At several conferences I’ve attended lately, it’s been no surprise to find that everyone wants to talk about analytics. From supporting artificial intelligence to customer intelligence, organizations are looking for better ways to get value out of their data.

For SAS, this is great news. Because this is what we do and will continue to do – add value to organizations’ data through the use of analytics. But for every successful end there must be a beginning. And for every insight gained from analytics there must be a strong data management foundation. To highlight this point, consider a recent article from Forbes titled: Better data quality equals higher marketing ROI. Figures from the Forbes article:

• According to Forrester, less than 0.5% of all data is ever analyzed and used.
• Just a 10% increase in data accessibility will result in more than $65 million additional net income for a typical Fortune 1000 company (Richard Joyce, Senior Analyst at Forrester).

These figures represent the power of data management. The fact that less than 0.5% of data is being analyzed is not an analytics issue – it’s caused by organizations not having the proper data management foundation in place to begin their analytics endeavors. For analytics programs to have any value, organizations must be able to access all of their data, integrate it from various source systems, cleanse the data and govern it proactively.

Data management solutions at SAS

At SAS, our data management solutions have been developed not only with operational use cases in mind, but also to support our customers’ analytics investments. We provide:

• Access. Get read and write access to data stored on different platforms and in many different forms – like relational database systems, flat files, etc.
• Integration. With enterprise-level tools that support extraction, movement, transformation and loading of data between systems, you can migrate and blend data between nearly any type of platform, database or file format. 
• Cleansing. Standardize, cleanse, enrich and correct data to support various business needs – from legal compliance to data address standardization to uniform content formatting.
• Governance. Create and manage data definitions, rules and policies associated with accessing, sharing and using data – and let users monitor, measure and interact directly with the data to resolve issues.

An example

One organization using SAS Data Management to enhance its analytics initiatives is a major insurance company in California. The insurer needed to support the reporting and analytics needs of more than 3,000 independent intermediaries. As business departments struggled to make faster, better claims decisions, the firm realized that poor data management was the biggest hindrance to those efforts.

The company was challenged to merge multiple disparate data systems into a single, user-friendly platform. It also needed a clear data quality strategy and auditable structure to prepare for upcoming regulations.

Using SAS Data Management, the insurer was able to integrate its data into an enterprise data warehouse that was designed with IT and business collaboration in mind. High-quality data helped streamline reporting and, most importantly, offered management a clearer view of the business. With this, they gained the confidence to make decisions based on information that they know is accurate.

It used to take several days for the insurer to add data to a spreadsheet (resulting in data that was outdated as soon as it was available). With SAS Data Management, the same amount of data the insurer once processed in a year can now be processed in less than a week. By turning better data into better analytics, the insurer can make better informed decisions and deliver the best service and pricing options possible to market.

Is poor data management a hindrance to your organization’s goals? Learn how you can have better data for better analytics by visiting sas.com/dm and by downloading the white paper below.

Download: 5 Data Management for Analytics Best Practices