Business Intelligence vs. Data Analytics
The year 2020 saw a dramatic increase in the shifting of business operations online due to the COVID-19 pandemic. Companies started tapping the online marketplace to cushion their business from the curbs put by various countries to control the spread of the virus. In this technologically advanced environment, everybody knows that data is at the heart of the modern business. Those who have learned the art of drawing insights from the raw data are ruling the market.
When trying to enter the data-intensive landscape, people come across various terms that seem confusing. For a layman, it would be difficult to know the difference between data mining, data wrangling, data analysis, data processing, and so on. This article is dedicated to solving your confusion between two such data-related concepts – business intelligence and data analytics. After getting a clear idea of business intelligence vs. data analytics, you will also be encouraged to take data analytics training or a business intelligence course to enhance your career prospects.
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What is Business Intelligence?
Oracle defines Business Intelligence or BI as a combination of strategy and technology for gathering, analyzing, and interpreting data with the end result of providing information about the past, present, and future state of the subject being examined. This actionable information helps business leaders and stakeholders make more informed decisions for their products and services. Companies that take BI initiatives are better able to enhance operational efficiency, get higher Return on Investment (ROI), and stay ahead of the competitors.
Business intelligence is an umbrella term that incorporates business analytics, data mining, data visualization, various data tools and infrastructure, as well as performance metrics and benchmarking. These things come together to design a comprehensive view of a business and allow stakeholders to make better decisions. Companies use data warehouses and data lakes based on Hadoop clusters for BI data. This data may be related to historical information or real-time data collected from various source systems.
The BI process starts with data collection in warehouses where it is cleansed using data integration tools. Next, data specialists organize and model the data sets for analysis and carry out analytical querying. They share the findings with business leaders who then compare existing business processes and KPIs (key performance indicators) to historical data and track performance against industry bests.
What is Data Analytics?
Data analytics is a term that refers to the process of extraction and categorization of data with an aim to identify patterns and correlations and draw conclusions from them. Data analytics has a broader focus and goes beyond basic business intelligence, reporting, and online analytical processing to various forms of advanced analytics. The collected data may be historical, real-time, structured, unstructured, or qualitative.
When you study data analytics, you’ll find it is often categorized into different types, namely – descriptive analytics, diagnostic analytics, predictive analytics, and prescriptive analytics. All these types can be applied to different industrial sectors like financial services, manufacturing, travel and logistics, transportation, healthcare, and more.
The data analytics process starts with examining how data is grouped (whether the data values are numerical or categorical). The next step is the collection of data from different resources and organizing them so as to make it ready for analysis. Any form of errors or duplicate entries are removed from the datasets, i.e., data cleansing is performed. Data professionals then apply data governance policies to ensure that the data follows the defined corporate standards. Next, they build an analytical model using designated tools and train it. Later, the model is run in production mode against the full data set and addresses any specific need.
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Business Intelligence vs. Data Analytics
Looking at the description for both the terms, you may find the process for both of them quite similar. But those who work in this field need to understand where both of them overlap and when they are different. Data analytics is more often referred to as the process that focuses on asking questions while business intelligence is more of a decision-making stage. When a company takes BI initiatives, it is looking for insights that can help them improvise processes right away. On the other hand, companies engage in data analytics when they wish to emphasize future trends.
When it comes to tools, many cloud-based software and tools are used by data scientists for BI as well as data analysis. For data analytics, some of the popular tools include SQL, Python, Tableau, R, SAS, Rapidminer, and Excel. For business intelligence, the top platforms include IBM Cognos, Oracle NetSuite, Power BI, Qlik Sense, and Sisense. Some of the software facilitates both BI and data analytics.
Business Intelligence emphasizes more on descriptive analytics – something that describes what has happened over a given period of time and how it happened. Business managers do not look at technical aspects much and are able to create BI reports if they are well-versed in using data visualization tools. Data analytics has a type called prescriptive analytics which is not covered in BI, i.e. providing suggestions on what actions to take that will result in optimal solutions. Further, in data analytics, professionals try to know why things happened as asking questions iteratively leads to better solutions for business problems.
To sum it up, there are many areas where BI and data analytics overlap and their overall purpose is to support better decision-making. Hence, learning any of them would be fruitful for your career. Both the fields are poised to grow as companies continue to embrace digital transformation. So, why not enroll in a training course dedicated to learning BI or data analytics. Online courses are preferred by professionals as they get to learn everything from scratch and gain practical exposure as well. Doing a data analytics course also demonstrates to the hiring managers that you are serious about starting a career in this domain.