Customer data strategy — getting it right first time!
In a recent Gartner survey 75% of organisations admitted that defective data had a negative impact on their business. 50% agreed that they’d had to spend extra revenue to fix and reconcile data.
Maintaining accurate data is complex but having a customer data strategy in critical.
Pulling together such a strategy is an intricate and wide ranging exercise. Many fail to develop a robust data strategy because they lack a basic framework. We’d like to share with you a framework that might help.
A data strategy should include the following key components:
- Customer data vision
A clear set of statements outlining how data will benefit of the customer and business.
2. Data requirements
A defined set of requirements that bring to life how you plan to use customer data now and in the future. These can be plotted as customer journeys or simple use cases. It often works well to think of data requirements as being for today, tomorrow and the future. Looking at data in this way will ensure you’re collecting, storing and analysing data that is fit for purpose.
3. Data taxonomy
Work hard to define a clear data dictionary and glossary that everyone understands. Having a single taxonomy can reduce confusion and create a consistent understanding of data across the business. Have definitions for structured and unstructured data, behavioural data, descriptive data and contextual data.
Having defined your vision, requirements and taxonomy you can focus on managing your customer data. Use a series of data ‘modes’ including collecting, storing, accessing, sharing, using and updating. You can frame the work through a series of questions.
Let’s look at these in more detail.
- Collecting
Think channels and platforms. How do you intend to collect customer data? From which touchpoints, across what channels and through which methods? What would your ideal single customer data file look like?
2. Storing
Think data warehouses and data repositories. How will you store data that you collect? How will it be stored? What about data that you’re collecting across borders?
3. Accessing
Think portals and user permissions. Who can see the customer data? What roles have access, permissions and privileges?
4. Sharing
Think transfer, especially with third parties. How do you intend to manage the sharing of sensitive customer data? What about encryption and secure transfer rules?
5. Using
Outline how you intend to use the data. Focus on use cases whether for marketing or service management.
6. Updating
Think cleansing and de-duping. How do you intend to keep data accurate? Are you planning customer engagement work to keep data updated? Who handles keeping data clean and of high quality? How do you manage opt-outs? Have you considered the Google ‘right to be forgotten’ debate.
It’s important to ensure you’re clear on the following 4 core areas:
Information security — keeping data safe and secure. Mitigating risk and failure through back up, encryption and protocols.
Data quality — maintaining accurate, useful and relevant data. Data cleansing and de-duping where necessary. Operate the ethos of ‘garbage in, garbage out’.
Stewardship — data ownership , access and controls. Permission management. Enacting and revoking data access.
Systems management — resilient infrastructure built for future business needs.
There’s a lot more to data strategy than but this will give you a starting point.
