Big data has become an integral part of information management. Nearly all organizations have some need to access big data sources and produce actionable information for decision-makers. Recognizing this connection, we merged these two topics when we put together our recently published research agendas for 2017. As we plan our research, we focus on current technologies and how they can be used to improve an organization’s performance. We then share those results with our readers.
Topics: data science, Internet of Things, Big Data, Data Integration, Data Governance, Data Preparation, Information Management, Machine Learning Digital Technology, Machine Learning and Cognitive Computing, Analytics
The business intelligence market is bounded on one side by big data and on the other side by data preparation. That is, to maximize their performance in using information, organizations have to collect and analyze ever increasing volumes of data while the tools available are constantly evolving in the big data ecosystem that I have written about. In our benchmark research on big data analytics, half (51%) of organizations said they want to access big data using their existing BI tools. At the same time, as I have noted, end users are demanding self-service access to data preparation capabilities to facilitate their analyses.
Data preparation is critical to the effectiveness of both operational and analytic business processes. Operational processes today are fed by streams of constantly generated data. Our data and analytics in the cloud benchmark research shows that more than half (55%) of organizations spend the most time in their analytic processes preparing data for analysis – a situation that reduces their productivity. Data now comes from more sources than ever, at a faster pace and in a dizzying array of formats; it often contains inconsistencies in both structure and content.
Qlik helped pioneer the visual discovery market with its QlikView product. In some respects, Qlik and its competitors also spawned the self-service trend rippling through the analytics market today. Their aim was to enable business users to perform analytics for themselves rather than building a product with the perfect set of features for IT. After establishing success with end users the company began to address more of the concerns of IT, eventually creating a robust enterprise-grade analytics platform. This approach has worked for Qlik, driving growth that led to an initial public offering in 2010. The company now generates more than half a billion dollars in revenue annually, making it one of the largest independent analytics vendors. Of which based on their company and products was rated a Hot Vendor in our 2015 Value Index on Analytics and Business Intelligence and one of the highest ranked in usability.
On Monday, March 21, Informatica, a vendor of information management software, announced Big Data Management version 10.1. My colleague Mark Smith covered the introduction of v. 10.0 late last year, along with Informatica’s expansion from data integration to broader data management. Informatica’s Big Data Management 10.1 release offers new capabilities, including for the hot topic of self-service data preparation for Hadoop, which Informatica is calling Intelligent Data Lake. The term “data lake” describes large collections of detailed data from across an organization, often stored in Hadoop. With this release Informatica seeks to add more enterprise capabilities to data lake implementations.
The big data market continues to expand and enable new types of analyses, new business models and new revenues streams for organizations that implement these capabilities. Following our previous research into big data and information optimization, we’ll investigate the technology trends affecting both of these domains as part of our 2016 research agenda.