Gartner’s November 2016 Magic Quadrant for Data Quality Tools places SAS in its Leaders quadrant, based on completeness of vision and ability to execute. This is the 11th year that SAS has been named a Leader*.
“Many UK businesses find themselves held back and their initiatives stymied by data quality issues” said Hugo D’Ulisse, Head of Analytical Platform at SAS UK & Ireland. “The impending new General Data Protection Regulation will demand greater control over personal data, which will bring the need for data quality governance into even sharper focus. SAS seeks to provide businesses with the advanced analytics, data profiling and orchestration capabilities needed to transform their organisations.”
According to Gartner, “Leaders demonstrate strength in depth and breadth across a full range of data quality functions, including profiling, parsing, standardisation, matching, validation and enrichment. They exhibit a clear understanding and strategy for the data quality market, use thought-leading and differentiating ideas, and deliver their product innovation to the market.”
SAS Data Quality delivers trusted data by supporting traditional and emerging data sources – such as Hadoop, Impala, Amazon Redshift and more – throughout the entire data life cycle. By improving data where it lives, SAS provides faster and more secure data access. With data constantly flowing in and out, businesses rely on SAS to establish repeatable processes that build and maintain high-quality data.
“SAS has made its goal as an organisation to constantly improve upon its offering to customers. We strive to ensure that they have the insight they need to enhance their decision-making processes and take on the challenges of machine learning, IoT and big data” added D’Ulisse.
The report noted the importance of data quality for organisations of many types and sizes:
“The data quality tools market remains vibrant, owing to greater adoption on the demand side and consequently growth in market revenue on the supply side. We continue to see high demand for data quality tools from many verticals and organisation sizes, including midsize organisations (which traditionally tended not to buy them). This demand for data quality tools is driven both by organisations continuing to invest in digital business initiatives as well as organisations seeking to cut costs and optimise business operations. Therefore, we see data quality tools being applied in a wide range of scenarios, such as BI and analytics (analytical scenarios), MDM (operational scenarios), information governance programmes, ongoing operations, data migrations, and interenterprise data sharing.”
Learn more about the importance of data quality.