Coming to a decision around customer data is a complex mental process involving weighing up and choosing from a number of options.
Each option you might choose has various differentiating characteristics, what you decide upon is not necessarily a reflex action.
When we make a master data decision there are always some expected and foreseen consequences. There are possibly also unexpected outcomes or unforeseen consequences. One might have predicted, for example, that some data might fall foul of privacy laws at some point data collection about consumers was the wild west where anything goes, and in some places, this is still the case.
You should take note of the point that in making any data mastery decision, you are planning the use of information that has been accumulated in your brain through your past experiences. You would take these big and micro-decisions right up to the final moment that you commit your decision.
Every decision we make, also involves some element of risk, as there is a degree of uncertainty and even incompleteness or imperfection around making our decisions. For master data, many decisions can have long-term consequences on our organization and data quality. If you decide to not capture certain pieces of data at the time of initial contact, will you be able to recover them at some point later?
As an example, is your customer, a customer from the moment you first capture their data or the moment you first capture a transaction related to them? You might say, simply enough, the transaction drives the decision point but that’s not true if that customer has been a loyal customer for years but was anonymous.
Machine Learning Artificial intelligence and automation, when combined with structured data curation is a triumvirate of intelligent technologies with the added data management assurance through manual or automated methods. But we really want to minimize the mental effort associated with what for many, is a mundane but necessary activity – namely the management of that customer data.
Data syndication
We talk about data syndication with Pretectum’s CMDM as a systematic process of data distribution and availability.
When dealing with customer data, data syndication is the automated distribution, access or export of the schemas, rules and actual customer master data. You do this from your Pretectum customer data schemas and repositories to other users in your Pretectum Business Area and receiving or feeding systems in your defined systems landscape.
Pretectum CMDM is able to be configured so that the control, use and access to data and metadata entities in Pretectum by you, require minimal intervention by end-users depending on your use case.
Pretectum also allows you to run remote demand and push requests with secure credentialing via the Pretectum CMDM API stack.
Using the CMDM APIs, Pretectum CMDM takes care to keep the datasets updated with the latest master data from source systems of record or in target recipients of syndicated data.
Within Pretectum you use mapping rules to align schemas and APIs with datasets.
Pre-programmed data syndication helps you to configure the frequency of syndicated master data in the targets you require.
Show me the ML and AI
So where are the ML and AI you might ask? Well, behind the scenes, from the moment you add a schema traditional data management and integration practices become amplified in their capability and effectiveness through three key aspects:
Best Practice – You will be familiar with how most common data should look to the naked eye. Sometimes, particularly when dealing with digital data, unwanted artefacts are introduced that we may want to avoid or limit. Pre-processing or filtering records is a natural activity in the data curation process. Many of the methods and approaches that you likely use, are standard data engineering data prep activities. Accordingly, Pretectum tries to handle most of those for you, avoiding you having to apply those actions manually.
Templates – the most common systems of record, have predefined schemas. Data schemas are often stacked by industry vertical and within those specialisms, you will have some minimum expectations around how you gather, collate and manage customer data. Pretectum CMDM brings templates to the front of how you might consider your customer data curation and data management practice.
Recommendations – through the power of community across different industries we are able to relate the kinds of curation criteria that look most appropriate to your customer master data schema definition and suggest which attributes should be mandatory, the types of data patterns and in-ranges you might expect and the validation lists that you should be using.
Your data management decisions must always be considered to be evolutionary. The decisions that you take today may need to be different for the scenarios you encounter tomorrow.
Some data management decisions require a high level of thinking and calculation with referral to past experiences and results, and they require taking the long-term outcome of the decision into consideration. For long-term decision-making, we voluntarily focus on various information sources when we do this mentally, and then we decide what is and is not relevant in achieving our long-term goals. The reality though, is that our decisions continually change according to changes in our environment and our systems should adapt accordingly.
This decision-making is another area where the platform also learns from your decisions and behaviours within it. Over time, recommendations, decisions and results will evolve to be tightly aligned with your organization’s unique needs. The CMDM platform is there to support that.
To learn more about the Pretectum CMDM platform – contact us.