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Successful in the data economy

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Unlike other resources, data grows at an unstoppable pace. To put it in numbers: The total volume of data is expected to exceed 175 zettabytes by 2025. But that means little unless the data is harnessed and used. The data economy provides an environment for organizing, transforming, analyzing, and sharing data to derive value from it; Organizations participating in the data economy gain insights into current and future trends to enable innovation, customer acquisition, product development, problem solving and more.

The concern, however, is that despite all the analysis tools, data is massively underused. Estimates for idle data range from . Worse, even when data is used, its potential is not being realised: when Infosys MIT Technology Review Insights recently surveyed 255 executives and executives, 45% admitted to only using data for rudimentary insights and decisions.

The biggest obstacles to the use of data come from legacy systems, whose inflexible architecture and data silos make it very difficult to integrate the latest digital technologies – advanced analytics, machine learning, deep learning – and process huge amounts of information in real time. This also prevents companies from tapping into semi-structured and unstructured data that resides inside and outside the organization, or alternatively forces them to waste time and manual effort to refine these datasets into structured information.

Building a robust data economy

In order to be able to fully use data – the most valuable resource of the future – companies must be data-driven and actively participate in the data economy. Barriers that prevent data sharing must be broken down to allow organizations to share information both within and with third-party ecosystem members to create value for all.

The obvious first step is to eliminate these data silos that deny organizations access to insights gleaned from a spectrum of inputs. The importance of this was highlighted in the aforementioned survey, where 35% of respondents who shared data for collaboration said it paves the way to value and significant business outcomes.

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There is also a need to “free” data from the custody of giant tech firms, who own most of it, so that it can serve broader needs and be more accessible. Since data is a competitive advantage, these companies protect it tightly in their own repositories. Invariably, the data is underutilized when it could create so much more value if it were available for social purposes. There is no better example than the countless collaborative efforts that have helped the world understand the coronavirus and develop vaccines in record time.

Directives such as the EU Digital Markets Act and the Digital Services Act require (in certain circumstances) interoperability with third-party providers for data giants such as social media platforms and online marketplaces, allowing customers to easily move their data to another location if required can bring. However, the fact remains that the security and confidentiality of data must be ensured before companies or individuals are asked to part with them. The data economy must have clear rules – the GDPR is a good start – and appropriate guidelines. It also needs secure infrastructure and cybersecurity laws and standards to function properly. Last but not least, individuals need transparency and control over how the data they consent to share is used in the future.

The data economy is completely global. Researchers from different countries work together to discover problems and solutions; Enterprises collaborate 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. Developing a unified data policy can be very challenging when considering multiple regulatory jurisdictions and commercial interests. However, given the importance of data sharing in creating a better world, there is a strong case for continuing efforts to build a resilient data economy.

Why companies should participate

Even at the individual company level, the value of the data economy is undeniable. 53 percent of participants in the Infosys & MIT Technology Review survey said that participating in the data economy has given them new business models. An Australian telecoms giant plans to combine environmental data — on waste, air, water, etc. — sourced from IoT-enabled devices with microclimate information to yield useful, practical insights for the agribusiness industry, which it could offer a fee. A multinational waste management company also integrates 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 breakdowns by facility, region, event, etc.

Just over half of survey respondents cited faster innovation (52%) and customer acquisition and retention (51%) as benefits of the data economy. For 42% of participants, entering the data economy represented an opportunity to increase sales.

B2B companies working to create value in the data economy will also appreciate the pull of building data businesses – think data-driven products and services that are more relevant to customers than competing offerings or data-driven experiences that Deepen customer relationships through contextual engagement.

Successful in the data economy

1. Develop data skills

The first step is to develop the organization’s data capabilities. Remember the earlier reference to how much data is wasted. The MIT Technology Review survey provides some explanation: For example, about a fifth of respondents (18%) face challenges collecting, managing, and processing data; The unspoken implication is that their analysis is hampered, at least to some extent, by dirty, inaccurate data. Many companies are also struggling to share information seamlessly and securely. They need to have the right systems and processes in place to collect, analyze, manage, monitor, and control their information. The 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 view their data as yet another product. Just as product companies create a “baseline offering” and spin out variants or allow customization to address different needs, these organizations also provide standard records of key attributes that different business units and teams can use for their own purposes. A mining company offered a live GPS data feed of the location of their trucks to increase ore recoveries. Although that was the original purpose, another team used this information to eliminate bottlenecks in their transportation system by developing a truck routing optimization tool.

3. Create a supportive organization and culture

It may be necessary to change the structure and culture of the organization to better position it in the data economy. These changes must start at the top, with leadership taking responsibility for a successful push into the data economy. This, in turn, may require leadership to be familiar with data best practices – for example, in terms of visualization or governance.

From there, the training and education must percolate through the ranks of the organization so that everyone has ownership of building a data-driven company. As with any type of change, getting employees to embrace a data-driven culture may initially face resistance. Equipping them with the right knowledge, skills, and tools will make the onboarding process easier. Last but not least, the organization should communicate and gradually demonstrate the value of participating in the data economy. Ultimately, numbers speak louder than words.

About the author: Sunil Senan is Vice President of the Data & Analytics Unit at Infosys. In this role he is responsible for the growth of the Data & Analytics service line for Infosys. He works closely with several of Infosys’ strategic clients on their data and analytics initiatives. In his 20+ years of professional experience, he has worked with many Fortune 500 clients on their enterprise solutions and digital transformation. Sunil holds a Bachelor of Engineering with a specialization in Computer Science and a Masters in Business Administration (Executive-MBA) from the renowned Indian Institute of Management (IIM) Bangalore. Sunil has authored several articles that have been published online in industry publications such as Forbes, Changeboard.com and DataQuest.

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