IDC Digital Leadership Community – Think Talk Summary September 2022

By | October 25, 2022

We were pleased to host the third quarter of the IDC Digital Leadership Think Talk of 2022 on September 29. Around 40 digital leaders from across Europe joined the call to share their challenges, successes and experiences with data and how it is managed in their organisation.

The discussions were moderated by Mark Dowd and Tracey Keeling of the IDC Executive Advisory Group.

About all information

At the beginning of the session, Mark Dowd asked the audience to think about how they manage access to data, data security, managing data cleansing, data integrity, replication, creating a data culture, and how to align the business. and IT with data.

Our first CIO contributor explained that you need a data glossary because everyone has a different definition of what data is and what it includes, as well as a data directory. Second, it is important to have a clear view of the types of data, including human and machine generated data, and ultimately how to manage it through governance.

Another contributor described how he used data journey maps: to show where data came from or was created by the organization, how it was changed, live or archived, and basically how ownership of the data was given to those closest to the business. This helps them manage the difference between dealing with external data and your own data, which may in some cases be returned to the owner or returned to you by the vendor at the end of the contract if you switch to another product.

Ownership and management

It drew a lot of comments from other CIOs on the call – one talked about democratizing data, starting with regulations and GDPR, to put limits on data so you can decide who can access it, how they can access it, and how long it should last. stored.

Our next contributor, from the government industry, answered the question of whether Information Management is an IT function or a business function.

Comparing IT to a car leasing company, IT provides an asset, a set of rules, for the business owner to drive the car the way they want (using data). This was echoed by another member who said machine generated IoT data, the largest source of which is managed by technical teams.

Another comment was on data management in government. A CIO believes that it depends on the size of the organization and that he should sit in IT, but only if the organization is too large. If it’s a private sector organization, they think it’s best to push ownership to the business side and give business ownership of data quality. IT can then manage the side of the system where the data sits and flows.

Another member of the pharmaceutical industry explained a separate model they use. One region has a local person responsible for local test data, a central team provides management, and IT provides the infrastructure to display and manage its integrity.

Practical examples

Then the discussion turned to practical examples. We have discussed low code/no code and how data is managed in this. Although they functioned differently from standard applications, participants still needed system architects to create a reliable system.

An example of this was an AI low-code/no-code chatbot solution for doctors. Another participant highlighted the complexity by explaining that they can consume data from 25 different sources. Healthcare business stakeholders are responsible for data quality and have set up content checks to ensure that what physicians create is understandable.

Another CIO said that before they generate data, they ask the important question of whether the data they intend to collect or generate is really needed. Once you’ve determined what you need and why it’s important to check the quality of incoming data. For example, there has been discussion around automated systems, where they teach relationships between subjects and how to eliminate bias in algorithms to get better information. Another noted the need for independence in the process and diversity of people developing/reviewing algorithms for automatically generated data.

The talk continued by presenting a wide range of data management strategies and use cases. This exchange of information demonstrated the value of these meetings, as well as the value of peer interaction and experience.

Moving to best practices

One contributor talked about how they started their data journey with a small step, for example, if you are exploring data mesh or data virtualization, it is best to start with a PoC in one area. If you are successful, you can produce a company-wide business change champion to face other areas of the business.

To improve data management, you can focus on business development and data governance, regulatory compliance (stick), risk, compliance teams, etc. You can “blame” them, but then you can explain their meaning to the business. (carrot) from the intelligent use of data.

Another idea discussed involved having Line of Business leaders “sell” the data initiative to the rest of the organization, then returning to IT to provide tools, sometimes resulting in business-focused colleagues asking to step in – but that’s a good problem to have. to own.

Mark Dowd asked how you manage a request pipeline to handle data that requires multiple responses. The first is if the business is very excited about new data streams, and so on. you need a steering committee to prioritize data investments based on business revenue. Some contributors have arranged regular reviews with the data management board once a month, with the executive committee making the final decision.

We discussed the carrot or the stick approach and which contributors use in their business. The biggest sticking point is regulation, which often requires a separate analytics platform that collects data from multiple systems, including humans and machine-generated data for compliance.

It is also important, even for data quality, and even if there is a big “stick”, business users should understand the meaning of the “Why” exercise with a quote from the work of Simon Sinek.

Leadership must understand that data-driven decisions are better than pure intuition, and must be knowledgeable enough to know that all measures are in place to trust the data. And they need to spread that trust through the organization to deploy data as an integral part of business operations.

By the time we finished the session, it was clear that the problem of data is complex because it not only involves creating and traveling through organization, management, and storage, but also issues related to ownership and corporate culture to make data interesting and fun to optimize. Use to manage and create insights.

The IDC CIO Advisory team would like to thank everyone who attended the call for their input. It’s always inspiring to hear from those who work with business data challenges. We hope that this session was valuable and brought many things to you.

Our next session will look more closely at ICT governance – does the new environment mean we need to change? We will examine new trends such as Agile and Pervasive management.

If you have received invitations to our sessions, I hope to see you there. If you would like to join this community, please email us mdowd@idc.com.

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