. Natural language processing (NLP)

Natural language processing applies machine learning models to teach computers how to understand what is said in written and spoken language. Because of its rich and growing applications, natural language processing is arguably one of the top branches of AI in overall economic value. It’s becoming especially popular as consumers adopt voice interface technology like Google Home or Amazon Alexa. Instead of writing or interacting with graphics on a screen, we talk to devices that can understand our casual language. 

Natural language processing can be divided into two sub-applications: 

  • Natural language understanding, which consists of a machine reviewing a text and accurately interpreting its meaning.
  • Natural language generation, where a system generates a logical response to a text or input.

Key natural language processing applications: Sentiment analysis, chatbots, machine translation, automatic summarization, auto video captioning.

Natural language processing case study: YouTube uses Natural Language Processing technology in many applications across the platform. One use most people will be familiar with is auto-generated captions. Speech recognition software ingests a YouTube video and returns the output of video captions. This technology first went live on the site in 2009 and has been fine-tuned and translated across a dozen languages thanks to the growing dataset available to the company — the videos uploaded every day to the platform. 

Suggested course on Udemy: Deep Learning and NLP A-Z™: How to create a Chatbot

3. Reinforcement learning

In its most simple explanation, reinforcement learning is an input- and output-based system that trains itself over trial and error to reach a certain goal, while using a reward system to reinforce its decisions. So, an AI takes as input some data and returns as output an action. When it does this correctly, it receives an award. The better it performs its task, the more rewards the system is given and vice versa. 

Imagine training an AI agent to predict whether an object is a carrot or a wood stick. If it accurately predicts a carrot, we give it a reward of plus one and if it erroneously predicts the wood stick, we give it a reward of minus one. 

Key reinforcement learning applications: Personalized recommendations, advertising budget optimization, and advertising content optimization.

Reinforcement learning case study: Alibaba, the popular Chinese e-commerce site, leveraged reinforcement learning to increase its return on investment for online advertising by 240% without increasing the advertising budget. In a research paper, the Alibaba team explains how reinforcement learning was used to optimize a sponsored search campaign by creating a bidding model for impressions each hour and performing real-time bidding accordingly. In the paper, you can see how this reinforcement learning system outperformed the benchmark of the other bidding systems. 

Suggested course on Udemy: Deep Reinforcement Learning 2.0

4. Edge computing

With smartphones, smartwatches, and Internet of Things-enabled devices in our homes and cars, there is a lot of data flying around. Processing all this data is a complex exercise requiring information sent to cloud computing machines based on servers hundreds or even thousands of miles away. Lose a Wi-Fi connection and your smart device becomes a very expensive brick. 

Enter edge computing, which takes the servers and data storage required for devices to access their smarts, and puts it directly on the device. This is real-time data processing that results in much faster computing responses and avoids network latency. If cloud computing is big data, edge computing is instant data. 

Another type of edge computing is performed on nodes. An edge computing node is a mini-server close to a local telecommunications provider. Using a node creates a bridge between cloud and local computing options. This technique results in lower costs and less time spent on data computation, making for a faster experience for the consumer.

Key edge computing applications: the interconnection of more devices, growth of Internet of Things technology.

Edge computing case study: Consider the Amazon Echo on your kitchen counter. The Alexa assistant technology on the Echo is not actually in the device. It recognizes the “wake-word” of “Alexa,” but the Echo must connect to Wi-Fi to process your audio query via a cloud-based server, no matter how simple or complex the request is. 

With a specially designed AI chip enabling edge computing, Amazon hopes to resolve simple questions such as “What time is it?” directly in the device, reducing the response time and providing a better user experience.

Suggested course on Udemy: Learn BERT – most powerful NLP algorithm by Google

5. Open-source AI frameworks

The programming world is built on libraries and frameworks that take redundancies out of everyday coding work. For example, JavaScript libraries like React and Angular help developers build websites quickly and with fewer errors since they supply common components. Likewise, open-source AI programming frameworks have allowed the development of AI technology to expand quickly. By democratizing these AI tools to programmers, data scientists, and technical teams of all levels, AI research is not exclusive to Silicon Valley professionals or Ph.D. candidates. 

Thanks to the libraries and platforms built for AI functionality, highly complex artificial intelligence algorithms, models, pipelines, and training procedures are now accessible to those with an interest in the technology. Say you want to build a computer vision-based project, some open-source AI frameworks will allow you to implement a computer vision system with very few lines of codes. 

Key open-source AI framework applications: Prototype and train complex AI algorithms; build pipelines to define, optimize, and assess an AI model; automate the training of a reinforcement learning module; build neural networks with just a few lines of code. 

Open-source AI framework case study: TensorFlow is an AI framework developed by Google that can be used across any branch of artificial intelligence. With TensorFlow, you can build a convolutional neural network for image classification. Some TensorFlow modules will also help simplify the creation of NLP systems.  This is among the most popular AI frameworks, especially since the development of TensorFlow 2.0, which allows users to create even more advanced AI systems.

Suggested course on Udemy: A Complete Guide on TensorFlow 2.0 using Keras API

There are many more open-source frameworks and libraries helping the advancement of artificial intelligence applications. We dive deeper into frameworks as well as the real-life business use cases of these AI trends in the full webinar, so be sure to watch the webinar here. 

Find out how you can train your team on these hot AI skills with a Udemy for Business subscription.

New Data: Inclusive Decision-Making Drives Engagement

Can you name the three things your company is doing to ensure your decision-making is inclusive? If you can’t, then it’s likely your decision-making process is unconsciously exclusive. However, there are key steps you can take to right this issue and enhance your organization’s employee experience.

Four years ago, the team at Culture Amp partnered with Paradigm to release the industry’s first-ever Diversity and Inclusion Survey. The survey is designed to analyze the employee experience across seven measures of diversity and inclusion. 

In our most recent Workplace Diversity, Inclusion, and Intersectionality report, our data showed that inclusivity in decision-making is a top driver of engagement. This means improving the inclusiveness of the decision-making process can create a more engaged workforce.

Here’s how you can get a head start to ensure inclusive decision-making at your company, and in turn, support a highly engaged and diverse organization.

What the data on decision-making tells us  

Over 165 organizations from around the world, representing a range of industries including Technology, Non-Profit, Education, Media, and Professional Services have used the Diversity and Inclusion Survey to collect feedback through the Culture Amp platform. 

The analysis of our diversity and inclusion data showed: 

  • Of our top ten Diversity and Inclusion drivers most highly correlated with engagement, three of these questions are about decision-making: 
    • I am satisfied with how decisions are made
    • Perspectives like mine are included in the decision-making at my company
    • I am included in decisions that affect my work
  • 21% of all ideas or “inspirations” for addressing specific focus areas related to employee experiences on our Diversity & Inclusion survey were related to decision-making.
  • Decision-making was the lowest-scoring factor in Culture Amp’s 2019 Diversity & Inclusion benchmark by favorability. Just 59% of respondents answered the questions in this factor favorably.

Take small steps to make decisions more inclusive

One of our key insights from the report suggests that tailored small wins can lead to greater change. Oftentimes, improving employee engagement or diversity and inclusion can seem like a daunting task. To solve them, people think big reforms are the answer. However, small steps can have a great impact as well.

Create a transparent decision-making framework

We found that when teams take a “small wins” approach tailored to their unique needs, they can expect to see a 4–8% uplift on scores related to their areas of focus. We define a small win as a concrete, implemented outcome, such as increased transparency in decision-making. 

Culture Amp’s in-platform Inspiration Engine is essentially a library of small wins. It is a curated list of micro learnings — easy-to-digest ideas and actions that our customers and others in our People Geek community have used with their employees.

For example, one of our top diversity and inclusion inspirations for decision-making is creating a transparent decision-making framework.

Once you’ve documented your decision-making process, you’re able to share it with everyone in your company. You can include the objectives, risks, and alternatives, and anything else that impacted your decision. People get insight into what options and consequences were considered and will be more informed to ask thoughtful questions with context.

Udemy’s courses on Mastering Your Decision-Making Skills and Diversity & Inclusion: A Beginner’s Guide are great primers for those looking to learn how to create a more inclusive decision-making process.

Get more diverse voices in the room

One of the main benefits of having a diverse workforce is to ensure you have diverse decision-making processes. If a homogenous group of people consistently makes all of the key business decisions, you’re missing opportunities to make smarter, more innovative moves as a company.

To get a range of different opinions contributing to decisions, consider encouraging your teams to have more skip-level meetings. A skip-level meeting occurs between a people leader and someone (or a group) two levels below them — without the manager who those people report to attending. 

When a level of management is skipped, it can allow employees to have conversations they wouldn’t normally be involved in, and also communicate with higher-ups who don’t often hear their opinions.

Moreover, getting a more diverse collection of voices into the room where decisions happen can result in more effective and inclusive initiatives in other areas.

Explore more data on workplace diversity, inclusion, and intersectionality 

In addition to the importance of decision-making on inclusion and engagement, our 2019 Workplace Diversity, Inclusion, and Intersectionality Report provides data on employee representation and experience, and details the importance of considering intersectionality when reading data related to diversity and inclusion. 

The more organizations can consistently measure demographics of race, ethnicity, parental status, or disability in addition to the traditionally gathered demographics of gender and age, the richer our data and insights will become.  

Check out the full report by Culture Amp for more information on the state of diversity and inclusion in the workplace today.

How Deep Learning Can Predict If Your Customer Will Buy Again

Online stores are a gold mine of data for applying AI and deep learning in the retail and e-commerce business. Each data point builds a detailed understanding of customer habits. To harness this data, the most successful e-commerce companies leverage not only data science basics, but also deep learning techniques. Deep learning and AI can provide business-critical predictions like whether or not a customer will buy again. 

Any business can capitalize on deep learning techniques as long as two criteria are met: 

  1. Access to a large volume of data
  2. Investment in the infrastructure and the people who can make sense of that data

Luckily, ad providers like Facebook and Google allow small businesses to collect data with almost zero effort simply by pasting a script on their website. Additionally, Google Ads and Facebook Ads provide access to deep learning-based purchase intent models to all business customers on their platforms. So, although your team may not build the deep learning model, it can leverage the technology developed by these tech giants. This democratization of AI has reinvented marketing by creating new subfields like customer analytics. It’s also positioned machine and deep learning as a key player in e-commerce for the upcoming years. In the Udemy course Customer Analytics in Python we explain how to leverage deep learning to glean personalized customer insights.

What data does the e-commerce industry use?

In the e-commerce world, customer data is abundant. Companies can leverage all types of data for a given customer — from demographics and geolocation to income range. Smartphone apps and cookies embedded into websites can recognize customers’ devices and build profiles for brands and ad platforms based on customer preferences. 

Some of this data can even be used as a proxy for more important indicators to derive customer insights. For instance, a customer using the latest iPhone model can be assumed to make a higher income bracket than a customer using a five-year-old iPhone model.

Rich data like this allows companies to fine-tune data insights to better understand and serve customers. However, a data analyst can slice and dice the data all day, but that would not allow them to generate a reliable prediction on customers’ future purchasing behavior on an individual level. This is where a nuanced use of data — built on deep learning algorithms — can play an important role. How can companies take advantage of this information about their customers? Enter deep learning — an outstanding resource for predicting purchase intent.

How does deep learning inform purchase intent?

As a subfield of artificial intelligence, deep learning has been instrumental in some of the most transformational products available today. Self-driving cars, facial recognition, and translation apps are just some examples of consumer-facing offerings based on deep learning techniques that are already available. 

But the applications of deep learning are not reserved for high-tech products only. In fact, many e-commerce companies are empowering their marketing and sales teams through deep learning technology. These techniques are used most often when predicting purchase behavior at an individual consumer level.

This brings us to purchase intent. Purchase intent shows whether a customer is ready to purchase a product. For example, when you enter a board game shop, your purchase intent to buy a board game is high simply because of the nature of the store you visited. On the other hand, visiting a grocery store implies that you want something to eat, but doesn’t reveal much about your purchase intent to buy eggs, for instance. Fortunately, in the world of e-commerce, the wealth of data available to companies lets them uncover detailed customer preferences and profiles. 

By using data science and deep learning practices, we can quantitatively analyze purchase intent. In mathematical terms, purchase intent is the probability that a consumer will buy a product or a service. With a mathematical representation of purchase intent and enough data points about our customers, we can create deep learning models that show with near certainty whether a customer will buy our product. 

In the Udemy course Customer Analytics in Python we show you how to do this. We outline how to pair datasets with deep learning techniques to predict the likelihood of a repeat purchase from a customer. In the example used in the course, we build a dataset from the real data of a popular audiobook app. By using metrics such as number of purchases, minutes listened, last login date, reviews, and so on, we predict the probability that a customer will purchase another audiobook from the platform.

What is most intriguing, though, is that deep learning models can make a specific prediction for each customer. If you use business intelligence dashboards or other everyday data analysis tools, you would only get a general picture, but never predictions on an individual level. 

Such advanced insights are reached through ‘will buy’ or ‘won’t buy’ predictions (usually represented by 1s and 0s, respectively). However, on the back-end, we have purchase intent models that actually output a probability (e.g. we are 67.24% certain that Alice will buy again in the next 3 months; so we presume that she will purchase again). Such findings could then be used in various ways — most notably for marketing purposes. We could stimulate specific people with higher discounts, influence others with more features, and so on. This type of insight also helps marketers decide how to best allocate their advertising budget.

3 ways the retail industry uses deep learning

Measuring, evaluating, and predicting a customer’s purchase intent isn’t the only use for deep learning in the retail industry. Other deep learning applications include:

1. Predicting churn rate

Churn — AKA attrition — is a term used for subscription businesses to measure the number of people who unsubscribe from and stop using a service. You may have also seen churn used to describe the rate of employees leaving their jobs at a given company. This concept is the opposite of a growth rate. For a company to succeed, its growth rate should be higher than its churn rate. As retailers predict purchase intent, which is often used as a proxy for growth rate, they also will want to forecast the churn rate.

2. Recommendation algorithms 

Sites like Netflix, Amazon Prime, and, even Udemy are great at acquiring new customers. In order to maintain a low churn rate, these companies must understand how to retain customers by keeping them satisfied and engaged with the product. Relevant recommendations have become an essential tool to keep customers engaged. These deep learning recommendation algorithms use data from the customer’s habits on the site and with product use to recommend shows, products, or courses.

3. Fraud prevention 

While financial fraud may attract the most headlines, tiny fraudulent activity happens all the time and can disrupt your customers’ interactions with your brand. There are fake likes on social media platforms, fake emails sent by lookalike companies, fake reviews to boost a product’s profile, and fake social media profiles to make a community look more popular than it is. All of these types of fraud can be identified or prevented by leveraging deep learning algorithms. Companies that employ deep learning to prevent fraud of all kinds do so in order to improve or maintain a customer’s positive associations with their brand. 

Is your mind racing with ideas for how to use deep learning in your e-commerce business operations? Join us in the Customer Analytics in Python course and unlock data science techniques that will help your company keep customers happy by understanding which products and services matter most to them.

Preparing for the Future of Work: Build vs. Buy Talent?

Preparing for the future of work is a top priority of CEOs and business leaders. In the past, organizations tended to lay off employees and hire new ones when they wanted to move the business in a new direction. However, with tight labor markets and the fast pace of technological development, business leaders are beginning to recognize that retraining existing talent for new roles is more effective than competing for scarce talent. While reskilling for future skills requires long-term planning, the cost of disruptive layoffs and hiring can be more expensive than providing continuous training for employees.

In this post, we’ll consider why it makes sense to build vs. buy talent by reskilling your existing workforce and look at a few of the ways leading companies are approaching this strategy.

The case for building your own talent

One of the biggest reasons companies are prioritizing building their own talent is the cost. Josh Bersin calculates that it can cost as much as six times more to hire from the outside than to build from within. In a recent article on this topic, Bersin writes, “It’s quite simple: the economy has created a bidding war for people with critical skills, increasing the cost and risk of hiring from the outside.”

It’s not just about the price tag, though. Companies that dedicate resources to reskilling existing employees build a learning culture while promoting career development and employee engagement. This will be especially important as younger generations enter the workforce. A study by Robert Half found that 91% of Gen Z say professional training is an important factor when considering employers.

Our research shows that many companies are already beginning to reskill employees. A significant percentage — 59% — of L&D leaders said they’ve reskilled 10–20% of their workforce in the last year, and 64% of organizations already have an informal or formal reskilling program. And employees show an increasing openness to reskilling: 29% of L&D leaders surveyed by Udemy said their employees are excited to learn new skills and only 12% were afraid they would lose their job. 

According to research by McKinsey, there’s a clear connection between company performance and willingness to reskill employees: an overwhelming percentage — 63% — of high-performing organizations favor in-house training for strategically important analytics roles.

Find out how Udemy for Business can help your organization build your talent internally with continuous online learning programs.

Let’s explore how companies are taking the “build” approach by reskilling their existing workforce.

4 ways L&D teams are building talent internally

1. Booz Allen Hamilton introduces learning at scale

Booz Allen Hamilton, a management and technology firm, strives to be a game-changer in the data science field. Aiming to help their clients harness data in ways they’ve never used it before, they set a goal to employ 5,000 data scientists over 3 years. Due to the talent shortage, they knew they couldn’t rely on hiring data scientists externally and instead decided to double down on training existing employees for new data science roles. In order to meet this goal, the L&D team at Booz Allen Hamilton created a personalized learning program at scale.

The lean L&D team played the role of “learning experience architects.” They developed an ecosystem with curated content, technologies, and platforms using Udemy for Business and Degreed. This program relied on four tactics to scale learning: online assessments to tailor learning, personalized online learning pathways, a blended learning model that focuses classroom time on hands-on projects, and mentor circles that guide the learning journey. To learn more about this program, see How Booz Allen Hamilton Is Winning the War on Talent.

2. Skills mapping helps L&D identify gaps

One important aspect of building talent from within is understanding which skills will need to be updated. Josh Bersin writes, “Every employee in the company should understand what skills are most in-demand and how they will be used, so they can prepare themselves for jobs of the future. This type of transparency creates excitement and empowers people to join, contribute, and reinvent or transition their roles.”

While all employees will want to have a sense of which skills they’ll need in the future, L&D teams need to have a detailed understanding of their company’s gaps and how to address them. Skills mapping is a visual representation of skills needed to perform desired roles as compared to the existing skill base of an organization’s workforce. This exercise helps HR and L&D leaders identify key skill gaps. Skills mapping, also called competency mapping, can be done for individual career planning as well as future skills training for an entire workforce.

As organizations tackle workforce reskilling, they’re beginning to hire experts to help map existing workforce skills and future skills. JPMorgan is working with the Massachusetts Institute of Technology’s Initiative on the Digital Economy to forecast emerging skillsets for its workforce. JPMorgan is also piloting a “skills passport” platform in its IT department. The platform enables employees to assess current skills as well as browse new roles and the necessary training to achieve this next step in their career.

We expect that over the coming years, organizations will take a deeper dive into skills mapping to prepare their workforce for what’s next.

3. Focused Capability Academies are replacing ad hoc training

Companies often use an ad hoc approach for their talent-building efforts, according to McKinsey. They hire new workers equipped with the desired skills or apply ad hoc training when needed. But these quick-fix tactics aren’t enough to transform an organization and continuously keep up with the pace of technology and business change.

According to McKinsey, “While hiring new talent can address immediate resource needs, such as those required to rapidly build out an organization’s AI practice at the start, it sidesteps a critical need for most organizations: broad capability building across all levels.” This is best accomplished by training current employees using in-house capability programs.

We’re beginning to see Capability Academies being implemented at Udemy for Business customers. For example, Publicis Sapient, a business and technology consulting firm, reorganized their organization and learning & development team by “capabilities.” For the artificial intelligence capability, they launched an AI Academy to provide in-depth training for existing employees in AI and data science. See Learning at the Speed of Business.

4. Communities of practice are keeping skills up to date quickly

Josh Bersin reports that the average time required for a worker to gain enough knowledge to successfully reinvent their career due to AI or automation-based job disruption is around 15 months. This means that companies will need to seek many ways of reinforcing and sharing knowledge. To complement in-depth learning offered by Capability Academies, social learning communities are on the rise to support learning on the job.

In the software developer world, for example, industry standards change quickly and are often set by consensus. A group of developers may share a best practice, and the industry will move in a new direction almost overnight. This makes it hard to keep up with best practices and curate the right course material to upskill your team. 

To keep skills aligned with the latest trends in the industry, we see companies increasingly rely on communities of practice. For example, when developers run into a problem with a line of code, they naturally like to collaborate and ask their peers for help. But instead of only tapping the shoulder of their neighbor, they’re creating a virtual community of developers to serve as a collective brain.

Communities of practice aren’t just an organic peer learning effort. Learning & development teams are also creating structured learning around their communities. This might include monitoring Slack for commonly asked questions and creating content for in-person and virtual sessions around these issues. Online courses or lectures can be assigned as pre-work while in-person sessions focus on hands-on practice and discussion.

In addition, social communities are also being used for nurturing soft skills like sharing management tips on how to give feedback or be a good mentor. See How Slack Promotes Social Learning at Work.

As new technologies disrupt the world of work, L&D teams will need to look for ways to reskill their workforce and adapt to these changes. We’ve covered a few of the ways ground-breaking companies are taking the build vs. buy approach to reskilling their workforce. Read more in our 2020 Workplace Learning Trends Report.

Why Splunk Certification is a Top Skill for Data Scientists

Data, like so many other words, is a borrowed word in English. It comes from the Latin datum, meaning gift. In my world of data science, I tell my fellow data engineers that to acquire highly focused data skills, such as earning a Splunk certification, is to become a gift-giver. You’re giving to your career and employer the ability to engage with largely unseen business data and leverage it to reach business goals.

The gift of data is the lifeblood of any organization. It’s the set of building blocks from which emergent business capabilities arise. Every decision, from executive strategy to project management to facility maintenance, revolves around accessing the right data in the right format at the right place and time. 

Think about this question: What is your team’s gift to the company? What if it was the ability to derive immeasurable value from business and machine data? What if you could unlock latent business capabilities that the business didn’t even know it had? What if your team propelled the business to the top of its domain in the marketplace, thwarted cybersecurity breaches before they even happened, and made business predictions? These “gifts” can become a reality with Splunk, one of the top 10 hottest growing technical skills on Udemy from 2016-2019 as noted in the annual 2020 Workplace Learning Trends Report: The Skills of the Future.

Splunk certification data science employees

Who should get a Splunk Enterprise Admin credential?

There are many tools on the market for engaging with the vast amounts of data that businesses generate. Specialized tools exist for data streaming; extracting, transforming, and loading (ETL); artificial intelligence; business analytics, and more. Splunk, though, is a single tool that combines many of these capabilities into one experience. Companies using Splunk in its data stacks can:

  • Consume streaming data from forwarders or network input
  • Transform data using regular expressions to find and replace data elements
  • Build, train, and test AI models using an integrated comprehensive machine learning toolkit 
  • Integrate with open-source tools like Tensorflow
  • Use the Splunkbase community of mostly free apps and add-ons to extend data functionality
  • Point Splunk to remote or local files and directories. For example, any data that someone on the accounting team stores in Microsoft Notepad is still consumable by Splunk.

To earn the Splunk Certified Admin credential proves you or your team can build and manage a scalable Splunk infrastructure. Employees with the certification can help their organizations derive value from the vast amounts of data they are already generating — and likely not using. Certified Splunk Admins have a deep understanding of the architecture that makes Splunk the most powerful “Data-to-Everything Platform,” and how to fine-tune it to make it sing.

Learn how to get your entire data science and IT teams Splunk-certified with a free Udemy for Business demo.

What are Splunk’s use cases?         

In our data-driven world, teams of all sizes must understand how to derive valuable business insights from different types of data. Splunk allows teams to easily and efficiently engage raw data. Saying your business wants to intelligently use data is one thing, but understanding the tactical uses for a tool like Splunk might not be as obvious. Let’s dig into the use cases of Splunk:  

  • Cybersecurity predictions – Do you want to predict when an internal information security breach might happen? Security teams can use Splunk to build predictive data models using the Splunk Enterprise Security and User Behavior Analytics apps.
  • Identify business inefficiencies – The Splunk Process Flow product can analyze data from business processes and discover bottlenecks that might be costing a company money. The tool can correlate processes and group events from disparate systems for a single view of your entire business.
  • DevOps – In DevOps, fast, comprehensive feedback loops are essential. Splunk can help DevOps and Site Reliability teams monitor their releases, build statistical reports and dashboards for successful vs. problematic releases, and give visibility into the whole deployment pipeline. By using Splunk, DevOps teams can discover configuration issues before they become a problem.
  • IT operations – The IT department is a company’s beating heart that keeps it alive and competitive. If critical technical systems go down, the business suffers. Splunk enables teams to monitor every aspect of IT operations. From applications to hardware to networks, Splunk can monitor it all.

Top 10 skills you need for the Splunk certified admin credential

The Splunk Certified Admin credential exam focuses on the foundational infrastructure and architecture that makes Splunk a powerful tool. In my course, The Complete Splunk Enterprise Certified Admin Course 2020, I cover everything you need to know to build and architect scalable Splunk environments for your company. This starts from the basics and moves to a deep dive including the following 10 topics: 

  1. Deploying Splunk in distributed, high availability environments
  2. How Splunk licensing works in distributed environments
  3. Managing users and authentication, including Lightweight Directory Access Protocol (LDAP) and multi-factor authentication (MFA)
  4. Understanding the apps and add-ons in Splunkbase
  5. Creating, modifying, and managing configuration files
  6. Understanding indexes, buckets, and a directory known as the “fish bucket”
  7. Streaming data into Splunk with forwarders, network inputs, and agentless inputs
  8. Configuring Splunk inputs, such as monitoring, uploading, and the HTTP Event Collector (HEC)
  9. Understanding distributed search and indexer clusters
  10. Manipulating raw data, data transformations, and how Splunk processes data
Splunk certification benefits

What’s new in Splunk 8?

In 2018, I outlined the benefits of Splunk and compared it to its competitors. At that time, Splunk was on version 6, and it’s since evolved to version 8. With the newest version comes noteworthy improvements. Some of these include:

  • As of January 2020, the Python Software Foundation will no longer be supporting Python 2, which some of the Splunk internal code is written in. Splunk 8 now offers Python 3.7 support as well as legacy 2 support. 
  • Back-end changes implemented with Splunk 8 now accelerates searches, data models, and employs some security enhancements.
  • New Splunk Analytics workspace makes creating time-series charts easier when using metrics and accelerated datasets.

Splunk is actively developed and has a thriving user and developer community. You can get involved in local user groups, post questions at any level on the Splunk Answers forum, and join other Splunk enthusiasts at the annual worldwide Splunk .conf conference. It is one of the best tools I’ve used for making sense of the staggering amount of data companies see.

Understanding how to derive business value from data is a gift not only to your team but also to your resume. Ready to fill your data science toolkit with one of the most diverse multi-tools available in the industry today? Start the path to bona fide Splunk expert by passing the Splunk Enterprise Certified Admin exam.

Become an L&D Hero with Learning Playbooks at Udemy for Business

Here at Udemy for Business, one of our goals is to make our customers L&D heroes and expand the impact of learning at their organization. We know all the hard work that you as L&D professionals put into your programs and we want to enhance your efforts with proven material from Udemy’s own Learning team. We’re excited to announce Learning Playbooks as one of the practical ways our customer success team at Udemy for Business partners with you to achieve your organization’s learning goals — elevating you as an L&D hero. 

We understand most L&D professionals are working with limited time and resources. As L&D teams shift from content creation to content curation, we’ve developed Learning Playbooks to help you turn Udemy’s online courses into multi-touch, blended learning programs designed to enhance critical stages of your employee lifecycle.

The transformation of L&D in recent years has been well documented. For example, according to an article by Deloitte on the future role of learning, the highest performing companies are rethinking what L&D strategically means for their business. They take a learner-first approach to learning and spend time building a culture of learning that is a company-wide responsibility.

Become an L&D hero: Implement  Learning Playbooks and an effective blended learning program with a Udemy for Business subscription. Learn more here.

Quick recap: What are the benefits of blended learning? 

Blended learning is the practice of learning in multiple modalities. This can include online learning and classroom training, which are the two modalities the Learning Playbooks primarily focus on. 

There are many benefits to blended learning. It helps reinforce learning by giving employees the opportunity to practice and reinforce what they’ve learned through spaced repetition. When you use a blended learning approach, it means people can learn online first at their own pace to become familiar with new concepts. You can then focus valuable classroom time for people to practice and apply what they’ve learned. For many adults, the social learning that occurs in the classroom with their peers is also important to building their understanding of the topic.

For more on the benefits of blended learning, see Reimagining Blended Learning: Best Practices from 8 Leading Companies.

Udemy for Business Learning Playbooks: A closer look

Mapping learning along the employee lifecycle

Our initial Learning Playbooks focus on topic areas from the employee lifecycle — the compilation of all the processes, trainings, and tools used to support employees throughout their tenure with a company. We know HR and L&D teams are often tasked with creating learning programs to support employees throughout this cycle. However, creating these programs can be challenging and time-consuming, so we decided to create playbooks to help support our HR and L&D leaders launch effective learning programs.

Our first two Learning Playbooks focus on Continuous Feedback and Manager Training. We’ll be adding new Playbooks in the near future that address other key areas of the employee lifecycle, such as Career Development and Diversity & Inclusion.

In the Continuous Feedback Learning Playbook, we give suggestions for how to create a culture of feedback in your organization. It starts with developing a shared understanding during a workshop with employees. Once everyone has a common vocabulary and knowledge of feedback, you can incorporate the feedback framework into your manager trainings, one-on-one meetings, and performance reviews to keep the learning going.

The Manager Training Learning Playbook is designed to help you run a year-long manager training program using a combination of online courses and short, social learning workshops we call Manager Labs.

In early 2020, we’ll also have Learning Playbooks to help you design a career development program in your organization that facilitates employee self-reflection, manager coaching, and mentorship, as well as a Diversity & Inclusion playbook to help you roll out learning programs to reduce unconscious bias in your organization and foster inclusion and belonging.

Who are the Playbooks for?

We’ve designed the Playbooks to be flexible enough to be used by a wide range of L&D professionals. If you’re an HR or L&D leader at a small organization working alone or with just a few colleagues on your team, the Playbooks are designed to give you all the tools you need to roll out a full learning program without needing to devote many hours to planning and creating one yourself. 

If you work at a large organization, you can use the Playbooks to supplement your existing programs or scale them globally by sharing them with local team or office leads.

When it comes to using the Playbooks, they function like a menu at a restaurant. You can follow them section by section (the equivalent of a prix fixe menu) or you can “order à la carte” and just choose the parts that are most relevant to you. You can use them to supplement your existing trainings and programs, on their own as an entire program, or any other way you’d like. And the courses we include are suggestions, so you can easily switch out the courses we recommend and replace them with other courses.

How are the Playbooks designed?

All of the playbooks are laid out in the same four steps: Communicate, Facilitate, Evaluate, and Integrate.

Each Playbook is designed with four parts: Communicate, Facilitate, Evaluate, and Integrate

The first step — Communicate — is all about planning the program, getting all of the logistics in order, and then communicating it with the organization, assigning the course, scheduling trainings, and generating excitement. We provide project planning templates and sample emails you can use to make the job easier. The Facilitate section includes a facilitator guide with a lesson plan, presentation deck, and all the materials you’ll need to run a live blended training based on the course or courses recommended in the Playbook. The Evaluate section offers strategies for evaluating the impact of your learning programs. It’s not just about evaluating the live training, but measuring the impact overall of your program on employee performance. The Integrate section provides ideas for folding the learning into your work culture and ensuring that people feel like they’re applying their learning over time.

An overview of what you’ll find in the Facilitate section of the Playbooks.

Some of the Playbooks use the Learning Paths feature in Udemy for Business, too. Rather than assigning a full nine-hour Manager Training course, you can use Learning Paths to assign just the relevant sections of multiple courses. If you’ve never done this before, don’t worry — we give guidance and instructions on how to set that up. Read more about Learning Paths on Udemy for Business

Applying the Playbooks across the employee lifecycle

If you are a Udemy for Business customer, you can work with your customer success manager to determine which Playbooks would work best for your organization and how to adapt them to your unique needs. Your customer success manager can also work with you to make the most of your Udemy subscription by setting up Learning Paths for your employees and tracking their progress.

Everything you’ll find in the Playbooks is based on work that our Learning team has done here at Udemy. We’ve tried and tested the concepts and distilled our learnings to make it as easy as possible for you to put them into practice right away. And remember — this is intended to be a guideline, but ultimately you can make the decisions about which elements will work best for your organization.

Learn how you can partner with our Udemy for Business customer success team to roll out effective online learning throughout the employee lifecycle. Request a demo.

Why Swift UI Should Be on the Radar of Every Mobile Developer

Swift UI is a user interface framework intended to make it easier to build Apple platform apps in the Swift programming language for mobile development. It was introduced at the annual Worldwide Developers Conference (WWDC) in 2019, alongside many new APIs and frameworks, all intended to grow the base of mobile developers fluent in developing for Apple products. As the Cupertino-based company explained, “Swift UI is an innovative, exceptionally simple way to build user interfaces across all Apple platforms with the power of Swift.”

As Apple plans for the next decade, this new UI framework is Apple’s effort to make iOS development more approachable for beginner mobile developers. Though Swift UI is still in its infancy, its potential to shift how Apple apps are developed is so significant that we mobile developers should start to take note of it. Job descriptions requiring Swift UI expertise are likely to appear in the next few years.

Why is Apple prioritizing Swift UI?

The Apple App Store of today looks very different from that of 2008 when it was first introduced to the world. With older Apple product models (iPod Touch, first-generation iPad, etc.) still in use today, there are dozens of screen sizes accessing content from today’s App Store. Auto Layout has long been the default Swift system for managing layouts on various screen sizes and orientations. But with so much fragmentation in the device landscape, mobile developers have been asking for a much simpler and more intuitive way of building apps that can scale across all Apple devices. This is why Swift UI has entered the scene, with features including:

  • Drag-and-drop code creation: Using Swift UI, developers can drag a button or other component from the object library and drop it onto the canvas. Swift UI automatically writes the necessary code. This drag-and-drop method is even applicable to attributes like font weight. 
  • Vertical-Horizontal-Z Axis Stack: The VHZ stack lets developers create complex designs simply by dragging and dropping elements in orientations either vertical to, horizontal to, or along the Z-axis of other elements. It’s similar to building within rows or columns, with no manual coding required. This is akin to using the Bootstrap library to build complex interfaces for web design. 
  • Reusable UI components: Once you’ve created layouts in Swift UI, they can be reused throughout your app. For example, if you’ve built an appearance comprised of a photo left-justified with a precise caption design to the right of the image, that component can be reused by extracting a new subview.
  • Build across Apple platforms: With Swift UI, Apple’s made it easier to build across Apple platforms like WatchOS, TV OS, and macOS by using the subview components made in one app across other apps.

How will Swift UI change mobile development?

2019 saw the popularity of declarative programming skyrocket, mostly thanks to the rise of React, one of the most popular front-end frameworks used today. Much of the excitement and expertise React developers have for the framework’s functionality has made its way to the world of mobile development. Other examples include Google’s shiny new cross-platform UI framework, Flutter, as well as the Kotlin-based JetPack Compose. React, Flutter and JetPack Compose all use a declarative style for building UIs and managing state.

With Apple entering Swift UI into the ring, we’re moving further into the declarative world for mobile development. Hopefully, with continued investment and development into Swift UI, it will become a more enjoyable way of creating iOS apps and adopted by the next generation of iOS developers. I believe the simpler syntax and more straightforward state management will encourage more people to pick up Swift and iOS development.

Swift UI vs. Flutter

Flutter is a UI framework developed by Google to build native cross-platform apps using the Dart programming language. It’s been widely embraced by mobile developers and ranked as one of the most loved frameworks in the latest StackOverflow survey. Having taught courses in both Swift UI and Flutter, I’ve seen many similarities between the two.

These similarities include the use of a declarative style of programming, easily customizable components, and simple implementation of animations. However, those similarities end when it comes to platform use. Flutter is used to create native cross-platform apps. A developer can use Flutter to build Android apps, iOS apps, web apps, and even desktop apps for Linux, Windows, and Mac. Swift UI, though, can only be used for apps in the Apple ecosystem.

What does Swift UI mean for my current skills?

Does Swift UI mean that your existing knowledge of Swift is irrelevant? Not at all! Swift UI is currently only supported on devices running iOS 13 or later. It won’t serve as the primary tool in your tool belt just yet.

Apple likes to take things gradually, which we saw in the transition from Objective-C to Swift many years ago. So, UIKit is not phasing out any time soon; your knowledge of the tool is still highly relevant. At WWDC 2019, as Apple unveiled Swift UI, it also introduced new APIs for UIKit. I suspect both UIKit and Swift UI will be developed in parallel. Because UIKit’s been available for so many years, Apple has been able to correct common issues as well as build a wealth of related libraries. These pieces could act as important building blocks in the refinement of Swift UI.

Most companies will still want their apps to support users with iPhones that use iOS 12 and older, so the market for jobs requiring Swift UI expertise hasn’t opened up just yet. Mobile developers of iOS apps will want to continue to demonstrate UIKit knowledge on their resumes and in technical interviews. However, I do expect to see more mobile development job descriptions require Swift UI knowledge in the next two years. 

As Swift UI development by Apple and the programming community grows, one thing I always recommend to students is to keep your skills fresh. If you start learning Swift UI now and get familiar with its functionality, it’ll be much easier to keep on top of the framework’s changes later. 

Ready to get ahead of the learning curve and start experimenting with Swift UI today? Enroll in my course iOS 13 & Swift 5 – The Complete iOS App Development Bootcamp for the latest in iOS development. 

Register for a Udemy for Business demo and discover how to grow the skills of your mobile development team.

The Top 8 Learning & Development Priorities for 2020

The beginning of the new year and decade has many people thinking about their goals and plans for the future. This is especially the case for L&D professionals who are responsible for helping their employees gain the skills they need to adapt to this rapidly changing work environment. We recently surveyed 200+ HR and L&D leaders to better understand their priorities for 2020. Here are the top 8 priorities for L&D in 2020. 

To learn more about our other findings, download the report 2020 Workplace Learning Trends: The Skills of the Future.

1. Creating a growth mindset of continuous learning

The top priority for HR and L&D leaders in 2020 is creating a growth mindset of continuous learning among their employees, with 56% of respondents naming this their #1 priority. In fact, this was also the top priority in 2019. It makes sense — in order to adapt to changes like digitization and automation, employees will need to constantly learn new skills. 

But it’s not just about digital transformation — soft skills are increasingly important in the workplace, according to our 2020 Workplace Learning Trends report. Based on data from our 50+ million learners on Udemy, we also named growth mindset one of the top 10 soft skills for the workplace in 2020. Being open to developing both technical and soft skills through learning and feedback is at the heart of the growth mindset, as defined by Stanford professor and motivation research pioneer Carol Dweck. Dweck also found that organizations that have a growth mindset report employees who are more empowered, loyal, and innovative.

Learn how software development firm ITX fosters a growth mindset among employees starting on day one and throughout their tenure.

2. Reskilling the workforce to keep up to speed with disruptive technologies 

Keeping up with technological advancement is challenging, and experts at Deloitte estimate that the half-life of a learned skill is 5 years which means that what you learned 10 years ago is likely obsolete and half of what you learned 5 years ago is already irrelevant. It’s not surprising, then, that reskilling the workforce to keep up to speed with disruptive technologies has moved up from the #7 priority in 2019 to #2 in 2020 with 43% of respondents naming this a top priority.

Gartner’s Top 10 Strategic Technology Trends for 2020 include things like: 

  • Hyper-automation (the application of advanced technologies, including artificial intelligence and machine learning, to increasingly automate processes and augment humans) 
  • Multi-experience (evolving the idea of a computer from a single point of interaction to include multisensory and multi-touchpoint interfaces like wearables and advanced computer sensors) 
  • Human augmentation (the use of technology to enhance a person’s cognitive and physical experiences). 

Other tech trends predicted by Deloitte include ethical technology and trust, innovation in finance and IT, and digital twins that can increase efficiency in manufacturing, optimize supply chains, transform predictive field maintenance, and aid in traffic congestion remediation.

In 2020, L&D teams may be helping their companies familiarize themselves with these disruptive technologies, or they may be using these technologies themselves to upskill or reskill employees.

3. Using L&D to drive engagement and retention

2019 saw the unemployment rate drop to 50-year lows in both the US and the UK and the number of unfilled jobs in both countries hit an all-time high. This means that job-seekers have plenty of options and offers from multiple companies. HR and L&D teams were clearly feeling the pressure of the tight labor market since using L&D to drive engagement and retention was their #3 priority this year, with 40% of respondents naming this issue. It’s also worth noting that this was a new entry to our top 8 this year.

Research shows that employees crave learning and growth opportunities and this can be a powerful way of boosting retention. A study by Price Waterhouse Coopers showed that millennials rank learning and development as the top benefit that an employer can offer — above flexibility and financial incentives. Research from Deloitte echoes this sentiment, saying, “Millennials and other young employees have grown up in this self-directed learning environment. They expect it as part of their working lives and careers — and they will move elsewhere if employers fail to provide it.” Culture Amp also found that learning and development opportunities are also strongly linked to employee engagement levels and better rates of employee retention. According to their research, employees who stay with an organization are 24% more likely to say that they have had access to the learning and development they needed.

4. Embedding learning better in the workflow to meet on-the-job needs 

As the pace of change quickens and workplace expectations increase, it’s no longer realistic to expect employees to take time away for offsite or extended trainings — learning generally needs to happen in the flow of work. In 2020, 39% of L&D leaders said embedding learning better in the workflow is their #4 priority, up from #8 in 2019. This represents a shift in thinking about learning from a one-off event to a continuous process. Deloitte finds that high-performing companies “place the employee at the center of a new architecture and new vision that treats learning as a continuous process, not an episodic event, and as a company-wide responsibility, not one confined to HR.”

At Home Depot, for example, there’s a need to train 200,000 associates working across 2,000 stores in the US. Training was traditionally conducted via e-learning modules, which meant spending valuable time away from the sales floor. According to Brandon Carson, Home Depot’s former Director of Learning, associates viewed the e-learning modules as less valuable than their regular work. Introducing a pilot project with a mobile app allowed associates to learn in the flow of work, and the initial results were impressive: 90% of associates agreed that the mobile app helped them assist customers and improve their own knowledge. See How Digital Transformation Is Disrupting Learning.

5. Aligning learning to business outcomes 

For the modern L&D practitioner, it’s becoming increasingly important to prove how learning programs connect to business outcomes like employee engagement, productivity, and retention. Moving up from #6 in 2019, this was named the #5 priority in 2020 for 38% of survey respondents.

According to our State of the ROI of Learning report, 44% of organizations gave themselves a score of 5 out of 5 on their ability to measure the ROI of learning programs. At the same time, the majority of companies were relying on metrics like training satisfaction and completion rates. These metrics are useful to measure, but they aren’t linked directly to business outcomes. This disconnect may explain why only 33% of business leaders think the L&D function impacts business outcomes, according to the CEB

Creating a clear connection between L&D and business outcomes is the first step Udemy’s Senior Vice President of Human Resources Cara Brennan Allamano recommends to build a business case for learning. Cara suggests starting with your business leaders’ concerns and mapping your learning & development programs to those needs. For example, if a business leader shares they need 3 more mid-level managers within 18 months, you can address that need through L&D by offering management training. Find more tips from Cara in How to Make the Business Case for Learning.

6. Delivering learning faster and in a more agile way as the business changes

“The traditional waterfall approach to organizational L&D — define, design, deliver — is too slow to implement and doesn’t account for the changing nature of work. L&D leaders must channel an agile approach and modernize their solutions to manage constant disruption and deliver learning and development at the speed of business,” writes Shelley Osborne, Head of L&D at Udemy.

Considering this perspective, it’s not surprising that adapting to business changes with an agile approach to learning was named the #6 priority for 2020 by 35% of our HR and L&D respondents. 

Universities are not always known for their agility, but Geneva Business School is aiming to change that perception. They’re taking an agile approach to education by offering continually updated online content on the latest skills in real-time. Professors assign Udemy courses to students so when they come to class, they don’t have to sit passively and are more prepared to engage in active discussions. 

“If there is a new technology release or trend, we can quickly tap into the world’s leading experts on Udemy for Business to enrich our curriculum. Our professors don’t have to be experts on every topic. Instead, they can lean on Udemy for Business to complement their curriculum,” write Dr. Steven Mallon and Dr. Roy Mouawad, Executive Dean and Professor at the Geneva Business School. To explore more of Geneva Business School’s agile approach to learning, see How the Geneva Business School Stays Agile in the Digital Age.

7. Addressing talent shortages through internal training

In the past, organizations often laid off workers to address obsolete skills and then hired for new skills to move the business forward. However, with today’s tight labor markets, business leaders are beginning to recognize retraining existing talent for new roles as more effective than competing for scarce talent. While reskilling for future skills requires long-term planning, the cost of disruptive layoffs and hiring can be more expensive than providing continuous training for employees. According to a recent study by Josh Bersin, it can cost six times more to hire from without than to build talent from within. 

This may be why addressing talent shortages through internal training was ranked the #7 priority by 31% of HR and L&D leaders. Many L&D teams are looking for ways to train existing employees rather than hiring externally for in-demand skills.

At Booz Allen Hamilton, a management and technology firm, they strive to be a game-changer and leader in the data science field. They wanted to innovate and change the conversation around data to help their clients harness data in a way they’ve never used it before. That’s why they set a goal over 3 years to employ 5,000 data scientists. Due to the talent shortage, they knew they couldn’t only rely on hiring data scientists externally. Instead, they doubled down on training existing employees for new data science roles. To meet this goal, their L&D team set out to create a personalized learning program at scale using Udemy for Business and Degreed. Read Booz Allen Hamilton’s guest blog for more.

8. Proving the ROI of learning

No matter what programs and initiatives you bring to your organization, you’ll need to demonstrate the impact they’re having. Measuring the outcomes of your work is a critical step to securing budget and buy-in from executives, which is why proving the ROI of learning was ranked #8 by our survey respondents. 

Udemy’s Senior Vice President of Human Resources Cara Brennan Allamano puts it this way, “Whether you’re a full-time L&D professional, a leader responsible for developing your department, or just an advocate for learning, making the case for employee L&D initiatives isn’t always easy. And in fact, you should face challenges — you want everyone in your organization to take a thoughtful approach to adopting and rolling out new learning solutions.”

Cara’s recommendations including considering your employees’ ROI (which outcomes do they need to achieve and how can your programs help them meet those needs) and finding champions and partners throughout your organization. Read more in How to Make the Business Case for Learning.

Some companies are finding that they need to shift their thinking about how they measure the success of their programs. Bob Wagner, Learning Program Leader at Crowe LLP — a global public accounting, consulting, and technology firm — shared how his team shifted from tracking volume metrics like learner satisfaction and completion rates to measuring actual behavior change on the job. Within 60–90 days after learners completed a training, the Metrics That Matter tool would survey learners and their managers about behavior change. The initial results were promising, with 84% of learners saying they had applied new knowledge on the job. To learn more about Crowe’s approach and their results, see the on-demand webinar Proving the ROI of Learning.

To read about the top L&D priorities last year, see The Top 9 Priorities for Learning in 2019.

The 10 Hottest Technical Skills in 2020

We’ve covered the technical skills every web developer, data analyst, and IT specialist should have on their resume in 2020. Now it’s time to look ahead at which technical skills are growing in popularity on the Udemy Marketplace. These frameworks, languages, and cloud computing platforms represent skills that have grown in popularity based on data from Udemy’s 50+ million global learners over the last three years from 2016–2019. As you consider where to invest your learning in the decade ahead or which tools to adopt within your organization, look to these 10 hot technical skills. For more learning and workforce training insights, download the 2020 Workplace Learning Trends Report: The Skills of the Future.

The top 10 hottest technical skills of 2020

1. TensorFlow

The #1 hot technical skill for 2020 is TensorFlow, a deep learning Python library developed by Google. It’s used for an artificial intelligence specialization called neural networks, which we’ll also cover in this article. The library is used to uncover insights and predictions from large datasets. The fall of 2019 saw a much-anticipated update to the popular library with the release of TensorFlow 2.0. Since its initial release, TensorFlow has amassed an ecosystem of tools and the ability to use the library in languages other than Python, including JavaScript and Swift. 

These tools, built by a community of TensorFlow enthusiasts, include TensorFlow.js, a JavaScript library that trains and deploys models in the browser; TensorFlow Extended which prepares large data pipelines for machine learning tasks from start to finish to deployment; and TensorFlow Lite, which uses less processing power than a desktop computer to deploying models on mobile or Internet of Things devices. TensorFlow 2.0’s biggest update is the integration of Keras as the default API, which allows for simple and fast prototyping.

Course: Complete TensorFlow 2.0 and Keras Deep Learning Bootcamp

Learn how you can prepare your workforce for a new decade of technical skills with a demo of Udemy for Business.

2. Chatbot

According to a Gartner prediction, by 2021, 15% of all customer service interactions will be handled completely by artificial intelligence — chatbots, specifically. Chatbot technology is software powered by AI to mimic human conversations. A boon for scaling customer service teams and offering round-the-clock support, chatbots recreate the way a human interacts with customers to solve administrative tasks, sales, or frequently asked questions (FAQs). In a Gartner survey of CIOs, chatbots were named as the primary use of AI in their organizations from finance to healthcare to retail, which is why it lands at #2 in our list of hot technical skills for 2020. 

Conversational UI, or the natural way humans tell computers what to do through messaging or speaking, is built on a complicated backend of artificial intelligence techniques like machine learning and natural language processing, as well as data mining. However, there’s no need to worry about how to upskill your technical teams on chatbot creation from scratch. Chatbot engines like DialogFlow (owned by Google) do the heavy lifting of building and deploying conversational interfaces for companies. By combining chatbot engines with development teams equipped in JavaScript and Node.js skills, organizations can adopt chatbot technology without completely restructuring teams. 

Course: Deep Learning and NLP A-Z: How to create a ChatBot

3. Microsoft Azure 

While Amazon’s AWS cloud computing platform remains the market leader in cloud providers, Microsoft’s Azure cloud services are becoming a popular option for enterprises requiring strong security implementation and alignment with the suite of Microsoft services already in use by the organization. A multi-cloud approach is also rising in popularity as IT leaders recognize the cost savings and efficiencies available in leveraging different cloud providers for different purposes. The growth of Azure demand landed this technology at #3 in hot technical skills for 2020.

So that IT specialists can verify their skills in Azure, Microsoft offers 10 different certifications ranging from the generalist Azure Administrator to the advanced Azure Architect. Even if your team isn’t using Azure solutions currently, Udemy instructor Scott Duffy suggests learning fundamentals for the platform to propel personal career growth and to get ahead of any technical shifts your company could make as it migrates more IT practices to the cloud. 

Course: AZ-103 Azure Administrator Exam Certification 2020

4. OpenCV 

Through a branch of artificial intelligence called computer vision, computing systems learn to identify and analyze static images and videos. This technology is now applied to help self-driving cars identify obstacles on the road, accurately diagnose medical conditions, and even identify old photos of your parents. The computer vision industry is projected to grow to a market value of $26 billion by 2025. Democratizing the use of computer vision for developers of all experience levels is an open-source library called OpenCV, which is our #4 technology to watch in 2020. 

OpenCV has Python, C++, and Java interfaces and runs on macOS, iOS, Linux, Windows, and Android operating systems. The library supports applications like facial, gesture, and object recognition systems. This technology was once available only to advanced scholars, but thanks to OpenCV, developers with JavaScript knowledge can build computer vision models. 

Course: Deep Learning and Computer Vision A-Z: OpenCV, SSD & GANs

5. Neural networks

Part of the deep learning branch of artificial intelligence, neural networks are algorithms built to function like the neurological pathways of the brain. By mimicking the complex way humans learn, these algorithms can recognize patterns within complex datasets and generate independent insights on the data. Rounding out the top 10 hottest technical skills on Udemy, building neural networks is a skill you and your team will want to master for 2020 and beyond. 

As Udemy AI instructor Kirill Eremenko explains “neural networks have been able to increase the usefulness of computer systems and more benefits are sure to come.” How exactly? By using programming languages and frameworks like Python and TensorFlow, developers create complex graphs made up of layers of nodes and edges. Every node in a layer is connected to every node in the next layer, transmitting data as a human brain does. This approach allows neural networks to make much more complex predictions than other machine learning approaches. For instance, neural networks can identify objects in images, detect fraudulent credit card activity, or predict a city’s housing prices with near-perfect accuracy. 

Course: Deep Learning: Convolutional Neural Networks in Python

6. LPIC – Linux Certification

Year after year, Linux, an open-source operating system based on the Unix operating system, takes top honors for the most commonly used platform and most loved platform categories in Stack Overflow’s developer survey. It’s estimated that 16,000 developers have contributed to the Linux kernel since 2005 and the OS can be found on 96% of the world’s top one million web servers. 

Linux doesn’t show any signs of losing popularity in the coming decade, so companies want to ensure their teams can properly administer and maintain their internal Linux instances. At #6 in Udemy’s hottest technical skills is LPIC prep to pass the Linux Professional Institute’s exam for Certified Linux Administrators. The certification validates a professional’s ability to perform key tasks like maintenance on the command line, installation and configuration of computers running Linux, and basic networking configuration. 

Course: LPIC-1 Practice Exams

7. Ethereum

Ethereum is an open-source, decentralized software platform based on blockchain technology. While interest in blockchain technology as a currency fluctuates, Ethereum’s use for smart contracts and distributed applications has propelled it to the #7 hot technical skill on Udemy for 2020. While Ethereum does have a cryptocurrency, Ether, developers are creating applications that run on Ethereum because they know it will run as programmed without downtime, censorship, fraud, or other third-party interference.

Ethereum applications are built off a network of computers (also called nodes) which are used to transfer money between parties and store data. Each node has a full and separate copy of the blockchain, which acts as a database to store every change made to a transaction or application. As an example of Ethereum in use today, because of its decentralized network, Ethereum has been used by the United Nations for an efficient and secure distribution of refugee aid.

Course: Ethereum and Solidity: The Complete Developer’s Guide

8. Splunk

From servers to IoT sensors to syslogs, a staggering amount of the data companies process is machine-generated rather than human-generated. Monitoring, analyzing, and reporting on this high volume of data is a challenge for even the most skilled IT teams. This is where Splunk, the #8 hot technical skill on Udemy for 2020, becomes an important tool.

Splunk is software that ingests both structured and unstructured data from nearly any source and easily mines that data to provide business insights through report visualizations and alerts. Splunk uses Search Processing Language (SPL), a proprietary hybrid of SQL and Linux that’s simple for non-developers to use. Splunk is already a popular big data analysis tool and continues to see growing adoption thanks to an engaged user community that collaborates on APIs and Software Development Kits (SDKs) to extend the product’s capabilities. 

Course: The Complete Splunk Beginner Course


Quantum geographic information system (QGIS) is a term many professionals aren’t familiar with, but this #9 hot technical skill has an increasing use in the age of mobile-first and Wi-Fi-equipped devices. QGIS is a type of GIS software that stores geospatial data — data with a geographic component such as map coordinates. Spatial data can be found everywhere from Google Earth to satellite data to your GPS-connected fitness tracker. 

Understanding how to use GIS tools is becoming more important for technology teams as location tracking services, car navigation systems, and IoT devices become more embedded in our daily lives. QGIS is an open-source, cross-platform GIS that allows users to edit, manage, visualize, analyze, and even compose spatial information or maps. Industries including telecom, agriculture, consumer goods, retail, network services, and many more have functions that can expect to call QGIS a necessary skill. 

Course: Core Spatial Data Analysis: Introductory GIS with R and QGIS

10. Kotlin

In the top five of developers’ most loved languages is Kotlin, a programming language used for Android development. Because it’s a language for the Java Virtual Machine, Kotlin can be used anywhere Java is also used. Google even made Kotlin an official language of Android apps. Its popularity is only continuing to rise, which is why it rounds out our list of 10 hot technical skills to watch for 2020. 

Kotlin is a developer favorite because it’s a language with a clean and concise design. This drives a more manageable code base for the future and helps development teams ship code faster. Additionally, developing in Kotlin reduces the likelihood of error, as its compiler facilitates searching for bugs and performing checks to avoid runtime errors. 

Course: The Complete Android Kotlin Developer Course