Next Practices For Intelligent Enterprise Retail Companies

Joerg Koesters

Last year, a Gartner study uncovered something shocking. Among six industries facing major disruptions, retail is the most vulnerable. Only 3% of companies surveyed “are harvesting results from delivering and scaling digital business initiatives.” Why?

The data is there – historical, transactional, and now Internet of Things data on customers, products, and services. Transformative technologies like mobile, cloud, machine learning, artificial intelligence, and analytics are also there to get that data and make it useful. And retailers don’t lack motivation; disrupters like Amazon, Alibaba, and Flipkart are quickly building market share.

Retail data analysts already know the benefits of analytics: automating repetitive tasks, enhancing the customer experience, fine-tuning planning and forecasting, reducing costs, and so on. Perhaps the Gartner finding revealed something more fundamental: that gathering, processing, and utilizing data of diverse types from different sources for analytics on multiple use cases was extremely complex. Until very recently.

From our global experience working with the leading, most innovative retail companies, here are three SAP “next practices” – capabilities and outcomes to help companies utilize data and analytics on a much grander scale for a powerful new class of applications.

Integrate diverse data sources

Data is scattered. It’s in multiple applications, files, data warehouses, data lakes, and public and private clouds. Each silo walls off the data with proprietary rules. You need visibility into that data. Without it, you have a disjointed picture of the business. With it, you can understand which customers, products, services, and channels are contributing to profitability – or not. You can analyze returns to understand issues and reduce them. You can better understand promotions and any cannibalization that might occur. And much more.

Next practice #1: Integrate your data by combining data sets – including Big Data, process data, product data, analytical data, etc. – as needed, into a single data universe for much greater visibility.

Make data more useful

Your data comes to you structured, semi-structured, and unstructured. It may be spatial, chart, numeric, geographic, time-series, relational, JavaScript Object Notation (JSON), and so on. Integrating all of these different types of data is extremely complex. But without it, your company is at a competitive disadvantage, squandering available resources.

Next practice #2: Integrate your data sources using orchestration and governance solutions. Go from raw feed to intelligence with real-time analysis of vast datasets. How? With solutions to understand, integrate, cleanse, manage, associate, and archive data to optimize business processes and analytical insights.

Simplify your data landscape

Centralized. Easy-to-use. Automated. That’s what you want from your data analytics platform. And those features have been a challenge because of all the different databases, apps, and clouds in your IT and business environment. But now a centralized data management solution is available that manages all facets of a retail company’s data universe. Represented visually, the architecture is easy to share and understand. Stakeholders assigned to an architecture team within your company can collaborate through a user-friendly Web application in the planning, design, and governance of the architecture.

Next practice #3: Create and maintain a complete landscape architecture that is easy to share and understand. Open up this landscape to an array of company employees and managers to jointly manage your data environment as an agile, strategic tool.

A growing number of data analytics use cases for retail companies

Data analytics is being recognized as a vital tool for retail companies that need to digitally enhance the shopping experience; deliver products and services in real time; generate new business models to counter competition and gain market share; and connect things, consumers, and enterprises with digital technologies. This is how retail companies can involve to become intelligent enterprises.

SAP customers in retail use a reliable, easy-to-use platform to capture, ingest, process, orchestrate, compute, and consume data at tremendous scale. With such a platform in place, they are tapping into an increasing array of data sets for use cases that include:

  • Profitability analysis and margin assurance
  • Product markdown analysis and optimization
  • Returns analysis and prediction
  • Live 360-degree customer view
  • Basket analysis for promotion and placement

These are just some of the many quickly evolving, creative ways that larger and diverse data sets are being put to work by intelligent retail enterprises today. Some use cases are relevant to every type of organization within the industry. Others are more suited to different types of businesses, geographies, markets, and other unique characteristics.

For more on how retail companies around the world are transforming into intelligent enterprises, read the new SAP white paper “The Data-Driven Retail Company – Data Management for the Intelligent Enterprise.”

And please listen to the replay of our “Pathways to the Intelligent Enterprise” Webinar, featuring Phil Carter, chief analyst at IDC, and SAP’s Dan Kearnan and Ginger Gatling.

About Joerg Koesters

Joerg Koesters is the head of Retail Marketing and Communication at SAP. He is a technology marketing executive with 20 years of experience in marketing, sales, and consulting, Joerg has deep knowledge in retail and consumer products, having worked both in the industry and in the technology sector.