7 takeaways from Gartner London and Orlando 2024

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Gartner capped off its Orlando and London data and analytics summits a few weeks ago. We were named a Leader in the 2024 Gartner Magic Quadrant for Augmented Data Quality Solutions, and Ataccama, along with a few of our customers, attended as key representatives of the data management community. Gartner emphasized the imperative need and desire to create business value, linking this to most of the topics discussed at the summits.

Gartner divided this "value" into three categories: Value Creation, AI Ambition, and Collective Intelligence. Centered around harnessing the power of AI and other new technologies for data initiatives, Gartner’s advice and best practices on these three topics targeted companies wanting to drive success in their data initiatives.

Read on to hear our seven biggest takeaways from the events.

#1 AI literacy, what it means, and how to achieve it

If you've heard of "data literacy" in the past, you shouldn't have a problem grasping this concept. AI literacy measures how well your organization is prepared for and uses its AI functionalities. AI and data literacy also heavily impact your organization's future. Companies that prioritize them will be at a great advantage when mitigating risks down the road.

Gartner clarified that these maturity markers aren't just measured by the foundations you have in place or the tools at your disposal. By adhering to concepts like AI responsibility, AI culture, and AI ethics, you can drive further innovation and prevent unexpected mishaps by committing every organization member to initiative success. Hitting these data and AI literacy benchmarks is a great sign you're on the right track.

#2 How to drive change in your organization

Business decision structures can be rigid landscapes. Whoever is in charge might be adverse to significant shifts in mindset or legacy practices. Realizing those who don't change will be left behind, Gartner dedicated a section of the summit to strategies for pushing these evolutions forward and bringing your business into the modern era.

More specifically, Gartner highlighted the need for cultural change, which ultimately leads to everything else. As a means of checking your organization's ability to accept change, where it wants to go, and who is willing to contribute to achieving that goal, they highlighted core questions that business leaders need to answer, such as:

  • Are we there yet?
  • Do we know where we are now?
  • Do we know who's with us?

They also outlined the following three-step process:

  1. Establish your change team. Build a group of individuals who understand the value of the change and are willing to work toward it.
  2. Develop data-driven change stories. Use data to demonstrate your change's power and justify your pursuit of it.
  3. Address change resistance and barriers to adoption. There will always be obstacles and naysayers. Identify them early so you know how to work through them when the time comes.

#3 AI success = data quality

Gartner has emphasized this narrative for a long time and has shown no sign of faltering: successful AI must be built on high-quality data. They introduced the concept of "data fit for purpose" or "data fit for LLM," meaning data that is designated and prepared specifically for use in AI and ML models, emphasizing that this data needs to be of the utmost importance and quality.

Building the same model as a competitor on better data will inevitably lead to a competitive advantage. Additionally, automation can aid you in the pursuit of AI excellence. Automation in your DQ tool for manual and repetitive tasks (or efforts for which you don't have the resources) will allow you to dedicate that time and investment to more advanced tasks.

We've also written a lot about this at Ataccama.

#4 Team structures that facilitate value creation

Creating obvious and immediate value can be challenging for a data and analytics leader. Teams need to be able to work independently with centralized principles that keep their outputs uniform and usable by the entire organization.

Gartner highlighted "data products" as a prime example of effectively executing these team structures, emphasizing their findability, readiness, and inherently governed nature. When paired with a self-service architecture, these concepts are even more valuable, growing user accessibility.

Finally, they clarified that this is only possible with internal buy-in. To build team structures like this, you need:

  • Organizational flexibility
  • Focus on AI and data literacy
  • New leadership paradigms
  • Empowered teams with guardrails
  • Cross-functional teams

In other words, your organization needs to be flexible, independent, centrally governed, and fully appreciate the value of its data and data teams to facilitate value creation.

#5 Generative AI in data management

As predicted in many industries, Gen AI is set to transform the data management space radically. Gartner is fully aware of this, leading to a Gen AI and Data Management session at the summit, where they covered what's happening now and predictions for the future.

Their boldest prediction: Generative AI will cause up to a 20% cost reduction for data management initiatives by 2026.

Overall, they divided the impact into three categories: work offloaded, work done differently, and new enhancements.

Work offloaded

This category refers to mundane tasks done initially by humans that will now be done almost entirely by AI.

It includes:

  • Documentation
  • Optimizing code
  • Controlling complex systems through natural language interfaces
  • Data discovery
  • Data detection

Work done differently

Gen AI will also change the way we do several data management tasks.

These include:

  • New ways of curating data catalogs
  • Standardization, transformation, and enrichment
  • Data profiling and identifying errors
  • Identifying trends, patterns, anomalies, and biases

New enhancements

This category covers new available capabilities Gen AI has made possible.

It includes:

  • Suggesting and generating new DQ rules
  • Creating new pipelines
  • Running root cause analysis
  • Creating data products
  • Evaluating the price-to-performance ratio of pipelines and workloads

#6 The future of MDM or the end?

Gartner's perspective on master data management (MDM) is nuanced. While there's only one dedicated MDM session with a telling title, the topic is woven into data governance and AI readiness discussions. MDM is even touted as the sole AI-ready data, highlighting its ongoing relevance. Gartner analyst Helen Grimster confirms that MDM inquiries remain steady, indicating consistent interest, but she didn't indicate significant growth.

The takeaway? MDM may not be one of the trendier topics, but it remains a crucial tool for technical professionals. Its value proposition lies in its technical capabilities, making it indispensable for those who understand its importance. While the business world may not be captivated by MDM discussions, the need for this discipline persists. Recognizing that the target audience is primarily technical can help MDM professionals tailor their communication and advocacy strategies, emphasizing the solution's value to those most in need.

#7 The growing importance of metadata, data quality, data products, and data observability

When discussing data analytics and governance platforms, Gartner kept returning to these four key features/capabilities: metadata, data quality, data products (one of the sessions with the highest attendance), and observability. Their opinions align on all these topics and signify a shift towards comprehensive data management and governance solutions, a vision that resonates with our existing platform.

Gartner's emphasis on metadata management and governance further underscores their significance across various data-related disciplines, including AI. While metadata is now considered a commodity that every data governance platform should offer, ensuring our capabilities meet market demands remains crucial. Similarly, focusing on governance, particularly policies, necessitates reevaluating current features and communication strategies.

By actively addressing these aspects, companies can position themselves as leaders in this evolving landscape, especially as the anticipated Magic Quadrant for Data and Analytics Governance Platforms gains prominence.

We're in Gartner's Magic Quadrant!

We've been supporters of Gartner for a long time and were thrilled when they recently named us to their Magic Quadrant for Augmented Data Quality. Priding ourselves on automation, Ataccama ONE checks all of Gartner's boxes regarding data quality solutions.

Check it out yourself by scheduling a demo!

Marek Ovcacek

Written by Marek Ovcacek

Marek is our field CTO and has nearly two decades of experience in the technology industry. With deep expertise in data quality, master data management and data governance, he works closely with customers to help them extract value from their data to deliver better business insights and decision-making outcomes.

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