It can be difficult to get a data program off the ground, but without one, your company may be unable to synthesize crucial data and derive significant insights. To be competitive in the twenty-first century, every organization must be data-driven. Being data-driven does not imply automating people’s tasks, but rather equipping them with the tools and information they need to be more productive in their professions.
We believe that being more data-driven is everyone’s responsibility within the organization. After all, regardless of your role, we are all likely to come into contact with data in some way. There is no such thing as a one-size-fits-all data program. The goals of each data program will differ depending on criteria such as industry, organization size, and team structure.
1. Choose a top-down or bottom-up approach
The top-down approach to building a data program is related to the larger data and analytics initiatives within an organization. This requires strategically building out your program to standardize how analytic models and workflows are integrated across teams.
The bottom-up approach is about generally raising the capabilities and skill levels of all employees in a data and analytics career path. For example, AXA XL’s Analytic DNA initiative identifies experts in different technology and analytics areas, and looks to them for ideas to leverage to scale more broadly across the organization.
The great news is that people have been really willing to step up and provide their guidance and expertise, and it helps us shape our program into something that’s relevant and aligns to our strategic initiatives.
2. Develop personas and identify their goals
At DataCamp, we’ve identified several data-related personas or roles that our customers typically use: Data Consumer, Leader, Data Analyst, Citizen Data Scientist, Data Scientist, Data Engineer, Database Administrator, Statistician, Machine Learning Scientist, and Programmer. We advise our instructors to consider who the relevant personas are when building their courses, and we advise our business customers to consider who the relevant personas are when building out their data programs. Each persona has a different relationship with data.
Two primary personas for AXA XL are Actuaries and Data Scientists, and they’re currently focused on converging the skills required of both roles to further the business.
There’s always been an interesting divide in many [insurance] organizations around the difference between an actuarial background and what I would call more of a pure data science or analytic background. In our organization, we’re bringing those two things together. We’re actually teaching actuaries some of those more statistical machine-learning techniques that they can apply in their actuarial processes. It’s not an “us versus them” anymore. It’s “how do we bring these capabilities together,” which is pretty exciting.
3. Identify gaps for each persona
We advise all of our customers to take a broad inventory of their in-house data skills using a skills matrix. This comes in many different forms, but generally involves visualizing strengths and skill gaps at either the organization, department, team, or individual level.
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Rachel at AXA XL advises that you can identify gaps for each persona by asking questions like:
- What is it that you want to know?
- What would you like more of?
- What more could we provide, e.g., training, hiring, skill development, or project-based work?
- Are we aligning these capabilities with our strategic projects correctly?
For analytics professionals like data analysts, data scientists, and data engineers, companies may wish to investigate whether there are gaps in important skills like importing data, conducting data analysis in spreadsheets, natural language processing, and building and maintaining data pipelines.
For Data Consumers and Leaders, skills needed for success include soft skills like communication, understanding data visualization, and design thinking.
Design thinking is [about] creating a solution, whatever it is: technical, data, [or] model. How do you do that in a way that’s relevant for the customer? Then, how do you communicate the results effectively?
4. Create learning journeys for each persona
A holistic learning approach requires using design thinking to create an effective learning journey. It isn’t only about building technical skills—but also about building important business skills. For instance, AXA XL requires their actuaries to be able to translate data into meaningful terms so that decision makers like underwriters, who evaluate insurance risk, can put a company’s risk assessment into context and draw useful conclusions, like how much risk to assume. The actuaries’ learning journey must include the ability to contextualize data and translate their implications to a broader audience.
To help organizations discover their team’s data skill level across personas, we offer a free skill audit via a benchmark report.
After assessing skills, we advise organizations to build custom tracks so that everyone can achieve the baseline skill level required for their persona or role. Learning tracks for Data Consumers and Leaders may skew toward our theory courses, while data professionals take our hands-on coding courses. For instance, since so much of AXA XL’s data is in PDFs, learning journeys for their actuaries and data scientists include several of our courses on natural language processing.
Data analysis is a rapidly growing field with solid job prospects for the future. According to the United States Bureau of Labor Statistics, employment of computer and information research scientists (which includes data analysts) is projected to grow 15% from 2019 to 2029, much faster than the average for all occupations.
Data analysts can expect to start their careers in entry-level positions, such as data analyst or junior data scientist, and progress to mid-level positions, such as senior data analyst, data scientist, or data engineer. With experience and further education, data analysts can advance to leadership roles such as data analytics manager, business intelligence manager, or chief data officer.