Excavating the data goldmine for organizational planning

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Information is growing at an exponential rate and businesses are sitting on a goldmine of organizational data that largely lies buried. This article looks at four common misconceptions around excavating this goldmine and how your business can unearth value that also grows exponentially.

Data is the fabric that weaves all elements of an organizational system together. But for many companies, the basic building blocks they need to extract value from organizational data are either absent or inadequately managed. Quality control, consistent data labeling, and record counting using a common taxonomy are all fundamental to meaningful analysis that can influence business decisions. Without them, your data will remain buried.

Building a reliable data foundation is essential to designing and sustaining your organization over the long term. Being able to accurately blend data insights is the key to unlocking the goldmine of information that companies are sitting on, but four common misconceptions prevent many from taking this most crucial step.

Myth one: Data and technology are scary

Unlike marketing, where data has transformed the industry in less than a decade, only recently have those working with people data begun to see analytics as a priority. In 2013, Gartner found that only 15% of CEOs had changed a business decision as a result of people data. Today, it’s hard to ignore the prominent rise of people analytics and data-driven ways of working for HR.

Many HR professionals think of analytics as spreadsheets and complicated formulas. They can take comfort from the fact that marketing wasn’t exactly a hotbed of data science before its transformation. And now there are many software tools that can do the heavy lifting, requiring little knowledge of analytics to use them.

What’s needed though is a change of mindset, an appreciation that data and analytics underpin every aspect of business today. It may take some time to get used to a different way of doing things, but the value it’ll bring will be more than worth it. Analytics improves HR’s contribution to the business dramatically and having a reliable source of data will make it that much easier.

Myth two: Organizational data is too hard to process

By nature, organizational data is complex and messy. Because it’s often scattered across spreadsheets and disparate systems, it can feel overwhelming to gather and process the data. Research has found that cleaning and transforming organizational data is a very time consuming and repetitive part of the analytics, typically making up 80% of the work. Understandably, such a laborious and seemingly thankless task is not a welcome prospect.

To tackle this perception, it’s important that those working with the data understand exactly how analytics will produce significant business value that would otherwise be lost. By beginning to build your data foundation, you’ll be able to see what information is valuable and where improvements can be made.

There’s no doubt that gathering and processing organizational data can be hard work in the beginning. But done right, your data management workload will be much lighter in the long run.

Myth three: One system fits all

Despite a wide array of specialist software available to help those handling organizational data, many people still use general software that can’t handle the more sophisticated aspects of the work.

Instead, there are four alternative technology options to consider. Data warehousing is the usual choice but can be complex and cumbersome. Enterprise resource planning is good at integrating data sources but falls short on analytics, reporting, and modeling. Intermediary software offers a middle ground but is usually designed for very specific functions, while visualization software allows you to run advanced analytics but may lack some of the modeling capability.

Although there is no single software tool that can extract all the value from your organizational data, the trick is to work out exactly what needs to be done and choose software to fit your requirements. Having the right tools will make the data less scary and easier to process.

Myth four: More data means more insight

Organizational data may be a rich, untapped resource, but that doesn’t mean you’re obliged to exhaust it in one go. There’s so much data around that often the challenge is to specify what you actually need. Too much information just leads to confusion.

If you think of data as a value chain, you start with the source systems and end with actions and behavior. Collecting too much data will overburden people and cripple your data collection. Much better to collect high priority data and then add to your baseline as you go. There is nothing to stop you from collecting more data further later on but start with what’s important.

Dispelling data myths

Most of these myths come down to mindset. By approaching data in the right way, you can overcome many of the obstacles that have kept organizational analytics out of business decisions for so long. Much of the challenge is simply a question of confidence and discipline when building up and maintaining a reliable baseline of data to work from. Practical steps you can take to break down old ways of thinking include being prescriptive in what you decide to do with your data analysis. Set parameters at the outset to inform your choice and determine the best outcome for your work. Be focused and precise; don’t try to do too much at once, however tempting that may be. And look at how you can use technology to clean and aggregate data quickly, so you can spend more time on analysis.