Data, analytics and AI predictions for 2023
As we look back on 2022, it’s been exciting to see the rapid advancements in data, analytics and AI that have helped shape the way organizations operate. It was a turning point for many businesses as they began to realize the true potential of data-driven insights and the power of AI to drive innovation. As we head into 2023, we can expect even more breakthroughs and developments that will change the way companies leverage data and analytics to gain a competitive edge. Let’s take a closer look at my predictions for the coming year and explore how organizations can prepare for the future of data, analytics and AI.
A boost in M&A activity. Expect to see more M&A activity in 2023 as vendors look to tell more unified, end-to-end and comprehensive stories. This is already happening with two recently announced acquisitions: Qlik/Talend and Confluent/Flink. Of course, the macroeconomic conditions help. We’ve heard from several executives across markets that having cash on hand matters now more than ever while looking to broaden their portfolios, fill gaps and/or enter new markets. So, what markets are ripe for more activity than others? I’m keeping a very close eye on DataOps, data observability, data science platforms and MLOps platforms.
A focus on data sharing with trust and governance. Answering the question “Where’s my data?” remains a big challenge, and organizations are often using more than just their own data. They’re using third-party data. This highlights what I expect to be a heavy focus on data sharing in 2023. Data sharing must be considered a critical pillar of overarching data objectives. With that comes the assurance of trust. This is where governance comes in. Governance is no longer just about compliance and privacy. It’s also not just about using the right data catalog or leveraging data lineage capabilities. It’s more about using intelligent capabilities to automatically discover data, create lineage, inform the right stakeholders and maintain optimized workflows without disruption. I’m expecting governance to have greater ties to data quality and data observability as organizations look to better empower customers and end users to reliably bring trusted data and insight to every decision.
A reduction in data management costs. Organizations can anticipate a significant decrease in costs associated with the handling and management of large-scale data collected from various sources. With the emergence of cutting-edge automation and AI/ML technologies, there will be less need for human involvement in the analysis of these sizable data sets, enabling fast, reliable identification and extraction of crucial metadata. These technologies will also increase the accuracy of identifying failures in notoriously complex data ecosystems, accelerate the process of gaining insight and cut down on unnecessary resources spent on ineffective methods and/or DIY approaches.
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