Gartner: Key Trends of Modern Data Quality Tools

By Farnaz Erfan

The accelerating rate of digital business, growing complexity of incoming data, and limitations with existing data quality tools are producing a complex data landscape and generating heightened data quality requirements.

According to Gartner, “Data quality tools are experiencing fundamental changes in eight key areas: audience, governance, data diversity, latency, analytics, intelligence, deployment and pricing.“*

As a preview, here are some excerpts and insights:

• Audience: Data quality is essentially a business discipline, considering that business people are the primary producers and consumers of data and are best positioned to manage data quality initiatives. However, because many data quality tools in the market today require deep technical expertise, data quality projects are often relegated to the IT department.Nevertheless, according to Gartner, “Business people have become the primary audience of modern data quality tools and the agents of information governance.”* As such, analytic leaders need to choose data quality tools that can support business people.

  Fortunately, data quality tools are finally maturing to a level at which they can enable a business audience by offering capabilities such as self-service and interactive data prep, data profiling, and self-documenting rule management.

• Data Diversity: Data and analytics leaders must deal with issues related to increasing data diversity, including analyzing large volumes of data from diverse formats such as cloud applications, NoSQL databases, social and sensor data, and semi-structure file formats such as XML and JSON. Additionally, “Organizations are also increasingly curating external data to enrich and augment their internal data.”* Consequently, they need to select data quality tools capable of tackling these concerns.
• Intelligence: With the upsurge in data volume, variety, and velocity, data quality vendors are introducing a range of new technical innovations and intelligence capabilities, including machine learning and natural-language processing. These advancements will augment human intelligence and ultimately will improve data quality outcomes. Therefore, data and analytics leaders must seek out modern data quality tools with these competencies in order to manage their data quality initiatives with speed and agility.

*Gartner Evaluate and Adopt Modern Data Quality Tools Based on Eight Changing Trends, Mei Yang Selvage, Alan Dayley, Saul Judah, Ankush Jain, 12 June 2017