Supporting you data team helps build the next generation of data leaders
getty
When I taught my first class in the MS in Analytics Program at Northwestern University’s School of Engineering, I noticed a gap in how aspiring data scientists were trained. Their technical skills were superb, but I felt I needed to help them frame their thinking around what they do – and why they do it.
The response was overwhelmingly positive. It confirmed what I suspected – that building the next generation of data leaders requires something between technical skills and leadership training. Rather, it requires data-specific, value-added skills that communicate and elevate the value of their work.
I’ve developed a four-point framework that can help data professionals shape and organize their thinking and, as a result, facilitate better communication and collaboration with business leaders. This isn’t merely about “speaking business,” but rather developing a shared data language that empowers business leaders, thereby elevating the value of data teams’ work.
By categorizing their projects and pursuits into one (or more) of these four points, data scientists can better describe what they’re doing—and why.
Four Ways That Analytics Helps Businesses
● Planning – This is arguably the simplest way to use advanced analytics and prediction. Data scientists build models to predict what will happen, so that the business can be prepared. For instance, aggregating the predicted conversions and deal size in a sales pipeline can yield an accurate and flexible sales forecast. Including predicted retention and spending from existing customers would yield a revenue forecast. The objective is to generate accurate predictions and prepare for the trends that emerge.
● Selecting – This describes the screening that is a significant part of analytics—that is, selecting optimal groups for specific business purposes. For instance, professional sports teams try to predict player injury and performance so that they can trade for or draft the right mix of athletes. Another example is when companies seek to identify factors that predict customer value and then try to acquire more high-value customers. When businesses use analytics for selection, they aren’t trying to change individual behaviors. They are accepting those behaviors as fixed and merely creating groups (teams, customer bases, etc.) that reflect the behaviors they want.
● Targeting – Business leaders will no doubt be happy to hear about this one. This involves increasing the business value of an existing population, such as customers or employees. Targeting involves changing individual behaviors based on a prediction of what is likely to happen; for instance, predicting likelihood of customer retention and intervening with selected higher-risk individuals.
● Ideating – Finally, models help us identify the factors that might cause better business outcomes. This might mean using model results to think about how to increase customer traffic, how to make a website more “sticky,” how to improve the employee or customer experience, and myriad other business questions.
Using this four-point framework, data professionals become clearer in their thinking and, as a result, their communication. Instead of merely being “data-driven” and caught up in technical expertise, the data team shifts to evidence-based thinking. In presenting results to business leaders, the data team provides evidence-based answers that allow them to discuss solutions, opportunities, and obstacles. It literally changes the conversation, empowering the business leaders to integrate analytics into their business processes.
Moreover, this framework helps hold the business team accountable. …….