By David Thain, Product Marketing Manager at Privitar 

Last week, Privitar’s team joined over 5,000 senior data, analytics, and business leaders for the Gartner Data and Analytics Summit in Orlando, Florida. Under the overall banner of “Unleash Innovation, Transform Uncertainty,” Gartner laid on a three-day diet of analyst insights, customer expertise, and vendor perspectives in more than 250 individual sessions. Hot topics of discussion included how to drive from analysis to business decisions and outcomes, how to organize teams for success, and how to govern increasingly complex data and analytics efforts – even if many conversations started with estimations of how close the local alligators might be.

Here are our key takeaways: 

Ask the right questions 
The opening keynote by Gartner’s VP Analyst Gareth Herschel and Distinguished VP Analyst Debra Logan set the tone for the Summit by focusing us on the mindset and soft skills that lead to innovative, value-driven data and analytics.1 They called on us to ask: “Do I have the right data? Am I finding the right insights? Will my analysis drive outcomes?” As much as great technology in needed, analysts and data scientists need to ask the right questions to their data. In turn, great analysis can prompt us to ask better questions about our business.

We were encouraged to establish when good enough is good enough. Gareth and Debra championed “small data:” “We don’t want lots of data — we only want the data that makes us smarter.”

Debra equated analytics with art, where the ability to interpret what’s in front of us is critical, and quoted the American author Toni Morrison: “All art is knowing when to stop.”

Fortunately, the Summit itself was just getting started, and there were plenty more insights to absorb.

It’s the people that make it
People and people skills were generally top of mind at this Summit. Many analysts highlighted the importance of getting team structure right and promoting collaboration in our organizations. Across several sessions, we heard that data engineering is a team sport, governance is a team sport, data science is a team sport, and self-service analytics is a team sport. There may have been other team sports we missed!

This emphasis on teaming was a potent reminder of how easily organizational roles end up in siloes and the operational importance of breaking those down with streamlined collaboration.

VP Analyst Kurt Schlegel’s session on self-service analytics pushed this imperative particularly strongly, using the metaphor of franchises to advocate for a balanced organizational model where a centralized team works collaboratively with decentralized analytics teams.2 Assembling multidisciplinary teams that blend IT skills, analytic expertise, and domain knowledge seems obvious when you hear it, but the all-too-common communication issues between technical experts without business knowledge and business leaders without technical skills suggest.

We heard that collaborative approaches have applications across data and analytics programs and planning. Distinguished VP Analyst Frank Buytendijk brought this thinking to the complex area of ethics: “In putting together a digital ethics strategy, it makes sense to do that with a diverse interdisciplinary team, to catch multiple perspectives.”3

According to his maturity model for data ethics, mature organizations are characterized (among other factors) by teams collaborating and communicating how they apply ethics in their use cases, learning from each other.

A little more conversation
A variation on the theme of collaboration and communication, we heard sage advice about the importance of conversations that make analytics meaningful, and describe its value, to senior leadership in your organization. Distinguished VP Analyst Mike Rollings positioned similar principles as foundational to modern data and analytics pro: “Transform conversations about strategy with a fresh vision and expectations for value.”4

Managing Vice President Neil Chandler provided one of the most disconcerting statistics of the week: 67% of business decision-makers aren’t comfortable basing decisions on data pulled from their current technology.5 Simply put, data alone is not enough to make compelling cases; we need visualization, narrative and context to tell the full data story. Gartner predicts that data stories will be the most widespread way of consuming analytics by 2025.

VP Analyst Saul Judah encouraged us to use conversations to understand the CEO’s values, since these drive a company’s vision and strategy. These will help establish your data and analytics vision. He stressed the importance of engaging in meaningful conversations that engage stakeholders with real strategic choices, rather than trying to simply “facilitate” a person to a decision you’ve already made. “The choices must be real.”6 

Control and enable
Governance— including data quality, privacy, security, and more— was a key topic at the summit, but consistently looked at through the lens of how we unleash innovation while retaining the right amount of control. As early as the opening keynote, Gareth Herschel and Debra Logan asked “Does governance help or hinder innovation?”7 Their answer, echoed in many other sessions, acknowledged that good governance liberates data and analytics teams to innovate. 

Data leaders were called on to resolve the never-ending debate between control and freedom. Kurt Schlegel emphasized, “We must create a data architecture, organizational model, and governance framework that delivers the benefits of both.”8 For best outcomes, organizations need the advantages of control— consistency, shared best practice, and consensus— paired with the benefits of freedom— autonomy, agility, and innovation.

Mature approaches to governance move from ensuring control to providing agility and autonomy, but become even more challenging to apply in increasingly dynamic and complex organizations. Saul Judah (surely one of the busiest analysts at the Summit, presenting no fewer than four sessions) advocated for a connected governance framework that achieves cross-enterprise business outcomes through a virtual governance layer that spans business functions, geographies, and legal entities across an organization.9 He predicted 20% of high-performing organizations will use connected governance to scale and execute on their digital ambitions by 2026.

Metadata cures madness
This felt like the Summit where data fabric came of age, even as analysts stressed it’s still rising on the hype cycle and confirmed best practices don’t exist yet. Sessions related data fabric to business value and gave practical advice on implementation to the point it seemed a given that complex organizations should be looking into this emerging data management design.

Metadata is critical to prospects of success. Data fabric utilizes metadata to create flexible, reusable data pipelines for faster, automated data access and sharing. Distinguished VP Analyst Mark Beyer explained the unifying and simplifying potential of data fabric, leveraging metadata: “[Data] can always be “hooked” together even when it is in different platforms with different designs.”10 One application of this would be resolving existing metadata into automated governance.

VP Analyst Ehtisham Zaidi argued that data preparation without effective metadata management, becomes “brittle, chaotic and siloed.”11 In contrast, active metadata promises remarkable gains when it comes to reducing human effort and improving both trust in data and use of data. In his session on data fabric, Ehtisham told us that Gartner predicts automated functions in the data fabric, assisted by active metadata, will cut human effort in half by 2025, and quadruple the efficiency with which data is utilized.12

It’s clear that there was wealth of insights shared over the course of the Summit, and we look forward to putting those insights to work.

Check out the In:Confidence Podcast for more insights from data, privacy, and analytics leaders. 

Footnotes:
1 Gareth Herschel and Debra Logan, “Gartner Opening Keynote: Unleash Innovation, Transform Uncertainty”
2 Kurt Schlegel, “Rethink Self-Service: Establish Analytic Franchises to Drive Adoption, Break Bottlenecks and Maximize Value”
3 Frank Buytendijk, “Digital Ethics: Where Are You Now and What Will Be Next?”
4 Mike Rollings, “The Foundation of a Modern Data and Analytics Strategy”
5 Neil Chandler, “Data Storytelling: A Better Way to Engage Decision Makers With Data”
6 Saul Judah, “How to Have an Engaging Conversation With Your CEO About Data and Analytics”
7 Gareth Herschel and Debra Logan, “Gartner Opening Keynote: Unleash Innovation, Transform Uncertainty”
8 Kurt Schlegel, “Rethink Self-Service.”
9 Saul Judah, “Connected Governance Needs a Data and Analytics Governance Platform”
10 Mark Beyer, “Metadata is the Key to Self-Learning, Augmented Data Governance”
11 Ehtisham Zaidi, “Utilize Self-Service Data Preparation to Ease Rising Data Engineering Challenges”
12 Ehtisham Zaidi, “How to Design a Data Fabric for Augmented Data Management and Integration”