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.
