The Power of a Successful Data Strategy Part 1

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Harnessing the power of data is a critical component to any large company’s operating strategy. For the past 10 years, large businesses have invested heavily in building out the infrastructure needed to capture, standardize and report on transactional data from disparate critical access points across their enterprises. As a result, companies who have executed a successful data strategy have reaped enormous rewards. Obvious contenders, such as Amazon, Apple, Alphabet, Uber and numerous marketing analytics companies, have used data not only to fuel the growth of new products, but to streamline their operational efficiency, allowing them to operate at a higher level than their competitors.

As large companies and investment firms have come to a greater realization of the power that data holds, they have started investing heavily in their data infrastructure, both organically and through acquisition. According to a report by EY, companies that are focused in IoT and “Big Data” have seen increasing mergers and acquisitions activity from both tech and non-tech buyers alike as firms look to diversify their holdings and make a play in the data world. When data architecture and project investment go as planned, companies are setting themselves up for strong growth and lower operating costs in the coming quarters.

On the flip side, many companies fail at understanding how a data strategy can be implemented effectively across their enterprise. Even when they do understand, they often fail at execution due to a variety of reasons, ranging from poor planning, misunderstood business goals and lack of data quality to legacy system debt, misaligned resources and under-funded projects.

However, there is good news regarding the exposure and investment that large companies have made over the past 10 years: the cost of hardware and software required to undertake data projects has begun to fall sharply, and the breadth of tools available to take on such projects has increased. This eliminates two key components of project failure. Technologies that used to be reserved for Fortune 500 companies are now available to mid-market and small businesses, and software that used to cost hundreds of thousands of dollars now amounts to roughly a thousand per month.

Although the drop in price for data and analytics software reduces the risk of executing a data project, it does not help define the business goals and execution strategy required for success. Small and mid-market companies must adopt a consultative, well-thought-out approach in order to match their business goals and success criteria to available technologies.

Additionally, when a technology does not exist for their needs, a custom solution based off free or low-cost technologies may help to augment their purchase of required tools, giving them both a tailored made solution as well as a competitive advantage over their competition, who lack such tools and strategy.

Lastly, defining a data project may seem like a daunting task. But, like any successful project, it all boils down to breaking it up into manageable chunks and constantly realigning the project’s goals to match the goals of the business. Many data projects start out with unrealistic timelines. Taking an iterative, agile approach with early demonstrations, however, helps to allay stakeholder fears of a project gone awry.

While data projects have been among the last to adopt an agile methodology — due largely to their investment in foundational resources — research shows that this is exactly what should be happening. Innovative development methodologies, coupled with lower-cost technologies, can now open the door for small and mid-market companies looking to gain a competitive edge within their market. If these companies do not innovate, their competition will.

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