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No matter we realize it or not, our lives run on data.

Every time that we buy, we pick entertainment options and vacation spots, we make friends on social media, we recommend products and services, we find dating partners, we apply for jobs, or we locate houses to move in to, we are deciding. And these decisions are influenced through the innumerable data algorithms and recommendation engines that silently trawl our online personas, historical decisions, past behaviours, and social preferences, to churn out the future-likely choices.

In 2006, Clive Humby, the British Mathematician and an entrepreneur in Data Sciences, coined the phrase “Data is the new oil”. He elaborated by saying, “like oil, data is valuable, but if unrefined it cannot really be used.Oil has to be changed into gas, plastic, chemicals, etc. to create a valuable entity that drives profitable activity; so, data must be broken down and analysed for it to have value.”

Arguably Clive himself wouldn’t know till much later the towering impact of his prediction. Let’s exemplify that effect by two statistics alone:

  • New information generated per second for every human being is 1.7 megabytes. Considering there are over 7.7 billion people on the planet, this amounts to new information equivalent to more than 25,000 hour-long videos. Per second!
  • Today, nearly 70% of leading enterprise companies have a chief data officer (CDO), according to a study from New Vantage Partners. That’s up significantly from 2012 when only 12% did.

Everyone wants to talk about the insights and value they can derive from data. But what about Bad Data? And the price it exacts?

Bad data is inaccurate data, missing data, wrong or inappropriate data, non-conforming or duplicate data. Bad data is costly.

In 2016, IBM estimated the yearly cost of poor quality data (to the US economy) as $3.1 Trillion. Gartner put the average cost for poor data at $9.7 million/year/business. These are stunning figures.

So given the primacy of data in decision making and the heavy penalties of bad data (across a company’s financial resources, efficiency, productivity and credibility), let us consider what makes a data culture.

Simply put, a data driven culture moves data to the center of decision making.

It treats data as a main resource (as opposed to gut or instinct) for leveraging insights across all departments.

Best data driven companies embed a cultural framework that rivets the following nuts and bolts: easier data access, clear governance around data usage and definitive quality standards, improved data literacy, and finally, incorporating apt technologies to prepare and analyse data.

Next let us examine few ideas that mature Data cultures use to approach clarity of purpose that enhances their effectiveness, and also increases speed to fruition for their analytical efforts.

These data culture practices are what eventually leads to precise decisions.

  • Data culture is not about cool experiments. The fundamental objective in collecting, analysing and applying data is to make better decisions. Period. Volume in data lakes by themselves mean little if the central focus is not on solving the business problem. Always.
  • Data culture is both top driven and bottom swelled. True and repeated commitments from the board and the C-suite is non-negotiable but it is in democratizing data that the richer gains lie. Like staple diet forms, data should be missed if not consumed every day. Seeding projects, instigating new conversations, prompting innovation by applying data to challenges, imbibing data-belief, celebrating data-victories and removing data bottlenecks – all are critical in sustaining a data culture
  • Early acceptance of Risk. Mature data culture organizations understand the imminent risks that come from getting analytics wrong. They accept the support and remedial costs. Punitive measures in these companies are replaced by building alert systems. Higher levels of sophistication are wired into their planning-processes-practices. Ground level structures are fortified to use data with assurance and freedom.
  • ‘Culture Connectors’ are critical. To bridge the world of the data scientists to the on-ground users, a few high-credibility data ambassadors are enlisted. These change agents are catalysts cum coaches that help others to embrace data culture.
  • Rewiring the talent organization. It is usual practice to deliberate on touchpoints that interlaces roles. Constructive data-driven engagement practices are used to bind disparate organizational jobs and functions.

In sum, today’s businesses accelerate through precise decisions when on-demand data is engineered for accuracy. This marriage of conscious contextualization and comprehensive competencies works when it is backed by intersecting domains and tech. expertise.

Being able to do this, day in and day out, is what creates a robust data culture.

This was originally published on MEA-Finance.com website and is being reproduced here.

Article by

Krishnakumar Ranganathan