Complexity of Reporting in DW Environment

The maturity of the DW implementation is based on the following functionality when it comes to Reporting.

Every DW begins with a handful of reports which focuses mainly on the management requirement. Typically 90% of the reports are static by nature, only arguments change thus the required data is represented in the following formats (Table, Cross tab, chart, combination of all etc). As time passes, business have more requirements and thought process which we cannot implement only through static reports, so more tools / technologies comes under BI / Reporting area. For example a business user wants to add one more column / object in the report when they are analyzing the data.

Types of work / reports typically comes out of DW

1. Static / Canned Reports (What happened or Known Questions)
2. Ad hoc Reports (Why it happened or Analysis)
3. Dashboards (One view based on the role rather than digging deep – KPI representation)
4. Data Mining (What will happen / Predictive analysis – more of statistics)
5. Business Activity Monitoring (Defining threshold against every KPI and taking automated actions based on the event -- it's also called as Active DW in some implementations)

One technology in BI (Reporting / OLAP) won’t solve all the above. So, typically we have multiple technologies in the BI reporting side compared to ETL / Integration layer in the DW.

So, when compared to Integration (ETL) Layer in a DW, OLAP will have different software packages to solve each one of the problem we have in terms of requirements.

Information as an Asset -- How to Start

I came across a word today "INFONOMICS" which is nothing but applying "ECONOMICS" on the DATA what we generate in the enterprise.

Any company which has a DW and deriving more value out of the data to take decisions which impacts the operational, tactical and strategic decisions are on its way to be INFONOMICS company.

"Data Monetisation" -- Finding ways to use the existing data to fine tune the process or finding a new process which either saves the money for the company or generates the money for the company.

To prove the role you are playing, link your work towards the revenue contribution, thus you also getting closer to business. When a technology person understands the business very well, you will make use of data very inventively in the business. When we reuse the data for new process, you saved the money which we spend to collect the information.

Two types of DATA -- ROO -- Record of Origin
                                     ROR -- Record of Reference

The more ROR we have in the organization, we can prove the reuse as well as the cost we saved to process and present the data on need basis very effectively.

Record of Reference Systems
1. Master Data Management -- which improves the Data Quality, Data Reuse and Operational efficiency
2. DW and Business Intelligence -- which holds the historical changes and integrated data for tactical and strategic decisions.

When we don't have any platform which cannot be used to analyze and improve the decision making process (it can be operational, analysis, visualization, Machine Learning (data mining) etc), then we are not considering the data as asset.

Start building the repository of information which can be used across organization is the place to start looking at the data as an asset.