Next Practices For The Intelligent Enterprise Tech Company

Thomas Pohl

New ways for technology companies to innovate have mushroomed in the last decade. The array of technologies that can be used to gather intelligence has grown to include cloud, blockchain, machine learning, AI, and the Internet of Things. Analytics now informs everything from product design and customer satisfaction to protecting the supply chain and the automation of industrial and business processes.

Data analysts in tech companies already know the benefits of analytics — growing revenue, designing products and services faster, reducing costs, enhancing the customer experience, and so on. But tapping data from across and outside of a technology enterprise for analytics on a grand scale was easier said than done — until very recently.

From our global experience working with the leading, most innovative technology companies, here are three SAP “next practices”— capabilities and outcomes to help your company utilize data and analytics on a grand scale.

1. 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 and complexity. You need visibility into that data. Without it, you have a disjointed picture of the business. With it, you can do things like blockchain-enabled smart contracts to validate outcomes and automatically execute terms. Or connected manufacturing, optimized energy use, data-driven operations and marketing, demand signal management, forecasting, 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.

2. 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 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 data sets. How? With solutions to understand, integrate, cleanse, manage, associate, and archive data to optimize business processes and analytical insights.

3. 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 an enterprise technology 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 technology companies

Data analytics is being recognized as a vital tool for technology companies that need to innovate faster than the competition, create new markets and design new products quickly, and attract and retain customers. The need for speed has grown — along with the diverse types and quantity of data. Becoming a truly intelligent enterprise requires a reliable, easy-to-use platform to capture, ingest, process, orchestrate, compute, and consume data at tremendous scale.

SAP customers in the tech industry that are intelligent enterprises are using data analytics fed by an increasing array of data sets for use cases that include:

  • Process mining to uncover hidden bottlenecks, deviations, and critical fraud patterns
  • Predictive customer churn and lifecycle analyses to reduce churn and add revenue
  • Optimizing inventory to lower costs across the supply chain
  • Managing total margin to prevent leakage
  • Geo tracking and condition monitoring of goods while in-transit to reduce costs and variability

These are just some of the many quickly evolving, creative ways that larger and diverse data sets are being put to work to guide tech companies 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 technology companies around the world are transforming into intelligent enterprises, read the new SAP white paper “The Data-Driven Technology CompanyData 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.


Thomas Pohl

About Thomas Pohl

Thomas Pohl is a senior director, Marketing, at SAP. He helps global high-tech and aerospace companies to simplify their business by taking innovative software solutions to market.