Data scientists are among the most in-demand professionals in the US right now, as more and more industries look to harness the power of data to drive productivity and innovation to new heights. Demand is high and supply is short, so these experts command remarkably high salaries. However, recent research has suggested that many companies’ data isn’t sufficiently high-quality to produce the kinds of insights managers are expecting. This is particularly true in the emerging field of talent analytics, where companies are making major investments but most aren’t seeing them pay off.
In addition to the data quality challenge, Data Quality Solutions President Thomas C. Redman suggested in a recent Harvard Business Review article that senior managers at many organizations are mismanaging their data scientists: placing them in the wrong part of the organization, not focusing the data science program on business outcomes, and not facilitating a transition to a more data-driven culture. He offers some suggestions for how companies can get more out of their data scientists:
First, think through how you want data scientists to contribute, and put them in spots where they can do so. The worst mistake a company can make is to hire a cadre of smart data scientists, provide them with access to the data, and turn them loose, expecting them to come up with something brilliant. Lacking focus and support, most fail. Instead, clearly define the opportunities you want to address using data science, and put your data scientists in places in the organization where they can best pursue those opportunities. …
Second, immerse data scientists in your business. According to LinkedIn, the top 10 skills for a data scientist include machine learning, R, Python, data mining, data analysis, data science, SQL, MatLab, big data, and statistical modeling. The focus is on skills, and many data scientists are perfectly content to apply those skills while sitting at their computers and plowing through ever-increasing amounts of data in the hopes of finding something interesting. But it is not enough to put data scientists in the right spots and let them work. You need to instruct them to fully engage in your business, show them how things really work, and help them connect with others in the organization.
This disconnect between the data science program and the heart of the business has been identified as a possible weak point in talent analytics, specifically, when data scientists fall into the specialist’s trap of pursuing the most interesting data questions rather than those most useful to the organization’s goals. Redman’s advice resonates with our research at CEB, now Gartner, on project scoping within talent analytics. Projects fail to drive action when talent analytics professionals are just given data and told to run with it. A few weeks later, these professionals return with an action that can’t be applied because the data or even problem is outdated.
To drive results, data scientists need to be held to a process that is centered on action from the very beginning: Action-oriented scoping focuses on hypothesizing possible insights and action as soon as possible, leading to a higher chance of clients taking action at the end of the project. In the analysis phase, the actionability of each recommendation should be tested and measured to keep the project on track. Once a conclusion is reached, results should be communicated along with a concrete set of action steps, which should not be surprising to clients as they have been involved in the process throughout. At this point, the client should be prepared to initiate planned actions.
Talent analytics professionals need to be deeply involved in the business so they can see the bigger picture and keep managers involved during all these phases of scoping, analysis, and communication. That’s why our research has zeroed in on the importance of relationship-building as the key to a successful talent analytics program. If the analytics function doesn’t have good relationships with the other parts of the business that form its internal clientele, it will have a hard time collecting reliable data and generating actionable conclusions from it.
CEB Corporate Leadership Council members can read more about this process here, and check out all of the insights from our recent study on Relationship-Powered Talent Analytics. Our case study on DST Systems’ and Omega’s scoping templates, which allow talent analytics professionals to make sure they are prioritizing correctly when choosing projects, gives a good example of how business leaders can keep their analytics program focused on business needs. Members can also watch our recent webinar covering the findings of this study in detail.