Big data, data science, analytics, BI…data has been subjected to buzzword bingo over the years. Perhaps because of the hype, the results of big data initiatives often do not match the promises. Gartner estimated in 2016 that 60% of big data projects fail, and then revised that figure upwards to 85% one year later. Since then, the situation does not seem to be improving significantly: in 2019, Gartner predicted that through 2022, only 20% of analytical insights will deliver business outcomes.
Yet, almost all organizations know they need to get a handle on their data, not just for reasons of regulation and to comply with the law, but because of the business insights the data holds. When we go through the process of deriving analytical insights from an organization’s data, and then turn these into actionable intelligence, we often call this “data-driven decision-making” for short. In some ways this shorthand conjures up the wrong image, as if the data does all the hard work and magically present implementable answers to us. It is worth remembering there is no inherent agency or wisdom in the data, it is all down to how enterprises harness it.
To put it another way, when a racehorse wins, it does not succeed in isolation. The factors driving its success include the jockey, the training regime, the horse’s heritage, diet and so on. Similarly, data can deliver success, but it needs to be tamed, harnessed and then put to work in order to achieve impactful results.
Understand the data
Data comes in all shapes and sizes — both structured and unstructured – and includes systems of engagement, systems of record, and social media. Most of it resides in different formats and varying locations. The ever-growing use cases of IoT only aggravate the challenge. Getting to grips with the nature of an enterprise’s data is one of the first considerations at the beginning of a big data initiative, along with understanding what the business drivers are. Digital Engineering firms like Apexon have additional data points from their experience working in different projects in different industries. These data points cover all data from technical, operational as well as people perspective.
Build a business case
The failure rates for big data are high. The story behind the numbers is often all too familiar. Big data analytics works best when they are closely aligned with a business goal. Organizations who invest in big data looking for non-specific benefits and nebulous successes are likely to fail. Usually, they have been sold on the hype that surrounds big data. That’s why we work closely with clients to build a business case. It is something we insist on. Whether the business case for big data is around driving growth (top line) or efficiencies (bottom line), once the goals are clearly outlined and understood, it is time begin working on big data strategies and architecture design.
Deploy and operationalize big data solutions
It is an open secret that great data scientists are in short supply, which is why organizations will often partner with a digital engineering specialist (like Apexon) who can provide expertise in the skills enterprises lack in-house. It is a multi-step, complex task developing a technical approach to deploying and managing data, as well as addressing the governance, security, privacy and risk requirements. Ensure your big data partner is fluent in the leading technology tool providers for each stage of the initiative from data ingestion to analytics to application development to machine learning. To learn more about the Apexon approach, take a look at our data engineering services page.
Turning insights into actions
The finish line is not at the point at which a team has extracted insights from a big data project. The real fun begins when organizations take what they learn from the data analysis and use it to implement change. Transforming an organization’s disparate data into actionable intelligence will involve process redesign. Businesses can expect to implement data-driven changes using tools for automated execution and agile methods for continual product enhancement, which leverage the data to speed up cycle times and drive new operational efficiencies.
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