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CLLAX Guide: Master Data Management (MDM) Collection of Policies, Processes, Framework and Tools

“Master Data Management (MDM) is the collection of policies, processes, framework and tools to enable the management of the master data in an organization. MDM is not a technology, rather it is the processes and governance around how you manage your data life cycle through acquisition, data quality and enrichment, sharing, storing, securing, synchronizing, archiving and disposing your master data. Tools and technologies will help you enable and automate these processes. In order to have MDM success, your entire organization need to be aligned with the MDM concepts, benefits and its goals. MDM initiative that originate as technology initiative, without business involvement is a sure recipe for failure.”

What are the components that define master data management?

Master Data Management components falls broadly into end-to-end data life cycle. But generally they are categorized into data integration, data quality management, metadata management, data security and data governance. Although these are not a hard and rock boundaries, mostly all MDM processes are associated within these areas. For example, when you define data quality, you are thinking it from end-to-end, from source to target and beyond by monitoring and reporting data quality based on your DQ measurements. At source, you may be conducting data profiling and at data integration point, you may validate your business rules, quality guidelines, hierarchy, merging, matching, sorting and other transformation rules. Across all these you need to build and maintain data lineage and develop metadata definitions that helps to create your business glossary.

What is master data management framework?

An effective Master Data Management framework is built on robust people, processes and governance policies, and tools and technologies that can enable them. These policies range from defining clear roles and responsibilities to defining rules on data profiling, data quality, data security and privacy, whom to contact when any issues related to certain data process happens, how it is monitored, who has access, how data is used,  negotiating with business and stake holders to mitigate issues, conducting data quality reviews and a standards based technology platform that could help perform these functions.

When do you realize that your organization have an effective master data management in operation?

Organizations that have experienced an organic growth by mergers and acquisition may potentially have hundreds of applications to support their day-to-day business. These growths may likely results in redundancy and replication and in most cases have evolved into silo and stove piped application clusters. The more disparate and heterogeneous this application clusters are, the more complex data redundancy it becomes, and business will find too hard to get a right, quality and accurate data . It will become more difficult to figure out which data set is the system of record and which data set needs to be treated as the right data. End users will catalyze this by creating their own daily reports with whatever data sources they could find. It will not be a surprise to find Excel spreadsheet be the fundamental reporting tool for forecasting department. This problem would grow like a snowball and eventually develop into a big pile of “data spaghetti” unless businesses implement a strong data governance and data management operating principle.

Data Governance brings discipline to data life cycle management and its processes of acquisition, storage, using, archiving and destroying. It brings standards, rules and processes surrounding an organizations data management activity. It enforces ownership, accountability, availability, quality, security and integrity to data through data governance policies set by data stewards and data custodians. Data governance has been traditionally applied to business intelligence in the past, is now applied to enterprise level data management. Data Governance is the best and only route to master data management success at your organization.

Data Stewardship on the other hand will help define rules for data quality management. These rules may include data access, data security, data privacy and data usage, who, when and what users are authorized to access. Data stewards and data custodians will work together to enable data privacy and security through these rules. They also define and enforce data transformation and business transformation rules and standards for naming and data conversion. The difference between a data steward and a data custodian is that the former is a business representative, such as a business analyst, and latter is a technology representative such as a data architect or database administrator. A data librarian can helps keep track the meta-data and catalog of all data management activities of your organization.

Data Governance council includes representatives from both business and technology. This may include stakeholders, data stewards, data custodian, data architects, and functional and technical data user groups. Data Governance council set policies and standards for data definition. They help resolve data related issues when business leaders and data stewards cannot compromise. DGC monitors your data management program and its activities and recommends best practices and guidance to your program. DGC represents the higher level of all data management decision making processes.

Data Management Center of Excellent consists of subject matter expertise from industry, business and technology. DMCOE mean not only for data architects or data management practitioners, but also brings people from a broader and external expertise. For example, introducing CMMI into data management will benefit organization to develop and implement re-usable components. If you are in a financial services industry, the DMCOE can provide with expertise with financial services and regulatory compliance requirements (Basel II and Basel III, AML, Check 21, RESPA etc.). Together, DMCOE and DGC can provide oversee and advisory for a successful data management practice.

Data Management relates to all activities that manage the flow of information and data. To simplify, these can be viewed as the processes and activities that manage the CRUD cycle (Create, Read, Update and Delete). However, in master data management, it focuses on non-transactional data. The non-transactional data which sometimes referred as reference data are those data that can identify your customer, product or vendors. For example, customer address, phone numbers and attributes relating to customer. These data are different than a transactional data which can be data related to day-to-day business such as invoices, orders, credit card transactions etc.

Once you choose to go a MDM route, your entire organization need to be aligned to what MDM means to your business. It may very possible that everyone sees MDM in a different perspective that applies to his/her day to day activities. It is highly recommended that you first develop a common consensus among the organization, executive leadership team and management before socializing MDM to the end users. Once you have defined MDM to your business, you could then define what Master Data is and what that you care about. What are you actually planning to manage? For example, if you are planning to have a conformed, cleaned a single source customer view, then Customer and everything around customer will be your master data.

How can I start Master Data Management Program?

In my personal experience, I have seen MDM started either enterprise level or as a project level. But what I felt important is to understand what capabilities of MDM that you are trying to implement first, second and there after. I have seen the MDM Maturity Model plays an important role to help define where you want to be in the near term and in the future. Both MDM reference architecture and MDM maturity model defines your MDM roadmap. Your organization may choose to improve MDM capability incrementally, as oppose to having a long lengthier program from begin to end. I had always recommended my clients to start MDM at very small scale and improve and expand it to other areas. Of course, there are other criteria comes into play, such as funding, program scope, resource availability, risk tolerance, and business priority. But, I have seen that MDM that starts with small and tangible criteria had more success rate.

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