Gartner Data & Analytics Summit 2023

That was this year's Gartner D&A Summit in Orlando. More than 4,000 data experts, mainly from the USA, Canada and South America. 

Trends in IT are usually like this: analysts formulate trends, companies listen to them and start projects in these areas, which in turn confirms to the analysts that many of their predictions were correct :-)

Nevertheless, I would like to try to summarize the main topics as they appeared to me.

There are so many exciting topics right now: Data Fabrics, Literacy, AI Governance, Citizen Data Engineering, Data Sharing, Digital Twins, Data Products, Composable Architectures, DataOps, Metadata, MDM, Value Optimization etc.

Gareth Herschel did the following grouping of topics, which I found very appropriate: 

1. "Think like a Business.

Data & Analytics can deliver the most value when it acts as a business rather than a supporting business function: Be proactive not reactive. Data and analytics as a product to be "sold" to the rest of the organization. Owning responsibility for data and analytics assets and outcomes. Balancing cost of delivery with the value delivered.

Topics such as Data Fabric and Data Product pay tribute to this trend. It's always about companies seeing data as a core business function rather than an IT function. Data represents real value. Data professionals need to learn much more than before to make the connection between data initiatives and business value. And we need to better understand how data sharing can be deployed, with data governance. Today I attended an exciting talk by Sally Parker on the role of the Chief Data and Analytics Officer (CDAO). The key message for me was that this is not an IT role, but rather a connection between IT and business.

It was also exciting to see that numerous presentations addressed the topic of sustainability. With regulations soon to be in place, companies will only have to calculate and report their carbon footprint. In addition, D&A can be used to ensure other aspects of sustainability.

2. "From Platforms to Ecosystems.

Data & Analytics ecosystems recognize the connectivity of not just technical platforms, but the broader role and connectivity of D&A: Increasingly comprehensive and integrated end-to-end architectures make adding new capabilities less costly. Better understanding and sharing of resources and capabilities enables greater value to be created. 

Integration of D&A into the organization and societal ecosystem has broad and diverse impact.

Data Fabric will arrive as a concept in most larger enterprises in the coming months. In most cases, business use cases are finally driving the developments. I found super exciting the research that companies are more successful when they observe how data is used by employees. The analysts advise to expect unexpected data usage and to investigate these cases especially intensively. 

AI has arrived in data ecosystems. As 'AI joins the team,' impacting roles, activities and processes, we have to make sure that change management and HR aspects are fully addressed in AI initiatives. Exciting: Gartner predicts that by 2025, 5% of employees will use AI tools without permission from their employers.

Ecosystems will become more composable and modular. At Parsionate , we have been incorporating the important contributions of the MACH Alliance into our projects for some time now.

3. "Don't forget the humans"

Data and analytics is sometimes seen as an effort to remove the human elements from decision making. But a balanced view of D&A requires different aspects: 

Driving adoption of D&A by working with business users to ensure accessibility, trust and relevance to business users. Recognizing human involvement across the range of decision making, from decision support to decision setting the context for decision automation. Ensuring D&A deployments consider multiple aspects of risk.

The AI solutions we will develop in the coming years require increasing consideration of risk as capabilities increase. It will be a matter of defining something like AI governance for companies. How do we ensure that AI decisions are fair and sustainable? How can we connect our corporate values and sustainability goals to it?