Innovate or die. The mantra of the tech world since its first days and nowhere has that battlecry been more omnipresent than in today’s data arena. From the rise of the Chief Data Officer and academia creating new data centric degrees/departments to the explosion of new Big Data startups and new legislation geared towards data privacy, the community has seen a drastic shift from the need to simply gather data, but to deeply qualify data patterns at a feverish rate. Enter the growing need for Business Intelligence (BI).
What is Business Intelligence?
Simply put, Business Intelligence is the use of data to influence and guide business decisions. While used in broad terms at times, at its core, it acts as a wide umbrella that utilizes analytics within a predictive environment in a post-analysis state. In other words, an analysis based on analytical data, of what has happened in the past in respect to large data trends and patterns. At the same time, the term often refers to tools, strategies, and plans that are involved with data-driven decision making.
What led to the rise of Business Intelligence?
While data-driven decisions are not new, and the term Business Intelligence has been around since the 50’s, the concept and growth of today’s BI has had some historically key factors:
- Increase in the amount of companies that collect customer and internal data
- The global rise of smartphones
- Social media activity
- Introduction of wearable device technology
- Increased data storage at decreased costs
What’s the difference between Business Intelligence vs. Business Analytics?
The two are often used interchangeably, as I’m sure you’ve heard during a networking event or dinner party; however, there are distinct differences and uses. If you feel that you might be guilty of this, don’t worry you are not alone. While both involve the aggregation of analysis of data, it is there where the two break off into specific distinctions.
Within Business Intelligence, the aggregation of data is to make it clear what is occurring within a business, whereas analytics are used to find causality or why it happened.
Similarly, the direction of time can also be used when making distinction between the two. Are we facing the past or looking toward the future? Is the focus on what happened, how it happened or why it happened? Business intelligence is specific to what happened in the past and what brought us to the present moment. It qualifies trends and patterns without delving into causality or future prediction. Analytics focuses on specific factors and causality, to make predictions of future trends.
How do companies store and manage data?
As the market continues to grow with a data-first mentality, the sourcing and housing of that data also continues to diversify. Below are a few examples of where data is stored:
- CRM programs (shameless plug for HubSpot)
- Marketing automation systems
- Social media
- Data warehouses/marts
What are some KPIs within companies of that utilize Business Intelligence?
Each industry is different and specific use cases are driven by business goals. So I put together some example KPIs.
- Finance: Cost and risk reduction, increased sales, profitability, working capital, inventory turnover, profit margins
- Marketing: Sales revenue, leads, customer value, inbound marketing ROI, traffic to lead ratio, customer acquisition cost, dormant customers
- Healthcare: Staffing, patient mortality rate, supply chain management, patient flow and utilization, bed turnover, readmission rate, patient satisfaction, average cost per discharge, claims denial rate
Business Intelligence uses data and to influence and guide business decisions. Combining Business Intelligence and Business Analytics can help give a complete story as to why and what is happening within a business and predict future trends. How has your organization used Business Intelligence to set itself apart as an industry leader? Share in the comments below.