Which MDM approach is right for you?

MDM, in the past 5 years, has come a long way in its maturity model. Most of the MDM implementations fall under 2 different kinds of approaches.

1. Operational MDM (the tougher among the two)
2. Analytical MDM

Operational MDM enables synchronization of master entities and their attributes between the transaction processing systems. Why does one need such an MDM? Let's take an example. ABC Corporation is a manufacturing firm. It conducts roadshows and marketing campaigns to advertise its products. The salesperson collect customer information during those roadshows and feed it into their IT systems for further followup. There are a different set of sales representatives who conduct feedback on their products sold, with their customers. They too enter the customer feedback into their IT systems. These are 2 different sets of CRM processes.

Typically what happens in a mature company is, there are a set of batch processes which pick up the master data from one system and transfer it to the other. Now this introduces delay, inconsistency, inaccuracy of data and lot of manual reconciliation (same customer name can be entered by 2 different salesperson or the latest survey from a salesperson can erase previously collected information about the customer). So the IT develops custom programs to clean up the data, write reconciliation programs but still cannot manage to do all this in real time.

This mess can be reduced or eliminated by deploying an operational MDM. Operational MDM tools solve the synchronization problem using complex match-merge algorthims. Some of the tools currently in the market are Siperian, IBM, Purisma, Oracle and SAP.

Analytical MDM is an architectural approach if the problem revolves around inconsistent reporting for business performance management. In simple terms, inconsistent hierarchies are getting reported out. This needs for a unified reporting view of the master data. The audience for this system would be the downstream data warehousing and business intelligence applications. Some of the MDM vendors selling their expertise in this area are Kalido, Oracle, IBM.

It is essential that an organization has to build both these models to address their MDM needs. But which one to chose first depends on which problem is in their high priority list.

Teradata's reseller alliance with Trillium

Teradata Corporation announced its reseller alliance partnership with Trillium Software. Teradata will now combine its warehouse product with Trillium's Data quality tools and its own MDM products. Overall, this seems to be a good strategy for Teradata, because now Teradata's customers can leverage Trillium's data quality abilities on their huge databases.

Because of this alliance, the customers will enjoy a powerpacked database, Data Quality tools and a MDM suite. Information Difference has ranked Teradata's MDM low in the quadrant though compared to the likes of SAP, Oracle and Siperian.

Buy or Make - Financial Analytics

Today, I had a consulting assignment with a company focussing on Server Virtualization. The objective was to narrate the factors influencing a Make vs Buy (mVb) decision and their risk quotients for a Financial Analytics Solution.

Some of them are :

1. What is the business requirement and is the requirement very unique?
2. How urgent is the application?
3. What is the technology Strategy of the Organization?
4. Does the off-the-shelf product address most of the requirements and does it have flexibility to customize it?
5. How does the present make-buy decision relate to the strategy?
6. Are their right people and support systems to support the application, in case of a build?
7. Does the financial tool address internationalization needs?
8. Are their security measures in-built in the tool, because it hosts sensitive data?
9. Can the Integration of the Packaged Solution into the process control system be done seamlessly?
10. What is the underlying technology? In this case, what is the ERP system? It would make sense to buy the analytical solution from the same vendor of the ERP system, if it addresses your requirements
11. Will the TCO be reduced because of the Buy approach?
12. Are their right people and support systems to support the application, in case of a build?
13. Does it reduce cost?

After these questions were answered, the following matrices were prepared which summed up the decision.

1. High Level Requirement x Priority x Effort Estimation Matrix
2. Benefit Comparison Matrix
3. Risk Comparison Matrix

Statistics and Data

I was reading an excellent text "Statistics for Business and Economics" written by Anderson & Sweeney. It highlights the importance of statistical measures in decision making. Many of the existing predictive analytical tools use most of the principles covered in the text. It also highlights the importance of collecting and preserving data.

One such example covered was to calculate the average wait time of a queue in a particular ATM in New York. Using this data, the bank would then decide to position a new ATM to balance the load in that busy place. The predictive model uses probability distribution and helps the analyst in making a decision. The models have to be refined so that they don't reflect any false positives.