Master Data Horror Story
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I am a credit card customer and I moved from 520 S State St, Chicago, IL 60605 to 1130 S Michigan Ave, Chicago, IL 60605. I changed billing address immediately but did not receive a bill for several months. One day, a customer care executive called me and asked me to upgrade my credit card. I agreed and confirmed whether they have my new mailing address and communication address which is of 1130 S Michigan Ave. Months passed I did not receive the card as it has been sent to 520 S State St every time. One day, I received a threatening phone call from the credit card billing department asking why the bill and joining fee of the new upgraded card has not been paid. I verified that they have the new address, and the billing department verifies that the address on file is 1130 S Michigan Ave, Chicago, IL 60605. I asked for a copy of the bill to settle the account.
After two more weeks without the new upgraded card, I called back and found the existing account has been deactivated and they have activated the new upgraded credit card account. This time, I find out that even though the address in the billing file was 1130 S Michigan Ave, Chicago, IL 60605 the mailing address is listed as 520 S State St, Chicago, IL 60605. After several phone calls and letters between various customer care executive and bank officials, the bill finally gets resolved and the credit card company has lost a customer for life.
In this case, the master copy of the data was accurate, but another copy of it was flawed. Master data must be both correct and not inconsistent. Even if the master data has no errors, few organizations have just one set of master data. Many companies grow through mergers and various acquisitions, and each company that the parent organization acquires comes with its own customer data master, item master data and so on.
The better solution is if we could just union the new master data with the current master data, but unless the company acquired is in a completely varied business in a different country, then there’s a very good chance that some duplicate customers and products will be present in both master data sets —usually with different formats and different database keys.
If both companies use the Social Security Number as the customer identifier, discovering which customer records are for the same customer is a straightforward issue; but that seldom happens. In most cases, customer numbers and part numbers are assigned by the software that creates the master records, so the chances of the same customer or the same product having the same identifier in both databases is obvious. Item masters can be even harder to reconcile if equivalent parts are purchased from different vendors with different vendor numbers.
In summary, merging master lists together can be very difficult since the same customer may have different names, customer numbers, addresses and phone numbers in different databases. For example, John Martin might appear as J Martin, Martin John, and Martin J. Normal database joins and searches will not be able to resolve these differences.
So, to resolve this we must create a Common Master Data List. Before creating the common master data list lets understand the benefits of performing this activity.
The Benefits of Creating a Common Master Data List
While creating a clean master list can be a nightmare, but there are many positive benefits to the bottom line that come from having a common master list, including:
- A single, consolidated communication / mailing address, contact details, which saves money of repeated courier cost, printing cost and improves customer satisfaction
- No concerns about sending the same marketing literature to a customer from multiple customer lists, which wastes money and irritates the customer
- A cohesive view of customers across the organization, that way users know before they turn a customer account over to a collection agency whether or not that customer owes money to other parts of the organization or, more importantly, if that customer is another division’s biggest source of business
- A consolidated view of items to eliminate money wastage and shelf space as well as the risk of artificial shortages that come from stocking the same item under different part numbers
A real-life business challenge when the business thinks that they have all their customer records stored in database but there are multiple record variations for each customer. If we create a single customer service that communicates through well-defined XML messages, we may think that we have defined a single view of our customers. But if the same customer is stored in five databases with three different addresses and four different phone numbers, what will our customer service return?
Similarly, if we decide to subscribe to a CRM service provided through SaaS, the service provider will need a list of customers for its database. Which list will we send at that point?
For all these reasons, maintaining a high quality, consistent set of master data for your organization is rapidly becoming a necessity. The systems and processes required to maintain this data are known as Master Data Management.
Why Every Retailer needs a PIM Strategy?
When companies are running an omnichannel business, they need to create a cohesive business that combines offline and online channels into a unified brand identity. And to manage the data, responsibilities, and multiple channels, you need a PIM strategy.
Why Every Retailer needs a PIM Strategy?
When companies are running an omnichannel business, they need to create a cohesive business that combines offline and online channels into a unified brand identity. And to manage the data, responsibilities, and multiple channels, you need a PIM strategy.
Why Every Retailer needs a PIM Strategy?
When companies are running an omnichannel business, they need to create a cohesive business that combines offline and online channels into a unified brand identity. And to manage the data, responsibilities, and multiple channels, you need a PIM strategy.
Back to ‘ABCD’ of Data: Master, Golden, Reference and Metadata
In this blog, we will discuss about the classification of data and describe the various categories of data (reporting, transactional, master, golden, reference, metadata, unstructured and big data).
Back to ‘ABCD’ of Data: Master, Golden, Reference and Metadata
In this blog, we will discuss about the classification of data and describe the various categories of data (reporting, transactional, master, golden, reference, metadata, unstructured and big data).
Back to ‘ABCD’ of Data: Master, Golden, Reference and Metadata
In this blog, we will discuss about the classification of data and describe the various categories of data (reporting, transactional, master, golden, reference, metadata, unstructured and big data).