Data, data everywhere—not just the right kind to make effective decisions! It wouldn’t be wrong to assume that this is the common lament in most enterprise decision-making circles today.
On the one hand, companies are drowning in an unprecedented flood of data, structured as well as unstructured. And, on the other, CIOs, CMOs and other CXOs are struggling to get a handle on all that data, put it into the right perspective, extract and massage it into a usable form and take quick, effective decisions. The ones that can earn their firm the much-prized moniker of an agile business or a data-driven enterprise.
While making decisions in any enterprise involves a whole battalion of executives, LOB heads, managers, supervisors and many others, I think the job of enabling the whole organization to take decisions based on analytics rather than hunches (and perhaps, lunches) is most suited to the CIO. The reason is simple: who else has an across-the-board view of the data ecosystem of the company? And that too with the additional knowhow of how the information systems work (or can be made to work)?
So, without further ado, here are five ways CIOs can enable an environment for adaptive, data-led decision making in their organizations:
Making speed count: I know one thing for sure: organizations of all stripes today collect all sorts of data. Through all sorts of forms. By making innumerable number of calls to customers and prospects. And by sources such as the usual enterprise data captured through ERP and other operational systems. But how fast are you with the data you collect? Does it lie buried into file cabinets or dusty disks? Simply putting the data to quick use can make a huge difference to the organization. Following up on a hot lead in quick succession of the data collection process, for instance, will translate into revenue; too much delay, on the other hand, will make the prospect turn to your competitors.
Knowing your data from your metrics: This may sound simple to some and unnecessarily complicated to others. Yet this article on the Harvard Business Review site illustrates the difference and the significance of the difference quite clearly. Authors Jeff Bladt and Bob Filbin cite in the article the example of a YouTube video, asking the reader to guess as to how many views would qualify a video as a success. Now, the particular video in question had garnered 1.5 million views but it failed to do what it was supposed to do: encourage young people to donate their used sports goods. So, despite the impressive views, only eight viewers signed up for donation—with zero finally making the donation!
Not all results (or metrics) will turn out to be in such low extremes. But the point is well-made: you need to specify clear metrics in any data collection or numbers related exercise that will reliably give the true measure of success for the initiative.
Data is data is data, right? Wrong: When data is to be put at the heart of decision-making in an enterprise, it matters all the more that the data be accurate, consistent and timely. So, one may be under the impression that all the data required for a project, say, a marketing campaign, is available, if the data quality is not up to the mark, the results of the campaign would certainly be below expectations.
According to a data quality study by Experian Information Solutions, 32% of U.S. organizations believe their data to be inaccurate and further, 91% of respondents believe that revenue is affected by inaccurate data in terms of wasted resources, lost productivity, or wasted marketing and communications spend. If that’s the case with such a data-rich economy, one could imagine how bad the shape of things would be in a country like India, where data collection and research are relatively new fields and far from being mature scientific disciplines. In this context, the need for best practices as well as tech tools in maintaining high data quality cannot be over-emphasized.
Democratization of analytics: How many of you can remember the era of generating sporadic MIS reports for the consumption of the privileged few? Well, that era is long gone. However, most companies are still chary of sharing key statistics or analytics data beyond the confines of top or senior mid-management. But gradually, this state of affairs, too, is set for a bold change. Some call the coming wave as the democratization of data or analytics, in which actionable data percolates to the lowest links in the organizational hierarchy.
Having said that, democratizing data does not mean dropping a huge spreadsheet on everyone’s desk and saying, “good luck,” as Kris Hammond, Chief Scientist at Narrative Science points out in this article. On the contrary, he explains what it involves simply and emphatically: “Democratization requires that we provide people with an easy way to understand the data. It requires sharing information in a form that everyone can read and understand. It requires timely communication about what is happening in a relevant and personal way. It means giving people the stories that are trapped in the data so they can do something with the information.”
Point well made: unless people can take “informative action,” the analytics tools or the extracted data will have little value for the people or the organization.
Analyzed this, have ya? Now visualize that, too: I’m not sure if you noticed but the Internet has been flooded with a new tool of information dissemination in the past couple of years. It’s called the infographic. For most of your searches on Google, there are now an eye-load of infographics, those illustrative diagrams that give you the needed information with icons, pictures, graphs and anything non-text.
Much less noticeable but equally important, a similar movement is underway within enterprises in the context of data analytics. Vendors such as Tableau Software and Qlik Technologies are leading the charge in this emerging segment, referred to as the visual analytics market
According to specialist consulting firm Atheon Analytics, visual analytics “brings together computer science, information visualization, cognitive and perceptual sciences, interactive design, graphic design, and social sciences.” (To see the power of visualized data in action, watch this slightly old but enormously impactful video, the Joy of Stats, of Swedish statistician Hans Rosling, who is often referred to as the “Jedi master of data visualization.”)
The above are only a few of the multiple ways in which CIOs can bring the hidden power of data to the forefront of organizational ability and agility. There are plenty of tools and technologies available but each organization must find its own best-fit path to data-driven success. The key is to start the data journey as early as possible and do so in right earnest.