Gartner Data & Analytics Summit 2023: A Brief Day-by-Day Overview
The world of data and analytics is eclectic and exciting. Here, the numbers only come to life and empower businesses to make strategic decisions backed by a wealth of information called insights and analytics.
The prime responsibility of any Chief Data and Analytics Officer (CDAO) is to build connected ecosystems across the company, guaranteeing smooth scalability, and using AI to enforce moral standards for data management and privacy.
Recently, Gartner hosted its much-anticipated Data & Analytics Summit 2023 in London between 22 – 24 May bringing together the finest minds from the world of data and analytics.
In this article, we will mainly cover some key takeaways (day-wise), some interesting anecdotes, and some profound thoughts and strategies from the world’s most significant leaders.
Day 1: Opening Keynotes by Pieter den Hamer, VP Analyst, Gartner, and Georgia O’Callaghan, Director Analyst, Gartner
This session highlighted the ideal data and analytics organizational model. The focus remained on empowering companies to strike the correct mix between centralized and decentralized functions to assist firms in becoming more data-driven.
The leaders tackled common questions like what is happening in the world of data and analytics that is causing these changes. How does it affect the D&A organization’s structure? What about the jobs, knowledge, and culture that exist inside the data and analytics industry?
Peter and Georgia emphasized innovative approaches for D&A In order to achieve leaders to communicate with stakeholders, address skills shortages, and lead with purpose.
“The most common roadblocks to the success of D&A initiatives are all human-related challenges, such as skills shortages, lack of business engagement, difficulty accepting change and poor data literacy throughout the organization.”
“D&A leaders are still unable to speak the language of business, but we are under tremendous pressure to show other business peers and executives the value of our initiatives.”
In the realm of data and analytics (D&A), the focus lies on a range of activities and responsibilities related to strategy enablement, which means this plays over a longer period of time.
The need of the hour is to reconsider how we approach discussions regarding data and analytics (D&A) investments in order to successfully illustrate their potential impact and capacity to meet the various needs of several stakeholders.
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Rita Sallam, Distinguished VP Analyst, Gartner presented the top D&A trends in 2023
Rita outlined the ways in which businesses may take advantage of these trends to promote innovation, efficiency gains, and corporate success. She explored the most recent developments in data and analytics (D&A) methods and techniques that have the capacity to predict changes and transform uncertainty into a world of possibilities.
The top 10 trends for D&A:
- Value Optimization
- Managing Your Artificial Intelligence (AI) Risk
- Data Sharing is Essential
- D&A Sustainability
- Practical Data Fabric
- Emergent AI
- Converged and Composable Ecosystem
- Consumers Become Creators
- Humans Remain The Decision Makers
To quickly identify, manage, prevent, escalate, and resolve data outages within established service level agreements, data observability refers to a company’s ability to maintain a complete view of its data landscape and the complex dependencies between different layers of data.
AI for sustainability
Organizations are increasingly using D&A and AI to benchmark and track their progress toward sustainability targets and other environmental, social, and governance (ESG) objectives.
Enabling responsible AI
It’s critical to understand that not every choice should or can be automated. We face the risk of developing data-driven businesses without conscience or a clear purpose if we only concentrate on advancing decision automation without taking into account the human aspect.
Impact of ChatGPT & Generative AI on Enterprises – Anthony Mullen, VP Analyst, Gartner
Due to their large scale and diverse potential applications, foundation models like ChatGPT have significantly advanced the science of artificial intelligence. The possible hazards connected to these models, however, are not yet fully understood.
Anthony discussed the benefits, dangers, and possibilities that these models offer during the conversation. The goal was to investigate how companies may enhance value while lowering associated risks.
“ChatGPT (and the family of generative AI foundation models) is the first widely known AI technology that challenges the one trait humans always thought they would have over machines: creativity.”
Although foundation models are a huge breakthrough, caution must be used when training them. Their opacity may produce unwanted results that fall short of expectations.
5 best practices that should be followed before beginning work with generative AI.
- Create a thorough position paper that details the benefits, dangers, possibilities, and a precise schedule for implementing basic models.
- Opt for task-specific pre-trained models that fit your unique requirements and goals.
- To proactively stop misuse and deal with undesired actions, promote responsible AI practices.
- By emphasizing UX redesign, establishing a feedback loop, and disseminating information on prompt engineering, we may improve machine-human interaction.
- A specialized incubation team should be assigned to manage the development and implementation of generative AI efforts.
Day 2: How to Avoid Data Lake Failures – Roxane Edjlali, Senior Director Analyst, Gartner
In the field of data and analytics (D&A), a data failure can have a major negative impact. It can stifle insights, impair decision-making, and reduce the overall efficacy of data-driven projects. Therefore, for successful D&A adoption, assuring data reliability, quality, and resilience is crucial.
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On day 2, the main areas of interest remained to combat Data Lake failures; comprehending the methods Chief Data and Analytics Officers (CDAOs) use to manage talent acquisition and retention, and pinpointing the crucial characteristics that make a good CDAO.
Data Lakes Complement Traditional Data Warehouses & Enable Advanced Analytics
As a complement to conventional data warehouses and a tool for advanced analytics, data lakes play a key role. Utilizing data lakes enables businesses to go beyond traditional data warehousing, opening up possibilities for more complex and thorough analysis.
“Watch out for operational dependencies – making your data lake the feed for operational systems may mean that you need to make it much more operationally robust.”
Failures of data lakes are frequently caused by ignoring important elements like project resources and appropriate technologies. Roxane clarified the many criteria of a contemporary, multipurpose data lake, enabling businesses to avoid failures.
Curate, Govern, and Secure the Data
Curation, governance, and security of the data in your data lake are essential to maintaining its clarity. You can stop your data lake from getting muddied or compromised by putting in place efficient data curation processes, establishing strong governance standards, and maintaining appropriate data security measures.
Strategies for Attracting, Retaining, and Nurturing Talent in D & A, and AI Teams – Jorgen Heizenberg, VP Analyst, Gartner
Ever wondered what’s the success behind assembling the ideal team to work on your data, analytics, and AI projects? It all comes down to finding great talent and keeping them inspired. Finding and keeping the greatest minds in the industry is essential to the success of your team. This includes providing competitive pay and growth opportunities, as well as building a supportive work environment.
“Get D&A talent from outside the organization by applying nonconventional approaches, such as hiring atypical employees. Hire for potential rather than experience.”
Consider using unconventional techniques, such as hiring outlier employees, to gain data and analytics (D&A) talent from outside sources. Consider alternative hiring criteria and give potential precedence over expertise. You can attract diverse talent that fosters innovation and advances your D&A goals by welcoming new viewpoints and untapped potential.
“Create a culture that prioritizes work-life balance, skills development and emotional well-being.”
Focus on creating a culture that prioritizes work-life balance, ongoing skill development, and emotional well-being in order to foster a flourishing workplace. By giving these things a top priority, you create an environment where workers feel encouraged, supported, and empowered to succeed on both a personal and professional level.
Retain D&A talent by creating more balance in the D&A team via the diversity of people, roles, and development paths.
If you want to keep your priceless data and analytics (D&A) skills, finding a balance within your team is key! Bring in individuals with various experiences and viewpoints to celebrate variety. To keep everyone interested and motivated, provide several responsibilities and career pathways. A balanced D&A squad has a high chance of success!
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So You Want to Be CDAO? – Presented by Sally Parker, Senior Director Analyst, Gartner
Data and analytics are the key ingredients for enterprise success in today’s business world. An accountable CEO is required to make data and analytics a reality. Sally Parker, Senior Director Analyst at Gartner, recently discussed the essential characteristics a Chief Data Officer (CDO) should have as well as the duties and responsibilities of the position. She also offered helpful advice on how to handle the duties after taking the job.
Decoding the CDO Role: Definition and Key Responsibilities
CDAO refers to the business leadership role that has the primary enterprise accountability for value creation by means of the organization’s data and analytics assets, and the data and analytics ecosystem.”
According to a Gartner survey, a CDO’s top three duties are developing and implementing information governance, managing data and analytics initiatives, and overseeing data and analytics strategy.
3 Key Traits of a Successful CDAO:
Conductor: Take leadership in directing the organization’s shift to a data-driven culture. Lead and coordinate the adoption of data-driven strategies as the change agent.
Composer: By using data effectively, you can spur business innovation and spot monetization opportunities. To encourage creativity and advance the organization, use data insights.
Performer: By putting data-driven initiatives into practice, concentrate on providing real and useful business value. Utilize the learnings from data analysis to produce impactful results and improve overall business performance.
“CDOs who embody all three are heavily involved in digital transformation initiatives, and more effective at generating business value.”
Day 3: What Every CDAO Should Learn About Data Management – Presented by Aaron Rosenbaum, Sr Director Analyst, Gartner
Data management centralization is not always a smart concept, but sometimes it really does work for you. Enterprise data can utilize federated and dispersed governance, allowing the owners to handle more than half of the governance labor. In this session, Aaron highlighted the various misconceptions about data management, the errors that resulted, and ways to prevent them.
“Many CDAOs fall into the trap of data hoarding or paranoid data control. Data itself is an example of entropy and a better model of balancing controlled chaos with observable behavior helps reduce the friction between governance, application developers and users.”
How to Avoid Falling into the Trap?
It’s important for CDAOs to resist judging one data design as “better” than another. The usefulness of the data coming from a system or app is constrained once it is given too much power rather than concentrating on the actual data. Aaron advises that it is better to start utilizing data as soon as it is deployed rather than delaying its integration. Right away, compare it to related data, and whenever possible, automate that process.
Principles of data management:
- Get insights from users who are currently utilizing the data.
- Try not to start from scratch or assume that data redeployment is necessary.
- Collect as much metadata as possible.
- Consider the vitality of purposeful redundancy.
- Start with passive metadata, but be mindful that you’ll eventually need to be able to use active metadata.
- Utilize current data management tools and concentrate on integrating them.
- Security, data mastery, integration, and data quality are still crucial and must not be ignored.
How to Improve the Performance of Stalled AI Projects – Presented by Leinar Ramos, Sr Director, Advisory, Gartner
Roadblocks in AI projects are a common occurrence for data and analytics leaders, and they can happen before or after implementation. Leinar Ramos, Senior Director of Advisory at Gartner, spoke on a number of AI performance accelerators during this event that can help overcome these challenges. These accelerators can either help stalled AI projects get back on track or improve their efficiency so they produce better outcomes. According to a Gartner report, 46% of AI projects are never put into production.
Leinar while pointing out the reason behind reaching a dead-end, said,
“The problem is that many AI projects hit a wall. We often make some good initial progress in AI projects, but as time goes by we hit a performance barrier.”
Measuring and understanding commercial benefits frequently ranks as the top difficulty when firms are asked about their biggest challenges in using AI.
He categorically mentioned that one should not confuse model performance with business performance. Models can predict the future, but taking action is essential for realizing corporate value. We must update our procedure and assess the elements that actually affect business value.
The four main directions of the Gartner AI compass
The four main directions of the Gartner AI compass are Strategy, Data, Modeling and Testing, and Infrastructure and Operationalization. This compass helps teams find their way through a variety of performance-improving strategies.
- Distribute the AI Compass to the teams, and have them use it to rank the importance of impending tasks.
- Keep the domain experts in the know to assure their participation and knowledge in AI projects.
- Broaden internal and external data collection initiatives.
- Devise a comprehensive action plan to capture value from AI projects
- Invest in AI engineering to hasten and improve the utility of AI on a bigger scale.
Data Observability: To Build Reliable Data Landscapes – Jason Medd, Director Analyst, Gartner
An organization’s data landscape, data pipelines, and data infrastructure can be evaluated for health using continuous monitoring, tracking, alerting, analysis, and incident handling, thanks to the new technology known as data observability.
Jason Medd, Director Analyst at Gartner, elaborated on how data observability may help create a more solid data environment in this presentation, particularly in the context of contemporary, complex, and dispersed data stacks.
“Data observability provides continuous, holistic, and cross-sectional visibility into complex data landscapes and synthesizes signals across infrastructure, application, and data layers to provide a comprehensive understanding of individual components, data pipelines, and system performance.”
Enterprises can evaluate the key elements of their data journey using data observability, experiment, make necessary adjustments, and put into practice data strategies that are in line with their needs.
Data observability focuses on the following five things:
- Observing data
- Observing data pipeline.
- Observing data infrastructure.
- Observing data users.
- Observing cost and financial impacts.
The smartest way to achieve tangible benefits is to work together with business stakeholders to determine and present the financial benefits of data observability techniques. It’s critical to monitor the improvement of data issue management throughout data pipelines.
The Gartner Data & Analytics Summit is a priceless resource for keeping up with the most recent developments, tactics, and best practices in the data and analytics industry. This year’s edition was no different. With insights from eloquent leaders like Pieter den Hamer, Jason Medd, Georgia O’Callaghan, and Roxane Edjlali, a volley of questions like data management challenges, advertising, the role of CDAO, the impact of ChatGPT and Generative AI were addressed.
A lot of emphasis was laid on a human-centric approach, purposefully leading the leading data as well as AI team while drawing a sharp contrast between creativity and innovation. The informative sessions have instilled the confidence and knowledge to design and lead the innovative, flexible organizations of the future.
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