Author: Mike Hendrickson

  • Five Tips for Solving the Technology Skills Gap

    Five Tips for Solving the Technology Skills Gap

    The insightful, and sometimes breathless, predictions regarding digital transformation are everywhere. We are right to be mindful of both the potential and the challenges posed by digital transformation. Digital transformation is increasing the rate of change in every business, in every line of business and even in every role.  

    Digital transformation requires new development practices, organizational culture changes, customer intelligence and focus, empathy and, above all, new ways of thinking. Only when everyone is committed to improving the way work gets done, can an organization thrive in a competitive environment. As organizations continue to grapple with how to prepare their workforce for an increasingly digital world, they have a technology skills development problem.

    Below are five considerations for bridging the technology skills gap brought on by digital transformation.

    Identify the Skill or Development Problem You Are Trying to Solve

    It’s essential that the technology skills development program be aligned in purpose and design with the intended business objective. That may sound obvious, but organizations have a way of accelerating beyond the original scope and intentions. 

    Ultimately, what you are trying to do will determine the combination of tools, content and even organizational commitment needed to be successful. For instance, are you trying to deepen technical knowledge for a set of employees? Or, are you tasked with improving specific performance objectives in data, DevOps and/or security?

    Before investing in training, it’s important to establish the targeted employees, the range of competencies to be covered, the degree of proficiency required and the time frame to complete the initiative. In all cases, build a program that can solve the problem in a time frame that meets the business needs.

    Establish the Performance Objective 

    For nearly all technology skill problems, the goal is performance: the target learners becoming sufficiently competent to perform at the required level. Understanding what the performance standard or measure of success is or will be helps focus all stakeholders on the same goal.    

    If specific performance objectives are required, monitoring a specific key performance indicator (KPI) can be the most valuable approach to determining training success or impact. If the KPIs are not available, using post-assessments can be a useful measure of knowledge gained and can help demonstrate the impact of the training.   

    Leverage Authoritative Content

    It’s important to understand the sources used to create the course material as well as who authored or collaborated on the material. This matters not just because the learning content should be accurate, but also because adult learners are sensitive to the credibility of the source material. Especially in technical areas, IT professionals expect to be provided with material from authoritative sources.

    IT professionals consider current practitioners, or well-known consultants the most authoritative voices on how to use technology. In some cases, industry associations or trade groups have established themselves as authoritative sources in their specific technical areas. Regardless, the source material should be highly reputable and authoritative. 

    Put User Experience First 

    Making learning as convenient for the target learner as possible is essential to a successful learning program.  

    Convenience has often meant anytime/anywhere learning—learners can take courses at home, while commuting, from their desk or wherever they work. This experience should be much like watching a movie on Netflix or Hulu: Learners should be able to start the course at one location, on one device and continue it at another time on a different device from just the right point in the course. 

    Training can also be seamlessly incorporated into the workflow of some applications. Think of it like a more sophisticated, less annoying version of “Clippy,” the Microsoft assistant that provided in-context help for Microsoft Office through the mid-2000s. For some types of applications and technical roles, having a context-sensitive learning engine that can identify and serve up nuggets of learning appropriate to the task can be very useful. 

    Offer the Right Mix of Push Learning and Pull Learning 

    Employers are beginning to realize the benefits of pull learning where IT professionals identify and select their own learning paths. This model allows motivated professionals to acquire skills, gain more responsibilities and, in some cases, promotions and new job opportunities. Employers have realized that when employees self-select a course, it is a strong indicator of self-motivation, and a strong predictor of professional dedication and success.  

    But pull learning isn’t appropriate for every problem to be solved. For example, maybe you need a group to learn the same thing about cloud security or compliance. In such situations, it’s not practical to wait until the target learners are motivated to take the specific courses. It makes more sense to solve these challenges by assigning specific courses to the right groups of target learners, assigning completion dates and monitoring progress.  

    Depending on what problem you are trying to solve, either push learning or pull learning may be more appropriate. 

    Setting Your Organization Up for Success

    Ultimately, successfully navigating digital transformation requires a team with well-aligned IT roles and skill sets. People are the make-or-break element of a high-performing IT organization. There is simply no replacement for people with the right skills, attitudes and traits. CIOs don’t have the luxury of hiring a totally new roster of people with “the right stuff,” so they will have to be creative in using a combination of hiring, mentoring and training, contractors, and partners to create the needed mix of capabilities. 

    Once you have hired and trained the talent in your organization, what’s your plan to help them continue their learning journey for the duration of their career?

    — Mike Hendrickson

  • Building Your Data Science Team from Within

    Building Your Data Science Team from Within

    The global machine learning (ML) industry is increasing at a compound annual growth rate of 42% and will be worth $9 billion toward the end of 2022. Despite this figure, there are many cases of data science teams’ failure to deploy a reliable artificial intelligence (AI) product or service.

    Part of the failings and shortcomings of AI concern the ramp or process for getting there. For an AI project to succeed, it is critical to have your data fully deployed, available, structured and cleaned. You also must have some algorithms (ML/deep learning/natural language processing) ready to pull the insights and focused data, effectively putting the intelligence into AI.

    This requires a very specific skill set and talent pool.

    Empower Your Data Analysts

    With so many different technology fields colliding, machine learning is a difficult area to master. Deep reinforcement learning (DRL), natural language processing (NLP), AutoML tools, ML Ops, neural networks and model-based reinforcement learning are just some of the subjects required to master ML.

    With a shortage of talent and shortfall of data scientists, this is easier said than done. Part of this is because the demand far outweighs the supply, with Bloomberg estimating job postings for data scientists rose 75% from 2015 to 2018. Almost every organization shares this same urgent need.

    When faced with a skills gap in their teams and stiff competition in recruiting, how can organizations effectively build a data science team?

    One way is to start by looking at your data analysts. They are often swimming in disparate, federated data sources, operating on legacy databases, working with mixed data sources and using Excel to do things it was never intended to do. There is an enormous opportunity to empower data analysts to become data scientists.

    From Data Analyst to Data Scientist

    Companies looking to hire data scientists from within need to think about how to upskill, reskill or preskill their data analysts to perform the roles needed to implement AI and ML fully. I’m a believer that if you provide prescriptive and progressive curricula around the essential topics a budding data scientist needs, you can equip them with the skills and knowledge to make a difference and move them forward in the organization.

    However, it’s not enough to simply equip these employees with a curriculum; the program should have numerous twists and turns. Why? When thinking about how you can train the prototypical ML expert of the future, you must consider all the deep tech skills needed. You also want the business and social skills that are required to attain the expert moniker. A well-rounded ML architect will see the business imperatives, understand how to communicate with engineering and business, and have the tech skills to deploy ML models that benefit your business and customers.

    When organizations take roles commonly found in the workplace, such as data analysts, provide them with a sequenced path of instruction that moves them toward an aspirational role, they’ll be well-positioned to realize the full potential of AI and ML.

    — Mike Hendrickson