Larger organizations such as Google and Facebook are iconic when it comes to using data to drive their business. There’s no denying that they have been leading the field both in terms of technical approach as well as culture.
Previously, big data was nothing compared to what it is today. Data volume, variety, velocity, and veracity were all at lower levels. This is mostly due to the fact that organizations didn’t have much experience working with big data and using it in their decision making. Organizations were left wondering if having all that data was actually worth it; now the value of data has been proven and everyone wants to get their hands on it. Once we start putting in the work to streamline access to data, we are given access to ideas that will help transform our businesses.
The first step in this journey is to acknowledge the effectiveness of data-driven decision making. Once we do this, we need to ensure the right infrastructure is in place, and adjust the culture of our organizations.
Data-driven decision making is not all that new, however, things are different in terms of scope. Collecting data and performing analytics used to be a privilege that many organizations could not afford. Business intelligence and descriptive analytics are terms that have been associated with these type of tools and approaches respectively. However, in the beginning, it meant having a stack of printouts on your desk. Those printouts summarized metrics and key performance indicators, such as production and sales. This information was overwhelming, but at the same time not enough. Imagine needing metrics for an organization with thousands of branches and/or employees. This required a substantial amount of time and effort just to scan through, let alone digest.
Even if someone put in the time and effort to review metrics, what if they discovered something that required closer introspection? For example, how would they be able to focus their attention on a specific branch that is under-performing, see historical metrics, and compare with other branches?
These issues helped drive the evolution of analytics. Big data went from stacks of printouts that required dedicated teams to process, to solutions such as visualization, dashboards, and data warehouses.
If you need assistance with pulling custom data reports from your CRM, accounting software or any other critical business applications, please contact us to see how we can assist!