More than 200 data and analytics leaders will get a taste of what the future has in store for them when they descend on London for Corinium’s Chief Data & Analytics Officers UK conference this week.
Featuring 60 speakers from innovative companies including Dyson, HSBC, Dixons Carphone, Sky and more, the two-day event will provide a unique window into how the industry will evolve over the next 12 months.
To uncover some of the key themes and issues that will be discussed, we asked industry experts from three of this year’s sponsors to share their top predictions for data and analytics in 2020.
As cloud initiatives become more mature, with more and more data migrating to the cloud, the centre-of-gravity of an organisation's data will shift. The balance will tip towards data being spread across both cloud and on-premises data sources.
Similarly, data and analytics platforms will transition to a multi-location, multi-cloud architecture, increasing the complexity of securing, managing and accessing this data.
Robotic process automation (RPA) will become an accepted mainstream technology in most organisations.
This will result in increasing pressure on IT to deliver the data to drive the RPA workflows – typically through REST APIs. Organisations will look for data platforms that support multiple data delivery methods to support traditional analytics, machine learning (ML) or AI initiatives and RPA workflows.
In 2020, the search for an effective integration platform to access and utilize data and deal with issues like data security and siloed information will continue. But increasingly, organisations will look towards using a data fabric to overcome these data challenges.
By combining historical and real-time datasets across multiple data silos, a data fabric offers a single, secure and consistent data management framework. It reduces data delivery times and supports the automation of data preparation and integration, enabling organisations to focus more on ML and AI initiatives.
I think 2020 will refocus attention on practical systems that augment human intelligence (to enhance creativity, reasoning and decision making) rather than systems that promise complete automation.
For those working in analytics, this should mean improved tooling to help profile, catalogue and explore data, as well as quickly prototype and test analytical pipelines.
For data analysts and data scientists, the importance of problem context and domain expertise should never be underestimated.
In 2020, I’d expect to see progress in how industry records and re-uses domain knowledge to facilitate faster collaboration between data professionals and domain experts.
Finally, it’s not just the sharing of data that is important to data professionals in an organisation. The sharing of analytical pipelines, analytical context and machine learning models is vital to reduce development time and repetition of effort for data scientists and analysts.
For 2020, I think we’ll see the emergence of platforms and databases that allow colleagues to better share and understand their approaches to analysis.
A great migration to the cloud is underway for businesses worldwide. But while there are fewer up-front IT costs associated with cloud platforms, they are generally more expensive overall, especially when resources are improperly managed.
Ultimately, companies that don’t actively develop a larger cloud strategy and manage cloud costs in 2020 will face an uphill battle to prove positive ROI with AI projects, racking up bills that aren’t necessarily offset by the financial gains or savings from the projects themselves.
In 2019, companies around the world created centralised teams to handle data initiatives and kick-start AI efforts.
These ‘centres for excellence’ give data teams the ability to get started and scale-up quickly while easily prioritising different projects. Yet, they also require quite a lot of discipline to ensure that the business is sufficiently involved in centre of excellence initiatives.
The trend for 2020 will be moving toward a more formalised way for data and business experts to work together: the ‘initiative-driven teams’ model.
This approach allows teams to be ‘spun up’ or ‘spun down’ based on the project for tight, expert focus, ensuring that the results align well with business needs and expectations.
Explainable AI, trust and bias were topics that took centre stage in 2019, but that does not mean they have now been resolved. What’s new for 2020 is that there is both internal and external pressure to make AI explainable, unbiased and trusted:
Ultimately, building internal trust will provide the foundation for external trust. This starts with trust in the data that is being used in AI systems. Data quality is one of the most basic but most important hurdles to overcome in the path to building sustainable AI that will bring business value.