![]() ![]() ![]() People would spend too much time and energy, and the lack of results would generate enormous lost opportunity costs. Everyone cannot fish convoluted source data structures for themselves if tried it would simply be too costly for most organizations to attempt. The bottom line is that there is a need for simplified, unified, subject-oriented, integrated, time-variant, and non-volatile data sources, for all kinds of reasons. Keep implementations as simple as possible. Or perhaps it is likely that, like the proverbial poor, data warehouses and data marts will always be with us. The success or failure of those tools will be the same as it is today-does a user still get what they want and need in a simple perspective of the available data, and get good query performance? Most tools today do not yet overcome all complexity and performance issues. Someday more complex configurations will work where data remains in one single structure, and that data’s “appearance” across more normalized, data-vaulted, or dimensional structures is nothing but an illusion instantiated via the latest and greatest data virtualization tool.Īnd that will be fine. Users need to be enabled to fish the data for themselves.īeing physical about much of what has been done and creating physical data warehouses and physical data marts was the simplest approach. And even with these essential reports, requests cannot be log-jammed in a queue for IT to work through in creating custom reports one by one. The work-a-day reports and basic analyses still must be supported. These analytic tools will still need to present data in a simple fashion to users, and then turn-around and provide rock star query performance. ![]() But any shortcomings in the implementation of the physical data structures must then be able to be hidden from those analytics users by the reporting and analysis tools used. If simple integrated data is not provided, either in a data warehouse or multidimensional data mart, it may not be the end of the world. It was to resolve the kinds of issues mentioned above that the concepts and architectures of data warehousing and data marts came into being. The average dashboard user cannot handle very much complexity nor should they need to do so. Data scientists largely understand that they will need to cope with inconsistent codes from differing sources. ![]() It is understood that beyond changing content, developers may have to handle record structures that change shape over time. And simple also implies that when they run the same query, for the same point-in-time that they get the same answers, or at least the ability to break down the answer to get what they received previously, plus knowledge of the changes applied (and why).ĭevelopers and data scientists can deal with complexity and inconsistencies in their data sources. The simple presentation should allow users to view data as it is today, or request and obtain the way the data looked at a previous point-in-time. As well, if a subject area naturally contains items from multiple sources, then these items should be integrated together in a coherent fashion, with data quality checks and standardizations also applied. Simple presentation requires that data be grouped in meaningful assemblies, like a subject area. While often not stated explicitly, the “simple” data presentation implies several characteristics. Dashboard users doing unsophisticated, largely repetitive, operational reports and analyses ask their IT support personnel to provide data in a simple way they demand fast performance from their queries and they demand new functionality be provided quickly. ![]()
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