The New Leadership Mindset for Data & Analytics
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For many senior executives, data presents a conundrum. Naturally, they want their data programs to succeed. Indeed, they’d like to help in some way, and even provide leadership, but beyond providing funding, they’re not sure how. Business leaders’ lack of technical knowledge exacerbates their uncertainty and makes them feel like outsiders to data science teams.
But data programs cry out for business leadership, and there are many ways senior executives can play a bigger role, even if they don’t fully understand data. This article explores seven ways leaders can accelerate their companies’ data efforts, derive near-term benefits, and gain a better understanding of the roles data can play in advancing their business objectives. Think of these as opportunities. Not all of them will suit your needs, interests, or style, so just pick one or two and get started.
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Focus on Quality
We begin with three opportunities related to quality. Counterintuitively perhaps, improving data quality saves money. Further, all data strategies (and more and more business strategies) depend on high-quality data.
1. Break the quality logjam. Many leaders readily admit that they don’t trust their company’s data, at least not enough to use it when it really matters. It’s no wonder, given how often they encounter bad data: Two numbers don’t jibe, some report looks too good to be true, and the finance team complains every quarter about the overtime needed to produce a routine report. Still, most companies manage to convince themselves that their data is “pretty good.” And an uneasy stasis results — you don’t have the trusted data you need but are frozen from doing anything significant about it.
To break the logjam, undertake a review to determine how good — or bad — your data is. Tell people there will be no rewards or punishments for good or bad results — it is just important to establish a baseline. Instruct your teams to make a simple data quality measurement, using what I call the Friday Afternoon Measurement. It entails laying out a sample of the most important data your teams use every day, marking the obvious errors, and then counting them up. This measurement returns a score between 0 and 100 — the higher the better.
Although calculating this measurement takes just a few hours, it may take longer for the team to work through the implications.