Organizations are currently shifting their priorities to Artificial Intelligence (AI), from their survival in the competitive market to simply keeping abreast of the latest developments. Regardless of the plan to incorporate AI into projects or workplans now or later, the most critical starting point for any relevant AI-initiative is to have AI-ready data. But have organizations planned for this next step? Read my blog.
To have A-I-ready data is already a very successful differentiator for organizations over the last few years.
Being able to use large amounts of data and provide visually appealing reports and dashboards helps fact-based decision-making and brings transparency, visibility and strategic, tactical and operational benefits.
The reports and the dashboards are the visible tip of the data iceberg.
Data experts are experienced in connecting various, heterogeneous data sources, performing data transformation, data cleanup, data storage, complex calculations and performance optimization to reach a level of report-compliant data and being able to deliver the reports using data visualization tools.
The chain of activities leads to a final product, which is the report and dashboard.
However, there exists a strong shift to this paradigm. This is heavily influenced by cybersecurity attacks that require better protection of the data. The sharp rise of AI does not simplify but rather complicates the scenario.
Data does not only lead to producing reports but also as a starting point for AI, while at the same time requiring heavy security measures.
The time has changed from thinking about reports and dashboards and building them with data to thinking data-first. With burning questions, some of which, center on how this data can be shaped so that it is and stays safe from potential attacks, is compatible to be shared with externals, is open to users as self-service to generate their reports, is compliant with Microsoft CoPilot or other generative AI tools to generate insights, is predictive analysis ready, and much more.
In conclusion, the time to deliver a report will increase, as all these critical steps will need to be taken to offer multiple doors for the data, and not only the sole reporting door.
Data experts will need to incorporate all these dimensions and associated skills. They will be even more in demand.
It's even clearer now that data is the most important asset for organizations, but how well they are prepared to come up with datasets will draw the line between data-ready organizations and the ones on the other side of the spectrum.
In this data journey, UNV has managed to break all data silos, centralize and connect all data, and implement processes to maintain clean data.
Moving along in 2024, UNV will be in a position to share data securely with UN entities, protect its data assets better, and start using the data through relevant AI cases.
Is your organization's data exploited to its full capacity?
Worth pondering.
End note: Data exploitation refers to taking the decision assumed to be optimal for the data observed so far.
