Making data analytics work for you--instead of the other way around

Making data analytics work for you–instead of the other way around

Making data analytics work for you--instead of the other way around
Does your data have a purpose? If not, you’re spinning your wheels. Here’s how to discover one and then translate it into action.

The data-analytics revolution now under way has the potential to transform how companies organize, operate, manage talent, and create value. That’s starting to happen in a few companies—typically ones that are reaping major rewards from their data—but it’s far from the norm. There’s a simple reason: CEOs and other top executives, the only people who can drive the broader business changes needed to fully exploit advanced analytics, tend to avoid getting dragged into the esoteric “weeds.” On one level, this is understandable. The complexity of the methodologies, the increasing importance of machine learning, and the sheer scale of the data sets make it tempting for senior leaders to “leave it to the experts.”

But that’s also a mistake. Advanced data analytics is a quintessential business matter. That means the CEO and other top executives must be able to clearly articulate its purpose and then translate it into action—not just in an analytics department, but throughout the organization where the insights will be used.

Read Also:
Why Is Artificial Intelligence So Bad At Empathy?

This article describes eight critical elements contributing to clarity of purpose and an ability to act. We’re convinced that leaders with strong intuition about both don’t just become better equipped to “kick the tires” on their analytics efforts. They can also more capably address many of the critical and complementary top-management challenges facing them: the need to ground even the highest analytical aspirations in traditional business principles, the importance of deploying a range of tools and employing the right personnel, and the necessity of applying hard metrics and asking hard questions. (For more on these, see “Straight talk about big data.” ) All that, in turn, boosts the odds of improving corporate performance through analytics.

After all, performance—not pristine data sets, interesting patterns, or killer algorithms—is ultimately the point. Advanced data analytics is a means to an end. It’s a discriminating tool to identify, and then implement, a value-driving answer. And you’re much likelier to land on a meaningful one if you’re clear on the purpose of your data (which we address in this article’s first four principles) and the uses you’ll be putting your data to (our focus in the next four). That answer will of course look different in different companies, industries, and geographies, whose relative sophistication with advanced data analytics is all over the map. Whatever your starting point, though, the insights unleashed by analytics should be at the core of your organization’s approach to define and improve performance continually as competitive dynamics evolve. Otherwise, you’re not making advanced analytics work for you.

Read Also:
The 10 Algorithms Machine Learning Engineers Need to Know

“Better performance” will mean different things to different companies. And it will mean that different types of data should be isolated, aggregated, and analyzed depending upon the specific use case. Sometimes, data points are hard to find, and, certainly, not all data points are equal. But it’s the data points that help meet your specific purpose that have the most value.

The precise question your organization should ask depends on your best-informed priorities. Clarity is essential. Examples of good questions include “how can we reduce costs?” or “how can we increase revenues?” Even better are questions that drill further down: “How can we improve the productivity of each member of our team?” “How can we improve the quality of outcomes for patients?” “How can we radically speed our time to market for product development?” Think about how you can align important functions and domains with your most important use cases. Iterate through to actual business examples, and probe to where the value lies.

Read Also:
Fixing The Big Data Skills Shortage Problem

Read Full Story…


Leave a Reply

Your email address will not be published. Required fields are marked *