Do you want to make data-driven decisions instead of ones made on gut feelings?
Introduce new offers to adjust to the current market situation? Successfully track KPIs by getting notifications? And even as much as getting comprehensive answers to all your complicated questions relating to your business? Then, you might find it useful to look into the possibilities of Business Intelligence.
From this article you will learn:
- What is Business Intelligence
- Business Intelligence Architecture
- The Value of Business Inteligence to business
- Comparison of BI and Business Analytics and the today’s relevance of BI
WHAT IS BUSINESS INTELLIGENCE
Companies across most sectors and industries aspire towards facilitating decision-making, driving innovation, reducing costs and improving quality. Today companies can achieve these objectives by harnessing the power of analytics, which has greatly evolved due to the explosive growth of business data and technological advancements.
Speaking very generally, Business Intelligence (BI) refers to an information system that converts data into knowledge with the intent to provide analytical information to users. A broader definition states that “Business Intelligence is an umbrella term that includes the applications, infrastructure, tools, and best practices that enable access to and analysis of information to improve and optimize decisions and performance”, according to a Gartner IT glossary.
It’s necessary to understand that the main objective of Business Intelligence is to find out what a company can gain from data. BI goes beyond data collection and includes business processes and data analysis procedures.
BUSINESS INTELLIGENCE ARCHITECTURE
Understanding more about the technology is worthwhile as it would help you realize the benefits that BI introduction could bring to your company and the way it can find its place in your business.
Therefore, let’s examine the key components of BI. A BI system can be loosely divided into three parts: Data Sources, Data Warehouse & OLAP cubes and Analytical Findings.
1) Data Sources
Data is an integral part of BI in particular and analytics on the whole. Modern companies deal with a lot of electronic documents, spreadsheets, invoicing, orderings and other documents. In order to gather, store, use and disseminate this information, companies utilize data organization systems, such as:
- Operational Applications
Operational applications are accessed to carry out regular operations of an organization. They allow us to enter, and retrieve large volumes of specific information, such as company legal data, financial data, personal employee information (HRM systems), sales and customer data (CRM systems), and many others.
Operational systems typically use online transaction processing (OLTP) databases. The common examples of applications that run on OLTP databases are: order-entry systems, stock management systems, bank systems that perform money transfer operations, retail sales etc. Such databases are designed to support fast processing of small workloads and transactions.
It’s worth understanding that the primary function of an OLTP database is to support daily operations, rather than perform complex analysis. You’ll be able to quickly identify a customer order status, but it would take much time and effort to determine how many of your customers who purchased your goods 3 months ago repeated the purchase again. OLTP databases are not designed for long queries, so they are unable to provide the sufficient level of analysis a company needs. For this, they can serve as sources for Data Warehouse.
- Flat files
- External data sources
After operational data is securely collected and stored, it needs to be analyzed before it can be leveraged to support decision-making activities. For this, with the help of Extract Transform and Load (ETL) process data is delivered to the next stage, that is Data Warehouse.
2) Data Warehouse & OLAP cubes
So the data from multiple sources has arrived at the next stage.
There is a need for data from disparate data sources to be integrated and a layer optimized for and dedicated to analytics. A Data Warehouse (DW) is exactly the solution that addresses these needs – it is a database that stores data in one format and allows easy analysis. DW is often considered as a core component of BI.
Most often data warehousing modeling supports Online Analytical Processing (OLAP), which encompasses a greater category of Business Intelligence. DW can serve as material for multidimensional datasets known as OLAP cubes. They allow managers to get insights of the information through fast, consistent, and interactive access to it. Many of the OLAP applications embrace business process management, sales reporting, marketing, forecasting, creating finance reports and others.
Additionally, a single department within an organization can take advantage of utilizing a simple form of a data warehouse called a data mart. A data mart is focused on a single subject or functional area, that can be production, finance, sales, marketing or any other one.
3) The Outcomes: Analytical Findings
The instrumental objective of BI software is to provide users with meaningful information that they can decide on. Analytical findings come in one or combination of forms: reports, dashboards, visualizations, graphs, charts etc. In fact, one of the hallmarks of BI programs is the rich visualization of the resulting reports.
It follows that creating a solid Business Intelligence solution that would support your business goals requires a highly skilled team of dedicated professionals with the corresponding competencies.
BI – BENEFITS:
- Single version of truth. An organization may have a number of business systems that track the same information. For example, a company may have customer information in CRM, financial and operational systems. As usual, each system has its own list of customers. With a BI tool, the information from these systems would be consolidated and a business user becomes able to get a full and true list of enterprise customers, without having to check different lists from multiple sources. Thus, you can leverage data from multiple sources and in diverse formats (like spreadsheets, json, xml, xls, etc).
- Data Persistence. Any piece of information is available at any piece of time. It means that even if the data is updated or deleted from its initial data storage, you’ll still be able to find it in a data warehouse. This is a really important feature required for analysis, as historical data is essential to establish key trends.
- Performance. As data is stored in a preprocessed condition in a data warehouse prior to analysis, data is processed and analysed in a timely manner.
- Flexibility. The possibilities for reporting and analysis are endless. Business intelligence tools are usually designed with flexibility in mind, so the solution can be scaled when needed.
- Efficient human resource management. People are not involved in complex calculations needed to perform analysis of the whole data sources, as a BI solution does this automatically.
Introduction of BI tools into daily operations of a company brings about accelerated decision-making, increase in operational efficiency, and cost improvement. The resulting benefits are also determined by the set issues the software was designed to address, such as, for example, spotting business problems that need improvement, identifying good opportunities for cross-selling your goods and so on.
BUSINESS INTELLIGENCE VS OTHER FORMS OF ANALYTICS
The practice of using data to make better business decisions is described not only by the term Business Intelligence, as Business Analytics has, in general, the same meaning. Still, these are not the same things and it’s worth knowing the difference.
Business Intelligence implies Descriptive and Diagnostic Analytics, while Business Analytics is used to refer to Predictive and Prescriptive Analytics.
Both involve collecting and analyzing data, and both share the same goal of enhancing a company’s efficiency by using data analysis, though these two perform different functions in business.
BI describes past or current state of business; it is all about extracting insights from data for telling you what happened, why it happened and what’s happening now. With its powerful reporting and data analysis mechanisms, BI is able to optimize and streamline current operations.
The next step of analytics is Business Analytics, which is also called Advanced Analytics. It goes beyond telling what has happened or is happening. Business Analytics is about forecasting upcoming developments based on all past and present data. The specific intent of BA is to get a business prepared for the upcoming challenges – by making predictions and identifying issues before they happen (Predictive) or by suggesting taking particular proactive actions (Prescriptive).
Following such a comparison, one might find implementing Business Analytics software a more appealing solution. So here comes the logical question…
IS BI STILL RELEVANT?
Should companies adopt BI when there is a more promising opportunity of achieving proactive advice by Business Analytics?
Things are not that simple. To predict the future, you still need to know what and why certain actions occurred in the past and what and why is occurring now. Advanced Analytics is commonly applied by companies on top of their BI solutions. It could be said that Business Intelligence is the first step in analytics adoption that could not be dismissed.
This means that Business Intelligence value is quite high as never before. Such software is sufficient for those who want to understand their work processes and improve decision making, and, with these insights, improve their business from the ground up.