Three Trends for Modernizing Analytics and Data Warehousing in 2019
Data analytics priorities have shifted this year. Growth factors and business priority are ever changing. Don’t blink or you might miss what leading organizations are doing to modernize their analytic and data warehousing environments.
Business intelligence (BI), an umbrella term coined in 1989 by Howard Dresner, Chief Research Officer at Dresner Advisory Services, refers to the ability of end-users to access and analyze enterprise data. According to Dresner, during a December 2018webinar, three major trends revealed themselves from new primary research conducted in 2018 which will influence the growth of the analytics and data warehousing market in 2019:
Dresner end-user research shows that big data use cases have seen a dramatic increase in adoption and have become a mainstream approach for supporting analytics. In 2015, only 17% of organizations surveyed had big data implementations. Only three years later, that number more than tripled to 59% in 2018.
The most common big data use case is data warehouse optimization. Big data architecture is used to augment different applications, operating alongside or in a discrete fashion with a data warehouse. A big data implementation may even replace a data warehouse entirely with a data lake.
Dresner says organizations are starting to distribute big data use cases across the enterprise wherever they make sense. There is a tremendous amount of diversity within the ecosystem in terms of the types of data and the sources of data organizations want to access. In all of these use cases, organizations are dealing with extreme volumes of data, often with data in motion, which make them well suited to big data architectures.
Given the ubiquity of search engines like Google and now voice response systems like Alexa and Google Home, you would have thought that natural language, search-based analytics, and BI would have already become the norm in organizations.
Research by Dresner indicates the emerging technologies of natural language analysis and streaming data analytics have jumped in importance over the last year.
