We generate nearly 2.5 quintillion bytes of data every day, more than 80 percent of which is unstructured data. Multiple industry reports and research surveys highlight the fact that only a handful of C-level executives have made effective use of this data, while the rest are still trying to cope with data overload.
In our previous article, we presented our perspectives on how to understand your organization’s requirement for speed, including key considerations to improve the speed of your organization to meet speed of business decisions. Investments in data analytics should be driven by the need for speed within the organization.
But speed in itself cannot create business value. It has to be complemented with actions.
Recent Forrester reports predict a 350 percent revenue growth - to $1.2 trillion by 2020 - for public companies that have embedded analytics and insights-driven action into their company DNA.
One such example is the online personal shopping giant Stitch Fix, which brought in $250 million of revenue in 2015, and is predicted to jump to $500 million plus in the next year or two. The C-suite title of ‘chief algorithms officer’ is a testament to Stitch Fix’s data and actions-driven culture. These insights-driven firms are predicted to grow 27 percent annually – a number that should compel organizations to think differently and focus on insights and actions.
So how do you ensure your organization has the right capabilities to drive faster and more effective business actions? There are three key considerations for understanding the correlation between data, speed and business actions:
1) Actions should guide your enterprise data requirements - not the reverse
Data in itself is not actionable, even when it is real-time, at hyperspeed. To drive effective business actions, data needs to be sourced, processed and shared with the end goal in mind.
Highly effective organizations ensure all data delivered to decision makers is relevant, timely, and actionable. It is important to strike the right balance between strategic foresight and analytical hindsight. The business world today is focused on “hypothesis-driven insights approach.” But is it enough?
To realize true value from data, this must be embedded in an iterative “test-and-learn” cycle:
The speed and frequency of the test-and-learn cycles, relative to your competition, will determine the ultimate impact of your analytics capability. Further speed can be achieved by minimizing the human element and investing in machine learning algorithms.
2) Data is ubiquitous, but insight is scarce – be judicious in your data strategy
Once you have identified your data requirements, you must determine how to best extract insight from this data. The decisions you make around data sources, collection methods, linkages, and structure will drastically influence the value of the output. Consider the following three aspects when designing your data strategy.
The data that you require may not reside within your organization’s four walls. Strategic partners, suppliers, and customers represent a wealth of untapped potential. Consider the case of designing a new product.
