Bridge The Trust Gap In Your Business Data Once And For All

Pam Barrowcliffe

Despite the emergence of transformative technologies like cloud computing and in-memory processing, the quality of business data – and its reliability in support of daily and long-term business decisions – continues to be a major concern for most CEOs. Their fear is well-founded. The percentage of companies that meet just the minimal requirements for data quality remains very low, which means that the vast majority of companies out there cannot fully rely on the integrity of their data management landscape and processes.

So, how do you bridge this data quality trust gap once and for all?

Take stock of your data

Today’s business data is not only voluminous, but resides in myriad places: structured data from multiple cloud and on-premises sources, unstructured content from email and other communication platforms, and even sensor data from physical assets. To effectively tackle data complexity, you first need to understand and document where all of this business data resides and how it moves – or doesn’t move – through your organization. You need to identify specific areas where data is being duplicated, where data integrity may be compromised, and any data sets that require cleansing, matching, or consolidation.

Once you have analyzed your data for completeness and integrity, you need to optimize your data management operation with the necessary controls, validation checks, and retention policies that will improve data quality moving forward. Next-generation data management tools can help. These tools can take advantage of intelligent automation to make the entire process much simpler and less error-prone than traditional data management approaches.

Ensure that your data is connected and meaningful

While companies are awash in business data, turning that data into meaningful insight remains a major challenge. Critical business data is often siloed off in remote systems. To make it useful to the business, data often has to be duplicated into a business-reporting system, which creates new opportunities for data errors and latency. This is especially true for transaction data that often must be processed through a separate reporting system for analysis.

The good news is that technologies like in-memory processing and new ERP data models eliminate the need to maintain separate layers for analytics and transactions. Instead of having to maintain separate data models and data sets for your analytics, you can run all your analytics in real time directly on your core transaction data. You can turn live transaction data into meaningful insight instantly and get it immediately to everyone who needs it, which means no more mass data duplication or constant reliance on historical data for critical business decisions.

Adopt an open and flexible data management system

Now more than ever, you can take advantage of new business opportunities by leveraging today’s data management and analytics technology. Look for a comprehensive data management solution that can deploy anywhere and support virtually any use case now and in the future. You can run a single solution for enterprise-wide data management and analytics, one that can support terabytes of transaction data from applications and sensor data from machine learning systems with equal ease.

This approach gives you maximum flexibility, enabling you to quickly and cost-effectively adapt as your business needs change. Want to maximize machine learning capabilities to support a new product, service, or business model? A single, open, and unified data management platform can speed that transition. You can deploy on (or connect to) any cloud, on-premises, or hybrid environment and launch new applications quickly because your overall transaction and analytics data model is now smaller and simpler.

The intelligent enterprise can bridge the trust gap

By connecting all the data across the enterprise into a unified, highly governed landscape, you can build-in trust in your business data.

Learn more about how SAP is bridging the trust gap in our data management infographic. Then check out our SAP HANA Data Management Suite solution brief to learn how to use our open, modular, hybrid, and cloud solution suite to build your data foundation for the intelligent enterprise. You can read Gartner’s Magic Quadrant report to learn about different vendors’ data management solutions.

Finally, learn more about SAP Data Warehouse Cloud.

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.

Pam Barrowcliffe

About Pam Barrowcliffe

Pam Barrowcliffe is a marketing director for SAP Data Warehousing at SAP. She has many years of experience working with large and midsize companies as well as OEM, systems integrator, and independent software vendor partners solving challenges around a variety of data problems including; mobile data, multi-temperature data, and enterprise data. Pam works within a larger group at SAP to shape, define, and communicate SAP's digital platform and data warehousing strategies.