Business Intelligence vs. Data Analytics

Business Intelligence and Data Analytics are popular terms used in business environments today. Both these terms help businesses make better present-day decisions or a prediction or forecast decision for the future.  The words, at times, are used interchangeably, but they are different despite using some of the same tools.

What is Business Intelligence (BI)?

Business intelligence consists of strategies and advanced technologies such as Artificial Intelligence (AI) and Machine Learning (ML) that help users draw conclusions from analyzed data. The primary goal of business intelligence is to help businesses make informed and data-driven decisions using BI tools that present analytical findings in user-friendly reports, graphs, charts, dashboards, and maps. 

Key features of BI

Business intelligence tools use AI methods and practices, including machine learning, to collect, process, and analyze large amounts of data. After data analysis, the data is presented in a readable format. Business analytics, data mining, data visualizations, business performance management, location intelligence, Interactive reports, and predictive analysis are all examples of BI tools used to provide insightful and actionable information to help managers make faster and better decisions.

What is Data Analytics?

The primary purpose of data analytics is to modify raw data into a meaningful format for business users, while BI’s goal is to enhance decision-making for companies. Data analytics prepares data by cleansing and transforming the data into a presentable format that managers can use to improve company productivity. Data analytics creates or improves business processes, generates business growth, and enhances decision-making. Data analytics uses four analytical reports that can be used for business improvements or decision-making:

• Descriptive analytics describes what has happened already.
• Diagnostic analytics helps users understand the cause and event or why something happened.
• Predictive analytics predicts future events using historical data to make better decisions.
• Prescriptive analytics provides recommended actions to take to affect a future outcome.

Key features of data analytics

Microsoft Power BI, Tableau, and Zoho Analytics are known for being excellent data analytics software. Good data analytics software solutions will have these key features: data wrangling and preparation, real-time analytics, data exploration, data mining, data visualization, data warehousing, and data integration. Data analytics also requires mathematical knowledge in linear algebra, probability, and statistics.

Is Data Analytics the same thing as Business Intelligence?

Data analytics main goal is to transform data into an understandable format, but the analytical products and the outcomes generated can also be used to make business intelligence decisions. Data analytics and business intelligence are more similar than they are different, and both are aligned to helping businesses make data-driven decisions in the present and future. 

What is the difference between BI and Data Analytics?  

The difference between business intelligence and data analytics can be hard to distinguish. Data analytics is one of Business Intelligence’s many tools used to make decisions. To summarize how businesses use BI and data analytics, Business Intelligence is used to make data-driven decisions from accurate and actionable information obtained from data analytics. Data analytics purpose is to transform and clean raw data into actionable information that can be used for multiple purposes, which includes BI.

What is the relationship between BI and Data Analytics?

Business intelligence is used to help businesses make timely, relevant, and smart data-driven decisions using past and present performances to optimize day-to-day business operations and future strategic decisions. Data analytics is a tool that transforms raw historical structured and unstructured data into actionable insights that can improve day-to-day decision-making and future decisions using predictive analysis. 

Data analytics uses advanced statistical techniques, data mining, and machine learning to analyze past and present data to formulate accurate predictions. Businesses using BI benefits from data analytics actionable insights. Therefore, data analytics can be considered a tool that BI software uses for future predictions.

How does Business Intelligence help a business?

Using business intelligence tools creates a smarter decision-making process that leads to improved and retainable financial performance monthly or annually. Customer Relationship Management (CRM) applications have built-in business intelligence that allows businesses to understand customers through customer engagement analytics better to discover preferences and customers’ future behaviors.

Tools like customer engagement analytics allow business intelligence to create a holistic customer profile that contains buyer behavior used to develop strategies for customers to make more purchases. Better customer knowledge will enable businesses to provide better customer satisfaction, retention, and customer referrals for new clients.

Business intelligence tools can anticipate market trends and consumer buying habits by connecting to their social media sites to recognize developing trends and customer impressions on products or services. Business intelligence tools are good at deriving conclusions from petabytes of data turned into actionable information that managers use to make data-driven decisions in the present or future, which will involve data analytics outcomes. Overall, business intelligence tools identify and remove bottlenecks, refine old business processes, automate daily tasks, and increase sales efficiency for better Return on Investment (ROI) and productivity. 

What are Business Intelligence Tools and Techniques?

Standard BI tools are software for reporting and query display, spreadsheets, digital dashboards, graphs, charts, business performance management, and Extract, Transform, Load (ETL) tools. Some of the business techniques were also considered tools, so there is no clear distinction between tools and techniques as online analytical processing (OLAP), data mining, data warehousing, predictive modeling, and analytics are considered techniques.

Popular BI tools

Though many BI tools are available in the competitive market today, two BI tools stand out. The two BI tools are Tableau and Microsoft Power BI. 

Tableau

Tableau is easy to learn and use for non-technical staff. Tableau offers a desktop and online edition allowing you to create workbooks and files that you can share on a Tableau server, though you’ll need Tableau Reader to view it. There is a free trial and version available. The vendor provides customizable dashboards that can be changed according to user roles. Tableau features anonymization techniques that hide personally identifiable information (PII) by using data masking. The vendor features a self-service data preparation option that allows you to clean data quickly by providing a direct path to prepare your data using the Tableau Prep Builder. 

Microsoft Power BI

One of Microsoft Power BI’s advantages is that it works well with its peer products like MS Office, Azure, and Dynamics 365. Power BI integrates well with Excel’s queries, models, and reports, combining multiple workbooks that can be visually displayed in a BI dashboard. Power BI has several built-in connections that can connect to Salesforce, Microsoft products, and SharePoint. Power BI lets you connect new connectors and import data from different sources. Another primary feature is Power BI’s AutoML tool that enables you to build, train, and implement your data models with the hands-on machine learning module.

How Does Data Analytics Help a Business?

Data analytics plays a significant role in successful businesses today by aiding managers in making informed data-driven decisions, increasing business productivity and efficiency, and making it easier to identify customer behavior and trends. Analytical software, statistical and quantitative models derive data-backed conclusions that managers use to make informed decisions.

Data analytics reduces cyber security risks by highlighting potential risks and recommending preventative measures. Statistical models can help determine which retail locations will require more inventory of a popular product or mitigate financial risks by determining the best time to reduce inventory or set a reasonable price on items during a clearance sale.

What are Data Analytics tools and techniques?

Data analytics and business intelligence are so closely related that Tableau and Microsoft Power BI also provide data analytical tools in their respective software packages. Data analytics is also mathematically intensive with quantitative data that require statistical tools and qualitative data, which considers the open-ended questions from humans. Open-source programming languages like R and Python are used for statistics and graphical data visualization.

Data analysis uses two types of techniques. One is mathematical, using statistical calculations, and the other involves AI and ML. Regression analysis shows which variables have an impact,  while dispersion analysis shows how values are spread out or tightly grouped. Time-series data is used to show trend analysis information. Other analytical techniques that use AI and ML are diagnostic, predictive, prescriptive, and descriptive. 

Popular data analytical tools

As mentioned, the two BI software tools previously covered are capable data analytical tools too, but we’ll look at some other vendors’ data analysis software solutions. Sisense and Google Data Studio are also popular solutions.

Sisense

Sisense can also be used for data analytics and BI. The vendor offers users various drag-and-drop options and interactive dashboards for easy collaboration. Sisense platform uses a customized In-chip technology that takes advantage of the available CPU caching that can compute up to a hundred times faster than RAM.

Google Looker Studio

This data analytical software solution provides a free dashboarding and data visualization tool that naturally integrates with other Google applications. This product is very good at analyzing Google data that marketers can use to understand their customers better and create advertisements that enhance customer conversion and retention rates.

What is the difference between a Data Analyst and a BI Analyst?

The difference between a data analyst and a BI Analyst can be difficult to distinguish when they use the same software tools and have the same business goals to improve some aspect of business operations. A data analyst’s primary job is to transform structured and unstructured data into an understandable format that a human can easily understand. Additionally, a data analyst may be challenged with solving a business’s process or financial problem, requiring programming and mathematical skills. A Data analyst’s job can be more investigational when needed.

A business intelligence analyst’s prime objective is to provide actionable insights that will influence business decisions. Business intelligence analyst primarily works with multiple structured datasets linked and converted into digestible dashboard information that can be easily understood. The BI analyst needs to be able to do prototyping, understand structured data, and use design tools like Microsoft Visio. The data analyst job is more involved than the BI analyst. 

Can a BI Analyst do a Data Analyst job or vice versa?

The data analyst job is broader in scope and requires additional skills than what a business intelligence analyst needs. Using a programming language like R or Python and the mathematical skills necessary for quantitative analysis makes it hard for a BI analyst to perform the duties of a data analyst. A data analyst is capable of doing a BI analyst job. The data analyst learning curve will be shorter than a BI analyst trying to learn programming and the required math involved in some analytical equations data analysts use.

The Best BI software on the market

Now you have been exposed to what business intelligence and data analytics do for a business, you will want to purchase the best BI software tool that incorporates some of the functionality of data analytics tools too. All BI software tools have a feature or two that is comparably better than the other BI tools, even though all BI tools have the same features and functions.

With BI tools relying on some data analytics tools and functions, you will need to consider what business intelligence software will provide the full capabilities of both BI and data analytics. Also, with both jobs being eerily similar, do you hire a BI analyst and a data analyst or just one? Each business will need to decide on whether to hire one or both positions, but in the meantime, here are some of the best BI software tools you can begin researching to help you decide on a BI solution.

FAQs

What is the difference between Business Intelligence and Data Analytics?

Business Intelligence (BI) and Data Analytics are both data-driven decision-making tools. However, they have fundamental differences. BI converts raw data into meaningful information for business interpretation, helping organizations understand their historical and current situation. On the other hand, Data Analytics involves processing and analyzing data to uncover patterns and predict future trends, making it more forward-looking.

Which one is more suitable for my business – Business Intelligence or Data Analytics?

The decision depends on your business’s specific needs. If your business seeks to understand its historical performance and make informed decisions based on this data, BI could be the ideal tool. If your business is looking to leverage predictive insights for future strategy and wants to forecast trends, Data Analytics might be the better choice. Many businesses use both, as the two approaches complement each other.