Three Trends for Modernizing Analytics and Data Warehousing in 2019

Categories: Data Warehouse

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 2018 webinar, 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:

  • Big data technologies and architectures are now mainstream alternatives to traditional database options for business intelligence (BI).
  • Natural language analytics and streaming data analytics are emerging technologies that will impact the market.
  • Cloud computing has passed the tipping point, with most organizations comfortable moving critical data and applications to the public cloud.

Big Data Technologies and Architectures

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.

Other common use cases include customer/social analysis, predictive maintenance, clickstream analytics, fraud detection, and IoT.

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.

Natural Language Analysis and Streaming Data Analytics

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.

Natural language analysis, which includes natural language queries and natural language generation, is the ability to translate spoken or written language queries into something a machine can understand then querying a database to get results and analyses back. Despite being a nascent technology, natural language analysis has increased in priority by 23% from 2017 to 2018, according to Dresner’s survey.

Streaming data analytics, the ability to analyze vast amounts of data in motion, has gained even more traction in the past year. More than 75% of respondents indicated streaming data analytics was important to their business.

Though natural language analysis and streaming data analytics remain relatively low in priority, these emerging technologies have seen a significant jump in importance over the past year. Both offer a first mover advantage in the marketplace as long as you have an appropriate use case. This means you need to educate yourself to understand where these technologies fit in, how they support your business, and who the user might be.

There is a real market opportunity as long as you identify the appropriate use cases and user constituencies.

Cloud Computing

Cloud computing has become an established market, and Dresner has been tracking it for more than seven years. BI requires a lot of data, making the public cloud an attractive and cost-effective solution. Today, more than 50% of organizations surveyed either currently use or plan to use the public cloud for BI.

Through education and market observation, organizations now see the public cloud as a relatively safe place to implement their applications.

Modernizing Analytics and Data Warehousing

Arcadia Data and Cloudera customers are a reflection of (and really driving) the same trends in the market. When dealing with big, fast, and complex data, you need to be able to visualize your analytics and BI in real-time. There has been a significant shift in the way data warehouses are being used to support new business processes and models. A modern data warehouse plays a foundational role in analytics and BI. Check out the on-demand webinar to dig deeper into the research insights and learn more about how Arcadia Data and Cloudera provide the next-generation of modern data warehousing and analytics.

Written by Steve Wooledge, VP Marketing at Arcadia Data


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