During the last few years, we have learned that being data driven has little correlation to size or geography and has only a marginal correlation to vertical industries. In fact, data-driven companies range from small health-care firms to large banks–and even include mid-sized non-profits.
While the traditional categorizations of businesses have little to offer, we have observed a few common characteristics.
In this Data Points article, which utilizes research by Experian Data Quality, eWEEK reporting and industry insight from Alation CEO and co-founder Satyen Sangani, we highlight characteristics that help define data-driven organizations.
Data Point No. 1: Don't assume that everyone is born data literate.
The most progressive data-driven organizations continuously educate analysts and non-analysts in how to use data correctly. This data literacy training ranges from understanding how key metrics are calculated all the way through 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 the continuous shifting of roles
Data Point No. 2: One person's data trash is another's data treasure.
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 wrongly calculated. 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 Point No. 3: Data must be kept lean and clean.
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. 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 business. According to Experian Data Quality, inaccurate data directly impacts the bottom line of 88 percent of organizations and affects up to 12 percent of revenues.
Data Point No. 4: Garbage in/garbage out applies tenfold.
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 you produce could be completely misleading. The biggest problem is that the data or report does not tell you this information; so you have to resort to tribal knowledge to validate whether a given bit of data is right or wrong.
Data Point No. 5: Complexity matters, size doesn't.
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.
Data Point No. 6: Available data does not mean unprotected data.
Nothing kills transparency like an information breach. Data-driven companies zealously guard their data from outside threats, while employing internal policies which restrict access to sensitive data while empowering analysts and decision makers to access the data they need. Balance is key.
Data Point No. 7: Data is all over the place.
One would think that the data is 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.
Data Point No. 8: Fast insights emerge from technological freedom of choice.
Generally, the principal concern of people in data-driven businesses is 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 Point No. 9: Data flows in every direction within an organization.
Data should empower more junior employees to make decisions. Leaders often use data to communicate the rationale behind their decisions and to 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.
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