SAP HANA Data Management Suite

A blog by Ratnang D. Desai, SAP Data and Automation Leader, Deloitte Consulting LLP

Having worked at Deloitte Consulting LLP for 23 years and leading our SAP Data Competency practice area, I can tell you first hand that Deloitte is always working to stay one step ahead and anticipate the data needs of our customers. In that spirit, we have been rapidly ramping up our data management capabilities for the past couple of years to prepare for the growing challenges of enterprise data management.

With the recent announcement of SAP HANA Data Management Suite (HDMS), I am very excited that Deloitte customers now have access to an entire suite of solutions—from SAP HANA and SAP Data Hub, to SAP Enterprise Architecture Designer and Big Data Services—that are integrated by design to offer a complete end-to-end data management solution.

After an immersive review of its capabilities, I believe HDMS goes beyond working with data at an enterprise level and elevates the business and strategy of data management to an ecosystem—collapsing the walls and organizational borders of the enterprise by accessing social media data and other public data available for consumption. This type of data orchestration of structured and unstructured data can be rapidly integrated with an organization’s enterprise data for a complete and trusted view of all available data.

Coupled with the ability to interact with cognitive and machine learning concepts and services within HDMS, a strategic information-making framework congruent with the business goals of an organization is easily attainable. Not only is HDMS the foundation for data and analytics but it is also now available as a service and on the cloud. This can significantly reduce the total cost of implementing and operating HDMS.

Let’s take a closer look at how I would envision HDMS providing value to Deloitte clients.

A 360-degree View of Data

First and foremost, HDMS has the power to provide businesses with a 360-degree view of their customer data—what we in the data management world might call the holy grail view —something everybody has been trying to achieve for years now. A company needs to know their customers inside and out—purchasing power, what they are thinking, what they are looking for, whether it’s quality, price, or unique differentiators. The channels customers now use to interact with businesses are multifold—website usage, social media, email, etc. Integrating these data channels with known purchasing data allows businesses to convert the raw data into powerful and valuable information that allows organizations to best position a product to any given customer. It sets a powerful stage for improved strategy in any organization, especially in retail, where understanding the customer profile and knowing how to drive the right behavior at the point of sale is crucial.

HDMS even takes it a step further with cognitive and machine learning capabilities. With a set understanding of customer behavior, an organization can predict what their customers will want in the future. HDMS is a dynamic solution that allows companies to interpret a customer's behavior patterns in real time and creates a decision-making environment that is predictive rather than reactive, which is really what’s at the heart of that coveted 360- degree (“holy grail”) view of customer data.

Pricing and Profitability Gains

HDMS also offers tremendous value when it comes to pricing and profitability. Most companies know what their customers are buying, but may not know much beyond that. With only static reports based on historical buying behavior, pricing actions can be somewhat arbitrary—for example, a company sees that inflation is 2% and then decides to raise prices by 3%, the philosophy being: stay above water with pricing, and we’ll still make a margin. But with real-time data, companies can segment customers using their purchasing behaviors. Then, based on the volume of purchasing, customer’s profile and publicly-available competitive and industry information, a company can appropriately adjust price and margins on a given product for a given customer at a given time —a much more targeted and dynamic approach to pricing and profitability.

Before HDMS, that kind of targeted purchasing data analysis required taking the customer and materials master data, along with competitive information from public sources, and put all of that into a custom server to analyze. With HDMS, there’s no need for complex maneuvering. Users can take data from an external data store, pull in buying behavior from an existing system, and then compare the data and make an informed pricing decision.

Supply Chain Predictive Analytics

HDMS also offers significant value through data analytics on the supply chain side of a business. Consider, for example, automotive manufacturers. Customers report defects or issues that can lead to recalls. No one recalls one car or 100 cars; they recall hundreds of thousands, even millions of cars. If a company can diagnose the problem and resolve it before consumers begin reporting complaints, they could potentially prevent lawsuits, bad publicity, and loss of brand equity.

Deloitte has helped manufacturing customers anticipate issues with their products by incorporating different data points—historical defects and recalls, manufacturing data quality control, and social media chatter from customers—in order to come up with a score that indicates the likelihood of a major defect or issue with any given product.

With HDMS there’s no need to stitch together three or four different data sources, a process that leaves significant room for error. HDMS offers big data services, machine learning algorithms, the capability to work with structured and unstructured data, and all the analytic tools required not only to crunch the data but also to present the findings in a digestible format that allows customers to take timely, informed action.

Added Value to Health Care

HDMS can also add value in health care. Currently, a patient may have to seek a variety of sources—doctors, diagnostic tests, along with their own research—in order to determine the best treatment path. HDMS can provide one precision medicine data store that compares patient symptoms or test results to clinical data, genomic data, bio-specimen data, imaging data, research data, and other external reference data—in order to make a diagnosis and recommend an effective treatment path, as well as provide patients with practitioner matching and scheduling services. Just one example of how this powerful tool can be deployed in health care scenarios—HDMS can take a patient X-ray and compare it to millions of other X-rays in order to make a diagnosis that is 99.9% accurate.

Concerned about that one-tenth of a percentile? Machine learning algorithms make HDMS more intelligent with every iteration. And, what’s more, the robust security and privacy features embedded and enabled in HDMS are designed to support patient data that is self-owned, private, and protected by anonymous digital identities.

Benefits are available not only to patients, who could receive more reliably successful treatments while also getting the opportunity to take a more proactive role in their own health, but also to the health care system as a whole, which has the potential to grow increasingly effective and efficient.

HDMS: An Invaluable Data Management Suite

I am eager to implement HDMS for customers seeking a powerful and innovative solution for their increasingly complex data management needs. Whether you are in retail, manufacturing, health care, or any other industry, HDMS offers an invaluable end-to-end data management solution for trusted data that can help you add untold value to your business.

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