Between downloading white papers and submitting enquiries to contact centres, there are dozens of ways customers interact with your business.
If your customer data is accurate and well managed, you can study it to inform business actions. However, those decisions must adhere to a framework that ensures the quality and security of the information for which you are responsible.
Creating a data governance framework
In 2007, University of Arkansas at Little Rock’s Department of Information Science Chairwoman Elizabeth Pierce delivered a presentation on data governance at the Massachusetts Institute of Technology (MIT).
Dr Pierce maintained that companies develop rules that determine how their personnel and departments manage physical and financial assets. In an age of ubiquitous data, it makes sense for organisations to develop a framework that dictates:
- Who is accountable for ensuring data is of high quality
- How employees gather, log and use information
From a holistic perspective, the purpose of developing a data governance plan lies in positioning information as a strategic asset. The video below provides an overview of the practice:
What are the first steps?
Before implementing a data governance framework, you must familiarise yourself with how your company collects, processes, stores and uses customer information. Afterward, you need to determine who will be in charge of monitoring and enforcing protocols applicable to those actions.
As you can imagine, completing the preliminary stages successfully is only possible under a collaborative environment between chief decision makers, the IT department and vendors who may develop data management practices. This enables you to create protocols applicable to the following areas:
- Business requirements
- Information architecture
- Data quality, security and privacy
- Input, processing, output and boundary controls
From there, those at the top can figure out which protocols are conducive to positioning customer information as a strategic asset that’s protected under best practices.
Addressing data quality
As you’ve probably gathered so far, a huge part of data governance involves ensuring the quality of that information. Doing so necessitates a thorough assessment of how your company inputs and processes information.
— Tableau Software (@tableau) October 27, 2015
One segment of your business may gather data manually, while another may collect information automatically. For example, when a prospective customer enters his or her email address to download a case study, a back-end web program makes a record of that interaction, possibly sending that record to a customer relationship management solution.
However, what if this isn’t the first time the prospect has interacted with your company? What if he or she subscribed to a newsletter or called a customer representative? Depending on your data management protocols, it’s possible a profile detailing the customer’s information already exists.
To eliminate duplicate or inaccurate data, you can employ a customer data mastering (CDM) strategy. This constitutes using either an on-premise or hosted application that processes the data you own. The program will:
- Eliminate multiple entries for the same customer across all your systems
- Ensure the accuracy of customer data
- Connect subject-oriented data to the same customer, no matter where it’s located
- Decrease the time it takes to locate the right information pertaining to a customer
The uniformity associated with CDM also enables you to better enforce security and compliance standards. Using a master account to identify data locations enlightens IT personnel to the sort of protective measures your company should take.
— InfoWorld (@infoworld) November 2, 2015
An iterative process
A company’s mission may change depending on various economic factors. Such a repositioning often affects how financial and physical assets are managed. Therefore, an information governance framework must be adaptable.
You can sanction this adaptation by continuously reviewing benchmarks. Dr Pierce maintained that businesses should develop their frameworks to a point where internal best practices are regularly employed.
You won’t be able to create an optimised data governance program overnight, but iteratively assessing the model to eliminate deficiencies and faults will allow you to reach operational success.