Data warehouses (DWs) and requirements for them continue to evolve. DWs are more relevant than ever, as they support operationalized analytics, and wring business value from machine data and other new forms of big data. Hence, it’s important to modernize a DW environment, to keep it competitive and aligned with business goals, in the new age of big data analytics. Yet, user organizations struggle to stay educated about trends and take the right action to modernize their DW investments. Many users need to catch up by deploying a number of upgrades and extensions to their existing DW environments and by adopting modern development methods. Once caught up, they need a strategy for continuous modernization.
Philip Russom, Ph.D.
Content Provided by
TDWI, IBM, Pentaho, SAP, SAS, TimeXtender
Demand is accelerating across organizations for better and faster access to data. Business executives, managers, and frontline users in operations want the power to move beyond the limits of spreadsheets so they can engage in deeper analysis and use data insights to transform all types of decisions. Newer tools and methods are making it possible for organizations to meet the demands of nontechnical users by enabling them to access, integrate, transform, and visualize data without traditional IT hand-holding.
The continued growth of interactive businesses combined with the explosive diffusion of online, mobile, and IoT (Internet of Things) touch points has enabled organizations to develop business applications involving millions, if not orders of magnitude more interactions and transactions. The success of the business, though, depends on driving the customers and users toward profitable transactions. Examples include purchasing products viewed on an eCommerce web site, recommending an article to a friend, or triggering automated controls within an industrial environment to avoid a part failure. These are examples of scenarios that are informed through behavioral analytics.
A recent TDWI survey shows that Hadoop clusters in production are up 60 percent over two years. This is no surprise because use cases for Hadoop in data warehousing, business intelligence, and analytics are well established. In addition, applications of Hadoop for archiving, content management, and operational applications are emerging into prominence. These developments show that Hadoop usage is diversifying broadly across and within mainstream enterprises, such that Hadoop will eventually be a common platform for many purposes in many IT portfolios.
Fern Halper, Ph.D., Philip Russom, Ph.D.
Content Provided by
TDWI, IBM, Cloudera, MapR, MarkLogic, Teradata
We all know that data warehouses and users’ best practices for them are changing dramatically today. As users build new data warehouses and modernize established ones, they are turning to cloud-based elastic data warehousing, because the automation of elasticity yields agility, ease of use, scalability, and performance, while reducing maintenance, tuning, capital investments, and other costs.
Philip Russom, Ph.D.
Are access and authentication enough when it comes to securing your data, especially an organization’s most critical data? The short answer is no. In 2015, many customers of large and small companies including T-Mobile, Excellus Blue Cross Blue Shield, UCLA Health, Scottrade, and more fell victim to data breaches. No industry is immune. TDWI has noted for years that most data warehouses rely on user-centric authorization almost exclusively, with little or no use of data-centric security. Given the ever increasing number of data breaches, security upgrades are certainly needed for data warehouses and the larger evolving data ecosystem.
Fern Halper, Ph.D.
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.