Data Modernization

The Role of Data and Analytics in Digital Transformation

22 August 2022

The Covid-19 pandemic was a sharp reminder of how vital digital transformation is for an organization. A report by McKinsey stated that companies accelerated the digitization of customer and supply-chain processes and internal operations by three to four years after the pandemic hit.


Industry 4.0 has resulted in some powerful innovations that can help businesses greatly enhance operational efficiency, product and service quality, customer satisfaction, and other KPIs that will result in more revenue for the organization. Yet, according to a report by Accenture, nearly 30 percent of companies that have invested in digital transformation are lagging in scaling efforts. The primary reason for this is a lack of know-how and the inability to realize the full potential of today’s digital innovations.

That brings us to data and analytics. Any organization looking to beat the competition and develop scale needs a business intelligence solution—a strategy for acquiring data, analyzing it, and drawing insights from it. Why? Because data analytics is one of the most important pillars of digital transformation, in this blog, we will explore why. 

What does Business Intelligence Involve?

Business intelligence is the process of deriving actionable insights from accumulated data. If we take a step back, business intelligence encompasses all activities involved in acquiring, processing, storing, managing, and analyzing data and presenting the analysis visually (through reports and insights), as illustrated here.


Business intelligence is a more modern term used to define the infusion of artificial intelligence in data and analytics. At its crux, BI reporting is the use of data to make better business decisions. This brings us back to the question, why are data and analytics the cornerstones of digital transformation?

Digital Transformation Challenges a Business will Face when a Proper Data Strategy is not in Place.

Every company collects business data either through careful planning or in the course of transactional operations for those who are less methodical. A company that has not yet begun its digital transformation journey will also have excel sheets with lead information, procurement and sales numbers, customer details, and so on—that is nothing but data! Often though, the data is dispersed in silos; no platform can use this data to give the business an idea of the past, present, or future.

Without a strategy, a company will face these issues:

  • Data Siloes: Data is stored in standalone excel sheets or databases that are not connected. The data should flow into one location for the analytics platform to access the legacy data and extract insights.
  • Poor Data Quality: Poor data quality includes data stored in wrong formats or data fields that are not correctly captured. This again results in poor analytics results.
  • No Picture of the Past or the Present: Without a proper data and analytics strategy, the organization cannot picture how its business processes have worked and what results have been achieved against its KPIs.
  • No Vision for the Future: Without data and analytics, the organization cannot prepare for the future. To improve processes and grow, the organization needs to know where it can improve, and these insights can only be derived from a data analytics solution.

Implementing a Data Analytics Strategy does not have to be Complex

Without a proper data analytics strategy, a company’s digital transformation efforts will fall short.

An analytics system provides the tools needed to gauge the performance of the processes (manual and digital) and identify the avenues of optimization.


Implementing a data analytics strategy can be summarized into:

1. Defining Data Collection Points

The first step in implementing data analytics is defining data sources. What business data are you looking to collect? This could be data about leads, revenue, performance, etc. Once you know what you want to collect, you can easily define where data collection tools (APIs, sensors, and so on) need to be installed.

2. Data Acquisition and Storage

The next step is installing data collection tools at locations from where you want to collect data. To collect website data, you need to have a tracking code. In addition, all lead acquisition platforms must be connected to the analytics system through APIs if you want to collect lead data. It would help if you also defined where and how this data will be stored. For example, data can be stored on on-premise databases or in the cloud.

3. Data Processing and Analytics

It would help if you had a solution to process accumulated data, consume it, analyze it, and extract insights from it. There are many robust AI-powered tools available today that you can use for analytics right out of the box.

4. BI Reporting

The final step is presenting these insights in an easy-to-use format through a dashboard. These can be visual representations like pie charts, bar graphs, or tabular models. A front-end BI dashboard helps your analysts understand the insights derived by the analytics system. Most analytics systems today come with in-built BI dashboards.

Data Analytics Drives the Future of Digital Transformation

As a business moves ahead in its digital transformation journey, it must understand that data and analytics will be the skeleton on which all digital (and manual) processes will be built. Data analytics should be your first digital implementation; from then on, it must tie into every step of your digital transformation journey.

If you’re looking to get started with a complete data and analytics solution, you might want to consider partnering with an expert who can help you get off the ground faster and on the right foot.

Visit our service offerings page and get a quote for your business here -> Data and Analytics Solution.

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