In a flurry of AI-in-analytics announcements, Gartner asks for some fundamentals
After a fortnight full of analytics and data management announcements, Gartner has cautioned that consumers are not keeping up with analytics vendors fashion-following eagerness to inject some AI serum into pretty much anything they can get their hands on.…
Jason Medd, Director Analyst at Gartner, said many clients still had to catch up in terms of data quality despite the flurry of announcements from Microsoft, SAP, and Google as the Data & Analytics Summit came to a close in London this week.
When something new and shiny comes along, data quality is frequently disregarded. People try to adopt it and gain some value, but then quality problems start to appear. Bad data can enter a system in a variety of ways. He told The Reg that as people pursue the latest dazzling object, they begin to lose sight of the important things.
In order to better support their digital business objectives, according to Gartner researchers, half of organisations will employ modern data quality technology by 2024.
This week, Microsoft relaunched their analytics platform as Microsoft Fabric, which includes a data lake named OneLake, Data Science, Data Warehousing, and Power BI. Copilot, according to Microsoft, will enable users to design machine learning models, establish dataflows and data pipelines, produce code and full functions, and interact with results using conversational language.
While this was going on, Google joined forces with enterprise software giant SAP to integrate Google Cloud’s data and analytics technology, including its AI and machine learning (ML) models, with Datasphere analytics tools.
But according to Medd, consumers are still having trouble identifying the best business case for analytics rollouts.
Connecting the business use case [to the technology] is difficult. People asking how to create value out of data was one of the things they did notice frequently at the conference. According to him, it doesn’t really matter how quickly technology advances until the business case is solidified and until it is clear how data will be used to provide value.
In addition to the technology, Medd said it was difficult to create the correct culture and raise awareness of the value of business data.
Data quality is advised to be achieved through a four-step procedure by Gartner. Understanding which data has the greatest impact on business results is the first step. Next, data quality accountability is introduced, followed by data quality validation and, lastly, data quality integration into company culture.
