Business Intelligence and Analytics – 5 Level BI Strategy to Empower Your Company 
Find 5 levels of business intelligence and analytics that should be used by all business leaders.
Also learn how to implement this 5 level strategy to increase your business productivity.
Let's dive in!
Unleash the potential of your company’s most carefully guarded and valuable asset – Data. There is no shortage of them. Most will agree that there is too much data, but understanding it is not enough. According to a recent survey, 60% of executives receive more information than they can use. The main thing is to reveal the meaning of the data, turn it into a basis for action, for making decisions and improving business efficiency. Research shows that top-performing organizations are twice as likely to use business intelligence and analytics as their strategy for the future and the need for day-to-day operations than underperforming organizations.
How successful is your company in providing you, as an executive, with the timely information and interpretation you need to make critical business decisions?
Companies still spend millions of dollars on transactional applications and IT infrastructure. As a result, they collect mountains of data that are often buried in huge databases and are poorly used. However, a growing number of companies are using this dead asset. They gain a competitive advantage by transforming data into actionable insights and a basis for action to help them answer critical business questions and improve business efficiency.
According to a recent IBM study, 50% of executives believe they are not getting the information they need to make critical decisions, and three out of four believe that forward-looking information would lead to better decisions. In addition, two out of three companies are still in the early stages of using business intelligence.
In this article, I will outline five levels of business intelligence and analytics that should be used by all business leaders. Let’s start with the preparation of reports by individual applications.
Level 1. Preparation of reports by individual applications
It is about preparing reports in a prepared form using transactional applications such as SAP or Oracle Financials. These reports provide historical data and answers to questions like “What happened?” and “What have we achieved?”
Level 2. Corporate reporting using multiple applications
Many companies fail to merge corporate data due to organizational and technical issues. The technical means are well known for building a viable infrastructure using enterprise data warehouses or data marts and for developing procedures to extract, transform, load, manage, and cleanse data. Business units and functional groups are often reluctant to share their data, which negatively affects the company’s performance. The problem can only be resolved if senior management approves the development of an enterprise business intelligence and analytics application.
A reliable corporate data warehouse allows management to receive complete management information (Single Version of Truth, SVOT). It should be noted that data warehouses used for analytical purposes must be designed accordingly.
Focus on solving urgent business problems and data. Don’t try to cover all the data in the company. Start not with data, but with business questions and problems. This will help prevent the data warehouse from going into the “death tailspin” that occurs when businesses try to do too much too quickly.
It is not uncommon for companies to get bogged down in trying to improve corporate data and spend years aggregating, integrating and cleaning it instead of solving today’s most important business problems. Unfortunately, the drive to “boil the ocean” results in significant losses of time, money and, ultimately, loss of management support.
There are excellent business intelligence tools for working with data warehouses, allowing managers and users to view customized reports from a variety of applications. For example, using dashboards and scorecards, executives can monitor and track key business metrics, requesting more information as needed. If some indicators go beyond the specified limits, then the management will learn about it from the automatically generated reports on deviations.
Multiple table queries, pivot tables, and online analytic processing cubes provide an in-depth look at the relationship between two sets of variables (such as the effect of geography on revenue for a quarter or a year).
Acting to their own detriment, companies often stop at this stage and miss out on additional opportunities associated with complex analytics.
Level 3. Data extraction and statistical analysis
At this level, it is no longer a matter of simple queries and top-ten listing of relationships, trends, and patterns in data. Typically, this is done manually by people who are familiar with both technology and business and have an excellent knowledge of all the elements of corporate data. They are like prospectors who follow a gold mine to discover a new mine. Such hypothesis-based analysis helps to understand the data and answer questions such as, “Why is this happening this way?” “What if…” scenarios usually raise additional questions for further analysis and reflection.
Level 4. Analytical interpretation and predictive modelling
At this level, companies use sophisticated modelling software and analytical tools to identify the relevant relationships between multiple data sources and the variables that are tracked within the company. Through sophisticated computation and automation, patterns, trends, segments, and clusters in data can be identified to help better understand the situation. If the human brain can process and visualize two-dimensional graphs or three-dimensional, multidimensional representations of data (think of the 3D chessboard in Star Trek), today’s computers and sophisticated software can reveal relationships between different data using hundreds of variables simultaneously.
At the same time, modelling is sometimes used to predict future results, which allows a more efficient concentration of the company’s resources. At this stage, they receive answers to the questions: “Which customers are most likely to refuse our services?”, “Which potential customers should be interested in this offer?” etc.
Level 5. Resource Optimization
At this stage, companies optimize resources based on specific constraints and parameters. It provides answers to questions such as “What should we do?” “What might be the best outcome given the resources available?” “How can we optimize our staffing structure, inventory and service levels?” Just nirvana, and more!
Which of the five levels of business intelligence is your company at? What is your level of knowledge in this area? Are they tactical or strategic? Where are your resources concentrated? Are they centralized or decentralized? Are the capabilities of business intelligence used in any business function or not? Do you have a streamlined process for exploring new opportunities that promise substantial returns?
Implementating Five Levels of Business Intelligence and Analytics
Many companies, bogged down in day-to-day tactical operations, cannot take advantage of the wealth of data they have. This is often due to a short-term mindset that ignores strategic, long-term initiatives. According to one of the studies cited above, the top three barriers to improving business intelligence are organizational issues:
- misunderstanding of how to use analytics;
- lack of governance due to competing priorities;
- lack of qualified personnel.
If you’re interested, you have access to critical business intelligence tools that ultimately provide you with the knowledge to take your company to the next level.
As a first step, you need to make sure your data is reliable, and available over a long time. Some companies, with good intentions, periodically destroy data every four to six months to reduce storage costs. Now, however, with the widespread availability of inexpensive terabyte drives that power PCs, these costs tend to be low.
It is best to use multi-year data to accurately identify and analyze seasonal variations. However, even for one year, the data can reveal previously unknown trends and patterns.
Organizations often have isolated islands of business intelligence at different levels of development depending on specific business problems and the availability of specialists. In today’s competitive environment, a well-functioning corporate system for analyzing and using all the data available in the enterprise is of paramount importance to optimize the efficiency of the company.
Building Business Intelligence and Analytics Competence Centers
Centres of competence or excellence in business analysis sometimes arise within highly-minded IT, finance, or operations departments. However, it is recommended that they be separated from their day-to-day functions so that they focus on strategic, long-term objectives. Ideally, a team is created based on centralized resources that function as a data centre and help business units develop the potential of business intelligence.
This gradual process is sequential in nature. If there are opportunities in a particular department to use analytics and modelling, then there is no reason to wait until others are ripe for this. Analyzing a business unit’s mart from reliable data can be very effective. You should not wait for the corporate data warehouse to be created.
Building your business intelligence competence with an overarching five-tiered business strategy requires strong support from forward-thinking senior management. Seize this opportunity to better interpret data as a basis for better decisions and greater efficiency.
Start by identifying opportunities for building business intelligence and develop a strategy for building business intelligence and analytics competencies. Explore quick-hitting remedies to demonstrate quick success and gain support. Gradually, advanced business intelligence will emerge and analysts will emerge that will ultimately transform your company.
A more feasible option for developing a business intelligence and analytics system is to outsource it to experts. Custom Business intelligence and analytics software development outsourcing enables you to bring in highly sought-after experts with no need for a drawn-out hiring process. Businesses that use outsourcing maximize their productivity and gain a competitive advantage since they are freed up to focus on their core tasks.
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