The proliferation of the internet has led to a substantial increase in online user engagement. More and more users are conducting business, purchasing goods, or consuming content online and businesses are finding ways to store & manage the huge volumes of customer data thus generated. However, this data is generated at multiple sources. Thus, companies prefer a master data management (MDM) solution to get a comprehensive view of their customers. This brings us to the long-standing question regarding technology: to build or to buy?
The best to decide is to discuss both the approaches, and that is what we have done in this blog post.
Factors that Define to Build or to Buy
Before deciding on any MDM strategy, the primary task for an organization is to consider the following factors related to data storage, management, and usage.
- Currently available resources and budgets, along with the future requirements to support the MDM initiative.
- The level of data quality, data accuracy, and completeness to support business goals.
- Organization’s current data volume and its increase in the future.
- Current and future use of data with the collection of additional types of data while leveraging outside data to augment internal data.
- Real-time access to get a complete view of the data requirements.
- Impact of regulatory compliance for the organization.
Many organizations prefer to build an MDM solution because they believe they understand the data quality issues better than anyone else does. Thus, they can build better solutions than generic ones available in the market. Also, they will have to invest a considerable amount of time and efforts to configure the MDM solutions to suit their requirements.
Their argument also holds when you look at the impact of the in-house MDM solution.
- A focused solution, catering to business requirements, costs lesser than a vendor-supplied general-purpose solution.
- Building the MDM solution enables the company to scale to a larger architecture in the future.
- It is believed to be the most common best practice to deploy a tactical MDM solution and grow it into full information architecture.
Developing an MDM solution takes considerable time, efforts, directions, and knowledge. It is largely because an MDM solution is about not only maintaining data quality, but it is a complex solution related to data quality, governance, management, process workflows and integration of the solution across the landscape catering need of all the different systems.
That said, building the MDM solution is best for companies with:
- The low data volume of approximately one million records
- Matching criteria are straightforward and there are limited sources of data
- Uses customer data for back-end analytics or can otherwise accept certain levels of data processing latency
Organizations prefer to buy an MDM solution because building a flexible and scalable MDM solution can be challenging, specifically looking at the data quality problems. Another big challenge is to get the right IT support and people with the right skill sets.
The BUY solution is usually beneficial for companies:
- Dealing with high volumes of data
- Require a solution to conduct real-time transaction processing with low response time
- Adding more data from new customers or existing customers, either organically or through acquisition
Let’s learn what buying MDM solution brings to the table:
- Complete MDM functionality in a single package
- Efficient management of a large number of customer data
- Negligible latency time that results in a build scenario
- Matching and linking of data via deterministic or probabilistic algorithms to provide an integrated customer view (via web services)
- A higher level of data accuracy with lower deployment time and expense
- Real-time solution when customer data is immediately available throughout the organization
- Additional data management functions via data steward applications
Deciding between building or buying MDM solution solely depends on your business requirement. However, take business goals such as growth, acceptable accuracy and latency levels, data usage, availability of finance and human resource, etc. into consideration to make an informed decision.
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