Business intelligence and data warehousing has been around for over 20 years and both have experienced a burst of interest in recent years. A data warehouse serves an important purpose by providing a centralised source of core business data from disparate systems, but reality most often falls short of the original promises around the capability and return on investment for the business.
A data warehouse is seen as the central repository that allows an organisation to capture and model all of the required data for further business insight. The level of detail depends on the various factors, including accessibility of the data, type of data structures, and analysis requirements. A data model needs to be designed with consideration to what critical business questions you are trying to answer.
When building a data warehouse even the most advanced analytical companies can find that the time to value is long and if not executed correctly it can fail on its original promises. The concept can evolve from a lofty vision but the final solution may leave business users with a reporting structure that is slow, difficult to use and with limited useful data. Although I have seen companies who have made a great investment in traditional business intelligence who are now running into some serious capability limitations.
A data warehouse is a model of the business that focuses on reporting what happened with past activity within a contained set of data, while the BI tools allow for analysis and visualisation of this data. But every minute a huge amount of data is being generated both internally and externally from on-premise systems, machines, websites and application across the Internet. So how do we monitor what’s happening now, compare with what happened historically and predict what might happen in the future? A data warehouse will fall short in predicating the probability of an outcome and extracting patterns and correlations from large amounts of data in a timely fashion. In BI the expectation within a business is the efficient reporting and analysis of data patterns and metrics, while aligning these with key performance measures.
Business intelligence consumers are demanding insight rather than historic data analyst outputs, this is leading to the consumerisation of BI. We are seeing a massive growth in the mobile technology market and these devises are perfect for visualisation and quick access to analytics and big data. The velocity, variety and volume of data are increasing at a massive rate and have the potential of being converted into greater business value. IDC anticipate digital content growing by 2.7 billion terabytes in 2012 alone (see December 2011, IDC Predictions 2012).
The ability to optimise your processes in deriving value from data in a cost effective way is driving businesses to redefine how they manage their data warehouse and BI solution. You need to adopt quickly and evolve without historical broken promises and failures previously experienced. It is still important to start small, but to also aim to grow with your success and become more of data decision-driven business. The traditional data warehouse is being replaced by a modern take on how to manage your data, with on-demand BI in the cloud you can provision, model, load, operate, and optimise as you grow. Take the plumbing off your to-do list and focus on your value-add and start really dealing with your big data issues.
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