Nine Key Factors That Identify Data-Driven Organizations

Being data-driven has little correlation to company size and vertical industries. We highlight key qualities that define data-driven organizations.

The most progressive data-driven organizations continuously educate analysts and non-analysts about how to use data correctly. This data literacy training ranges from understanding how key metrics are calculated to understanding regulatory requirements for using data. However, even the most advanced enterprises admit that it’s an uphill battle, given the growth, constant change in systems and businesses, and continuous shifting of roles.

Today, only about 12 percent of data in an organization is analyzed. Eighty-eight percent isn’t touched at all—though that portion could contain useful insights—often because the teams that store it and the groups that need it are in different parts of the organization. Some of the data sets that are used shouldn’t be, because they’re stale, noisy or calculated incorrectly. Data-driven organizations break down the barriers of data siloes and let people access useful data across divisional boundaries. At the same time, they ensure that the real data garbage is marked or deleted.

Data quality is extremely important. Enterprises often position themselves as handling terabytes and petabytes of data, with teams of data scientists running Apache Hadoop clusters with data analytics that give them competitive advantage. Truthfully, many of them suffer from conventional garbage-in/garbage-out syndrome. Not only do they not have big data in terms of complexity or volume, but most actually have fairly diluted data, and it’s undoubtedly hurting, not helping, their businesses. According to Experian Data Quality, inaccurate data directly has an impact on the bottom lines of 88 percent of organizations and affects up to 12 percent of revenues.

Most data-driven organizations already know that the quality of the data matters as much as the data itself. If you have the right data, but half the values are missing or, even worse, wrong, the data might be useless. Additionally, even if your data is totally clean and accurate, applying the wrong calculations or definitions means that the metrics produced could be completely misleading. The biggest problem is that the data or report does not reveal this information; users have to resort to tribal knowledge to validate whether a given bit of data is right or wrong.

You would think that data-driven organizations have exabytes of data. However, size matters only to a point. What really matters is the variety of the data: Are people asking questions in different business functions? Are they measuring cost and quality of service, instrumenting marketing campaigns or observing employee retention by team? If you’re just getting a month-end report on revenue and profit, you’re probably not data-driven.

Nothing kills transparency like an information breach. Data-driven companies zealously guard their data from outside threats while employing internal policies that restrict access to sensitive data while empowering analysts and decision makers to access the data they need. Balance is key.

One would think that data should be well-organized and well-maintained as in a library, where every book is stored in one place. In fact, most data-driven cultures are exactly the opposite. Data is everywhere–on databases, inside business intelligence tools, on file servers and on reports on people’s PCs—all within the company walls. The key is knowing where the data is, not centralizing and confining it.

The principal concern of people in data-driven businesses is usually the ability to get insights quickly. Forcing analysts to learn and use IT-defined models and centrally specified tools slows down analysts and data scientists (and makes them harder to attract and retain). In the most data-driven enterprises, the person answering the question gets to pick the tools used.

Data should empower more junior employees to make decisions. Leaders often use data to communicate the rationale behind their decisions and motivate action. Data should empower everyone to make decisions without having to consult managers three levels up, whether it’s showing churn rates to explain additional spend on customer services versus marketing or showing revenues relative to competitors to explain increased spend on sales.