Business Intelligence – A path to monetize the data

1. What is Business Intelligence?

Business intelligence (BI) refers to a technology-driven process used for analyzing data and delivering necessary information that helps executives, managers, and workers make efficient business decisions. BI initiatives aim to get efficient and better business decisions that help the organizations increase revenue, improve operational performance, and gain competitive advantages over business rivals. BI uses a combination of analytics, data management, reporting tools, and various methodologies for managing and data analysis to achieve that goal.

2. Data – A Critical Raw Material for an Effective Business Intelligence Platform

The business marketplace of the modern era is a data-driven environment. Data is the new oil, and the organizations which are sitting on more data would be a driving force of this decade and beyond. The key role of data is to enable business leaders to make efficient decisions based on facts, trends, and statistical numbers. But, Having alone the data is not enough unless one knows how to use it effectively and draw a conclusive outcome from the data to drive predictability and decision making. With so much information available out there, businesses must be able to filter through the basket and get the right information to make the best decisions about strategy and growth by minimizing the risks.

For example, a business wants to market a new skincare product for women. There are various key factors the business must know to design and execute a marketing strategy for a new product launch. Although the new product might be useful for all women, will it still be branded extensively for the teenage crowd? Will it be marketed for the aging women trying to hold on to their youthful skin? The label and packaging design might vary for each segment of the population. A higher price might not appeal to a younger crowd, but research backing the claims might be required to sell a product with a higher price to older women.

In such a scenario, the business requires data on the goods’ cost price, with the product pricing and target segments of the population. It will then design the complete packaging around its target population. The analysis takes numbers and group sizes and what a specific demographic response to visually. Packaging for a young Millennial group is much different than it would be for the more aged demographic.

Every organization is now sitting on GBs of data that they have acquired in the last two decades. Initially, ERPs, CRM software, and other applications helped them create and store lots of structured data. Over time, due to inflexibility offered by those applications or not providing desired and effective information on the system from that generated data, it has become just like a database. Everyone started pulling the data and drive the conclusive outcome based on their requirement.

This has drawn millions of spreadsheets, duplicity, versioning issues, data loss, and many more problems than an effective solution. An effective BI solution helps organizations to draw a predictable outcome and a solid base for effective decision-making. Building an effective BI is an art and science both. Art helps in creativity to effectively visualize the data outcome, and science helps the effective utilization of the data to bring that outcome.

3. Stages of  The BI Platform Setup

BI Platform Setup 

Setting up an effective BI platform is not an overnight effort but a continuous journey to begin with a smaller effort and slowly build an effective BI platform. A business intelligence setup includes more than just BI software. Business intelligence data is typically stored in the data warehouses built for an entire organization or in smaller data marts that hold smaller units of business information for individual departments as well as business units, often with relations to an enterprise data warehouse.

Objectives and KPIs-

First and foremost, one should draw the objectives and identify high-level Key Performance Indicators (KPIs). Once the KPIs are identified, the company then has to frame plans to measure the changes in these KPIs; it is necessary to identify what data points are relevant for the measurements of KPIs.

Identify Data Points-

Secondly, the company checks the availability of data points within the organization. For generating any results using a certain technique and make data-based decisions, the first requirement is to host the data on one or multiple data sources. We should not target to bring the entire data under the BI platform at one go but draw a strategy basis the alignment and commitment of the stakeholders to support this initiative basis their vision and objective.

Selecting Tools-

Once we have our objectives and sponsor, we identify the tool which could be driven by various selection criteria and checklist. The selection of the tool, architecture, and approach is also driven by various parameters like data volume, variety, velocity, value, veracity, and variability, also referred to as the 6Vs.

Checking Data Accuracy-

Suppose an organization is having multiple sources of data. In that case, it is critical to identify the single source of truth and make it mandatory across the organization to maintain the veracity of the data. Once we have identified the data sources, the entire data is being collected at a single source to avoid any runtime connections with the live systems. Once all the data is being collected at a single place, often referred to as Data Lake, our next task is to identify the data basis of structured, semi-structured, and unstructured data. Suppose the data is having an accuracy issue. In that case, we need to make sure it is being addressed for legacy data, and there are reasonable steps identified and placed for the data generated in the future.

This data can be used to draw various data marts basis objectives, so the desired KPIs can be drawn.

Visualizing Data-

Finally, we get the data visualization tool that helps draw the right dashboards, reports, and drill-downs to help the user draw the right conclusion.

Many a time, organizations do the same in a phased approach; in the first phase, they typically draw reporting, analysis and monitoring. This is also referred to as descriptive statistics. In this, we typically describe and summarize the data like What happened? Why did it happen? What’s happening now?

In the next phase, they go from monitoring to forecasting and from forecasting to predictive. In this predictive statistics, patterns and trends are being monitored based on behavior and relationship. Based on this, one could say what may happen? What is likely to happen based on X? And finally, one reaches a prescriptive stage where it is often referred to as Machine Learning, where one would have potential outcomes based on complex interactions; what will happen? When will it happen? Why will it happen?

Visualizing Data

4. What are The Major Challenges Faced While Obtaining the Desired Outcome?

A matured BI Model is not an easy task to achieve. Many Organizations typically focus on it till the monitoring model, and few go beyond that to achieve predictive analysis. Going beyond that needs lots of data points and efforts, computing, patience, and organizational commitment. Few major issues, which make it hard to go to the next stage are-

1 – Data availability: Lack of data or the presence of incorrect data will cause a hindrance in achieving correct results.

2 – Lack of BI strategy: Devising a suitable strategy before adopting a solution is crucial as confusion may lead to the failure of the adoption.

3 – Lack of training and execution: Leaders need to be equipped with proper knowledge and tools to efficiently and effectively carry out the BI process.

4 – Organization’s focus: Organization should function on clear focus points to avoid confusion and inefficiencies.

5 – Leadership commitment: Leadership team should be dedicated, and their interests should be well inter-wined with the goals of the company.

6 – Lack of BI impact: The business must utilize the full potentials of the BI process to achieve growth in the long run.

Though many things have changed, one thing remains constant in the IT world, that is GIGO (Garbage In Garbage Out), so if any organization is not having accurate data, irrespective of whatever effort we put in, the progress to the next maturity level remains a distant dream.

5. How can CONCAT help?

Concat Team has identified this as one of the promising technology of the future. We are one of the best HR Shared Services and business intelligence companies having capable partners along with creative resources and thoughtful consultants. A combination of such types of trio can help our customers to incubate the idea and build the right BI strategy and objective to draw and get the early return on Investment. Some of the situations which can be catered to by using BI are-

1 – Monitoring business performance or other types of metrics;

2 – Supporting decision-making and strategic planning;

3 – Evaluating and improving business processes;

4 – Giving operational workers useful information about customers, equipment, supply chains, and other elements     of business operations; and

5 – Detecting trends, patterns, and relationships in data.