It should be obvious that data driven decision making over time yields consistently superior (and repeatable) results as compared with pure gut driven decision making.

And sure enough, studies have shown that too.  An article in Forbes references a report on “The Executive’s Guide to Effective Analytics” by Aberdeen Group which states that data-driven organizations experience a 27-percent year-over-year increase in revenue, compared to 7-percent for other organizations.

An Economist Intelligence Unit survey, on “The Complexity Challenge: How businesses are bearing up”, shows that companies that rate themselves substantially ahead of their peers in their use of data are three times more likely to rate themselves as substantially ahead in financial performance.

So with all of this, one would assume that most companies would automatically start to use data, both internal and external, in order to hone their organizational decision making processes, right? Wrong!

Let’s understand why.

The first (and probably the biggest) reason is Newton’s first law of motion – inertia. It just hasn’t been done before, it needs an effort to start figuring what data to collate, how to use and review it, and how to draw meaning from it such that it helps the decision making process. That’s daunting. And people don’t know where to start, or even that they can start to do something about it.

 Typically, to get over this inertia two things need to happen. One, a conscious adoption of a data-centric decision-making approach by some internal change agent in the firm (and this is ideally a C-suite champion) who can lead and guide this change. Two, people (even a few) beginning to experience the early benefits of the use of data. The benefits of a data driven approach can even just start with the ability to explain a situation more cogently with supporting quantitative data rather than relying on anecdotal information alone. Depending on the organization’s receptivity, this could soon lead to implementing and validating decisions based on data. Unsurprisingly, for those that make this switch from a completely gut feel driven decision-making process to a data supported decision-making process, even the ability to hone the gut instinct increases.

The second reason for resisting (or at least struggling with) a shift to using data for decision-making is what I am going to focus on a little more. It is tough to get relevant data at short notice, without wading through reams of superfluous information. And it doesn’t seem worth it at times, since it is hard to trust the quality of the data one uses. Once someone points out an error in the data, the entire decision that has used the data as an input seems suspect.

Let’s look at this second problem in the not-so-narrow world of business and financial data. Most , driven by requirements to comply with regulatory mandates. That’s a starting point for data that can be used within firms for decision making. CFOs should be able to, relatively easily, track their financial performance vis-à-vis peers in the industry using such published financial data. And they would like to.

The problem, however, has been that such published data is still not easy to use. It needs patience to dig deep through documents of peer companies to find what one is looking for, understand the nuances of how every company classifies the concepts you are looking for, what adjustments firms have made that probably need to be readjusted, and finally, entering the data in one’s own model for tracking and comparison purpose. And it gets tougher when it extends to business data. Things like productivity per unit of installed capacity, compensation structures across peers with comparable turnover, revenue per employee, average interest rate on outstanding debt, and more. More firms would leverage these kinds of data across peer sets to start drawing inferences and working through strategies, if only the process of doing all of this was easier.

It is getting easier. With data now available in standards like XBRL, users are able to pick what they are looking for across several companies in a more straightforward manner, since most companies map their internal data to the standard definition. For countries like the United States that allow non-standard concept reporting, a growing number of information vendors are looking to provide normalized views of these non-standard concepts. This allows users to get the comparison data they need using these normalized views, while still being able to cycle back to the ‘as-reported’ XBRL view easily.

XBRL data is also ‘trustable’, since it is pre-validated. And it also provides clear visibility to users on things like ‘how-is-this-number-calculated-and-where-did-it-come-from’. Finally, given that XBRL is also a machine readable format, it is easy to directly pull data into financial models quickly, without needing to manually tweak and update the model every quarter.

Many companies still work the good old gut feel method for making decisions. And honestly, gut feel is great, as long as the outcomes of the decision are visited, examined and modified if needed. Data helps do just that. By valuing data as much as gut instinct and experience, data-driven organizations can leverage data to produce relevant, tactical information when and where it is needed.

Read here to know more how ‘trustable’ XBRL data can be served to your lovingly-wedded spreadsheets.