In this first part on making data more valuable let’s consider how you view your first-party data

It’s debatable as to which customer data you have is your most valuable but without a doubt, the data that is unique to your business is the most important. It is that data which you are able to rely on to make critical business decisions and engage most comprehensively and effectively with your customers that is certainly likely to be valued far above other data.

It helps to provide a definition of what that unique first-party data really is.

First-party data is the data that you are able to obtain from your prospects, customers and audiences. Typically it is given first-hand by form entries, engagement, calls or transactions.

On one hand, you could think of it as the classic Rolodex entry but these days it is more than likely lurking in your ERP, CRM, CDP or POS system.

Your first-party data may also be present in emails, spreadsheets and of course a CMDM like a Pretectum Customer MDM.

Your first-party data typically carries all the essential information that you need to contact, transact and engage with a given customer or prospect. Over time that data may be enhanced through the addition of measures, insights and indicators related to transactional behaviour, preferences, tastes and engagement. Some of these enhancements may be unique to your business and its direct relationship, others might come from annual data refresh or contact update requests. Some may be inferred.

The reason you have this data is to minimize friction in the engagement and transacting with the customer or prospect. You minimize friction best by personalizing the customer experience every time they engage with your brand, message, people, processes and technologies.

But there is a problem with first-party data, a problem that is inherent in almost all data that is not appropriately managed and which devalues the data. That problem is related to the classical six core data quality dimensions.

• Accuracy
• Completeness
• Consistency
• Timeliness
• Validity
• Uniqueness

You can learn more about these on the web but they are just a handful of the 65 dimensions and subdimensions created by DAMA that flex according to the needs of different industries.

Data Quality dimensions were described by Richard Y. Wang and Diane M. Strong in Beyond Accuracy: What Data Quality Means to Data Consumers. They recognized 15 dimensions. DAMA International, a not-for-profit, vendor-independent, global association of technical and business professionals dedicated to advancing the concepts and practices of information and data management developed a more elaborate list containing 65 dimensions and subdimensions.

So the first challenge with ensuring that your data is initially, and then continues to be valuable, is ensuring that the data meets some sort of data quality objective. Defining data quality measures and then measuring the quality of your data is the best way to determine if your data is going to be useful.

Pretectum allows you to manage first-party data quality, by allowing you to define the measures of quality upfront before you even add data to your CMDM. When you eventually load or enter that data, the system then informs you of problems and keeps you aware of records that may not be complete or consistent with defined values. Other mechanisms allow you to verify the data against external reference sets to inform you on accuracy or validity as well as uniqueness. All the while, the latest version of the data is served up to you with the ability to examine the change history of the records over time.

Most companies have customer master data somewhere in their systems. The challenge with the customer data is that many companies find it difficult to maintain that customer data and ensure that everyone that needs access to the customer data, in fact, has access to it in its latest and greatest form. It is suggested, in various pieces of research, that as little as 20% of business user time gets to be spent on actually using the data. The remaining time, according to HBR, is spent seeking out and preparing data.

Data is central to how we run our businesses today and customer data is often slap bang in the centre. Global market intelligence firm International Data Corporation (IDC) projects spending on general data and analytics to reach $274.3 billion by 2022, you can assume a large chunk of that will be on working on customer data.

Customer data, wherever it is found, is often contaminated with additional data that is not necessarily pertinent or current. The best solution to having exactly what you need is the implementation of customer master data management (CMDM).

Having a CMDM creates a common definition and a single view of what a customer is, by centralizing data and creating governance, compliance and security.

With the infinite number of possible data combinations and the average number of systems that may be involved (an average of around a dozen or more), any CMDM project can be unnerving.

Getting started can be made easier though.

Here are some steps to make your customer master data management initiative successful.

Any master data management project’s starting point depends on where the business falls in the enterprise data management maturity model.

Every company is at a different stage and very few start from scratch or have only one source for the ‘customer’.

Clues to organizational master data maturity

• Business Divisions are focused on products, services or functions, so they don’t share customer data with others even if there is overlap or benefits
• Companies may have great customer data but have inadequate control and governance over that data, so data gets duplicated and potentially goes stale.
• Non-organic growth activities result in acquisitions of data from elsewhere but this data may be misaligned, inadequate or simply not integrated
• Meteoric business growth leads to systems not being able to adjust in alignment with the needs of the business, resulting in data proliferation and islands of information.

So, what are the choices available to you, to remediate these kinds of problems?

Research and Identification

Identify who should be the custodians of the Customer Master and which pieces they should be responsible for. This will inform you of the controls and processes you have around customer record creation and maintenance.

Identify who uses the customer master and for what purpose. They could be using the data to engage in outbound customer marketing, billing, service or almost anything

Take an inventory of all the customer data repositories that people are curating or maintaining or using. This will inform you of your data control and proliferation changes and also hopefully give you a statistical count on how many raw records you have.

Define the ideal customer master

Create one or more common definitions of what a customer is and what a customer should look like (we recommend you start with just one). Consider the different lenses through which different business units view and leverage the customer record.

Looking at the consumer customer record, consider that even a consumer may have many different pieces of contact data, emails, phones and actual addresses, some of which are for specific purposes. Home, work, delivery etc.

The idea is to identify data attributes that will be common to the majority of your customers and constitute the customer data point definition – this will become your customer master data model.

Get rid of the duplicates and bad data

After you have a master data model, you’ll want to ensure that your data is accurate and free of duplicates. If you have customer information in multiple systems, the chances are good that you will have duplicate customers with different information. You need to correct inaccuracies and converge on a unified view of the customer record. You will also need to clean up those records and report regularly on the effectiveness of your efforts!

Access and Control

Some sort of governance program will help you control your master data model and in particular the adjustments that will occur to it over time. You’ll also need controls baked into the model and the capture and maintenance processes to keep your data clean and accurate. Good governance ensures that customer data can be trusted and it provides accountability as you strive to keep it up to date and limit the control of and access to the data.

Some final thoughts

The Pretectum CMDM helps you define your participants in the master data curation process, the solution allows you to define one or more models for what you consider the ideal customer master and then it further allows you to establish controls and measures around what the data should actually be. But Pretectum’s CMDM goes a step further, it also allows you to physically build up your repository of customer records and syndicate them to whichever systems or users need the data in a hub and spoke approach using APIs and integrations.

The goal of any CMDM is to create a single place for all your customer information, a single data source that you can use to inform your business decisions and systems. If you consider the workings of customer relationship management (CRM) systems, ERP and CDPs, the value of a CMDM is clear. It is not a transactional system, it is a master repository with controls.

Many companies think that their shiny new CRM, ERP or CDP will solve all their customer master data issues but without a solid understanding of customer data and a strategy for how it will be managed. When you move to those new systems you can often compound the problem rather than solve it by creating yet another system with its own data repository and governance rules.

Why not consider joining the Pretectum CMDM Alpha trial and testing its usefulness for your organization?

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