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Blog > March 2018 > What You Missed at the 2018 Gartner Data and Analytics Summit

What You Missed at the 2018 Gartner Data and Analytics Summit

This past month, the 2018 Gartner Data and Analytics Summit exceeded expectations as the modern innovator’s playground, striking a balance between capturing the pulse of today’s data landscape and looking toward to the future of data-driven IT. The presentations and conversations provided considerable insight and sparked a lot of thinking around how the data landscape is evolving and how ASG could add value.

For those of you that couldn’t make it to the Summit, or for those of us who want to relive it, here are a few of my favorite takeaways from major Summit themes:

Driving Diversity in Data and Analytics

While managing and analyzing data was the lifeblood of Summit buzz, a few presenters took a step back to consider how the people behind the data can contribute to bettering analytics — diversity being one of the most momentous and important conversations. In her session, “Diversity in Data and Analytics Fuels Innovation on the Path to Digital Transformation,” Debra Logan, VP and fellow at Gartner, pinpoints exactly where digital transformation and diversity intersect: an industry need for talent. Organizations want to put analytics at the center of digital transformation, but the skills and experts needed to do so are scarce. By embracing diversity and inclusion, companies can recognize and move beyond their own human biases and can cast a wider net building a higher performing team that brings a broader set of skills and perspectives to bear.
Logan called on IT decision makers to build a business case for diversity, highlighting the role that diversity plays in innovation, broader customer understanding, brand enrichment and benefits for recruiting and retention. Closing out her presentation, Logan drove home that when it comes to jobs, data and analytics positions can be filled by a broad range of professionals — kindling Summit conversations around how to seek out diversity actively and reach into new areas for talent.


Data and Analytics Leaders

Talent and the role of IT were hot topics at the Summit, with presenters predicting a huge evolution in how IT will function within organizations, sparking both concern and curiosity. Based on various sessions, exhibits and sideline conversation, it’s clear that this change must start from the top. In fact, Gartner predicts that 80% of large enterprises will have a chief data officer (CDO) office in place by 2020.
As IT is redefined, the set of skills required of the modern data and analytics professional must also shift. In his presentation, “How to Create and Lead High-Performance Data and Analytics teams,” Alan D. Duncan, research VP at Gartner, cited Gartner predictions that by 2019, 55% of citizen data scientists will surpass data scientists in the amount of advanced analysis they produce. At the same time, Gartner predicts that 40% of data science tasks will be automated by 2020 — relieving professionals of time-consuming tasks and making way for more strategic work. Automation was a largely talked-about theme at the Summit, with several perspectives offered on job making or taking. In this case, however, Duncan cited that automated data science tasks will result in increased productivity and more and better insights by citizen data scientists.


workplace-1245776-1920-2.jpgDefining Data Engineering

The data engineer is a newer role we’ve heard popping up on the data and analytics scene — but what exactly is its purpose and where does it fit into your data and analytics program? In a room full of enterprise IT professionals, there were plenty of us looking for the answer to this question. Gartner’s Research VP Nick Heudecker provided that answer in their session, “Driving Data Analytics Success with Data Engineering.”
In addition to building the metadata that enables data pipelines, today’s data engineer has become the “data guru” across both business and IT — promoting the role and capabilities of IT around data and analytics and collaborating with business units to enable data access and understanding. The twist is that, while data engineering requires a diversity of skills, including analytical, IT and interpersonal, Heudecker recommended that core data engineering tasks should be automated. By automating as much as possible — from data maintenance and data quality to data insight — companies can drive efficiency and reduce data engineering drudgery. Another point for automation!


Prepping for Data Preparation

The faster we can glean insights from our data, the better — a race to the finish that most Summit attendees knew well. Yet according to Ehtisham Zaidi, principal research analyst at Gartner, in his session, “From Self-Service to Enterprise Data Preparation — The Next Wave of Disruption for Pervasive Analytics,” organizations are still reporting that more than 70% of their time is spent finding, accessing, preparing and sharing data for further analysis. Cue the rise of data preparation.


According to Zaidi, data preparation can shrink the time-to-insight for organizations. And while it was once used mostly by business or citizen users, or for self-service, data prep is now being leveraged for higher IT productivity. By incorporating data integration, data quality and metadata management, prepared data is becoming more trustworthy and can enhance organizations’ ability to monetize information — a key action needed to drive digital transformation forward. In fact, Zaidi predicts that by 2020, organizations offering users access to a curated catalog of internal and external data will realize twice the business value from investments compared to those that do not.

computer-3246121-1920-1.jpgInfonomics 101

Contrary to the new catchphrase for data (used even by Summit attendees), information is not the new oil, according to Doug Laney, VP distinguished analyst at Gartner, in his presentation, “Applied Infonomics: How to Monetize, Manage and Measure Information as an Asset.” Information is extremely valuable but it’s also nondepleting, regenerative, difficult to control, and as Laney says, “if you spill it, you can’t clean it up.”

There are three steps organizations can take to extract value from data: monetize, manage and measure. This approach to data meshes digitally-driven business initiatives with traditional (and still essential) data management — allowing organizations to know what information they have, where it came from and how to build a framework for effective use. Such insights can empower C-level decision makers to generate measurable economic benefits they can attribute to data assets — allowing them to better leverage the value at their fingertips.

As the value of data explodes, we need to learn how to harness it, fast. From the networking on the floor, to the engaging exhibits, to the powerful lineup of presentations, the 2018 Gartner Data and Analytics Summit did not disappoint. It was a powerful reminder of the momentum that can be created when the best minds and innovators come together for an exchange of information and vision. Of course, my favorite highlight was our session with ASG’s Sue Habas and American Fidelity’s Mark Nance, detailing how Mark used data to lead the digital transformation of his company.

If you didn’t attend the Summit, keep your finger on the pulse of industry happenings by reading this ASG blog post on what we expect to see from data in 2018. And for information on how to tackle and maximize those trends, visit our product page.
 
Posted: 3/23/2018 3:59:59 PM by Rob Perry | with 0 comments


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