As some might recall, in the 1966 World Cup, Sir Geoff Hurst, a former English international football (A.K.A. ‘soccer’) player, scored a controversial goal during England’s World Cup final match against West Germany at Wembley Stadium that helped his team secure a 4–2 victory. For many the ball hadn’t crossed the goal line and therefore wasn’t a score; however, the Russian linesman saw things differently and ruled the goal ‘Good.’
When this year’s Gartner Magic Quadrant for Data Quality Tools was published this past week, for a moment, I thought “where’s a Russian linesman when you need one?” But that moment quickly passed as I recalled my statement from earlier this year—when we received the results of the Gartner Magic Quadrant for Data Integration Tools—that it’s not about the dot, it’s about the journey.
I’m sure my American colleagues will appreciate the words of NFL football player and coach, Vince Lombardi, who said: “The price of success is hard work, dedication to the job at hand, and the determination that whether we win or lose, we have applied the best of ourselves to the task at hand.”
When you apply this to Talend’s positioning in the 2016 Magic Quadrant for Data Quality Tools, the takeaway is: it’s all about moving the ball down the field—making a significant jump within the Visionaries quadrant vs. our 2015 placement, which is an overall triumph for the betterment of our customers and the market.
When describing the market in this year’s report, Gartner notes that increasing numbers of “organizations seek to monetize their information assets, curate external with internal data using a trust-based governance model, and apply machine learning as they explore the value of the Internet of Things (IoT).” I believe our jump in this year’s Magic Quadrant for Data Quality report can be largely attributed to our focus on providing machine learning data quality capabilities for Big Data, and self-service data preparation capabilities with governance.
Over the last year, Talend has augmented our Data Quality capabilities in three key areas: Big Data, Self-Service, and Metadata Management. We believe these are the three necessary ingredients for enabling the next generation of workers with tools that can help them do their jobs faster, which are presented in a more consumable, simple, and integrated way. This approach is in-line with the same shifts Gartner sees taking place in the market.
Let’s look at each one of these areas of innovation in turn:
1. Data Quality on Big Data—Having high-quality data is a prerequisite for guaranteeing the business value that can be generated from analyzing and using the steadily increasing volumes of enterprise information. Otherwise, you just end up with a ‘garbage in, garbage out’ scenario, which results in poor decision making. Our latest data quality product innovations utilize machine learning to capture and automatically apply a lot of human decision making to data sets to cleanse, organize and verify big data information stores at scale.
2. Self-Service—Many business workers today can’t get the information or data sets they need from IT in a timely enough manner to get their jobs done. Add to this the fact that more and more millennials are entering today’s workforce, nearly a third of whom—according to a survey of 1,050 Americans conducted by Conversion Research—would rather clean a toilet than talk to customer service (or in this case, IT). The millennial generation has no desire to wait on anyone to do anything for them—they’d rather be able to do things for themselves (i.e. not involve IT to get the information they need to do their jobs faster). By introducing self-service data preparation, we’ve put data quality in the hands of business users vs. IT. Not only does this self-service access to information make more workers happy—particularly millennials—but it also allows informed decision-making to take place enterprise-wide and enables IT to scale with emerging business needs.
3. Governance—As businesses grow and enable more workers to access enterprise data lakes, IT needs to have tighter information security and auditing policies to ensure the quality of enterprise information is maintained over time. One way to do this at scale is to institute collaborative data governance; wherein, all users are making updates to the metadata to ensure it’s up to date and accurate at all times.
With the many different expectations being placed on IT by today’s evolving workforce, coupled with the demands of big data, how should IT adapt or provision their services to succeed? At Talend, we believe the answer lies in the right combination of all three ingredients listed above—and that our current Data Quality solution embodies this perfect recipe.
So to bring it back to what I learned from the results of this year’s Gartner Magic Quadrant for Data Quality Tools report, as Vince Lombardi also said, “Perfection is not attainable, but if we chase perfection we can catch excellence.”
Again, it’s all about moving the ball down the field.
 “Magic Quadrant for Data Quality Tools,” by Saul Judah, Mei Selvage, and Ankush Jain, Gartner Research, November 2016.