How can firms drive a robust strategy to gain insights from their data and deliver it with confidence?

A Q&A with Mukesh Jain, Chief Technology & Innovation Officer, Vice President & Head – Insights and Data Technology, Capgemini, and Noah Schwartz, GM, Product and Engineering, AWS Data Exchange.

Mukesh Jain leads the team responsible for building 890 by Capgemini – a fast-deployed, cloud-ready, platform-agnostic product that allows organizations to make actionable, data-powered decisions, backed by strong governance, privacy, and security. Jain discusses the need to embrace insights and analytics, the challenges facing organizations, and what they need from data-analytics solution providers.

Organizations generate a tremendous amount of data. How well are they taking advantage of that?

Jain: In my experience, the answer is: “Not as well as they could be.”

Why is that? What are the challenges?

Jain: When I speak with executives, I hear them express a number of challenges that many organizations seem to share.

First, they know their organization generates a lot of data that contains valuable insights, but their business users don’t know what that data is or what value they can derive from it.

Second, they believe building a data-analytics solution that meets their organization’s specific needs and situation must be done from the ground up, which can take anywhere from eight months to a year. The commitment of resources that represents is significant and often feels prohibitive.

Third, decision makers understand there’s a lot of data outside their organization that is valuable and relevant to their own activities – for example, data generated by partners. But they aren’t sure how to integrate that with their own data.

Finally, they recognize that any time an organization seeks insights from its data, it must do so in a way that complies with all applicable regulations governing data protection. Those regulations vary by jurisdiction and are constantly evolving. What’s more, the applicable regulations may change depending on what data is being collected, who is collecting it, and how it’s being used.

These challenges are huge impediments to unlocking the value of data – particularly when one moves beyond a test environment and scales out data analytics to encompass the entire organization and its partners.

Schwartz: It’s hard for customers to make sense of the first-party data they generate. We’re seeing more and more customers that need the ability to marry their first-party data to third-party data so they can run their business. For example, putting together a green investment strategy for a hedge fund requires all the typical market data, but also needs to be joined to environmental, social, and governance (ESG) data. Simply put, it’s unlikely a business possesses all the data within the organization to make decisions like this. And there is immense time pressure to make these decisions, so discovering, licensing, and being able to use data quickly are critical. Discovery and licensing alone can take weeks. Needing to build custom IT capabilities to integrate the data can add months and then must be maintained forever. That’s a real distraction from, for example, the goal of putting together a green investment strategy.

What does that mean about how organizations are leveraging their data?

Jain: While many executives want to take advantage of their data, only 50 percent of the organizations Capgemini surveyed in 2020 reported that they actively promote data-driven decision-making. The good news is that’s up from just 38 percent in 2018 – but it still means half of all organizations are not actively embracing data.

Even then, we found many of the decisions being taken are reactive. That survey found organizations used reactive decision-making approaches – such as basing decisions on what happened in the past, or why things happened as they did in the past – about 51 percent of the time. Many executives still tend to respond to what happened in the past, rather than being proactive with future-oriented data. And fewer than four in 10 respondents said they were able to harness their data to create a sustained competitive advantage, introduce new business models, or introduce new products and services.

Why do you think that is?

Jain: One word: “trust.”

Only about 20 percent of business executives trust the data that their organization is collecting. And only about 40 percent feel that their organization’s data and analytics strategy is properly aligned with their overall business strategy.

The reasons for this lack of faith in their data strategy include concerns over the quality of the data, its collection, and the organization’s data-access policies. Yet trusted data is crucial to enhance organizational agility and the organization’s ability to derive value from its data.

What are the benefits of a well-defined and implemented data-analysis strategy?

Jain: The right solution will allow businesses to become Data Masters. They have developed strong capabilities to harness their data – both in terms of the tools and technology required, and the DNA within the organization that encourages data analysis and data-driven decision-making.

These Data Masters are able to explore their data and unlock valuable insights – and do it while complying with all applicable regulations. They have figured out how to harness their data to create simulations, run various scenarios, and make better decisions.

And it works: In my experience, clients who have implemented 890 by Capgemini discover their data is more useful, and they end up using it more. We are able to bring in data from within the organization and external or third-party data from AWS data exchange and other data providers and make it available via API for people to use or blend to enrich their data.

Schwartz: At least 90 percent of what we build at AWS is exactly what our customers are telling us that they need. We heard over and over again that data providers wanted a managed solution for metering, billing, entitlements, security, and distribution at scale. Subscribers wanted the experience of licensing data in the cloud to be as easy as it is to shop online today. To do that, we developed AWS Data Exchange, making it easy for customers to find, subscribe to, and use third-party data in the cloud.

With our integration into 890 by Capgemini, AWS customers, with just a few clicks, have access to   more than 3,000 data products from more than 200 qualified data providers, including thousands of free data sets for immediate entitlement and use. The ability to discover, license, and leverage external data within existing workflows minimizes the time needed to reach insights from the data.

Jain: There are also benefits for data scientists and other technical users at an organization because they’re able to do things better and faster and reduce the pain their business users have experienced in the past when using other means to try to explore their data and extract value from it.

That said, Capgemini’s research has determined that only about one in six organizations can be considered Data Masters. The majority of companies we’ve surveyed are what we would call Data Laggards. They are weak in both the tools and the culture required to harness their data to their advantage.

What are the consequences of a poorly shaped strategy – or no strategy at all?

Jain: I think history is littered with examples of Data Laggards. Over the past 25 years, I’ve seen example after example of companies that have not leveraged their data, only to have a competitor overtake them. Look at the top companies on the Fortune 500 list: You don’t have to go back very far to discover past leaders that no longer exist.

The leaders of any organization need to take decisions based on data. It’s that simple. Yet it’s immensely challenging to absorb and interpret and understand all the data that’s available to them. Executives need tools to help them – and the right type of tool is critical.

With the right solution, executives can, for example, run alternate scenarios – they can model the impact that changes to marketing will have on their sales, or how a change to customer service might affect overall satisfaction, and many other features for various scenarios.

So, what should leaders look for in a data-analytics solution?

Jain: They must trust how the data is being collected, processed, and analyzed, so they must work with a solution provider with a proven track record in this area. The solution must feature robust rules of access and regulatory compliance, and it must easily integrate data from several sources – including older data sources and data from partners. Speed of deployment is important, as is security of data and scalability.

These were all must-have features when we developed the 890 by Capgemini solution – and it is why companies, governments, consortiums, and other organizations are embracing it.

With 890 by Capgemini, we deliver to our clients an organization-spanning workspace that’s fully configurable, with a rich suite of ready-to-use AI and analytics tools. We designed our solution to make it easy for all users to analyze, model, and visualize the data and the results, to drive insights and outcomes that create value for their organization.

In today’s environment, extracting value-generating insights from an organization’s data is no longer a choice. It’s the only choice.

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Authors

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Mukesh Jain is Chief Technology and Innovation Officer, Vice President and Head – Insights & Data Technology at Capgemini. He is a veteran in the data, analytics, and artificial intelligence space with 25-plus years of experience building large scale products at Capgemini, Microsoft, Jio, and NICE Systems. He is a known figure in the industry and often speaks at internal conferences on these topics. He is active in teaching at several universities and is the author of two books.

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Noah Schwartz is the GM of Product and Engineering for AWS Data Exchange. He joined AWS in 2017 and has spent most of his career in a role building technology to generate or use data to answer questions across FSI, CPG, and Sports. Prior to joining AWS, he was the CTO of Bloomberg Sports, Head of R&D at Dow Jones, and Head of Engineering for Risk & Regulatory Data at Bloomberg.