More and more enterprises are waking up to the importance of a sound data strategy to drive the process of extracting value from their data assets. An IDC report states that by 2020 the amount of digital information in existence will have grown to 40 zettabytes (from the 3.2 zettabytes estimated today). It is now common urban knowledge that data volumes are exploding whereby more data has been created in the past two years than in the entire previous history of the human race.
Organizations have begun to benefit from data by leveraging data science and modern big data technology. In layman’s terms, data science is a sequence of steps that is used iteratively to convert data into insights for executives in the organization. It is the process of making data valuable. Using data science, many organizations have repeatedly shown how to transform their businesses into responsive and data-driven enterprises. These organizations have driven profitability where the use of data has not only helped the topline but also the bottom-line. A report in Forbes mention that for a typical Fortune 1000 company, just a 10% increase in data accessibility will result in more than $65 million additional net income. McKinsey report “Getting big impact from data” quotes that a large retailer could increase their profit margin by more than 60% through big data analytics.
Success stories such as the above are not as many as the industry and practitioners such as myself had hoped. Fundamentally, data, while omnipresent in a modern enterprise, does not exist in a vacuum but in fact requires the active cooperation from all C-suite stakeholders to become valuable. An HBR article reinforces the need for organizations to start their data & analytics strategy from top. This means not breaking the organization into silos to derive value from data. It is essential that data strategy becomes a key contributor to the development and functioning of the business strategy, and offer insights into sales, marketing, supply chain, operations, customer experience and other core decision makers. Unfortunately, this tends to get overlooked by most enterprises embarking on the journey without expert help.
Rather than embarking on hiring scarce-and-expensive so-called “data scientists” from the market, organizations need to start from what they are already very good at and have abundance expertise in – the domain. The data science process becomes relevant producing meaningful results only by having a concrete understanding of the domain – business knowledge, operating process, product knowledge, competitive landscape and market forces.
People and process have been the fountainhead of domain knowledge that is required for businesses to operate, while technology plays the role of an enabler. Data science, when carried out outside the context of a domain, provides only numbers that are not useful for the business, since it discounts the importance of that fountainhead of knowledge and context. Usually such exercises tend to focus on the technology and platforms instead, which suits the enterprise software vendors and their system integrator partners well! That explains why studies by The Guardian show that, at the moment, less than 0.5% of all data is ever analyzed and used!
Simply collecting data will not unleash its potential value. It is important to add to the mix human intelligence to analyze, distill and communicate information to capture opportunities. According to a McKinsey survey, only 18% of companies believe they have the skills necessary to gather and use insights effectively. At the same time, only 19% of companies are confident that their insights-gathering processes contribute directly to sales effectiveness. Diverse set of analytic techniques are required to extract more value from a wide variety of data types. And the velocity with which big data accumulates and ages implies analytic insights can improve business performance only when they can be quickly brought into operations and drive actions in a live manner. Where not sufficient human knowledge experts are available, technology can come to the rescue what with the advent of excellent innovation in modern AI that powers everything we touch and feel as consumers to eventually traditional enterprises.
The increasing availability of cloud processing, storage services and analytics services has opened the opportunities in making data driven decision making accessible to businesses of all sizes across many industries. Data and analytics can be significant contributors in the core development and execution of business strategy impacting the employees, customers, market opportunities and more. Coupled with the global adoption of cloud as an enabler, a sound data strategy that emphasizes on operationalizing data science becomes effective at driving value. After all, data without action is meaningless.
[Disclaimer: The views expressed in this article are solely those of the authors and do not necessarily represent or reflect the views of Trivone Media Network’s or that of CXOToday’s.]