Business intelligence is a set of tools and techniques that integrate business analytics, data mining, and data visualization. BI infrastructure and best practices assist businesses in making better data-driven choices.
In practice, contemporary BI allows you to get a full picture of your company’s data. It also enables you to use that data to drive change and respond rapidly to market and supply changes.
BI as we know it now began in the 1960s as a method for exchanging data across enterprises. The usage of BI got associated with computer models for decision-making and data conversion during the 1980s.
Modern BI systems work on self-service analysis. They also manage data on reliable platforms and empower business users. This article will serve as a complete overview of BI.
What is Business Intelligence?
The answer to “What is BI?” every time revolves around the word DATA. Business intelligence, as defined by Thinklayer’s specialists, is a system that integrates exploring data collection. It also acts as a storage and knowledge management tool along with data analysis.
This system aids in the evaluation and transformation of complicated data into useful, actionable information. Thus, helping to utilize this information. Consequently improving strategic, tactical, and operational insights and decision-making.
How does Business Intelligence work?
BI takes four important phases to turn raw data into easy-to-understand insights. These phases can be used by everyone in the organization. The first three steps, data gathering, processing, and visualization, prepare the groundwork for the ultimate decision-making phase.
Before embracing BI, businesses perform much of their analysis manually. But BI tools automate many of the procedures, saving time and effort. All business intelligence strategies begin with a plan. There are many phases to implementing BI regardless of the project management approach, one employs.
Company intelligence environments are made up of several technologies, applications, products, and technical architectures. These are used to gather, analyze, display, and disseminate internal and external business data. BI uses software and services to turn data into useful information.
Step 1: Gather information from a variety of sources
To collect structured and unstructured data from numerous sources, business intelligence systems often employ the ETL technique. It involves Extraction, Transformation and Loading.
This data is then changed and renovated before being stored in a central area. Here it may be conveniently analyzed and queried by apps.
Step 2: Identify patterns and contradictions
Data mining is also known as data discovery. It is a type of data analysis that uses automation to swiftly examine data. Consequently helps to uncover patterns and anomalies that give insight into the present state of affairs.
BI solutions frequently include exploratory, descriptive, statistical, and predictive data modelling. It also involves analytics. Thus allowing users to dig deeper into data, forecast patterns, and make suggestions.
Step 3: Present your results using data visualization
Data visualization is used in BI reporting to make results easier to understand and disseminate. Interactive data dashboards, charts, graphs, and maps are some of the reporting ways. These things allow people to understand what’s going on in the organization right now.
Step 4: Take immediate action based on your findings
Companies can swiftly go from insights to action by viewing current and historical data in context with business actions. BI also provides real-time adjustments and long-term strategic changes. It helps businesses to reduce inefficiencies and respond to market movements. As a result, resolving supplier challenges & customer complaints.
Importance of Business Intelligence
By displaying current and historical data within the context of their business, BI may assist firms in making better decisions. Analysts may also use BI to give performance and competitive benchmarks. It will help the company in a long run to function smoothly and effectively.
Analysts will also be able to recognize market trends more readily, which will help them enhance sales or revenue. The usage of appropriate data may assist with employment initiatives. A few examples of how BI may assist firms in making better, data-driven decisions are:
- Determine BI strategies to boost profits
- Examine the behaviour of your customers
- Compare information with those of rivals
- Keep track of your progress
- Streamline your processes
- Determine your chances of success
- Recognize market trends
- Identify difficulties or problems
Advantages of Business Intelligence
The advantages are different for different businesses but overall BI helps any business to grow at a faster pace. For example –
Faster Analysis and Reporting
Companies are now having difficulty putting up methods to incorporate large amounts of data. It’s also very time-consuming. However, BI has evolved into a tool that allows clients to develop reports and insights much more quickly. Hence, by using templates or customized reports based on several data sources you can perform better. BI allows you to show the collected data in a variety of ways, such as dashboards and graphs, so you may choose your preferred medium.
Improved Data Quality
Data quality is a prerequisite for information before it can be used in the form of data sets. The improved data quality is claimed to boost the possibilities of corporate growth.
You should construct a “virtual quality data firewall system”. Hence, it helps to enhance effective data quality via the benefits of business intelligence. It will also safeguard your firm by conserving relevant data and rejecting erroneous information.
Improved Operational Efficiency
Intelligence-based data analysis aids businesses in making more successful judgments by allowing them to make higher-quality decisions.
According to a recent study, 63% of businesses that used business intelligence software saw significant improvements in their operations. As a result, these businesses were able to better leverage and optimize their historical data with the help of BI.
Comparing Data with Competitors
Companies can use BI techniques to search down their competitors’ sales records and marketing success. You may learn about raising sales and overcoming any challenges the firm is encountering. Businesses can do it by focusing on internal statistics and the accounts of other rivals in the market.
In terms of discovering possible prospects, Business Intelligence tools can assist organizations in determining the strengths and weaknesses of their competitors.
Valuable Business Insights
BI also assists businesses in delving deeper into certain datasets by optimizing all data through regular schedules. As a consequence, firms gain insight into shorter periods.
It will allow them to better inform and develop strategies that can assist increase corporate performance while being safe. Hence, the benefits of BI tools can assist a corporation in identifying its shortcomings and discovering its strengths.
Improvement in Customization
Business intelligence has also aided organizations in understanding and learning about their clients’ demands. This data analysis may provide firms with insights about targeted adaptations as well as new ways to engage their customers. B.I. technologies have also aided businesses in establishing strong customer relationships by allowing customers to submit feedback and resolve product and service concerns through numerous customer portals.
Low Costs / Reduced Overhead
If your firm isn’t correctly using AI and making judgments based on stale data, corrupted and inaccurate data, or worse, no analytics tools at all, you might be losing money.
BI, on the other hand, may save firms since it can safeguard them against data security concerns and assaults. eCommerce development organizations also use Business Intelligence. Furthermore, they can handle clients on a wider scale and save millions of dollars.
Benefits of Business Intelligence
Organizations face the hurdles of generating insight from the pool of raw data. Therefore they use business intelligence to rectify their problems in a better way. It allows them to make better and smarter business decisions.
Businesses may acquire a complete perspective of their organization’s data and transform it into insights about their business operations by using business intelligence solutions. BI helps organizations to –
- analyze data with a historical context,
- optimize operations, track performance,
- accelerate and improve decision-making,
- identify and eliminate business problems,
- identify market trends and patterns,
- drive new revenues and profitability,
- increase productivity and accelerate growth,
- analyze customer behaviour,
- compare data with competitors, and
- gain a competitive advantage over competitors.
Applications of Business Intelligence
1. Sales Intelligence
One of the most important applications of BI is Sales Intelligence. Customer negotiation is a valuable skill that each sales department should cultivate. Hence, moving leads through the pipeline and persuading potential consumers to buy your product or service may be tricky.
Thanks to the application of business analytics and intelligence, this process is becoming easier and more predictable.
Business Intelligence collects data on specific KPIs, such as customer demographics, conversion rates, and sales metrics. Then they organize data into graphs, pie charts, and scattergrams, among other structured displays.
Finally, they use this data to uncover trends. Later on, they use this data to gain insight into consumer behaviour and business operations. Companies also use the reports created by BI to support assertions made to potential clients.
Managers may use the data gathered from BI analysis to make data-driven forecasting choices. BI systems gather data that keeps managers informed about where their firm stands on various KPIs. It will ensure that the companies will never face any issues.
In every sector, planning is one of the most crucial aspects of staying ahead of the competition, and BI makes it easier than ever before.
Business intelligence software makes use of a variety of data analysis techniques to evaluate and manage data about your company’s activities.
The business can monitor logistics, sales, productivity, and much more using this data. Finally, the companies use Infographics to represent these data. Some BI tools allow users to create bespoke reports with their specifications.
It is critical for cognitive processing to convert data into visual forms. Business intelligence solutions enable even the least experienced employee to get insights from data by presenting it in simple visualizations and easy-to-understand formats.
You may evaluate and present your data to shareholders, other departments, or your teams instead of depending on expert data scientists.
Reporting is the critical business use of BI. As previously mentioned, business intelligence technologies collect and analyze unstructured data sets, as well as organize and generate a variety of reports.
Staffing, costs, sales, customer service, and other procedures are examples of these. Reporting and data analysis are similar in purpose, delivery, tasks, and value, but they differ greatly in purpose, delivery, duties, and value.
Reporting is the practice of compiling data into summaries with the goal of monitoring company performance. Analyzing data, on the other hand, is the process of extracting insights. Managers use these insights to enhance company procedures.
In a nutshell, reporting converts data into plain text, whereas analysis transforms data into actionable insights. Both aid firms in improving their performance and keeping track of their operations.
Reporting shows users what is going on, while analysis explains why it is going on. Both procedures can but do not have to, be accomplished by utilizing visualizations. For dealing with dynamic data, BI tools are excellent.
Interactive dashboards that update in real-time are available in modern BI applications, providing a new level of usefulness and agility in data analysis.
4. Performance Management
Organizations may use BI tools to track target progress using customized timeframes. Project completion deadlines, target delivery times, and sales targets are examples of data-driven goals.
Thinklayer provides customers with dashboards that show performance management information. Users may arrange the system to notify them when they are approaching a target or when the time restriction expires and they have not yet completed their task.
This allows managers and staff to keep track of their progress and keep teams focused on their objectives. Users may also track target completion and utilize progress data to assess an organization’s overall productivity.
Unlike situations where more time is lost tracking down urgently needed data, information is always readily accessible. This saves both time and money for the businesses.
Types of Business Intelligence Tools
- OLAP (Online Analytical Processing) – One of the first BI technologies, OLAP tools allow users to evaluate data across several dimensions, making them particularly useful for sophisticated queries and computations.
- Mobile business intelligence – It allows users to access BI apps and dashboards from their smartphones and tablets. Companies develop mobile BI solutions with an emphasis on ease of use. They use it more to display data than to analyze it.
- Operational Intelligence (OI) – This is a type of real-time analytics that distributes information to managers and frontline workers, also known as operational BI. OI apps intend to help with operational decision-making and allow for quicker responses to situations.
- Software-as-a-service BI – SaaS BI products give data analysis capabilities to consumers via cloud computing platforms hosted by vendors. The SaaS alternative, often known as cloud BI, is increasingly offering multi-cloud compatibility.
- Embedded BI – Embedded business intelligence software integrates BI and data visualization into business applications. These allow business users to conduct data analysis within the apps that they use to execute their jobs.
- Collaborative BI – This is more of a method than a technological solution. It entails combining business intelligence (BI) solutions with collaboration technologies to allow many users to collaborate on data analysis and exchange information.
- Location intelligence (LI) – This is a type of BI that allows users to identify the geographical data and location. It also includes map-based data visualization features. Insights into spatial features incorporate data and processes provided by location intelligence.
Business Intelligence Architecture
A business intelligence architecture is a framework for the numerous technologies used to operate BI and analytics applications in a company. It refers to the computer systems and software tools that are used to gather, integrate, store, and analyze business intelligence data. It provides company leaders with information about business operations.
The underlying BI architecture is a critical component of a successful BI program, which uses data analysis and reporting to assist an organization in tracking business performance.
Optimizing & identifying new revenue opportunities, improving strategic planning, and making more informed decisions in general for business.
Importance of a BI architecture
Business intelligence architecture articulates the technical standards and data management techniques that enable an organization’s BI operations. It’s a technical framework for gathering, organizing, and managing business intelligence data.
It subsequently aids in the preparation of the data for analysis, data visualization, and reporting. Policies that regulate the use of technological components are also part of a good BI architecture.
Using such a framework, a BI team may design an enterprise BI program that fulfils the organization’s data analytics needs in a coordinated and disciplined manner. The BI architecture also aids data managers in developing an effective method for handling and managing data brought into the environment.
Enterprises benefit from an effective BI architecture by using the insights generated by business intelligence tools.
Components of BI Architecture
Source Systems – Transaction Processing Systems
This is where every BI endeavour should begin. These databases are where organization data is originally produced. It’s worth noting that you can’t analyze data if the operating system does not capture it.
Operational systems extract data and place it into a data warehouse through an ETL process. ETL (Extract Transform Load) is a term that refers to the process of extracting, transforming, and loading data into a database.
Analysts take data from multiple operating systems. They then convert it to ensure consistency and place it into the warehouse for analysis. Datawarehousing will aid in the cross-functional analysis and data summation.
To maintain a high-performance and scalable BI system, it’s critical to make judgments on hardware needs. We offer data warehouse appliances that integrate the server, database, and data storage into one system, in addition to server configurations.
Business Intelligence Techniques
Business intelligence tools use advanced statistics and predictive analytics. They use it to assist firms to conclude data analysis, uncovering trends, and forecasting future occurrences in company operations. The following are examples of common business intelligence techniques:
- Data mining – It is the process of analyzing huge datasets utilizing databases, statistics, and machine learning to find patterns and correlations.
- Querying – It is the request for specific data or information from a database.
- Data preparation – It is the process of integrating and organizing data to prepare it for analysis.
- Reporting – It shares the analyses of operating and financial data with decision-makers so that they may draw conclusions and make choices.
- Benchmarking – It is the practice of comparing current business processes and performance measures to historical data.
- Descriptive analytics – It is the process of interpreting historical data to make comparisons and better understand how a business has changed.
- Statistical analysis – It entails compiling the results of descriptive analytics and using statistics to spot patterns.
- Data visualization – Visual representations of data, such as charts and graphs, are provided for easier data analysis.
Business Intelligence Dashboards
Business intelligence dashboards are data visualization and information management tools for analyzing data. Content makers may integrate charts, graphs, and reports on a single page for snapshot overviews using interactive features like filters and actions.
BI dashboards include multiple data visualizations. They provide users with a consolidated view of key KPIs and trends. It will help them in both operational and strategic decision-making
BI dashboard featured components
A customized interface, dashboard templates to speed up the creation process, and the ability to draw in both historical and real-time data are all vital elements of current BI dashboard software, in addition, to support for interactivity.
Dashboard tools also include tabs, pull-down menus, and other navigation capabilities. They also include libraries of widgets, icons, and pictures. Businesses can add these tools to dashboards to automate functions and increase usability and aesthetic appeal.
Major BI dashboard components
- Data visualizations – You may add different forms of data visualizations dashboards to display different performance indicators and other information. Visualizations supported vary from simple line and bar charts to more complex visuals including bubble charts, heat maps, and scatter plots.
- Data tables – BI dashboards may include data tables that give an instant overview of key data values. Colour-coding or other graphical treatments highlights certain information in tabular data, such as a drop in sales or financial losses.
- Drill-down capabilities – By clicking on data visualization, users may acquire more information or examine and analyze detailed data. This sends visitors to further visualizations or data tables, which are generally organized hierarchically so that individuals may dig down to numerous levels if they want to.
- Filters – These allow users to alter date and time ranges, geographic settings, and other factors in data visualizations to receive a more concentrated perspective of the information being displayed.
- Tooltips and text boxes – Dashboards frequently incorporate freestanding text bubbles and pop-up information tooltips that explain the data being displayed or give further context and information about the analytics findings.
Benefits and limitations of BI dashboards
Business dashboards are becoming more widely recognized as vital tools for assisting businesses in extracting useful business insights from their enormous data warehouses. Dashboards that are well-designed offer a variety of business advantages. They –
- enable better-informed decision-making and strategic planning by business executives, for example;
- make number-intensive data and business analytics metrics easily understandable for users who aren’t skilled analysts;
- assist in the identification of business trends so that a company may take action to seize opportunities and handle issues;
- enable business analysts, managers, and employees to deploy and use self-service BI systems; and
- provide greater information sharing to facilitate more collaborative data analysis and decision-making processes.
Dashboards, on the other hand, provide difficulties for both BI professionals and business users. The possible downsides of BI dashboards are caused by how they are utilized and the expense of generating, deploying, and maintaining them, rather than by the software’s architecture. The following are some of the problems that businesses encounter while creating successful dashboards:
- dashboards with an overly flashy or cluttered design that are difficult to use;
- giving context to help end-users understand how KPIs and other data are meaningful to them;
- establishing drill-down channels to plumb the data behind surface metrics is tough; and
- inaccurate analytics are the result of misrepresenting data in dashboards or omitting crucial data.
Business Intelligence Platforms
A business intelligence platform allows companies to make use of their current data architecture and construct unique BI apps that allow analysts to query and view data.
Self-service analytics is supported by modern business intelligence solutions, making it simple for end-users to build dashboards and reports.
Users may connect to a variety of data sources, including NoSQL databases, Hadoop systems, cloud platforms, and traditional data warehouses, using simple user interfaces mixed with flexible BI backend software to produce a unified picture of their heterogeneous data.
Business intelligence is evolving as a consequence of advances in artificial intelligence and machine learning, allowing users to incorporate AI insights and use the power of data visualizations.
Oracle, Microsoft, IBM, and Salesforce are among well-known BI platform suppliers.
Business Intelligence and Data Analytics
Both BI and data analytics, as one might imagine, follow similar methods for gathering data, evaluating it, and generating insights. The data gathering process is particularly important since achieving the best outcomes necessitates ensuring that the information acquired is accurate and thorough.
Both of these phrases are included in the reporting process. This indicates that the data has been structured and presented in a way that makes it easy to visualize.
While raw statistics are vital, data becomes more valuable when it is visualized, making insights simpler to find and act on.
Business intelligence and data analytics may also be used to identify areas where firms are underperforming or not performing at their best. In other words, they use the data they collect to demonstrate where pain spots exist. Consequently providing businesses with a better understanding of where they could be falling short.
Differences between Business Intelligence and Data Analytics
While both BI and data analytics entail the use of data to uncover insights that will help the business, there is one significant distinction to be made. Simply said, business intelligence is concerned with the now, whereas data analytics is concerned with the future.
Companies may utilize BI to improve their productivity by using aggregation, visualization, and thorough analysis. A company may be able to better sell to clients or give greater incentives for staff based on the data collected and processed. All of the activities resulting from business intelligence may be carried out right now.
On the other side, data analytics focuses on the future. Data analytics entails data mining, which is examining a set of data to find patterns. It will also aid in the prediction of future trends, which will assist enterprises to determine what they should do. It is easy to understand how beneficial this may be to any firm.
Businesses may position themselves for the future with this knowledge. Data analytics teaches an organization how to prepare for the years ahead, whereas business intelligence lays out the game plan for an organization to implement right immediately.
If there’s one thing that distinguishes business intelligence from data analytics, it’s its accessibility. There are various types of BI tools, and most of them are meant to be used by a wide range of people. Except for people with prior knowledge in the industry, data analytics is increasingly complicated to comprehend.
Managers play a critical role in the success of an organization’s operations. Managers must make decisions that have a direct impact on the organization. A faulty judgment made by an uninformed source will have severe consequences. Managerial choices will be improved as a result of BI implementation.
The BI systems will provide all employees with sufficient information and enable them to make decisions. By utilizing technologies such as OLAP and Data Warehousing, BI solutions can make managing large volumes of data simple.