Thriving in the Data Economy

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Unlike other resources, data is growing at an unstoppable pace. To put a number on it, the total volume of data is expected to cross 175 zettabytes by 2025.  But that means little unless the data is harnessed and put to use. The data economy provides an environment for organizing, transforming, analyzing and exchanging data to extract value from it; organizations participating in the data economy gain insights into current and future trends to enable innovation, customer acquisition, product creation, problem resolution, and so much more.

However, the concern is that despite all the analytical tools out there, data remains massively underutilized. Estimates for unused data swing between  . Worse, even when data is used, it is not used to full potential: recently, when Infosys MIT Technology Review Insights surveyed 255 business leaders and senior executives, 45% admitted to using data for only rudimentary insights and decisions.

The biggest barriers to exploiting data arise from legacy systems, whose inflexible architecture and data silos make it very hard to integrate the latest digital technologies – advanced analytics, machine learning, deep learning – and process massive volumes of information in real-time. This also prevents enterprises from tapping semi-structured and unstructured data lying within and outside the organization, or alternatively, forces them to waste time and manual effort in refining those data sets into structured information.

Building a Robust Data Economy

To fully utilize data–the most valuable resource in the future–companies need to be data-driven, and active participants in the data economy. Barriers preventing the exchange of data need to be dismantled so enterprises can share information both within and with third-party ecosystem members, to create value for all.

The obvious first step is to get rid of those data silos that are denying enterprises access to insight garnered from a spectrum of input. The importance of doing this was highlighted in the survey mentioned earlier, where the 35% of respondents who were exchanging data for collaboration said it was paving the way to value and significant business outcomes.

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There’s also a need to “free” data from the custody of giant technology firms–who own most of it–so it can serve more broader needs, more accessibly. Because data is competitive advantage, these companies guard it ferociously within their own repositories. Invariably, the data ends up being underutilized, when it could create so much more value if it were available for social purposes. There’s no better example than the umpteen collaborative efforts that helped the world understand the coronavirus and develop vaccines in record time.

Policies, such as the EU’s Digital Markets Act and the Digital Services Act stipulate third-party interoperability (in certain circumstances) for data biggies like social media platforms and online marketplaces, so customers can easily take their data elsewhere if they need to. But the fact remains that the security and privacy of data has to be assured before asking corporations or individuals to part with it. The data economy must have clear rules–the GDPR is a good start–and adequate guardrails. It also needs secure infrastructure and cybersecurity laws and standards to function properly. Last but not least, individuals must have transparency and control over how the data they are consenting to share will be used, going forward.

The data economy is completely global. Researchers from different countries collaborate to discover problems and solutions; corporations work with distributed ecosystem partners to innovate products and services; industry 4.0 uses IoT data to produce in smart factories around the world; and distribution systems use customer demand data to plan their deliveries in international markets.  Evolving a unified data policy when there are multiple regulatory jurisdictions and commercial interests to consider, can be very challenging. But given how important data sharing is for creating a better world, there is a strong case for persisting in the effort to build a robust data economy.

Why Enterprises Should Participate In It

Even at the level of the individual enterprise, the value of the data economy is undeniable. Fifty-three percent of the participants in the Infosys & MIT Technology Review survey said that participating in the data economy gave them new business models. An Australian telecom major is planning to combine environmental data–pertaining to waste, air, water, etc.–sourced from IoT-enabled devices, with micro-climate information, to generate useful, practical insights for the agricultural industry that it could offer for a fee.  A multinational waste management company is also integrating data from multiple transactional, operational, and billing systems, across multiple regions to deliver near-real time operational waste metrics, including industry-first emissions and carbon footprint data, with breakdown by facility, region, event, etc.

Just over half of the survey respondents named faster innovation (52%) and customer acquisition and retention (51%) as the benefits of the data economy. For 42% of participants, joining the data economy meant an opportunity to improve revenues.

Enterprises in the B2B space, as they work to create value in the data economy, will also grow to appreciate the draw to build data businesses–think data-driven products and services that are more relevant to customers than rival offerings, or data-driven experiences that deepen customer relationships through contextual engagement.

Succeeding in the Data Economy

1. Develop the Data Capabilities

The first step is to develop the organization’s data capabilities. Recall the earlier reference to how much data goes unutilized. The MIT Technology Review survey provides some elucidation: for example, about one-fifth of respondents (18%) face challenges in collecting, managing and processing data; the unspoken implication is that their analysis will be tainted by dirty, inaccurate data to at least some extent.  Many enterprises also struggle to share information seamlessly and securely. They need to put the right systems and processes in place to be able to gather, analyze, manage, monitor, and govern their information. Cloud–arguably the biggest enabler of the data economy–is the obvious answer to these problems.

2. Take a Product Approach

Many of the best data-driven organizations think of their data as another product. Just like product companies create a “base” offering and spin off variants, or allow customization, to cater to different requirements, these organizations also provide standard data sets packing the most important attributes, which different business units and teams can use for their own purposes. A mining company offered a live GPS data feed of its trucks’ location in order to increase the yield from ore. While that was the original purpose, another team used that information to ease out bottlenecks in their transport system by building a tool for optimizing truck routing.

3. Create a Supportive Organization and Culture

There may be a need to modify the organization’s structure and culture to position it better within the data economy. These changes must start at the top, with the leadership taking responsibility for a successful foray into the data economy. That may, in turn, require the leadership to be exposed to data best practices – in visualization or governance, for example.

From there, the training and education must percolate through the organization’s ranks so that everyone shares responsibility for building a data-driven enterprise. Like it is with any kind of change, getting employees to embrace a data-driven culture may encounter some resistance at first. Equipping them with the right knowledge, skills and tools will ease the process of adoption. Last but not least, the organization should communicate, and progressively demonstrate, the value of participating in the data economy. Ultimately, numbers speak louder than words.

About the author: Sunil Senan is Vice President with Data & Analytics Unit at Infosys. In this role, he is responsible for growth of Data & Analytics service line for Infosys. He works closely with several of Infosys’s strategic clients on their data & analytics initiatives. In his 20+ years of professional experience, he has worked with many fortune-500 clients in their Enterprise Solutions and Digital transformation journeys.  Sunil holds a Bachelor of Engineering degree with a specialization in Computer Science and has completed his Master’s in Business Administration (Executive-MBA) from prestigious Indian Institute of Management, (IIM) Bangalore. Sunil has authored several articles that have been published in Industry journals like Forbes online, Changeboard.com, and DataQuest. 

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