Most businesses use the terms data analytics and business intelligence interchangeably. Forbes does point out, however, that business intelligence isn’t the same as data analytics . Using them as such creates confusion which can be difficult to dispel. While in many cases, the end goal of business intelligence is the same as data analytics; they approach the problem in distinctly different ways.
Data Analytics vs. Business Intelligence
The difference between business intelligence and analytics goes deeper than mere semantics. Tableau mentions that business intelligence and analytics are used to predict outcomes and trends, while data analytics has a broader use, outside of just within a business environment. The common thread between both of these terms is that they both use data to help a business gain insight into their operations and meet their goals.
Business intelligence primarily deals with the collection and storage of data from business operations as well as the analysis of that data to provide insights into the company’s performance. Business intelligence allows a company to understand what it’s doing well and the areas it can improve upon to deliver better service or be more efficient in its operations. Business intelligence looks back on data the company has collected to guide the company to do better as it moves forward.
Data analytics takes data collected from a business’s operations and uses it to attempt to predict the most optimal path for the company moving forward. Companies can utilize database software such as Apache Druid and consult their Druid docs to process the massive amounts of data the company has. Using analytics software allows the company’s data scientists to figure out the decisions the company should make to capitalize on emerging trends in their customer base. Analytics looks at current data and seeks to go forward in time.
Types of Analytics
Analytics comes in multiple different categories. Both data analytics and business intelligence aim to guide the business into making better decisions. They do this by leveraging different methodologies of analysis of data. When taken generally, analytics can be broken up into three broad categories:
Descriptive analytics turns data into something anyone can visualize. That includes taking the data and turning into graphs, charts, and other visual representations of numbers. It allows for the dissection of the historical performance of a company. Through the interpretation of results, executive members and management can get answers to questions about the company’s current state. Ideally, descriptive analytics should be run on a regular, recurring basis.
Predictive analytics tries to peer into the future to see what’s in store for the business. Using data the company has collected, predictive analytics reveals insights and leverages data science to predict the probability of something happening. It works best with larger data sets, and businesses that have massive amounts of data collected from several sources can use this methodology most efficiently.
Prescriptive analytics offers data-based advice on the steps a company ought to take. It explores the potential outcomes of individual decisions and models how the business would be affected if management were to take that course of action. Prescriptive analytics requires using advanced mathematical modeling, as well as analytic algorithms, to determine the best decision a business should make in the given situation.
Leveraging Data to Generate Insight
While they are distinct and separate in how they approach the problem and its solutions, data analytics is a way to bring business intelligence up to date. As it stands, business intelligence seeks to advise the business based on its previous failures and successes. Data analytics makes it so that business intelligence doesn’t have to depend upon the data that the company has collected in years past, but can also incorporate current data sets into the calculation.
Business intelligence will have to change because the companies that are at the cutting-edge of technology are the ones that will succeed in the twenty-first century. Business intelligence is still a valuable tool, but only if it’s kept informed by the current trends in data. Data analytics has made it so that the lag time between data collection and business intelligence is far less. In time, with real-time data collection and processing, that lag time might even reduce to zero.
Is One Better than the Other?
Both business intelligence and data analytics play a crucial role in the future of decision-making. Business intelligence will always have a place within a company. A business needs to understand where it is to know what it has to do to achieve its goals. Data analytics is still a relatively new field, but companies that have adopted it have seen a lot of benefit to including it as part of their business intelligence departments. Insights are only as useful as the data used to achieve them. By combining data analytics and business intelligence, a company can get the best of both worlds – decisions backed by current data and insights that come from studying the company’s current track-record.