Data, Analytics, and AI Trends for 2023

Data, analytics, and AI trends for 2023: What is at stake and what are the opportunities

In the recently concluded Israeli American Council summit in Austin Texas, Generative AI was one of the top trends that were billed to be prevalent in 2023. The summit was meant to provide an opportunity to connect with Israeli-American technology leaders and know their opinion on what trends would define 2023. No wonder generative AI found a place in the list for its clever ability to reinvent ways of work and technology usage per se. Data, analytics, and AI are three critical factors that drive generative AI and it makes it more than meaningful to understand what trends they would set in this year. The trio has come to a point where businesses see it as an essential part of day-to-day operations and a vehicle to drive innovation. Take a dive into data, analytics, and AI trends for 2023.

Mergers and Acquisitions will get a flip

Clearly, 2023 will see more M&A activity as vendors would see potential in providing end-to-end solutions. The recent acquisitions Qlik/Talend and Confluent/Flink are cases in point. With a conducive macroeconomic environment, the executives from across markets having cash on hand started looking forward to expanding their portfolio, filling gaps, and entering new markets by buying out companies. The markets operating in DataOps, data observability, data science platforms, and MLOps platforms will certainly have more transactions.

Data Governance to the fore

In the hyper data-driven environment, users are the last entities who have clue about who is using their data and for what. And this is a double-edged challenge for model building for AI and ML models that heavily depend on data and mostly third-party data. Perhaps, this challenge calls for more data sharing amongst the companies. In fact, it should be considered a crucial one among all the data objectives. For data companies to have self-regulation is a mere impossibility calling for government regulations that go beyond just privacy, using the right data catalog, or leveraging data lineage capabilities. Regulatory provisions pertaining to using intelligent capabilities to automatically discover data, create a lineage, inform the right stakeholders, and maintain optimized workflows without disruptions also come under the umbrella. The linkage between data quality and data observability would bring customer empowerment by including trusted data and insights.

Data Mesh and data as a product

For uninterrupted delivery, planning, and data management operations, companies would require customized datasets. Delivering data as a product leveraging data mesh would become mainstream with companies looking for structuring data management operations according to the client’s needs. But this strategy has its own hurdles to cross. Unless issues like data quality concerns, problems with excessive tools, under-skilled teams, and rising costs are addressed, it is highly unlikely that companies can deliver data as a commodity.

AI and augmented AI determined efficiency and productivity

There is a common belief that AI will replace employees with changing macroeconomic conditions. New AI tools like chatGPT might redefine the way people run businesses and do their job. Fears of job loss are widely prevalent but that idea can be saved for the next decade. At present, AI will drastically change, the efficiency levels of humans taking it to a new level, a level you have never experienced.

Automation and Employment on a rock and roll

As automation and self-service take the centre stage, there is a very much chance that employees will leave companies if do not integrate them into employees’ work. There are already signs of employee burnout because of automation, as they are assigned duties outside their job descriptions. Therefore, as the year 2023 sees more automation, employees would like to migrate to companies that provide a holistic working environment enhanced by intelligent automation that allows them to work on only strategically important tasks.

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