The “data gap” between companies is tightening, but the “insights gap” continues to widen. New technologies have made it easier for companies to collect data, but most realize only a fraction of the potential value. The discrepancy between the collection of data and the effective use of the data creates an insights gap that requires a significant culture change to address.
As a technology partner, you have an inherent responsibility to help your clients take advantage of technological advancements. Your clients, interested though they may be, care more about obtaining their goals. Accordingly, to deliver what your clients need requires an understanding of both the technology AND the fundamental operational and tactical changes necessary to turn information into meaningful action.
The “Insights Gap”
Investments in data collection and analytics continue to rise, providing tremendous amounts of information available to an organization. While the amount of data has increased at exponential rates, the use of it for many companies has lagged. One component of the insights gap is the lack of aggregation, leading to data silos. A second factor is that companies do not understand which data are critical to deriving valuable insights. Thirdly, most businesses lack the operating structure and culture needed to prioritize and take data-driven action.
The “Insights Gap” exists between the data capabilities on one side, and the decisional uses on the other. Data, unincorporated into decision making, provides little value. Decisions, absent supporting data, are powerless. The gap closes when organizations properly pair the critical data necessary to empower data-driven decision making.
Advances have been made not just in the technology available but in the way data and analytics are delivered. The volume of data available continues to grow exponentially with the expansion of digital platforms, billions of smartphones, sensors, cameras, and various other data sources. The increase in raw data produced a corresponding growth in storage capacities and computing power, but the data continues to be underutilized.
Perhaps the most valuable aspect is the migration from on-site collection and storage to cloud-based services. Software-as-a-service (SaaS) platforms have spawned variations such as Storage-as-a-Service and Data-as-a-Service models that facilitates providing data products to end-users on demand. These platforms offer endless flexibility and scalability opportunities for businesses to collect, measure, prioritize, and make use of their data - making them attractive additions to companies looking for a data “solution.”
As companies continue to invest in data collection; however, they will ultimately fall short of their objectives unless there is a corresponding investment in data aggregation. Disconnected systems will only increase the gap between data and insights if not combined and analyzed in real-time. The key to unlocking the value of “as-a-Service” models is in the aggregation and comprehensive analysis from all the sources.
Becoming a data-driven organization is not as simple as many companies think. It requires digitization of all data assets, expert implementation of the technology components, and a general understanding of how to use the data paired with a commitment to use the insights in day-to-day operations. Without the organizational changes necessary to make use of the data, technological investments will provide a fraction of their potential value.
A critical requirement for accurate analytics is comprehensive data inputs. Many businesses have been slow to embrace a complete digital transformation, which has created data silos where some information is digitized while other datasets remain in analog form. Organizational change requires the complete digitization of data assets to provide the information necessary to generate accurate insights.
Companies also need to cultivate the ownership and responsibility culture necessary to derive insights. It’s one thing to have raw data, and it’s another thing to know what to measure and how to measure it. Understanding what to measure is a business concern. Understanding how to measure it falls into the realm of the data scientist. Creating a culture of data ownership and responsibility begins at the intersection of business and data science, where the technology and business minds collaborate to develop innovative solutions to the most critical problems.
Finally, companies need to adapt their business processes to integrate data insights into day-to-day operations. Integration requires that the insights are easily consumable at multiple levels within an organization. Insight silos will evolve when only data scientists or high-level executives have access to - or comprehension of - data insights. Key metrics, simplified into readily discernible pieces of information, inform and empower the people who have the ability and responsibility to impact the key drivers.
The Tech Intermediary’s Role
As a tech intermediary, your approach needs to adapt from a technology-first to an insights-first approach. Your value stems from your ability to guide organizations in using insights - then employing the technology necessary to provide them. Technology alone, without organizational culture change, will serve only to open the insights gap, not close it.
Grind Analytics is a great channel partner because we understand both the technology and the use of technology. We are experienced business leaders in addition to technology experts. We not only know how to extract insights from data but also how to best apply the data to make data-driven decisions and cultivate a data-driven culture.