Data science is becoming essential to organizations seeking to gain greater business value from data. Yet, finding and keeping dedicated, high-pedigree data scientists is not easy; some even say it’s like “chasing unicorns.” A better strategy is to develop data science teams and empower business users – executives, marketing decision-makers, line of business (LOB) managers, and more – to engage in data exploration, experimentation, and development of insights that they can apply to improving business outcomes. This requires not just technology but training, attending to people, process, and governance issues, and helping personnel to define the right questions so that they can apply the most relevant analytic methods and technologies.
While many believe that the maturation of end-user tools supporting visualization, reporting, and analytic signals the imminent demise of the data warehouse, nothing could be farther from the truth. The increasing business user demand for information highlights the need for a centralized nerve center provided by the organization’s data warehouse. In turn, the future data warehouse requires technologies that accelerate design and development, improve cycle time in producing reports and analyses, and enhance the IT-business collaboration.
What good is analytics if no one takes action on it? Operationalizing and embedding analytics is about integrating actionable insights into systems and business processes used to make decisions—at the point of decision making. These systems might be automated or provide manual, actionable insights. Analytics is currently being embedded into dashboards, applications, devices, systems, and databases. Examples run from simple to complex, and organizations are at different stages of operational deployment. Newer examples of operational analytics include support for logistics, asset management, customer call centers, and recommendation engines—to name just a few.
Fern Halper, Ph.D.
Information Builders, OpenText Analytics, Pentaho, SAP, SAS, Tableau Software, Talend
Excellence in analytics is a competitive advantage in nearly all industries. For this reason, organizations need their data scientists, business analysts, and business users to be able to access and interact with more sources and more types of data than ever before. The Hadoop ecosystem is flourishing, producing innovative technologies and frameworks such as Apache Spark, Apache Apex, and more that are becoming important for providing processing speed and power as well as data integration and preparation capabilities for fast, visual, and interactive analytics.
Cloudera, Talend, DataTorrent, Platfora
There’s a lot of buzz currently about how many types of IT systems need updates, upgrades, extensions, and replacements, due to recent changes in business and technology requirements. Current parlance refers to these collectively as “modernization” projects.
Philip Russom, Ph.D.
The term “data-driven” has become an accepted principle for modern organizations, but to drive modern, agile businesses, each data consumer’s view of enterprise data must both align with individual data quality and usability criteria and remain consistent with other data users in the organization. While traditional data quality/data preparation tools were intended to ensure accuracy and trust, the conventional wisdom centered on a technical, IT-centric usage model.
Analytics are today’s business weapon of choice. Changing business environments and competitive pressures have driven companies to seek a new edge from innovative technologies such as Hadoop, specialized data stores, and the cloud. This expanding and constantly evolving set of data sources means the enterprise data warehouse can no longer be the singular physical location for all large-scale information management.
Claudia Imhoff, Ph.D.
TDWI and IBM Content