It has become something of a cliché that data is a natural resource essential to running a business. It must be harnessed and turned into insights and competitive advantage.
Companies are certainly wise to adopt a data-driven approach, particularly now, when technologies like artificial intelligence, cloud and automation need a solid data strategy to work effectively.
But where to begin? Many organizations aren't sure, especially because of the massive, ever-expanding quantity of data. By 2025, 463 exabytes of data will be created daily -- equal to nearly 213 million DVDs, according to the World Economic Forum. Then, there is the needle-in-a-haystack task of finding the right data, which is scattered inside and outside the enterprise.
Companies need a strategy. Here are four considerations to keep in mind.
Connect with analytics experts to create an industry-led augmented intelligence strategy. Partnerships, especially ones that integrate technology and industry expertise, increase access to talent and allow the creation of a culture of industry-led augmented intelligence. The balance between human expertise and the power of machines is vital. When these elements come together, they can solve some of the most challenging business problems and transform experiences for employees and customers. Genpact, a professional services firm based in New York, worked with a global medical company that had to manage such large volumes of disparate data that analyzing sales performance, competitor activity and customer behavior became extremely difficult. Analysts were spending more than 80% of their time extracting and cleaning data for reporting, leaving little time to turn this data into insights. They often had to rely on manual spreadsheets, which led to inaccuracies. Working together, we developed self-service analytics for our client's commercial team so they can generate their own insights on day-to-day activities. Among the many benefits realized, employee productivity has risen 98%.
Create the foundation for a sustainable future of data-driven decisions. Data-driven transformation isn't simply applying digital technologies to obsolete processes. Those processes must be identified and transformed. For example, if the invoicing process at a global retailer is slow and cumbersome, some may suggest introducing automation technologies to increase speed and volume. More likely, there are issues within the process that need to be resolved before automation is applied. In this instance, automation would create more problems than it would solve. One excellent way to uncover process inefficiencies is by process mining, which identifies problems in a process by using algorithms to analyze data from underlying systems.
