There’s a lot of buzz on social media about Big Data, Cloud, Web Based Analytics and more in Business Intelligence (BI), but how does it really size up for those making the final decisions?To find out, we recently asked our followers what they would like to hear about in our next blog post. And mwheeler_DDG posed a poignant question:
“How does agile data warehousing increase BI adoption rates?”
We fielded the question to our Solutions Architect @NPolselli to learn more about the difference between a Data Warehouse, an Agile Data Warehouse, and how they increase BI adoptions. Here is his response…
The waterfall is drying up. As is the case in many product creation industries, the rise of agile development is pushing the waterfall methodology towards extinction, and rightfully so. This archaic method of development is infamous for increasing project costs and time while decreasing effectiveness. Building a data warehouse (DW) is no exception. Businesses are not static beings; they are constantly transforming organisms. While a thorough requirements document will help build the foundation of a DW, failing to develop an agile framework to validate results with the business along the way will produce a DW that has not been thoroughly vetted. A DW will be produced that, while ultimately fulfilling the initial requirements, will not be as in tune with the different business units, needs and functions. This last part is a key component for BI adoption rates.
Agile Data Warehousing (ADW) should contain tangible incremental targets that deliver value to the business. Defining a proper data model, ETL architecture, and report structure will ensure that the businesses receive useful data. Combining these three notions will yield a DW that is specifically targeted towards BI that end-users want, even if they are not yet aware of it. Maintaining scrum will also ensure that a DW stays succinctly in line with the business, thus making the BI platform that utilizes the DW that much more robust. The rise and fall of a BI Competency Center (BICC) is often directly correlated to the quality of its data. It’s quite simple- if users trust and relate to the data, the BICC will grow. If not, the lackluster adoption rate will destine it for failure. Additionally, incremental DW deployment will allow new BI functionality to be built and delivered in a constant stream. Through increased report availability, more reliable and relatable data, and sensitivity to the rules of the business, an agile DW will set the groundwork for a flourishing BI ecosystem. The rest, they say, is history.
What insights can you share on Agile Data Warehousing?