by 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?
1)Take stock of your data.
Today’s business data is not only voluminous, but resides in myriad different places: structured data from multiple cloud and on-premise 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, as well as 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.
2)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 only 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 instantly get it to everyone that needs it, which means no more mass data duplication or constant reliance on historical data for critical business decisions.
3)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-premise, and 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 literally build in that trust in your business data.
- Learn more about how SAP is bridging the trust gap with 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.
- Also, read the Magic Quadrant report from Gartner, Inc. to learn about different vendors’ data-management solution offerings.
Learn more about data warehousing solutions from SAP.