"Two weeks to go," Santa said to himself, with millions of toys stacked up on the shelves.

Each year worry hit at the same time – "How do I get the right toy to the right child without losing my mind?"

Though Old St. Nick didn't have a computer science degree, deep down he knew he was talking about data quality.

Verify those addresses to reduce mailing costs, optimize delivery routes to save the reindeer from frost.

Elves built the toys straight from the heart, but they need standardized terms so they have the right parts.

Without data they could trust, decisions were bad. And with inaccurate inventories, not enough toys were had.

Santa had inconsistent reports at each elf workstation, and problems meeting the new Arctic regulations.

Too few Hatchimals delivered to the wrong city. Santa knew this was a job for SAS Data Quality.

Each year Gartner rates data quality vendors in what is known as the Magic Quadrant. Leaders are grouped in the top right quadrant. Before this comprehensive document is released, my team responds to requests for information including a briefing and sometimes demonstrations of SAS Data Management software. Such evaluations require a lot of work on the part of solution providers (like SAS) as well as the analysts. We do our best to concisely convey the extensive set of capabilities and benefits our software can deliver.

We're happy to report that for the eleventh year in a row, SAS has been evaluated as a leader by Gartner, based on completeness of vision and ability to execute.* The Gartner Magic Quadrant for Data Quality Tools 2016 can be found here.

Gartner describes data quality as having capabilities that include data profiling, parsing, standardization, matching, issue resolution and enrichment. Organizations need all of these capabilities to have well-rounded data quality solutions. For example:

• Profiling helps organizations better understand the data in terms of uniqueness of a given field or maximum and minimum values.
• Parsing helps organizations extract more meaning from the data, which can help separate a first name and a last name from an address.
• Matching removes duplicates so customers will be treated appropriately and multiple mailings won’t be sent to the same address.
• Enriching involves augmenting the data with additional attributes like ZIP codes (which could be used to optimize delivery routes).
• Related capabilities like data connectivity adapters, visualization and metadata management make data quality solutions even stronger.

To meet the evolving needs of our customers, the product management team for SAS Data Management focuses on empowering business users, simplifying integration with big data and incorporating emerging data integration patterns. This strategic focus helps our customers meet the challenges of managing data in this era of high-volume, low-latency data – which increasingly comes from external sources.

Empowering business users

For business users, empowerment means software that’s easy to use – particularly in terms of being able to create business rules to monitor the health of data. It also means being able to introduce business-facing remediation queues and workflows that allow subject matter experts to resolve data quality issues.

Simplifying integration with big data via common metadata and easy-to-use tools

SAS is one of the only data management vendors that provides common metadata spanning the worlds of data management and analytics. This means users can share the same metadata about reports, database tables or analytical models – for example, predicting when a customer might go to a competitor – across the analytical life cycle of data preparation, data management and analytical modeling. Our approach overcomes traditional barriers that exist between data scientists (involved in the design phase) and the IT department (involved in deployment to operational systems). Coupled with solutions like SAS Data Loader for Hadoop, our approach helps simplify and democratize access to big data.

Supporting emerging data integration patterns

While ETL is still an important part of a data integration toolbox, the old days of 100% batch-run ETL processes are behind us. Today, organizations often need faster access to data to make decisions that will help them compete. Self-service data preparation via SAS Data Loader for Hadoop is one example of how to do this. This solution gives business users fast access to data without burdening IT to provision it. In addition, SAS Federation Server runs embedded, on-demand data quality functions that can, for example, standardize all of your state codes as the view is generated. Further, SAS Event Stream Processing can apply analytics and data quality in motion to hundreds of thousands of events per second.

Santa and data quality

While you're enjoying your gifts around the Christmas tree this year, keep in mind that data quality helped the elves build the right items, order the right parts and deliver them to the right child. And it helped Santa – the jolly old man himself – deliver them with glee.

Read more about the importance of data quality.

*SAS was named a leader in previous Magic Quadrants for Data Quality Tools under its former subsidiary name SAS DataFlux.

This graphic was published by Gartner, Inc. as part of a larger research document and should be evaluated in the context of the entire document. The Gartner document is available upon request from SAS.

Disclaimer: Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner's research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.