While comparing the amount of data collected by B2B retailers today, to that a decade ago, we find that there has been a great change in two aspects ? Volume and Variety. Back then, very little product data was collected and it focused largely on the storage and delivery of products; whereas, today the data encompasses every activity concerned with the product. For example, if a supplier delivered a truckload of files in the pre-ecommerce era, the volume and variety of data being dealt with by the supplier would be limited to a few product and delivery details. Today, with the advent of integrated ERP systems, the data available on a product will allow it to be tracked from the manufacturer to the customer. At this stage most retailers employ Master Data Management to structure the data for analysis.
However, there is one issue with the data at this point, viz., most of it is bad data; i.e., it is highly disorganized and multiform in nature. According to The Data Warehousing Institute, bad data costs U.S. businesses more than $611 billion each year. Therefore, the B2B supplier must now employ methods to obtain quality data which include cleansing, de-duplication and structuring. Data Cleansing refers to the process of computer-assisted analysis of the quality of data in a data set, and standardization of the data attributes and specifics through formatting and acceptance/rejection of suggestions made by the system. This is followed by the process of Data De-duplication which, put simply, is the process of removing duplicated data. This removes the possibility of the same piece of data being present more than once in a data set, or in two or more data sets. Once the data has undergone the process of de-duplication, the data is structured according to select attributes. This structuring is through Master Data Management and it lends the analyzing software the ability to sift through data at great speeds.
Master Data Management (MDM) is the term employed to refer to the activity of processing raw data into structured, analysis-ready data. MDM will help enterprises solve their data problems through formatting of the data into a uniform, reusable and easily analyzable set. This in turn, will hugely benefit the enterprises in the following ways:
• Comply with Laws:
There are data laws in all countries, in the U.S. for example, there is the Data Protection Act, under which the 4th principle makes it the organization?s responsibility to ensure that the customer data is accurate and up-to-date.
• Improve the Decision-Making Process:
The structuring of data allows easy analysis, which in turn allows the organization to make well-informed decisions.
• Reduce the Costs of Marketing:
The structured data can reveal the target audience most receptive to marketing efforts. This allows the B2B organizations to create focused campaigns, and reduce the overall cost involved.
• Increase Profits:
The targeted marketing of products and services can have a great impact on sales. The conversion rates on B2B sites will be greater than before which will translate to greater sales and profits.
• Improve the Operational Efficiency of the Organization:
MDM helps increase operational efficiency by removing all unnecessary data and organizing the relevant data. This creates a uniform and easily accessible set of data, which is more customer-friendly.
• Reduce the Errors in Processes:
B2B organizations have to run a huge number of processes before the products are ready for shipping to the customer.
• Build the Brand’s Image:
Losing a single customer has a far greater impact on the B2B supplier than it does to the B2C retailer. By using MDM to create customer friendly and easily digestible data, the B2B supplier can ensure that it is never the lack of or disparity in data that turns a customer away.
When approaching MDM it is important to remember that it is not everyone?s cup of tea. It is most beneficial to large enterprises and not start-ups or mid-sized firms. The key to employing MDM is the identification of quality data after the cleansing, de-duplication and structuring, and analyzing the same.