All around the globe, in every industry known to man, data is the topic du jour. Big data. Customer data. Social/sentiment data. Business leaders intuitively grasp its importance. Analysts, thought leaders and management gurus think the information age has morphed into the data epoch.

Why, then, do so many companies fail to treat their data like a high-value and potentially difference-making asset? It’s easy to talk about data as the “lifeblood” of the organization, which it truly is. Without data, companies can’t market and sell their products, run their processes or measure their performance. But the reality is that companies manage data more as a raw material than as an asset.

Just how bad is the problem? Gartner studies have shown that less than a third of business people can access the business intelligence (BI) and analytics tools they want and need. No wonder many CIOs believe that the enterprise realizes far less than the full business potential of its technology. For instance, "Enterprises realize on average only 43 percent of technology's business potential, according to a global survey of CIOs by Gartner, Inc.'s Executive Programs. That number has to grow for IT to remain relevant in an increasingly digital world.”

Even worse, the problem doesn't seem to be getting better. In 2008, The Wall Street Journal described the core issue: “A lot of valuable information is wasted in companies because employees don’t describe or understand information in the same way across the organization.” That statement is just as true today, and likely will still be in 2018, if current patterns persist. It’s clearly time to rethink and reevaluate the value and utility of the data we possess. After all, if everyone treated data like the asset it is, it wouldn’t be wasted or underutilized in the ways it often is today.

On the business side, companies must view data not as a static entity captured in standalone reports or pieces of software, but rather as a rich, fluid and flexible resource that can be used to understand multiple dimensions and facets across the business. It’s a dynamic asset that can clarify not just how well (or poorly) the business is doing, but also how well (or poorly) it could or should do. It’s a force for optimizing how the business is measured and how key business decisions are made.

To help facilitate such a rethink, IT must shift its view, too. In fact, IT in general -- and BI teams in particular -- have a substantive leadership opportunity in effecting this important cultural change. It must start at home, of course. BI and analytics solutions should be viewed more like products; better still, they should be seen as one unified offering with stable, high-value features providing access to high-quality, trustworthy data. That means moving past project-centric, release-driven approaches that have been the rule in IT for a long time. Culturally, IT leadership must open people's minds to be creative about the uses of data. Practically, it must drive to eliminate bottlenecks and issues that are self-imposed (like broken or inefficient development processes), without ever losing sight of the bigger vision of what data can be.

BI and analytics must be defined and communicated in terms of information delivery service, like a utility. The actual products, or apps, are updated regularly with minor enhancements and periodically with major releases. Data is the foundational asset that drives the ongoing process of information delivery, supported by the BI and analytics solution that IT develops, supports and improves over time. The business value is achieved when users across the business can more readily access, understand and more creatively use data.

Defining our Terms

At its simplest level, an asset is any resource – ideas, patents, inventory, equipment – that companies can use to produce value (usually measured by revenue or profit). Data’s greatest potential value comes from the insights that can be generated by skilled users with the means to review and interrogate high-quality data . Similarly, investing in data capture and data management pays off when executives can make new observations about and good decisions for the business. Compelling promotional offers, better cross-selling and up-selling programs, clearer assessment of vendor and supplier performance, predictive accuracy on future financials, visibility into regional and seasonal fluctuations – there is virtually no part of the business or operational area where quality data cannot be used to valuable effect by the business.

So what’s preventing companies from doing more with their data? Veterans of the information delivery world understand that several factors are in play. This isn’t simply a matter of IT development teams in BI and analytical areas being overwhelmed with project backlogs (though that doesn’t help matters). It’s not that companies can’t access the right toolsets; there are plenty of high-powered platforms and packages on the market today. And it’s certainly not that companies don’t have enough data; if anything, they have too much, which prevents them from managing it properly.

The much greater challenges relate to culture, executive mindset and organizational structure. One of the most common and significant barriers is “informational hoarding” by different parts of the business. Sales, marketing, operations, finance, individual product lines and business units often maintain their own data and don’t share with other parts of the business. These hoards may result from turf wars, internal politics or simple organizational inertia.

Whatever the cause, these issues greatly limit the ability of business users to see relevant and potentially useful data across the enterprise. They make many common tasks – such as forecasting demand, closing the books or budgeting – more inefficient than they need to be, given that data must be reconciled or integrated, often in painstaking manual and error-prone processes.

Even more dangerously, decision-making can be severely compromised when different parts of the companies maintain their own data. This often occurs as a result of semantic confusion, another common cultural challenge when it comes to data. It’s painfully obviously that terms such as “revenue” and “customer” should be clearly and universally understood by all stakeholders across the enterprise. Too often, they’re not. Sales leaders hear revenue and think of a monetary amount on signed contracts (“booked revenue”), while finance defines in GAAP terms, such as “deferred revenue” on the balance sheet or “recognized revenue” on the income statement – all three of these are different. It’s easy to see how even the simplest management reports can be misleading when common metrics are inconsistent.

Collectively, these examples demonstrate a need for stronger enterprise data stewardship. For information management investments and programs to generate full value, the boundaries of data ownership must be expanded in support of broader governance approaches that synchronize data and its consistent meaning across functions, geographies and projects.

IT groups must change their thinking about the nature of their work with data. The traditional project-centric mindset must give way to a broader vision of the role data and information play in the enterprise. This culture drives a degree of accountability higher than the current state of affairs, where too often projects end with a sense of "Whew, that customer was tough, glad to be off that.” The new mindset should be to think of the way we deliver information as a product, much like software, with planned feature releases that expand its utility. This is a subtle but profound shift in thinking.

Ongoing relationships with the business should continue after one deliverable is complete. After all, user needs will change tomorrow, and they will need to figure out how to ask more questions of new, different and more data. For this reason, IT resources must work harder to engage more collaboratively and productively. A proven practice is to have customer-facing team members approach customers (or business users) to flesh out key requirements and identify the highest-value features that can drive product success. In this role, they behave as the product managers defining the market requirements.

They need to work with lean teams of product engineers that can quickly establish a view of what that feature will look like – call them prototypes that are up and demonstrable quickly, for instance within a week. With that kind of velocity, users will be engaged in the process of refinement. Product engineers will need to work with the product architects to assure the feature fits into a clean product release and won't break other features of the overall product. The product architects jobs are to have the long-term roadmap clearly in their head to fight off issues that might arise downstream across product releases due to narrow feature development.

For the product to be successful, there needs to be a "complete core" team that carries the product forward and retains the knowledge of the need and use of all features (data). The tooling is not as important as the solutioning and packaging. And BI teams shouldn’t overlook the marketing of their products, either. Product managers should be evangelists who also happen to have technical chops. Showing off just how effectively data can be accessed and interrogated through highly usable BI tools is a great way for “walking the walking” when it comes to viewing data as an asset. So is a customer-centric, service-oriented mindset; after all, the customer is always right.

Bottom Line: The New Rallying Cry

“Data is an asset, let’s treat like it one.” That should be the new organizational rallying cry. And IT must be first in the rallying line, shouting the loudest and evangelizing the most persuasively.

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