Business-led Analytics Goes Mainstream

Automation and Machine Learning Make Complex Data Tasks
So Easy That Everyone Can Master Working with Data

The recently published 2017 Gartner Magic Quadrant for Business Intelligence and Analytics Platforms* states that the “Market shift from IT-led reporting to modern business-led analytics is now mainstream.” We could not agree more. A little more than four years ago, we set off on a mission to automate every traditionally hard data task – from data prep to blending to discovery – with a goal of making it easy for everyone. We knew we had to radically change the user experience, making it largely self service, more engaging, and interactive. Why? Because ease-of-use combined with data automation transforms data analysis from rigid, manual and IT-driven, to agile, flexible and business-led.

This year, ClearStory is positioned for the second time as a visionary in Gartner’s Magic Quadrant for BI and Analytics Platforms (this time up and further to the right than last year). This is great validation of the strong alignment between current, mainstream market needs and the innovation that we bring to the modern BI and analytics market. While we obviously don’t (yet!) have the market scale and mass of Microsoft, Tableau, or Salesforce.com, we appreciate Gartner’s recognition of our innovation and product leadership in modern BI. Further, it’s great to see ClearStory rise into the top three vendors for BI and Analytics on the “Completeness of Vision” axis.

Here are a few highlights from the report, along with my commentary.

Automated Data Prep, Smart Data Discovery and Automated Data Blending and Harmonization at Scale is a Must.

The 2017 Gartner MQ report states: “Modern BI and analytics platforms are characterized by easy-to-use tools that support a full range of analytic workflow capabilities and do not require significant involvement from IT to pre-define data models upfront as a prerequisite to analysis (including at enterprise-scale deployment).”

We agree. We’ve innovated in all these areas, delivering automated prep of data via “Data Inference”, the elimination of pre-defined data models via innovations in “Intelligent Data Harmonization™”, the fast discovery of pertinent data via “Smart Data Discovery” and self-service business-led exploration via our innovations in “Explorable Interactive StoryBoards”. In all areas, we support collaboration and sharing in-context throughout. Because whether you’re working with data at its source or consuming insights to speed decisions, collaborating and sharing information is the lifeblood of business decisions.

Gartner’s characterization of modern BI and analytics platforms is well aligned with ClearStory Data’s strengths. We use fast, Spark-based processing to handle large data volumes. Our modern BI platform is an ideal choice for business users who need to combine, harmonize and explore multiple and varied data sources, including personal, cloud, streaming and syndicated data. Customers benefit from ClearStory’s machine-based approach that eliminates the long cycles and manual effort that traditional BI solutions require to integrate data sources and run complex queries.

What’s Next?

Today, at the Gartner Data & Analytics Summit in Grapevine, Texas, we unveiled another breakthrough with Automated Smart Data Discovery. It brings an easy, interactive way to find patterns and correlations across data values and dimensions, and lets users immediately zero-in on the most critical and relevant data to answer business questions. It is designed to work at scale, on complex, highly dimensional data from single or multiple harmonized sources. Read more about it here.

Beyond this, we’re working on integrating more machine-learning across our stack of capabilities in all areas from data prep to data harmonization, to smart discovery, to how key insights are automatically surfaced to business users, without business users having to waste time drilling through visualizations or hunting for what’s most important – especially when data and insights update hourly, daily or weekly. What’s great is we’ve built a deep foundation of capabilities and innovations already, which makes taking them even further with machine-learning extremely relevant and suitable for our architecture.

With these capabilities, ClearStory gets even better and smarter over time. And the result for business users is a “smart machine” that makes complex data tasks easy at scale, and finding insights dead simple, no matter what your skillset. We’ve only scratched the surface of how we can integrate ML into our stack, but we predict these capabilities will have a major impact on the modern BI and analytics market.

It’s an exciting time for ClearStory, as organizations worldwide have realized that it’s time to adopt a modern BI and Analytics solution and go from IT-led to business-led analytics. We are thrilled to be at the forefront of this transformation and to help companies realize that working with data does not need to be the sole province of IT and data modelers. Data analytics can be as easy and awesome as it is powerful for the business.

From Then to Now

I recently passed the 20th anniversary of my first job in Silicon Valley: working for Jim Barksdale, CEO of Netscape. He often said: “If we have data, let’s look at data. If all we have are opinions, let’s go with mine.” In that era, we didn’t have a ton of data, so often we went with Jim’s opinions. These days, the world is “one big data problem,” as Andrew McAfee says, and opinions don’t cut it. It’s time for everyone to know how to work with data and unlock the secrets inside. That’s only possible when the job of data wrangling and discovering data insights is automated and dead easy. We think we’re finally there – so come along with us, and experience the difference of modern, scalable BI.

Come see ClearStory Data live at Gartner Data & Analytics Summit or request your trial today: http://www.clearstorydata.com/trial

*Gartner, Inc., “Magic Quadrant for Business Intelligence and Analytics Platforms,” by Rita L. Sallam, Cindi Howson, Carlie J. Iodine, Thomas W. Oestreich, James Laurence Richardson, Joao Tapadinhas, February 16, 2017.