Filed under: Data Integration, Metrics, Performance Measures, Replication
In my last post, I introduced the need for operational synchronization, focusing on the characteristics necessary for a reasonable methodology for implementation. In this post, it is worth examining some example use cases that demonstrate the utility of operational synchronization in a more concrete way. Read more
In my last post, we discussed two (presumably) complementary business drivers for instituting a standard enterprise-wide strategy for data availability: the desire to absorb massive amounts of data for analytical purposes (AKA “big data”) while simultaneously enabling accessibility to internal data stored across a variety of different siloed systems that have evolved organically over the years. Yet while the desire for decreasing the latency for data access, often to the point of what is fuzzily referred to as “real-time,” drives the expectation for immediate accessibility to all data sets, it is valuable to take a step backward and consider the characteristics of the environment that need to be effectively addressed: Read more
Almost everywhere you look these days, there is talk about big data, big data analytics, and the value of massive data volumes, and underscoring the demand for exploiting big data is the need to manage big data. This will be critical when dovetailing the desire for instituting analytical systems and addressing real-time needs for operational decision-making. Whether your company is looking to streamline supply chain management and inventory control, or deriving insight for enhancing customer experiences using numerous data streams linked with existing customer profiles, the best advantage comes from enabling the integration of analytics with operational systems in real time, or at least within the window of a defined (typically short) time frame. Read more
Filed under: Business Intelligence, Data Analysis, Data Governance, Data Integration, Data Quality
In my last post, we started to discuss the need for fundamental processes and tools for institutionalizing data testing. While the software development practice has embraced testing as a critical gating factor for the release of newly developed capabilities, this testing often centers on functionality, sometimes to the exclusion of a broad-based survey of the underlying data asset to ensure that values did not (or would not) incorrectly change as a result.
In fact, the need for testing existing production data assets goes beyond the scope of newly developed software. Modifications are constantly applied within an organization – acquired applications are upgraded, internal operating environments are enhanced and updated, additional functionality is turned on and deployed, hardware systems are swapped out and in, and internal processes may change. Yet there are limitations in effectively verifying that interoperable components that create, touch, or modify data are not impacted. The challenge of maintaining consistency across the application infrastructure can be daunting, let alone assuring consistency in the information results. Read more