A Gentle Introduction To Business Intelligence And Data Analytics

Informed decision-making plays a key role in running a successful business. These informed decisions are made on factual information. From where do you get factual information? This information is derived from the data available to the organization.  Data is generated from a variety of sources and can be used in numerous ways. Business Intelligence and data analytics are the techniques to make sense of a large amount of data for your business.

"Without data, you are just another person with an opinion." – W. Edwards Deming

Organizations can use business intelligence and analytics in many use cases to make informed decisions. For example, the inclusion of diversity has become a milestone for every business. HR can use data analytics to evaluate and include diversity in your organization. Another interesting use case of BI is in marketing. According to Forbes:

“Martech (marketing technology) is driving a continual increase in BI’s adoption in marketing over the last eight years as more enterprises look to quantify the financial contribution of marketing strategies.”

You might also want to read How Businesses Put Big Data to Work.

The terminologies of BI and data analytics are usually used together, oftentimes interchangeably as both rely on data for insights to help businesses. Let’s learn the fundamentals of business intelligence and data analytics.

What is Data Analytics?

According to Investopedia:

“Data analytics is the science of analyzing raw data in order to make conclusions about that information. Many of the techniques and processes of data analytics have been automated into mechanical processes and algorithms that work over raw data for human consumption.”

Data analytics involves the following essential processes:

Data Acquisition and Selection 

In this step, the raw data is collected, selected, and stored. These are the core responsibilities of data engineers, who are an essential part of data analytics teams. They design, build and install the data systems. Without proper data management and storage, data analytics cannot be performed.

Data Pre-Processing and Cleaning

After data acquisition, the next step is data cleaning and preprocessing. In this phase, data analysts clean, normalize, and transform the data. For example, they handle missing values or duplicate values in the data.

Derive Data Insights

In the third phase, analysts derive insights from the data that bring valuable information to businesses. The techniques to derive insights involve statistical analysis, data science algorithms, and data visualization. The following are four types of data insights:

• Descriptive analytics - describes what has happened.
• Diagnostic analytics - describes why something happened. 
• Predictive analytics - predicts what is going to happen in the future 
• Prescriptive analytics - suggests a course of action. 

You can see that all the above-mentioned types can add value to the business. So then what is business intelligence? Let’s explore.

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What is Business Intelligence and Analytics?

Business intelligence also provides value to businesses through data. 

According to Wikipedia:

“BI technologies provide historical, current, and predictive views of business operations. Common functions of business intelligence technologies include reporting, online analytical processing, analytics, dashboard development, data mining, process mining, complex event processing, business performance management, benchmarking, text mining, predictive analytics, and prescriptive analytics.”

This definition sounds similar to the data analytics description, right? So what’s the difference? The scope of business intelligence and analytics is somewhat smaller than data analytics. Generally, BI focuses on current business issues (that are known unknowns) while advanced data analytics can help in finding unknown patterns.

Take an example of KPI (Key Performance Indicator).  Evaluating existing KPIs is a business intelligence use case while finding a new KPI is a data analytics use case. Though there are disagreements of experts on the differences between business intelligence and data analytics, as many use these terms interchangeably.

At the core of Data Analytics lies the exploration of vast information stores, transforming raw data into actionable insights. However, the pursuit of data-driven excellence is closely intertwined with ensuring compliance with regulations such as the California Privacy Law. This regulation, championed by the California Consumer Privacy Act (CCPA), empowers individuals with greater control over their personal data and imposes responsibilities on businesses to protect consumer privacy. Therefore, as organizations embark on their BI and Data Analytics journey, they must not only unlock the power of data but also embrace ethical practices that safeguard both insights and individuals' privacy rights.

Read More: WHAT REALLY IS DATA SCIENCE?

Business Intelligence and Data Analytics Methods

Here is a list of common business intelligence and analytics methods:

• Statistical and mathematical analysis
• Machine learning and data mining
• Text analytics and sentiment analysis
• Data visualization 
• Social network analytics
• Monte-Carlo simulation

Business Intelligence and Data Analytics Tools

Here is a list of common data analytics & BI tools and Languages:

• Excel
• Tableau
• PowerBI
• Apache Spark
• Python
• R

This article is just a gentle introduction to business intelligence and data analytics. In case you need more information about business intelligence solutions, feel free to contact our experts.

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