Data as an asset 

Data is proliferating faster than centralized data teams can possibly manage, and that has created challenges related to use, compliance, and cyber risk. With the growing acknowledgement of the value of data – and the corresponding spike in its interest – there’s been a push to democratize data and get it into more people’s hands.

Nonetheless, several obstacles continue to hamper companies’ ability to put their data to work. From a data consumption and analytics perspective, people continue to rely too heavily on Microsoft Excel™ spreadsheets rather than some of the newer, more interactive analytics tools, like Microsoft Power BI™ or Tableau. On the provider side, the challenge is getting data structured and published in a way that facilitates downstream analysis.

Fortunately, a growing market of machine-learning tools can perform sophisticated analyses of data, and prepackaged models designed to solve specific problems are becoming more widely available. There are pitfalls to using these tools, however. It can be difficult to test their validity and effectiveness, and if the data is flawed, the results might not be valid. Additionally, historical data biases might need to be addressed so that machine learning can be used responsibly, and business users might not have experience with screening for such issues.

Despite all the excitement about analytics, the use of data is still relatively immature, particularly for smaller organizations. For companies in the early stages of exploring what data can do, there are a few things to keep in mind.

First, implementation of a master data management strategy can be an important step to help create consistency, uniformity, and accuracy of data. Beyond that, it is important to have clearly defined roles within the business about who will make data-related decisions: to understand what data the company has, to determine if it’s available, and to confirm its quality.

While there are many different ways to solve data-related problems, having a capable data staff goes a long way. Many organizations have more tools than they know what to do with, but they struggle to set up the right capabilities in house to use the tools properly and to appreciate their deficiencies. Capabilities are far more important than tools; after all, a fool with a tool is still a fool.

In addition to educating themselves about data and what it can do, executives in the midst of a digital transformation must be open to experimentation. Technology is constantly changing, new data is accumulating by the second, and our ability to generate worthwhile insights is evolving and growing all the time.

Companies are often reluctant to experiment because they fear failure. But with digital transformation, falling behind is a greater risk than a single failed initiative. Testing new ideas with small prototypes can be a low-stakes way to identify and understand opportunities and to exploit emerging technology.

Fully embracing the use of data in an organization – through collecting, storing, governing, managing, protecting, and analyzing it – requires significant commitment and a huge investment. But an investment in data can pay enormous dividends in supporting a wide variety of business decisions. In fact, executives can, and should, demand data to support business decisions and manage risk. Using data as the backbone of performance management can strip away the opinions and politics that can otherwise cloud executives’ judgment. Business functions that can’t be measured due to a lack of data should begin investing in data collection and analytics.

The life cycle of collection, protection, and modeling takes months, so it is important to underscore the need for proactive planning with data collection. Despite the immense value of analytics and data science, if you don’t have the data, you’re already behind.