Business Intelligence (BI) has existed since civilization started transacting goods and services. With the advent of tools and applications that ingest data and present them in user-friendly formats such as reports, dashboards, graphs, and charts, it has become a critical enabler of data-driven business decisions. However, with the advent of digital transformation efforts, this science has moved several notches up by structuring disparate data, ensuring their quality, and reducing errors that provide decision-makers with a single source of truth. One that generates insights that enterprises require to innovate around productivity increases, customer experience enhancement, and overall cost reduction.  

Digital transformation initiatives can potentially drive business outcomes around unveiling hidden insights, enabling data-driven decision-making, enhancing operational efficiencies across the organization, and driving innovation. Let’s look at how these get generated.

  • Unveiling hidden insights: BI tools leverage extract-transform-load (ETL) processes to organize data for storage, analytics, and machine learning (ML).  They pull structured and unstructured data from multiple channels and transform it as per specific business rules to derive standardized information that can be loaded on to centralized repositories. Businesses can then uncover hidden patterns and trends that are otherwise invisible for operations. The insights help them enhance different functions ranging from product development to marketing campaigns.
  • Enabling data-driven decisions: Companies that use BI software systems can move away from gut-based choices to informed decisions that are based on evidence. The analysis of accurate data gives them more opportunities to fuel business growth and profitability. For example, organizations using BI can make data-driven decisions on where to allocate their resources. It helps them focus on areas that are most likely to generate high RoI, instead of impulsively throwing funds at different projects.
  • Improving operational efficiency: BI allows businesses to automate the process of generating reports, saving significant amounts of time and resources in the long run. On the basis of such reports and analytics offered by BI, it also becomes easier to quickly identify and eliminate inefficiencies in their operations. This eventually leads to cost savings, enhanced productivity, and a better customer experience.
  • Driving innovation: BI helps organizations identify new opportunities for innovation. The analysis of data on customer behavior, market trends, and competitor activities guides enterprises to develop new products, services, and business models.
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Transforming Data into Profits: Successful Use Cases of BI

Let’s take a look at some use cases on how a focused approach around business intelligence resulted in digital transformation efforts. The first case involves an India-based textile solutions business with a presence in the US, UK, and European markets. The need for a single source of truth resulted in the company deploying significant consulting resources to automate and integrate activities across operational silos. The outcome was a robust data structure that provided data-led insights for smarter decision-making. The second use case involves an Indian eCommerce company structuring data from multiple sources, automating data collection, and creating a series of dashboards for the key stakeholders to conduct real-time reviews across cash flows, customer experience, supply chains, and logistics. Both these use cases highlight how a focus on innovating the business intelligence process results in the digital transformation of a business. 

By implementing BI systems, organizations can progress on their Industry 4.0 paths while becoming data-driven. Here are some of the real-world examples of how they support routine workflows across verticals: 

  1. Predictive maintenance in manufacturing: In the manufacturing space, IoT sensors collect data on the quality of products being created as well as the health of the equipment used in production. BI tools analyze this data and it can be displayed on dashboards from where teams would notice issues in time to take remedial actions. It helps to prevent costly downtime by ensuring that machines get the maintenance they need before they malfunction or fail. 
  2. Better customer segmentation in retail: Retail enterprises can use BI to integrate a variety of customer data from their website, CRM, social channels, and POS systems. Stored in a common location, this data is then analyzed to segment buyer groups and connect with them more effectively through targeted marketing campaigns and personalized offers. It paves the way for improved customer experience and more revenue.
  3. Fraud Detection in Financial Services: BI tools offer banks and NBFCs deep insights into their data, helping them to prevent fraudulent activities across various channels and touchpoints. Financial institutions can monitor account-related activities in real-time to identify atypical usage of cards and take proactive measures to block transactions that are not genuine. BI also helps these organizations to identify vulnerabilities in their own systems and take calculated measures to mitigate the risk of cyber-attacks.

Sectors such as logistics, utilities, agriculture, and healthcare can also derive value from data with tailored BI applications. 

Challenges in Deploying BI Systems 

Although BI systems are powerful tools for digital transformation, their deployment may not be easy for many enterprises. It is fraught with challenges that primarily include:  

  • Data silos: When data is spread across a variety of systems such as legacy infrastructure, in-house servers, and cloud systems, it can be difficult to collect and analyze it for digital transformation initiatives.  
  • Data quality: If the same data is available in inconsistent formats, or has incomplete details for further processing, BI will not offer desired results. For example, if a manufacturing plant using liquids mentions stock volumes erratically in gallons for some records and in liters for others, BI analysis can be incorrect.
  • Lack of data literacy: Even though BI manages several tasks once the systems are ready, there is a need for appropriate inputs to kickstart the process. Some organizations do not have the required in-house expertise and skills to unleash the potential of data for BI.
  • Change management: When an organization adopts BI tools, it has to bring disruptive changes to its processes. Employees may find them overwhelming. As per a survey by Gartner, 45% of the participating companies said that their employees are fatigued by change. 

Driving Digital Transformation Successfully with BI

For efficient results from BI, enterprises need to overcome the obstacles. This can begin with winning employees’ trust by communicating the benefits of digitization that BI will bring to their work. Announcements of big changes should be made weeks before people begin to feel its impact. Building a culture of team cohesion and data-driven decision-making will also help the workforce to improve their change management capability. In addition, organizations must invest in data literacy training or partner with technology experts to help their employees use BI tools effectively.

The next step is to establish a data governance framework, ensuring that data is amassed, stored, and analyzed consistently across all units. This will prevent siloed data and allow all users to access the same information towards common ends.

To improve data quality for BI, companies must deploy data validation rules and automated data cleaning processes that optimize the accuracy of details from different sources. 

In a nutshell, driving digital transformation does call for organizing customer-centric processes supported by data. And BI plays a significant role here. It expedites decisions and refines their quality with its lucid dashboards and actionable insights. The key to success is choosing the right platform and keeping it active with seamless data inputs.