MDM tools vendor selection criteria
MDM tools are fast turning into necessities. Start your MDM tools vendor selection process with these helpful tips.
Given the massive explosion of social media data, business intelligence (BI) tools are starting to be seen as growth drivers. Many organizations are doing the rounds to find suitable master data management (MDM) tools vendors to service their data needs. But data becomes powerful only when the right spells and potions are applied, and for that you need the right vendor. Here’s a quick look at what to consider during the selection of a vendor of MDM tools.
1. Get your priorities straight:
The foremost consideration when picking an MDM tools vendor is the existing configuration of systems in the organization. Are there disparate systems that need to be integrated with the MDM tool? Which factors affect business the most? These are the issues the MDM tools will help solve. The chosen MDM tools should be compatible with the existing setup to do its job well.
2. Multi-domain vs. product/ client master MDM tools:
Next appraise whether you need multi-domain MDM tools or a product/client master MDM. Ask what multi-domain customer platform expertise will be provided. There are special products available for product masters and customer masters. Figure areas you need MDM tools for primarily, and accordingly base your vendor selection. The project will depend on which stream of business you want to focus on. If you have a specific area of expertise, you may want multiple interfaces. Your MDM tools vendor should be able to provide that.
Before you select the MDM tool:
# Market reputations though important, focus on the problem that the MDM tool's implementation aims to solve.
# Do not be short sighted. Identify long terms goals that will be satisfied by the MDM tool and select the vendor.
# Do your research about the MDM tool’s lifecycle and how it will affect the licensing costs.
# Training is important, but remember automated processes in the MDM tool’s functioning has greater value.
3. Size of the company:
The priorities of small, mid-sized, and large companies are dissimilar. For a large company, getting business data under control is more important than the cost of MDM tools. Multi-national companies will seek vendors with a higher standing in the market. In large organizations, mergers and acquisitions create new data challenges, and MDM becomes critical. The vendor’s background and experience in the MDM space should be top considerations for large organizations.
For small and mid-sized companies, other factors come into play. Distributed systems may not be prevalent and thus, the need for control over data lifecycles will be greater. The selection of a vendor for MDM tools depends on the ability and willingness to custom-fit the MDM to your needs.
4. Price considerations:
The pricing of MDM tools depends on the kind of data to be tackled. The quantity of data to be processed will be assessed to create the pricing model. The size of the database, followed by system of recurrence and the system of records, are the key factors to consider while evaluating the cost(s) of the MDM tool(s).
The more the number of entities using the MDM, the higher the cost. The vendor selection will depend on the inclusion of the entire process of data cleansing. Check how willing the vendor is to negotiate. If the database is not too large, then pricing becomes a factor because you may not want to invest too much in a small project.
Storehouse of MDM knowledge:
Master data management tools guide for managers
MDM tools business case secrets
How to include data governance in an MDM development process
Evaluating and selecting a master data management (MDM) vendor
Master Data Management (MDM) Platform Product Directory
Six criteria for master data management (MDM) tool evaluation
5. Data profiling is a separate cost:
Data profiling is another exercise to undertake to use MDM tools effectively. How does the data flow in the organization? What are the data governance and quality standards? Remember that the cost of data profiling is separate. Ask if this is part of the cost of MDM tools, or if the vendor can give concessions on the data profiling tools. Profiling of how data resides in the organization and how it flows through the company must be completed. The data that needs to be cleansed will have to be hosted on a separate server. These services will not come along with pure MDM tools. So ask your vendor if it will package data cleansing as a part of your MDM project or not. Depending on this there will be an additional variable cost. Discuss these aspects before you select the vendor.
6. Change management:
In the MDM paradigm, change management is an ongoing process. Once the data is pulled into the MDM repository, there will inevitably be updates to the data. How the data will be maintained, and how the attributes will be added must be considered. For this, a module to extract, load, and manage the data should be part of the MDM tool’s package. The MDM tools should be able to store the history of the data. Having a subset tool is not feasible for all organizations. Manual intervention in this process should be low and the level of automation high. The automated workflow in the MDM tools has to be of good value.
7. Back up policy:
Usually, you will not be using your MDM repository to process your product environment. There has to be a separate MDM production server and its backup policy needs to be defined. This is similar to a backup production server in the ERP system. How the backup of that MDM instance will be satisfied needs to be considered. This will also work as a reference for all the other production systems that you have.
Last but not least, do not forget your users. User intuitive MDM tools will be easily accepted by the users.
About the author: Rajesh Parameswaran is a business intelligence consultant specializing in data mining and master data management (MDM). He is a Six Sigma Green Belt certified professional with 17+ years of IT experience. At L&T Infotech, he rudders the MDM practice, and has been instrumental in creating the CON-TXT Text Analytics Accelerator.
(As told to Sharon D'Souza)