4 Key Data Prep Capabilities to Create a Single View of Customer Vendor Background

3 Takeaways from Gartner’s 2018 Data and Analytics Summit

April 2, 2018
· 4 min read

Paxata was a Silver Sponsor at the recent Gartner Data and Analytics Summit in Grapevine Texas. From all the sessions and conversations, we took away three important themes. For those of you who did not attend the summit, we have cited Gartner research as the sessions predominantly reflected the most recent Gartner published papers.

1) People and machines come together to create a more powerful and agile experience

In Rita Sallam’s July 27 research, Augmented Analytics, she writes that “the rise of self-service visual-bases data discovery stimulated the first wave of transition from centrally provisioned traditional BI to decentralized data discovery.”1

We agree with that. Although some product solutions disrupted the operational reporting market, they require users to know the questions they need to ask their data. So, while these self-service solutions are easy to use and simple to understand, they essentially function as validation for pre-conceived hypotheses.

This paradigm has shifted and will continue to shift. Today’s data management and analytics products have infused artificial intelligence (AI) and machine learning (ML) algorithms into their core capabilities. These modern tools will auto-profile the data, detect joins and overlaps, and offer recommendations. With AI infused throughout, the industry is moving towards a place where data analytics is far less biased, and where citizen data scientists will have greater power and agility to accomplish more in less time.

2) Line of business is taking a more active role in data projects

In Eric Thoo’s research, Five Reasons to Begin Converging Application and Data Integration,  we learned about the convergence of data and application integration: “An increasing number of organizations are recognizing the value of managing staffing and leveraging skills in a consistent way across both application and data integration disciplines.”2

 Today, data integration is moving closer to the edges – to the business people and to where the data actually exists – the Internet of Things (IoT) and the Cloud. Business people don’t understand the difference between data integration and application integration, but in the new world that wants to be infusible, they won’t need to. In the new paradigm, according to Gartner, “data and analytics leaders must follow the example of English as a second language (ESL) and treat information as the new second language of business, government, communities and our lives,”3 embedded in all places and all applications.

This shift is driving a hybrid data integration mentality, where business teams are given curated data sandboxes so they can participate in building future use cases such as mobile applications, B2B solutions, or IoT analytics.

Additionally, Doug Laney’s report on Applied Infonomics helped us learn that “by 2020, 10% of organizations will have a highly profitable business unit specifically for productizing and commercializing their information assets.4 Data and analytics leaders, CDOs, and executives will increasingly work together to develop creative ways for data assets to generate new revenue streams.

The conference also defined a new role/persona with strong ties into the line of business.  In Nick Heudecker’s session on Driving Analytics Success with Data Engineering, we learned about the rise of the data engineer role – a jack-of-all-trades data maverick who resides either in the line of business or IT.

From what we have seen, regardless of the reporting structure, data engineers not only know how to build data pipelines, they also have a product or business mindset and can educate others across the organization by evangelizing data access and understanding.

3) The emergence of a new enterprise information management platform

Perhaps the most interesting part of the conference was Ehtisham Zaidi’s session, “From Self-Service to Enterprise Data Preparation — The Next Wave of Disruption for Pervasive Analytics.” In the session, Zaidi reiterated a prediction from his Market Guide for Data Preparation: “By 2023, machine-learning-augmented master data management (MDM), data quality, data preparation and data catalogs will converge into a single modern enterprise information management (EIM) platform used for the majority of new analytics projects.”5

We feel this corresponds exactly with Paxata’s vision from the inception as described by Nenshad Bardoliwalla, Paxata’s Chief Product Officer and Co-founder.

To achieve organization-wide data literacy, a new information management platform must emerge. While this information platform will have some of the essentials from traditional product suites, such as data integration, data quality, and MDM, it is the convergence of these tools into a new modern platform that blurs the lines between different roles, personas, and skill sets. This new platform will also serve many different use cases, including but not limited to analytics, application and data migrations, data monetization, and master data creation.


[1] Gartner, Augmented Analytics Is the Future of Data and Analytics, Published: 27 July 2017, Analyst(s): Rita L. Sallam | Cindi Howson | Carlie J. Idoine

[2] Gartner, Five Reasons to Begin Converging Application and Data Integration, Published: 12 March 2015 Refreshed: 05 February 2018, Analyst(s): Eric Thoo | Keith Guttridge

[3] Gartner, Information as a Second Language: Enabling Data Literacy for Digital Society, Published: 09 February 2017, Analyst(s): Valerie A. Logan

[4] Gartner, Applied Infonomics: Use a Modern Data Catalog to Measure, Manage and Monetize Information Supply Chains, Published: 26 February 2018, Analyst(s): Alan D. Duncan | Ehtisham Zaidi | Guido De Simoni | Douglas Laney

[5] Gartner, Market Guide for Data Preparation, Published: 14 December 2017, Analyst(s): Ehtisham Zaidi | Rita L. Sallam | Shubhangi Vashisth


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