Data analytics is not only being revolutionized by artificial intelligence (AI), but it cannot reach its full potential without emerging technology, according to a new report.
Global data Data analysis The report notes that the data analytics market is relatively mature, but capabilities for prescriptive analytics – the most advanced of the four main types of data analytics – are still maturing.
At increasing levels of sophistication, descriptive analytics simply summarizes what happened based on analysis of datasets, diagnostic analytics answers questions about why things happened , predictive analytics suggests what might happen in the future and prescriptive analytics provides recommendations to businesses on what to do next. .
The report explains: “Prescriptive analytics, often described as the last mile of business analytics, uses AI to process historical data, discover unseen patterns and relationships, create models to show the likelihood of scenarios or results, then advise an action plan based on the findings. Complex decision making is optimized using millions of decision variables, constraints and trade-offs.
“Using rules-based approaches and optimization techniques helps decision-makers by providing optimal recommendations and actions to achieve business goals and prevents people from relying on their intuition. Applications include demand forecasting, supply chain logistics, power generation and employee scheduling.
As an example, the report describes how food suppliers can combine historical data with forecasts (on upcoming events or weather, for example) to proactively respond to changing demand in supply chains and ensure the right products are in stores when needed.
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By GlobalData
The type and volume of calculations required to make such predictions requires sophisticated AI algorithms and significant computing power to facilitate them.
Regarding the importance of AI for data analysis, the report explains: “Without applying AI, particularly machine learning and generative AI technologies, to data analysis, it does not is simply not possible to exploit its full potential.
“One of the five Vs of Big Data is velocity. For data insights to be actionable and useful, they must be delivered quickly. Analytics processes must be self-optimized and capable of regularly learning from experience, an outcome that is only possible with modern AI capabilities and database technologies.
Data Analytics Market
GlobalData estimates that the total data analytics market was worth $112.3 billion in 2023 and expects it to reach $190 billion by 2028, with a compound annual rate (CAGR) on this period of 11.1%.
The data and content management category is the largest of the three main market segments, ahead of business intelligence and data discovery tools, then big data platforms. Revenue from the business intelligence and data discovery segment is expected to grow the fastest between 2023 and 2028, with a CAGR of 13.1%.
The report notes a forecast that suggests more than 175 zettabytes of data will be generated each year by 2025, adding that a zettabyte “represents as much information as there are grains of sand on all the beaches of the world”.
He explains the value of data analysis this way: “We are drowning in data, and it is becoming increasingly difficult to make sense of so much information. Data analysis tools will increasingly be needed to convert large amounts of complex raw data into valuable information and actionable knowledge.
