Is Your Data Useable?
How to Avoid the Pitfalls
Have you heard the saying, “Data is the new oil?” It’s a picturesque metaphor that in many ways fits.
Big Data and enterprise analytics are proving ROI and a direct impact on business. When British mathematician Clive Humby first used the phrase in 2006, however, he had something different than monetary value in mind. His full quote holds more thought: “Data is the new oil. It’s valuable, but if unrefined it cannot really be used.”
It’s true—gathering data for data’s sake does nothing to drive profit, yet some businesses have yet to figure out how to properly pull value from the vast amounts of data they have started gathering. This pitfall also wastes resources that could more effectively used elsewhere. Even passively collecting data incurs storage fees.
Collecting data without a focused data strategy is the greatest pitfall of organizations right now. The good news is that increasing data utilization is one of the single most profitable steps a company can take to turn this pitfall around. Research shows those who use data to inform their business strategy consistently see a five-percent boost in productivity and six percent more revenue than those who do not.
A second common pitfall is the people behind the implementation. While 85 percent of corporations have begun trying to incorporate data into their business strategy, barely one in three can say their big data venture has been a success. People, not technology, are holding these projects back. Organizational resistance, a lack of understanding among the senior leadership, and misalignment with organizational goals can lead to bottlenecks and slowed down implementation. This means companies know the value of data, but they lose steam part way through implementation because leadership can’t agree on the direction of the data strategy.
To avoid project collapse, executives need to clearly define the insights they are looking to gather and identify the data that best suites that. Rather than collecting data and looking for random but useful nuggets to drop out like gold, be deliberate in what KPI you are applying your data strategy to.
A good place to start can be to outline pain points and desired areas of growth that your organization would like to address, then apply the analyzed data to those specific business problems. Data refinement is done using artificial intelligence and machine learning software. This type of application allows computers to behave in “intelligent” ways. Given a large subset of data, computers and programs could make its own decisions about relevance and priority of information without predetermined guidelines for every possible situation. Machine learning is a subdiscipline of artificial intelligence that aims to give machines the ability to learn from previous experiences and use that knowledge in future interactions. Essentially, a computer is given a pile of data and a machine learning algorithm is asked to process it. It’s possible to dig much deeper into the differences and implementation of both, so for more details, additional resources can be found here.
For the common pitfall of people, assigning clear roles in the digital transformation is helpful. It’s not reasonable for a CEO to be expected to understand every aspect of the business. At the same time, the CTO has to do more than implementation. In the digital transformation process, there is heavy reliance on the CEO, COO, and CTO for success. The CEO is integral in championing the effort and ensuring the transformation stays on course, keeping a closer eye on implementation than in the past. Meanwhile, instead of focusing on the nitty-gritty of implementation, the CTO should be forward facing and proactive in explaining the technology to all areas of the business impacted, showcasing an understanding of the technology’s impact on the bottom line and the department. Finally, the COO takes the technologies suggested by the CIOs and CTOs and fits them into the overall business strategy devised by the CEO. To put it another way, they are the “how” to the CEO’s “why” and the CIO’s “what”.
Avoiding these pitfalls means data-smart companies can maximize their data usage and gain an edge over competitors. The recommendations and insights into overcoming them also sets up your data strategy to be fairly pain-free and streamlined. This level of organization, focus and care gets, and importantly, keeps, not only the management team but all impacted by the changes, on board and moving forward.
Humberto Farias, CEO of Concepta, is a seasoned technology professional with over 18 years of experience guiding companies around the world through the custom software development process. He leads a team of highly skilled software engineers and developers providing tailored web and mobile applications to enterprises.
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