Didn’t make Gartner’s Data and Analytics Summit this year?
Don’t worry. We did. Consulting Services Manager Kris gives us his download including key takeaways from specific sessions.
Business outcomes, business outcomes, business outcomes.
Ignite was founded on the belief that there are no data projects, just business projects that use data. The Garner summit reinforced this founding belief for AI and governance, focusing on business outcomes, not just the latest tech, compliance, or control.
Governance should be approached as an enabler, not a controller. We start our focus with Data Discovery, ensuring an end-user focus on understanding what is available, usable, and up to date. This can help inform governance efforts and overall data literacy across the organization.
Chief Data and Analytics Officers and their teams need to be value-focused, where the discussion should be about investment – not budgets. We understand from working with our clients that the value discussion can be hard, the key is not just thinking about short-term ROI, but how capability enables business strategy-aligned outcomes.
A few of Gartner’s Top D&A Predictions from the summit that really resonated – surfacing the themes of being Outcome Focused; Governance as an Enabler; and the challenge of being AI Ready:
- By 2026, 75% of CDAOs that have failed to make organization-wide influence their top priority, will be assimilated back into technology functions.
- By 2027, more than half of CDAOs will secure funding for data literacy and AI literacy programs, fueled by enterprise failure to realize the expected value from generative AI.
- By 2027, 40% of CDAOs will have rebranded governance as business enablement of strategic business initiatives from the outset.
- By 2027, 80% of data and analytics (D&A) governance initiatives will fail due to the lack of a real or manufactured crisis. By 2028, more than 50% of enterprises that have built their own large language models (LLMs) from scratch will abandon their efforts due to costs, complexity, and technical debt.
Keynote Insight: Generating Value Together: From Fundamentals to AI Readiness to Collective Intelligence
AI will require a focus on three key areas:
Focusing on business outcome, not technical outcomes.
- Prioritize execution over long strategies – get going and learn.
- Look to broader business outcomes – not just short-term project ROI which may not sell the whole value.
Governance as an enabler, not a blocker.
- Understand your ambition for AI.
- Set up governance as an enabler to this ambition, ensuring AI-ready data.
Creating collective intelligence through independent decisions, enabled by central controls.
- Be willing to explore new operating models – this may require enabling the edge to make decisions.
- Focus on data literacy – but extend to AI literacy. Everyone using it should understand the risks and appropriate use.
Session Insight: Why your D&A Governance Solution is Not the One You Need
Governance has often focused on technology, tools, or platforms. Complexity is then overlayed by different vendors with differing capabilities. This results in a focus on data quality, management, or metadata management, instead of the outcomes the business needs.
- D&A Governance is not about data. It’s about business outcomes and how data improves or impacts them. This means your team will be looking at the right problems and opportunities.
- Be explicit in the use case and work required – as a way of focusing policy.
- Use technology and capability based on the above – not for the sake of it.
- What are you going to do with the capability?
- What is your policy?
- Does it drive business outcomes?
Session Insight: CDAO (Chief Data & Analytics Officer) Agenda 2024: Reinvent or Become Irrelevant
CDAOs face increasing scope complexity. Be prepared to spread yourself thin and to trade off what not to do. Focus on business outcomes, selling value and making the conversation an investment one, not a budget one. Without this focus on the business needs, and deliberating working to influence organizational outcomes, D&A functions will be pushed back into IT and be technology-focused.
- D&A Governance is the key to AI-ready data – but it needs to be contextual and serve a use case scope.
- Need to be able to communicate the value of governance programs – use cases and focus on enabling business outcomes.
- Budgets are the biggest constraint – you need to be able to communicate and sell benefits.
- Make it an investment conversation, not a budget one!
- Develop storytelling skills.
Session Insight: Data and Analytics Governance: Foundations & Future
Governance must start with a focus on business outcomes –moving from control to collaboration, focusing on building business trust in your assets to enabling users to deliver valuable business outcomes.
- Connect all governance activity directly to business outcomes and priorities.
- Move from Control to Collaborate – move away from bureaucratic directives to enabling, improving information culture, and encouraging innovation.
- Recognize that not all information assets are equal – understand the required trust level required to make the changes you need to make.
Session Insight: Lessons from Real-life D&A Strategies – the good, the bad, and the ugly
D&A strategies should link to the business strategy, with a shared vision of success and a compelling call to action. They shouldn’t be created in isolation, have unconnected initiatives or unrealistic timeframes (3+ years).
- Only 33% of organizations have a D&A strategy and are implementing it (2022 CEO survey).
- The goal is to create a business strategy infused with D&A, not a data & analytics strategy.
- Must have concrete metrics to measure progress and success.
Session Insight: Data Mesh vs Data Fabric? Identify the benefits and Risks before making your decisive 2023 investment.
Data Fabric is about using metadata analysis to make recommendations – creating an intelligent orchestration engine (automation of recommendations based on what is actually happening – e.g., user logs indicating the recommendation for a new aggregation or model).
Data Mesh is an operating model and architecture principles – not a technology. It is about Data Products and putting the value/creation/management of this into the hands of domains.
Mesh has had some challenges. If domain experts are not willing to be accountable or responsible, there can be a proliferation of data products and an unwillingness to share with other domain teams.
85% of organizations don’t have maturity to really pursue these capabilities yet. In this case, it is better to focus on building a Logical Data Warehouse/Lakehouse to establish capability first.
Upcoming Webinar
How to optimise AI in your business
A discussion with Ignite’s Consulting Services Manager Kris Hunter and Solution Architect Nischay Thapa on how to translate Gartner’s trends into practical, business value with Ignite.
Further Reading
More from the Gartner Summit
Solution Architect Nischay Thapa shares his technical highlights from the 2024 Data and Analytics Summit.