Business Analytics Adoption Part 2: Data Warehouse
In my first blog post on business analytics adoption I looked at the first tentative steps taken by companies just starting out on their journey. We looked at how companies use Business Intelligence Reporting tools to extend their operational reporting capabilities and how after a time they start experiencing performance issues. We looked at how these issues are typically addressed with an IT solution, but how this solution itself leads to more issues.
In this blog post I look at some of the next steps that we see our customers taking along their journey to business analytics adoption.
STEP 3 – Data Warehouse
In this step our customer has quite often proceeded through Step 1 and Step 2 and is looking to get more out of the Analytics software. Sometimes customers will start at Step 3 because they are already aware of the limitation of starting with the early steps, and maybe they have engaged a consultancy like Triangle and heeded their advice about how analytics should be implemented.
So what is a data warehouse? This can mean many things to many people. I can remember upsetting a client when I referred to the copy of the database they created each night as a data dustbin or at best a backup. I was trying to explain to the customer that just copying a database to another server doesn’t mean you have a Data Warehouse. All you have is a backup of your operational database(s) and with it all the same issues of unsuitable structure for reporting. If we type data warehouse into Google we get Wikipedia’s definition:
Wikipedia’s definition is:
“In computing, a data warehouse (DW or DWH), also known as an enterprise data warehouse (EDW), is a system used for reporting and data analysis, and is considered as a core component of Business Intelligence  environment. DWs are central repositories of integrated data from one or more disparate sources. They store current and historical data and are used for creating analytical reports for knowledge workers throughout the enterprise. Examples of reports could range from annual and quarterly comparisons and trends to detailed daily sales analysis.
The data stored in the warehouse is uploaded from the operational systems (such as marketing, sales, etc.,). The data may pass through an operational data store for additional operations before it is used in the DW for reporting.”
In short, a data warehouse is a database organised in a way that is conductive to producing business analytics quickly and accurately. Relevant data is extracted from one or more operational systems and brought together in a single place (warehouse) to provide a business with a central store of data. The design of these databases normally adopt one of two main design methodologies, Kimball or Inmon which arranges data into a dimensional and fact based model.
So in this leg of our journey we look at how we can extract data from our various lines of business systems and apply any transformations required in order to build our data warehouse. This is known as the ETL layer or ETL process (Extract, Transform and Load). There are many ETL tools available from Microsofts’ Sequel Server Integration Services (MSSSIS), IBM’s DataStage, Oracles Data Integrator (ODI) to other Open Source products like Talend. All these products perform the ETL process with different levels of functionality available and different price points. At the end of the day you will need to select an ETL tool that suits your business and budget, remembering that its not just the initial software price that you should concentrate on, but also the level of technical experience required to build a data warehouse using the selected tool. This is something that Triangle has a great deal of experience in and again we are more than happy to help advise on the correct tool for the job.
So we have made a decision to embark on this leg of our journey to business analytics adoption. We start by kicking off our data warehouse project and we run business requirement workshops to understand the reporting requirements. Without this knowledge, we are unable to define the data elements required and where they can be found in the various business data sources. Once we have this information we can then look to design our DWH and the ETL processes required to build and maintain it. One mistake often made at this stage is to look at the data in the data sources and put what we think may be useful in the data warehouse. Although this may provide a sound solution, typically there has been no user involvement in the process and when it comes to adoption of the solution we find that users are slow to engage.
It’s at this point that things start to become interesting. The DWH offers levels of analysis not previously available from reporting over the line of business systems. However, I often see feature rich BI reporting tools used to produce mundane tabular reporting. Modern BI tools can do far more than tabular reporting. It’s like driving your car that comes with a six-speed gearbox down the motorway in first gear. This is often caused by the users asking for what they know, effectively an equivalent of an excel spreadsheet, in fact we quite often see them ask for just that! My response is what are you going to do with that excel spreadsheet when you get it? The usual response is I’m going to use it as input to another spreadsheet or even worse to load it into an Access Database and I’m going to produce some level of reporting using Excel. They are often surprised when I say we can save you the time and cost and produce that directly from the DWH using our BI Tools. The trouble is that users don’t know what they don’t know.
We need to encourage business sponsors to understand what is possible and to have a Vision and drive the business to that Vision. I have seen this over and over again where it takes a change of senior management in a business to have this Vision, and its quite often driven from their experience in a previous company where they have seen what is possible and want the same now they have moved.
These are all areas where Triangle can help our customers along the journey and show how Business Analytics can really provide financial benefits to a growing business.
In my next blog post in the series we will look at the next steps on our continuing journey.
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