Back to Blogs

Data-Driven Companies Are 19 Times More Likely to Be Profitable – The Role of Data in Digital Transformation

digital transformation
Published on Sep 22, 2020

Data-Driven Companies Are 19 Times More Likely to Be Profitable – The Role of Data in Digital Transformation

Data science tools and approaches are reforming company processes and propelling companies towards accelerated digital transformation. In fact, according to IDC, the worldwide spend on digital transformation will reach a whopping $2.3 trillion in 2023. However, accurate data, effective governance and an effective data strategy are at the centre of digital transformation which determines how data is used in the enterprise.

According to a report published by McKinsey Global Institute, companies that are data-driven are 23 times more likely to acquire customers, six times likelier to retain customers, and 19 times more likely to be profitable and successful.

The importance of data analytics for attaining digital transformation cannot be overstated; companies need to have the ability to derive insights that are not only progressive, but enable them to stay ahead of the competition as well.

That being said, even today, many companies do not leverage the benefits of data analytics. Gartner, in a report, stated that less than 50% of corporate strategies that are documented have data analytics as their key components for delivering value to their organization. On the other hand, companies that do understand the importance of data analytics don’t know how to effectively derive insights to attain successful digital transformation.

Business insights

Hence, companies that want to be leaders in their niche need to make use of data analytics to gain a competitive edge.

In this blog, we will discuss the importance of data in digital transformation and how businesses can use data to follow relevant digital transformation trends to stay ahead of their competition.

The relation between data & digital transformation

There is no question that most company activities are now inseparable from the IT networks on which they operate, whether or not an enterprise has initiated a structured digital transformation programme. As a consequence, if adequately handled, technical developments can directly transform into market advancements if used correctly.

Nonetheless, several businesses are struggling to bridge the gap between their current IT infrastructures and procedures and the value that emerging digital technologies offer.

Luckily, there is a straightforward way to maximise the efforts of digital transformation: to rely on the data. However, simply collecting data will fall short unless it is based on a sound foundation of “data transformation.”

In this case , data transformation does not only encompass the conventional data processing, cleaning, reformatting, and storage processes of “extract, convert, load.” It also involves the eventual review and utilising of gathered (or real-time) data to support a company’s decision-making, processes, and high-level strategies for digital transformation.

Everyone recognises that the vast volumes of digital data produced by business and customer operations are – at least technically – an extremely useful resource. However, in reality the ever-expanding data engine remains underused today.

That said, in recent years, modern and advanced technologies have streamlined and automated many of the more time-consuming data collection functions, even as data volumes and sources have multiplied. Other strategies, mostly with the use of machine learning and other artificial intelligence technology, may help process vast quantities of data.

Companies today may find themselves buffeted in tides of digital transformation, but they should use this latest wave of data management tools to create a solid, data-based foundation going forward.

How to use data to enhance digital transformation

Digital transformation projects are flying blind without data-driven perspectives. By comparison, a variety of advantages may be gained by organisations that make efficient use of data. According to a recent Qlik assessment, the top four areas that can achieve amazing returns on investment from data-driven transformation include:

Deeper customer insight – create comprehensive customer expectations, desires and preferences databases, and use that knowledge to develop new products, change supply chain processes and enhance customer service and loyalty.

Digital Transformation strategy

Reimagined processes – recognise redundant business processes and introduce streamlined replacements of workflows to speed up processing times, optimise the supply chain and monitor costs.

New business prospects – identifying potential challenges, prospects, and trends and leveraging this information to change business objectives, reach new markets, and make other strategic decisions.

Top digitization trends of 2020

There’s no doubt that data plays a major role in helping businesses adopt digital transformation. Taking it one step further, we will now discuss the various data digitization trends of 2020.

Harnessing data to build a data-driven business

You may be out of business by 2021 if your business is not ready to unleash the value of data and invest in analytics in 2020. The “following your gut instinct” way of running a company is passe.

Unlocking and evaluating the growing data volume, speed, and variety is not a simple job, however.

Without an intelligence engine to manage and analyse it, gathering mobile data, sensor data, voice data etc. is not enough. New legislation such as the General Data Protection Regulation (GDPR) also means that data over-preservation could contribute to data privacy and enforcement risks. It means that storing information without planning or management is a terrible move simply for the sake of it.

So, all in all, data analytics will be one of the most significant focal points of digital innovation in 2020, regardless of the sector.

Leveraging the value of AI & Machine learning

As discussed in the previously, the quantities of data are growing exponentially and we need machines to support us and make sense of them. Three different value propositions distill the value of AI and machine learning to data analytics: speed, scale, and convenience.

Automating data set analysis can offer major improvements in both speed and size. In a fraction of the timeframe it used to take just two years ago, AI and machine learning can help us to evaluate complex data sets. Owing to the software being more intuitive and accurate, the incorporation of AI and deep learning with analytics tools is even more easy.

Not unexpectedly, in the next two years, there are reports that expect a 95 percent growth forecast in AI adoption. Machine intelligence, robotic agents and simple task management are the most common use cases of AI.

Automated customer assistants

Personalizing customer experience

Sounds very simple, but it is really at the heart of digital transformation to offer linked and customised service to the consumer. What more and more clients have come to expect from their service provider is delivering a frictionless omnichannel experience that is available anytime from anywhere.

MuleSoft published a recent report which stated that the average number of applications used in most organizations is 900, however, only 29% of those apps integrate with each other. The main challenge that businesses face is increasing the complexity and plurality of various digital channels. Moreover, the MuleSoft report further stated that 69% of customers agree that disconnected customer service would propel them to change service providers.


In the “early adoption” phase, information as an advantage continues, making it a strategic differentiator for leading companies as they embark on digital transformation. Data and analytics, in essence, is fast becoming a strategic priority.