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    What is Business Intelligence (BI)? How Do Businesses Use BI?

    What is Business Intelligence How Do Businesses Use BI

    What is Business Intelligence?

    Let’s begin the article with the basic definition of the term “Business Intelligence” (BI). In its simplest sense, business intelligence (BI) describes the leveraging of technologies and business processes and methodologies to manipulate and transform data into actionable insights, helping business organizations make informed data-driven decisions. In other words, it supports your business' data & analytics efforts.

    Specifically, through various stages of data collection, data formatting, and careful analysis, and so on, along with the use of data infrastructure, tools, and visualizations, companies can get a comprehensive view of their current business state, delivered directly from executive to management of operational teams.

    The Advantages of Using Business Intelligence

    One of the main objectives of Business Intelligence is to collect and process data for better insight readability for faster decision making. Many times, a spreadsheet with tables and thousands of rows of records might prove difficult to derive actionable insights. This is where BI comes for the rescue. Through data visualization, BI can transform the analysis of various metrics and numbers into visual dashboards and reports where the business insight will be faster to understand and easier to analyze.

    It’s worth mentioning that Business Intelligence is descriptive, BI informs business about what’s happening in the present and the past based on historical data, and see if business is on track against your business plan. In comparison to manually analyzing the raw set of data, BI, through visual interactive dashboards and reports, can provide a full business picture with clear patterns or describe trends that may not be easily detected when analyzing data manually.

    Below is a number of ways that BI can help companies with operations and data-driven decisions making:

    • Visualization of data to help users (e.g. analysts, executives) understand data more effectively.
    • Improve connection and collaboration via 360-degree view of current business snapshots.
    • Faster reporting and analysis to enhance competitive advantage.
    • Analyze customer behavior and take corrective action.
    • Identify market trends to improve profitability.
    • Compare data with industry benchmarks and/or optimize operations.
    • Detecting potential issues or problems.
    • And many more.

    Companies that embrace a strong BI strategy are indeed already ahead of the curve. But long gone are the days that only multinational corporations can use BI. Nowadays, smaller enterprises can take advantage of data to make informed and profitable business decisions. Next, let’s take a look at the new trends of Business Intelligence in the upcoming period:

    Business Intelligence Trends 

    Along with the advancement of technology, BI is also evolving and changing, enabling companies to analyze their huge volumes of data more efficiently and effectively.

    It’s worth noting that many companies nowadays look to cloud-based or software-as-a-service (SaaS) solutions instead of on-premise ones to keep up with the complex scalability requirements and faster implementations. 

    • Daas (Data as a service): 

    D-a-a-S refers to a number of cloud-based, on-demand, data products - offering the advantages where the vendors manage data storage and preparation, delivered ready to use. D-a-a-S also provides several key benefits like quick set-up time, enhanced flexibility, automated maintenance, etc. among many others. 

    • Embedded BI (Business Intelligence):
      Embedded analytics or embedded BI allows the integration of reports and visual dashboards into users’ existing business applications, offering valuable insights without disruption or requiring users to switch from one application to another. All in all, embedded analytics enable users to understand their data right at the place where they work and where their data reside.

    How companies across industries use business intelligence 

    Business organizations across industries, including BFSI, healthcare, and travel, have been adopting BI to transform their business operations.

    For example, American Express Global Business Travel (GBT) uses BI tools to identify travel patterns and cost-savings opportunities, boost compliance, renegotiate rates with preferred suppliers, and enhance duty of care obligations.

    Many start-ups and business organizations also use BI to help them understand their customers better, specifically learning how customers use their products and how they can retain customers more efficiently. 

    Having all large volumes of their customers' interaction data in one place, BI makes it much easier to identify trends and insights. With BI, organizations can see all their data in aggregate or dive into individual customer data for in-depth examinations. For many product start-ups, this can help both the customer services team to identify patterns and solve problems as well as the engineering team for their subsequent release(s).

    Business Intelligence Processes

    Business intelligence encompasses a range of processes and methods which involves data collecting, storing, and analyzing data sourcing from various business activities and systems such as:

    • The organization’s ERP (Enterprise Resources Planning) system.
    • CRM (Customer Relationship Management) system.
    • Web applications and other portals.
    • Internal business applications (e.g. HR) as well as other external sources.
    • Etc.

    Data is cleaned and aggregated to produce accessible dashboards, showing decision-makers the current status of their organization's business. Below are some of the most common processes and techniques in BI and how they’re used. 

    • Dashboards:
      Dashboards are essential for data visualization and reporting, which can come in the forms of charts, graphs, etc, helping users to see the full picture of their business data, together with other analytics metrics and KPIs as needed.
    • Reporting:
      This involves providing the insightful results of datasets analysis to stakeholders so that they can make judgments and come up with informed decisions accordingly.
    • Data mining:
      This practice applies statistics, database systems, and machine learning technology to sift through and find hidden patterns in large datasets. The prerequisite for mining is pre-processing data.

    Data mining examines an organization’s historical data to look at past performance or “predict the future”.

    For example, through data mining, a digital retail business can discover which products are more popular on mobile channels and decide to further emphasize its presence there. If a manager or analyst is looking for a particular insight related to certain product categories, there can be a good chance that data supporting such insight already exists, they only need to leverage BI, specifically data mining, to find it.

    • Extract Transform Load (ETL):
      Refers to the extractions of data from various sources, cleanly formatting and transforming it (e.g. concatenation) according to business requirements for reporting and analysis, and then loads it into a data warehouse. On another note, ELT gives faster data pipelines to have data ready to be used, as some databases put more computing power into their technology (Snowflake, Vertica, etc.) - That is why the trend is now ELT vs ETL.

    Here, for ETL, data can be retrieved from single or multi-sources (e.g. CRM, Website, etc.) and come in different formats such as documents, spreadsheets, CSV files, relational databases, and so on. Some common types of transformation include deleting duplicate data, calculation, splitting and merging, etc.

    • Online Analytical Processing (OLAP):
      OLAP is a technique of performing multiple-dimensional analysis of business data. OLAP is helpful for completing tasks such as performing trends forecasting and financial budgeting, as well as other complex calculations.
    • Data Preparation:
      A Pre-processing stage gathers data from multiple sources and cleanly reformatting them in order to make it ready for analysis.

    Again, data retrieved from multiple sources may result in an increasing number of data problems that require cleanings, such as Missing value (e.g. Customer records without email address), Uniqueness (e.g. Two customers with the same ID number), Misspellings, And others.

    Business Intelligence Platforms and Tools

    There are a number of BI solutions available to allow users from any business line to take advantage of them, aiding users in analyzing business metrics and deriving insights in real-time. The primary features of any Business intelligence (BI) solutions include integration of data from various sources, data preparation and analysis, scheduled and/or ad-hoc reports, and pre-built or custom dashboards.

    Some of the most popular BI platforms and tools are as follow:

    • Business intelligence platforms:
      BI platforms usually require users with a certain level of coding/programming experience to operate on. A typical platform will have a number of general features such as cloud and/or on-premises deployment, ingestion of structured or unstructured data, analytics, along with collaboration and sharing of analytics results, data management, reporting, security protection, etc. 
    • Data visualization software: Suitable for users to track performance metrics and business KPIs. Here, users can build their own interactive visualizations from pre-built dashboards template or customize them based on their unique business requirements. Similar to a comprehensive BI platform, data visualization software also allows individuals or a team of users to set up multiple dashboards of their own, meeting the needs accordingly.
    • Embedded Business Intelligence: Embedded BI solutions provide the advantage of interoperability, fast deployment, and integration flexibility, enabling companies to integrate the BI capabilities with existing systems and business applications, web portals. Embedded BI offers many features such as reporting, interactive dashboards, data analysis, predictive analytics, and more.
    • Self-service Business Intelligence: Self-service business intelligence (BI) refers to the solutions that allow business users to access and analyze datasets even when they do not have any coding background or experience in data-related functions like data mining, data analytics, etc. These self-service BI solutions usually have pre-built templates to help users explore and visualize data without having to involve the organization’s BI or IT teams. 

    Many companies adopt self-service BI solutions to make it easier for their business users from different business lines such as sales executives, marketers, HR to get useful insights from BI systems, driving more efficient and data-driven decision makings, resulting in better business outcomes.


    Investing in a BI solution is a no-brainer if your business is looking to find insights in the huge volumes of data to make better decisions. Many modern BI tools can offer self-service BI capabilities helping companies to decrease the dependency on IT teams, at the same time allow decision-makers to quickly recognize new market trends opportunities, spotting performance gaps, etc. thus advancing their places in the competitive landscape.

    With years of experience in Business Intelligence consulting and implementation, KMS Solutions can help you implement a BI solution that aligns with your business goals, meeting budget and ensuring maximum ROI.

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