1. Data sourcing
The first step in the BI process is accessing the data. This stage deals with the storage, management, and accessing of raw data. Thus, data warehousing becomes an essential aspect of business intelligence. Sourcing data is of utmost importance as the quality with Business intelligence services and relevancy of data directly impact the quality of insights gained and the consequent decision taken by the leadership. Another aspect of data sourcing deals with identifying and exploring various data resources. It’s crucial for the following reasons-
Data can be related to each other
It can be unstructured (in the form of text, images, or other forms of unstructured data)
Data can have peculiar features (such as different data types etc.)
It is vital for a business intelligence analyst to know what kind of data is available and where and how they can access it.
2. Data engineering and analysis
After data sourcing, the next logical step is information engineering and analysis. To perform analysis on the data and to gain even the fundamental insights from the data, a BI analyst needs to have the data in a structure that is conducive for analysis. This requires data engineering that includes-
- Converting data into a structured (tabular) format
- Removing or imputing missing values
- Capping outliers
- Removing multicollinearity
- Once the data is structured and ready, Exploratory Data Analysis (EDA) is performed through which useful information can be synthesized, such as-
- Summarizing data using descriptive statistics
Finding associations in data
After EDA, Structured Data Analysis (SDA) is performed, which includes three types of analysis-
Trend Analysis:
It’s used for identifying patterns in the data. This includes, for example, assessing sales based on different geographical regions, the volume of product sales over a stipulated period, etc.
Mathematical Analysis:
It’s used for calculating performance and growth using mathematics. This includes, for example, assessing the margin of sales and growth in absolute and in percentage.
Statistical Analysis:
Inferential statistics is used to assess the statistical significance of the patterns and peculiarities being identified in the data. Statistics is also used to perform predictions, analytics, and forecasting using regression and other methods. The statistical coefficients can also be used to perform prescriptive analytics, identifying reasons for certain business phenomena. Therefore, the model building can take place at this stage of Business Intelligence.
To understand how data is analyzed, you must also learn about the univariate analysis of data.
3. Situation awareness
The core of business intelligence is to provide concerned individuals in an organization with knowledge helping in situational awareness. This stage in Business Intelligence deals with report creation and presentations that provide the decision-makers with essential and relevant information to help them be aware of the events in and around the organization. This information can make them aware of, for example, government policies, upcoming market trends, market forces, etc.
4. Decision-making
Once the decision-makers know the ‘what and why’ of the events in and around the business, the next stage is proactive decision-making and its evaluation. The insights, knowledge, and intelligence gained from analytics enable decision-makers to make data-driven decisions. This can be, for example, coming up with strategic decisions such as management change, management of products and categories, opening or shutting of offices, launching of new products, or can be operational, such as promotional campaigns, upsell and cross-sell campaigns, etc.
5. Decision support
This stage deals with supporting the proposed decision by evaluating it. Evaluation includes identifying the risks, opportunities, benefits, profit, pitfalls, cost-to-benefit ratio, return on investment, and estimating the business value of the proposed decisions. All of this helps in making efficient and effective decision-making.
Another way of setting the stages of a Business Intelligence Value chain is to lay them out in terms of complexity. In terms of complexity, the stages are-
Reporting: The most basic stage of BI is to understand what is happening and report the same.
Analysis: The next stage is to answer ‘why did it happen?’ and identify the cause and effect of the events in and around the business.
Monitoring: This stage deals with answering the question—’ what is happening right now?’ as this deals with keeping an eye on the business’s day-to-day activities.
Predicting & Forecasting: The next stage is significantly more complex than the previous one as this involves using predictive statistics and even machine learning. Here patterns and trends are assessed through a predictive or forecasting model. This helps answer questions like ‘what will happen given the past data?’
Prescriptive Modeling: The most complex stage involves prescriptive models to answer questions like ‘What will happen, when will it happen, and why will it happen?”. All this requires understanding complex relationships and interactions between the data features. This stage also deals with re-prediction and re-forecasting to create a feedback loop that helps make models better.
NB: The first three stages use descriptive statistics.
Understanding the value of BI
Business intelligence is essential for any business. Here’s how BI enables a business:
Cost reduction: The most significant value of Business Intelligence is that it can significantly reduce costs for an organization. This can be achieved by increasing operational efficiency, identifying and eliminating backlogs and delays, performing root cause identification, looking for a solution, and eliminating wasteful resources.
Increase in revenue: BI’s next significant value addition is helping an organization increase its revenue. Information sharing among leadership and end-users, better market analysis, upskilling the existing workforce, and analyzing customer behavior to design products can help increase revenue.
Enhancing customer satisfaction: For any business, increasing the satisfaction of its end-user is of paramount importance. While customer satisfaction can be achieved in many ways, BI can help increase customer satisfaction by providing customers with factual information, performing competitive analysis to understand the pros and cons of the product offered by competitors, and analyzing the issues faced by the customers to help in a quick resolution.
Several factors of a business intelligence system influence it and can make or break it. While there are hundreds of factors affecting a business intelligence system, we can group them in the following manner.