Skill-Based Approach to Improve the Practice of Data Science

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Our Big Data world requires the application of data science principles by data professionals. I’ve recently taken a look at what it means to practice data science as a data scientist. Our survey results of over 500 data professionals revealed that different types of data scientists possess proficiency in different types of data skills. In today’s post, I take another look at that data to identify the data skills that are essential for successful analytics projects. Additionally, I will present the Data Science Driver Matrix, a skill-based approach to identify how to improve the practice of data science.

Substandard Proficiency in Data Skills

In this ongoing study with AnalyticsWeek, we asked data professionals a variety of questions about their skills, job role, education level and more.

Data professionals were asked to rate their proficiency across 25 data skills in five skill areas (i.e., business, technology, programming, math & modeling and statistics) using the following scale:

Figure 1. Proficiency in Data Science Skills by Job Role. Click image to enlarge.

• Don’t know (0)
• Fundamental Knowledge (20)
• Novice (40)
• Intermediate (60)
• Advanced (80)
• Expert (100)

The different levels of proficiency are defined around the data scientists ability to give or need to receive help. In the instructions to the data professionals, the “Intermediate” level of proficiency was defined as the ability “to successfully complete tasks as requested.” We used that proficiency level (i.e., Intermediate) as the minimum acceptable level of proficiency for each data skill. The proficiency levels below the Intermediate level (i.e., Novice, Fundamental Awareness, Don’t Know) were defined by an increasing need for help on the part of the data professional. Proficiency levels above the Intermediate level (i.e., Advanced, Expert) were defined by the data professional’s increasing ability to give help or be known by others as “a person to ask.”

We looked at the level of proficiency for the 25 different data skills across four different job roles. As is seen in Figure 1, data professionals tend to be skilled in areas that are appropriate for their job role (see green-shaded areas in Figure 1 where average proficiency ratings are 60 or above). Specifically, Business Management data professionals show the most proficiency in Business Skills. Researchers, on the other hand, show lowest level of proficiency in Business Skills and the highest in Statistics Skills.

For many of the data skills, however, the typical data professional does not have the minimum level of proficiency to be successful at work, no matter their role (see yellow- and red-shaded areas in Figure 1 where average proficiency ratings are below 60). Specifically, there are 10 data skills in which the typical data professional does not have the minimum level of proficiency: Unstructured data, NLP, Machine Learning, Big and distributed data, Cloud management, Front-end programming, Optimization, Graphic models, Algorithms and Bayesian statistics. Furthermore, there are nine data skills in which only one type of data professional has the minimum level of proficiency to be successful at work: Product design, Business Development, Budgeting, Database Administration, Back-end Programming, Data Management, Math, Statistics/Statistical Modeling and Science/Scientific Method.

Not all Data Skills are Equally Important

Given that data professionals lack proficiency in many skill areas, where do they begin to improve their overall set of data skills? Are some data skills more critical to project success than others? Should data professionals focus on learning/developing certain skills instead of other, less important skills?

Table 1. Correlations of Proficiency of Different Data Skills with Satisfaction with Outcomes of Analytics Projects

In our study, data professionals were asked to rate their satisfaction with the outcomes of analytics projects on which they work. They provided their rating on a scale from 0 (Extremely Dissatisfied) to 10 (Extremely Satisfied). I used this score as a measure of project success.

For each data skill, I correlated data professionals’ proficiency ratings with the data professional’s satisfaction with outcomes to understand the link between a specific skill and the outcome of analytics projects. This exercise was done for each of the four job roles (See Table 1). Skills that show a high correlation with satisfaction with outcomes indicate that those skills are closely linked to project success (as defined by the satisfaction ratings). Skills listed in the top half of Table 1 are more essential to project outcomes compared to skills listed in the bottom half of Table 1.

On average, we see that data skills are more closely linked to satisfaction with work outcomes for data professionals who are Business Managers (average r = .30) and Researchers (average r = .30) compared to data professionals who are Developers (average r = .18) and Creatives (average r = .18).

The ranking of data skills with respect to their impact on satisfaction also varies significantly by job role. The average correlations among the rankings of data skills across the four job roles is r = .01, suggesting that data skills that are essential to project outcomes for one type of data scientist are not essential for other types of data scientists.

The Data Science Driver Matrix: Graphing the Results

Figure 2. Data Science Driver Matrix: Skill-based approach to improve the practice of data science. Click image to enlarge.

So, we now have the two pieces of information for each of the 25 data skills: 1) average proficiency rating (in Figure 1) and 2) correlation with work outcome (in Table 1). For each job role, I plotted both pieces of information of the 25 data skills in a 2×2 table (see Figure 2). I call this diagram the Data Science Driver Matrix (DSDM). In the DSDM, the x-axis represents the average level of proficiency across all data skills. The y-axis represents how essential the skill is to project outcome.

The midpoint on the x- and y-axes are 60 (minimum level of proficiency needed to be successful at work) and .30 (~average correlation of skills with satisfaction), respectively.

Interpreting the Results: Improving the Practice of Data Science

Each of the data skills will fall into one of the four quadrants of the DSDM. In Table 1, I list the quadrant number for each data skill for the separate job roles. The decisions you make about a specific data skill (e.g., whether to learn it or not) depends on the quadrant in which it falls:

1. Quadrant 1 (upper left): Quadrant 1 houses skills that are essential to the outcome of the project and in which the proficiency is below the minimum requirement. These data skills reflect good areas for potential improvement efforts because we have ample room for improvement. Improvements in proficiency could come in the form of investments in hiring data professionals with these skills, investments in training your current data professionals to acquire these skills or creation of teams with members that have complementary skills.
2. Quadrant 2 (upper right): Quadrant 2 houses skills that are essential to the outcome of the project and in which the proficiency is above the minimum requirement. These skills reflect data professionals’ strength that we know improves the success in analytics projects. You’ll likely want to stay the course on these data skills.
3. Quadrant 3 (lower right): Quadrant 3 houses skills in which the proficiency is above the minimum requirement but are not very essential to the outcome of the project. Be careful not to over-invest in improving these skills as they are not necessarily essential for the success of analytics projects.
4. Quadrant 4 (lower left): Quadrant 4 houses skills in which the proficiency is below the minimum requirement but are not very essential to the outcome of the project. Consider divesting resources from these skills and re-direct them to skills falling in Quadrant 1. These skills are of low priority because, despite the fact that proficiency is low for these skills, they do not have a substantial impact on the outcome of the analytics projects.

Data Science Driver Matrices for Different Data Roles

I created a DSDM for each of the four job roles: Business Manager, Developer, Creative and Researcher. For this exercise, I will focus primarily on data skills that fall into Quadrant 1 (i.e., low proficiency in highly essential data skills).

1. Business Managers

For data professionals who self-identify as Business Managers (see Figure 3), we see that none of the skills fall into Quadrant 2 (high proficiency in highly essential skills), while 12 skills fall into Quadrant 1 (low proficiency in highly essential skills). Skills in quadrant 1 include:

Figure 3. Data Science Driver Matrix for Business Managers. Click image to enlarge.

• Statistics / Statistical Modeling
• Data Mining
• Science / Scientific Method
• Big and distributed data
• Machine Learning
• Bayesian Statistics
• Optimization
• Unstructured data
• Structured data
• Algorithms

Figure 4. Data Science Driver Matrix for Developers. Click image to enlarge.

2. Developers

For data professionals who identify as Developers (see Figure 4), most of the skills fall into Quadrant 4 (low proficiency in non-essential skills). Only two skills fall into Quadrant 1:

• Systems Administration
• Data Mining

Figure 5. Data Science Driver Matrix for Creatives. Click image to enlarge.

3. Creatives

For data professionals who identify as Creatives (see Figure 5), most of the skills fall in Quadrant 4 (low proficiency in non-essential skills). Five skills fall into Quadrant 1:

• Math
• Data Mining
• Business Development
• Graphical Models
• Optimization

4. Researchers

For data professionals who identify as Researchers (see Figure 6), six skills fall into Quadrant 1 (low proficiency in essential skills):

Figure 6. Data Science Driver Matrix for Researchers. Click image to enlarge

• Algorithms
• Big and distributed data
• Data Management
• Product Design
• Machine Learning
• Bayesian Statistics

Researchers appear to lack proficiency in areas that are critical to the success of analytics projects.

Conclusions

Applying the right data skills to analytics projects is key to successful project outcomes. I proposed a skill-based approach to improve the practice of data science to help identify the essential data skills for different types of data professionals. Businesses can use these results to ensure they bring the right data professionals with the right skills to bear on their Big Data analytics projects.

There are a few conclusions from we can make from the current analyses.

1. Data Mining was the only data skill that was one of the top 4 data skills that was essential to the project outcome. No matter your role as a data professional, a key ingredient to project success is your ability to mine insights from data.
2. Proficiency in data skills appears to be more important for data professionals who are in the roles of Business Management and Researcher compared to data professionals who are in the roles of Developer and Creative. Improving proficiency in data skills to increase satisfaction with work appears to be a more realistic approach for Business Management and Researcher type data professionals.
3. Data professionals could likely be happier about the outcomes of their projects if they possessed specific data skills. Surprisingly, for Business Managers, business-related data skills are not critical to the outcome of their analytics work. Instead, what drives their work satisfaction is the extent to which they are proficient in statistical and technological skills. Unfortunately, these Business Management workers typically do not possess adequate proficiency in these types of skills.

Improving the practice of data science can be accomplished in a variety of ways.  While the current analysis suggests that you can improve analytics project outcomes by improving skills for specific data professionals, another approach is to build data science teams with data professionals who have complementary skills. As I’ve found before, Business Managers are more satisfied with the outcomes of analytics projects when they are paired with data professionals with strong statistics skills compared to Business Managers who work alone. Likewise, Researchers are more satisfied with the outcomes of analytics projects when they are paired with data professionals with strong business acumen. Using either approach, organizations can leverage the practice of data science to address their analytics projects.