Data Analytics: The Ugly, But Crucial Step You Can’t Ignore

The part of the process that involves cleansing, categorizing and standardizing data lacks the excitement and obvious returns of other investments, but it's the only way to ensure your data collection isn't a waste of time and money.

Today’s C-Suite leaders have high expectations of data. They want to be able to whip out their smartphones and bring up beautiful visualizations of trends that give them actionable insights into the business at a granular level.

That’s the dream anyway. In reality, many businesses are failing to get the most out of their data even though they’re collecting a lot of it.

What’s usually missing is the part in the middle that connects the raw data with those fancy presentations. It’s an ugly part of the process that involves cleansing, categorizing and standardizing data, and one that often flies under the radar because it lacks the excitement and obvious returns of other investments.

But any company that doesn’t take this seriously is missing out on the crucial insights that high quality, relevant data provides, and which increasingly mark the difference between success and failure.

Leaders only need to look around to see that the best companies are making data central to their business. This has become even more important as companies grapple with rising costs. Good data analytics allow companies to stay on top of their purchases and to roll costs over to their customers, a capability that is proving highly valuable these days in the manufacturing and automotive industries.

Companies with low data maturity tend to keep data siloed, using different criteria across departments to collect and interpret it. This leads to missed opportunities from not integrating data to generate information at a granular level. They may know if they just had a good month but are not able to see how that breaks down on a per-item level or how it compares to other periods to give them a better understanding of “why” they had a good month and how they might be able to proactively make decisions to repeat or even further improve results.

One manufacturing firm we know recently employed analytics to clean up its data and for the first time obtain a SKU-level visualization of the profitability of each item it sold. This enabled it to cut items that were performing poorly and improve its bottom line substantially.

The best data results come from a combination of solid leadership, investing in the right technology, and incentivizing the right people to make it all work. It sounds simple, but following these key principles are critical to success.

• Good data management starts at the top. Company leaders first need to define how they measure success—whether that is sales growth, SKU-level costs savings, profitability or any other metric—before considering how to harness data to support their goals. One of the most common, and frustrating, missteps that leaders make on technology is spending big on all-encompassing ERP systems and expecting that to solve their data problem. While these systems may have a data management component, they are not built specifically for that task.

• Purchase dedicated analytics software. The two pillars of a good data technology stack are governance tools, such as Alteryx or Informatica, and visualization tools such as Tableau or Power BI, to name a few. They are affordable and allow organizations to build up capabilities and expertise. Data governance tools enable companies to validate their data, catalog it and ensure that is cleansed and reliable before it gets piped into other software. Visualization tools then present that data in a way that is both granular and broad on key business trends, giving executives the insights they crave.

• Investing in the right people. Senior data specialists should be incentivized for good performance, with rewards triggered when their analytics work leads to company success. They should be involved in all decision-making processes and provide regular updates to leaders, becoming true stakeholders. Investing in people like this is never cheap and can meet resistance. But the expense becomes much easier to stomach if you run an opportunity-cost analysis to understand how much is being lost through bad data practices.

Having strong data systems and SKU-level rationalizations in place make businesses more efficient. But companies also need to remember the crucial human element by investing in IT and data leaders—and making sure they are truly valued.

When it all comes together, there’s nothing like seeing a CEO or CFO light up the first time they open a revamped data dashboard on their screen.  But getting to that point doesn’t happen overnight. If you haven’t started on that journey, you’re already behind most companies, but there is still time to catch up.

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