4 Key Reasons to Get Data Integration Right in 2021
The volume of data that is available to companies today is significantly greater than ever before. The velocity of that data, moreover, continues to increase as cloud computing, mobile technologies, and the Internet of things (IoT) gain broader adoption than ever. Smart companies are not simply managing that complexity; they are embracing it as a key enabler of competitive advantage in the 2020s. A sound, holistic approach to data integration and governance is essential if organizations are to unlock the immense value of all that data.
The problem that many organizations face, of course, is that much of their data resides in siloed applications, on mainframes, in ERP or CRM systems, in specialized billing or logistics systems, e-commerce, or even with external vendors.
For companies that are still living with data silos, 2021 is the year to take data integration to the next level. The COVID pandemic has been the catalyst for so many changes, not the least of which are rapidly shifting customer expectations and an intensified focus on winning new customers and retaining existing ones.
As we look ahead to the new year, here are some key factors that compel business leaders toward a renewed focus on big data integration.
In the coming decade, the companies that develop a competency for integrating, managing, and extracting value from their data will gain a long-term competitive advantage. In the insurance industry, leading companies are using data to refine risk assessment models, resulting in more accurate pricing. Retail organizations are transforming site selection from an art into a science, using location intelligence and mobility data to rapidly zero in on the best locations for new stores. Financial services companies are using data to develop a more complete and accurate understanding of who their customers are and to tailor product and service offers that appeal directly to their needs.
To effectively extract value from an increasingly complex and multi-multifaceted body of information, business leaders must begin with a clear view of their data assets and the silos that contain them. They must take proactive steps to break down those silos with enterprise integration.
We are all familiar with the old saying, “garbage in, garbage out,” but in the rush to extract value from big data, there is the potential to feed downstream systems with that poor data quality. Information can degrade over time, as individual customers change their addresses, names, or other key attributes. Commercial entities, likewise, often merge or go out of business. Data quality can suffer as a result of human error, changes to what is being measured (and how it is measured), or simply from data corruption or loss.
As businesses aim to incorporate artificial intelligence and machine learning into a broad range of business processes, the importance of breaking down data silos is greater than ever before so you can truly feed artificial intelligence and machine learning models with all the data needed to produce the best outcomes possible.
