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How to know your customer using data

With more customer data available than ever before, teams can finally identify critical trends quickly and make smart pivots for faster growth. Learn how to apply these strategies during COVID in this blog.

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Screen Shot 2020-09-03 at 10.32.35 AM

Bridging the gap from reporting to intelligence

by: Kunal Turakhia, Global Practice Head, Data Sciences, Wipro Limited

According to a Wipro study, 95 percent of business leaders consider artificial intelligence to be critical, yet only 17 percent leverage it across their organizations. 

The ‘State of Intelligent Enterprises’ report, based on a survey of 300 respondents across industries in the UK and the US, examines the current landscape and shows the challenges and the driving factors for businesses to become what we call Intelligent Enterprises. The report highlights that while collecting data is critical, the ability to combine this with a host of technologies to leverage insights is what creates an Intelligent Enterprise. 

What really is an Intelligent Enterprise? In Wipro’s perspective, organizations that transform by infusing AI across business processes to gain actionable insights powered by data are intelligent. This is the next frontier in using data and technology in the enterprise.

Just a few years ago, our clients were striving to become digital enterprises—moving systems to the cloud, embracing new technologies, and focusing more on the customer. Now the conversation is changing. 

In the last three years, we have been seeing a host of mergers and acquisitions that blur the boundaries between industries. Think of CVS acquiring Aetna; Amazon acquiring Whole Foods, and Ford acquiring Argo AI. 

A new kind of competitor has been showing up, one that is organized less around a product or service and more around orchestrating an ecosystem. For example, Rakuten Ichiba is a retail marketplace. It also provides loyalty points and e-money; issues credit cards; offers financial products and services; runs one of Japan’s largest online travel portals, and offers an instant-messaging app, Viber. There have always been conglomerates, but in today’s world they have a different organizing principle. As McKinsey states, “data sets and sources are becoming great unifiers and creating new, cross-sectoral competitive dynamics.”

These trends have been shifting the goal from being a digital enterprise to being an intelligent one. The pandemic has accelerated movement in this direction. 

So how can you build this intelligent enterprise? You must address the gap between what traditional data warehouse and analytic systems can deliver and what business teams expect. 

On the business side, all of us today are used to having data at the tip of our fingers in our personal lives. We expect the same when we're talking about corporate data and analytics, but we’re not there yet. That has resulted in this sort of guerilla warfare wherein business teams build something on their own and try to get fast answers, for the most part bypassing the data lakes and warehouses that companies have invested in so heavily.

That creates a fragmented process across the organization, with business leaders doing data grunt work; people working in silos, and a lot of wasted, duplicative effort. The quality of data and analysis is suspect because you have multiple operations being performed before the data shows up on a report and it's very difficult for people to go back down the line. This is why business users still feel that their systems or their organizations are not yet intelligent. They still can’t easily convert all this data that's available into something that is accessible and that they can trust and use. 

On the IT side, they have this monolith environment that they've built over a period of time, which is costing them money to manage, and yet is not meeting their internal customers’ needs. If the business asks the same questions day in and day out again, they are prepared to meet those requirements. But if somebody comes and asks a different question, one that requires new data, data from a different system, or from an external source, they have to build new machinery to be able to address it.

This gap has been with us for some time, but it has gotten wider and wider. While the amount of data that companies are dealing with has been growing in volume and velocity, in many organizations the strategy has been to put one more layer of paint on top of a wall that is peeling rather than try to figure out where the leakage is.

If you look at any analytics life cycle, operational reporting is probably the heartbeat of your organization, relied on at many levels of the organization. Then you have management reporting, where analysts are trying to infer some trends, and finally you have data scientists using AI and machine learning to redefine the way you do things.

When I started analytics work in 2000, my first project was building a data warehouse as part of a large engagement wherein we were installing Oracle and had to build reports on top of it using Crystal Reports and SAP business objects. Twenty years later, both of these tools are still very much part of core operational reporting for many organizations. 

That should be surprising given the fact that we've had a seismic change in the way that the technology in analytics has evolved and the new analytics tools that have come onto the market. But, the approach has been to leave operational reporting alone and put more layers on top of it—visualization, self-service, and other new tools--in the hopes of making it more accessible for business users without breaking it. Companies are moving ahead with data science, creating models on open source data or data on the cloud, but they have to pull data the way they want and then create a new sandbox to test out these models and operationalize them because the core systems cannot handle it.

This has resulted in a very complex environment to manage, data all over the place, different routes to accessing it, and no shared view into the data. The business is trying their own shortcuts to get to the root of the problem. IT is trying to figure out how they can make the cart go faster. 

What we’re doing at Wipro is trying to help our customers bridge this gap by bringing in partners such as Incorta. Everybody has gotten used to the process of first taking data out of the warehouse, creating a data model and then building some reports on top of it. With Incorta’s direct data mapping approach, we are able to actually make this a one stage process.

For example, we have an enterprise analytics client that has been growing through acquisitions over a number of years. They had a pretty robust analytics infrastructure in place but were still challenged to be able to assess potential acquisitions quickly, and then quickly integrate acquired companies. We introduced them to Incorta, and three years later they believe they are now the fastest organization in terms of being able to integrate a new entity into their business, primarily because of the Incorta insights platform that Wipro supports for them.

We have another client, a manufacturing company with a stringent cadence of Sarbanes-Oxley compliance reports that need to go out on a regular basis, with financial penalties for missing deadlines or presenting inaccurate or incomplete numbers. They were barely able to get their reports out to the auditor on time, let alone have somebody do a detailed analysis before handoff. How Wipro and Incorta partnership was able to help them was by quickly bringing the data together from multiple systems across the organization so they could turn around accurate reports with time to spare.

These are the kinds of problems organizations are looking to solve today—problems that are tied to a particular business result and that are not solvable by looking at dashboards. Now, the problem might come in different forms in different business and industries, but the common thread we see is that they’ve got enough tools and systems but still cannot get the answers they need.

Intelligent enterprises understand the fact that using the systems they built in 2000 they're not going to solve the new problems they face today. But it’s not just about systems and tools. In my view, the end game of intelligent enterprise is a proper synergy between people and technology. It's ultimately about having confidence in your decisions. This is the gap that must be bridged. When people can get the data they need and there is trust in the data, you will truly be an intelligent enterprise. 

Learn how the CIO of a global manufacturer moved past the limitations of expensive, rigid, and chaotic BI and data warehousing to grasp the true power of data, analytics, and AI/ML. Watch our recent TDWI Virtual Solution Spotlight: http:://go.incorta.com/virtual-accelerate-your-journey