For a long time, master data management (MDM) practitioners boasted about their ability to build a 360° view of customers by aggregating and proactively managing information coming from various business applications such as CRM systems, ERP applications, and other operational systems.
But was it really a 360° view? What about transactional and historical data? What about external data sources like social media? What about unstructured content such as emails or call records?
The full 360° view of customers is clearly broader than what MDM solutions typically provide, and there is a lot of information about customers that can be used to draw a more comprehensive picture of who the customers are, their preferences, motivations, behaviours, etc. This is the key for a truly cross-channel customer integrated experience – providing the right answers, at the right channel, at the right time.
Big data is the answer. And the technology to achieve this is now available.
For instance, retrieving information from social networks could provide information about someone’s hobby or his/her friends and family. Such information can be used to enrich the MDM hub and can then be leveraged in decision processes to be more efficient and service oriented when engaging with customers.
Linking master data to big data opens a new dimension of understanding customer information. Big data provides an opportunity to enrich insights and facts on master data entities that are not available through operational systems. Gaining this additional information empowers organizations to understand their customers even better, delivering improved customer experience and making business decisions that could not be done before.
How do you go about combining things that seems to be almost at the opposite spectrum of data types? On one side, master data is concerned about the well-structured, low-volume, slow-moving and slow-growing core data elements describing what customers are. On the other side, big data is concerned by a wide variety of high-volume, fast-moving, and fast-growing data such as transactions, web navigation, customer feedbacks, etc.
And despite all these differences, only the combination of master data and big data can actually achieve a true 360° view of customers.
Just as MDM needs big data to accomplish its goal, big data initiatives rely on trusted information to extract actionable insights through analytical means. The primary issue with big data is the trustworthiness of the data. With so many different data sources (internal and external), and less control on data quality, how well can we actually identify an individual with accuracy and completeness in the big data lake?
When analyzing big data, we can aggregate information for example to find out what the market thinks about a specific product. But identifying what customer actually made a statement on the other hand is difficult, if not impossible.
By providing the data lake with a reliable and consolidated source of customer data, MDM has the potential to greatly increase the organizations’ ability to create insight from the big data lake. The challenge though is to link the information from the MDM hub to the rest of the data. For this we need sophisticated technologies like fuzzy logic matching capabilities that are provided by the SAS MDM solution.
By using MDM as a primary source of data for searching, identifying and analysing customer data, organizations can approach their big data analytics in a more customer centric way. Something that is much more challenging without MDM.
In a nutshell:
- Big data is the only way to complete the full 360° view of customers (what MDM was intended to deliver)
- MDM is a fundamental component and a key success factor of big data analytics initiatives (providing a reliable source of core customer data for the data lake and ultimately more accurate reporting and analytics)
Master data and big data are two very different beasts but they desperately need each other!
