Big Data, Value Chains and Master Data Management

Big Data, Value Chains and Master Data Management

Companies are beginning to understand that their customer data is a goldmine of insight which enables them to better manage, optimise and forecast their supply chains.

For many years data has been viewed as “islands” dropped into data lakes (unstructured data) or data warehouses (structured data) but the returned value has been very disappointing. A report by Capgemini suggested that less than 10% of executives would describe their big data projects as ‘very successful’.

This article considers the relationship between Big Data and Value Chains and what needs to be considered to maximise the opportunity for success.

Value Chains were initially described by Michael Porter in 1985 and as with most good ideas it was conceptually simple and rapidly accepted, but for those that tried to implement them, not easy to deliver. Difficulties were due to:

• Lack of insight into customer value beyond customer account management engagement
• Lack of rich sources of value relating data
• Lack of feedback loops to ensure identified value was delivered
• The drive for segmentation which added cost and complexity which was difficult to manage
• Inability to keep pace with changing market environments

In 1995 Rayport and Sviokla found that as an analytical tool, the value chain can be applied to information flows to understand the value created by data and technology. A Data Value Chain information flow is described as a series of steps needed to generate value and useful insights from data.

There is a significant amount of investment required before the true value of data is released through usage, so to maximise the effectiveness of your move from Supply Chain to Value Chain Management supported by Big Data, careful planning is required.

A priority clearly needs to be placed on the Data Curation stage and that’s where Master Data Management (MDM) plays its part. The Gartner Glossary defines MDM as “a technology-enabled discipline in which business and IT work together to ensure the uniformity, accuracy, stewardship, semantic consistency and accountability of the enterprise’s official shared master data assets”.  It is the key to ensuring the data the business relies on is accurate and consistently held in and used by all systems that generate and consume it and changes to it are managed and owned.

In respect to MDM and Big Data, if you have already started on this journey you could already have come across vendors who may want you to worry about what your competitors are already doing in this area, and that you need to get in the Big Data as soon as possible. In reality, good data and good data analysis should be considered valuable elements of your business strategy which include focus on the necessary business goals and value chains, whilst making best use of the data that’s identified, relevant and available.  You should be aiming for data quality rather than quantity.

Your business strategy may already consider big data, but big data should not be a goal in itself and the actual amount of data should not be thought of as the most important factor: having the right data is.  An example of this is Uber, whose success shouldn’t be considered just as a function of the big data it collects, but of the results from the small amount of right data it needed to do the very simple task of matching cars and people. They just asked the right question: “Who needs a pickup, when and where are they?”

Master Data Management should be considered a necessary early step to the successful use of Big Data as it helps organisations in developing a ‘single version of the truth’. Extending that Mastered Data with Big Data has the potential to greatly expand the view of whatever domain has been selected and can help deliver a more comprehensive understanding of the truth. This is because the Big Data is tagged and related to the master data entities of the organisation.  Data management analysts can then help you to develop a better insight into such domains as your customers, although several challenges could remain. These include:

• Being confident in the ability to relate this data with customers
• Actually knowing who the customers are (mastered and deduplicated) across and within systems by improving lifecycle management and data cleansin
• Being able to identify the best customers (and understand why they are the best ones)
• Knowing how your customers are reacting to your product or service (in such external data sets as in social media)

According to a survey result by The Information Difference, 67% of survey respondents saw MDM driving big data, rather than the other way around, with just 17% seeing big data producing new master data, including the ability to use master data to automatically detect customer names in sets of big data.

Mastering data moves that data potentially from a single record to a mastered record set, de-duplicating records (example being single customer record to a customer profile) and providing a skeleton structure to which the body of big data can be added as linked attributes with a level of confidence. Analysing big data in this way, linking clean master data across such elements as locations, products, customers, cost centres, plants and other important master data domains provides the basis for better analysis and forecasting and the incorporation of other external but valuable data such as mobile, social, and cloud data.

This can provide increased points of reference between a customer and their experience with an organisation’s products. Big Data can with care, proper planning and governance enrich mastered data and add context and value which can lead to better and more timely analysis and forecasting with an associated increase in confidence in that information and value to the organisation.

To find out how Iter can help you on your journey take a look at our MDM implementation checklist…

Colin Prout, Delivery Director
Mark Grayham, Consultant
Iter Consulting