Most organisations have well-defined business and IT strategies. With data becoming key to business success, a coherent data strategy is now also important.
This blog gives a quick summary of those discussions. And for more on this topic, download these Econsultancy reports:
Benefits of having a clear data strategy
To begin with, here’s a run through of some benefits of having a clear data strategy:
Personalisation and targeting. If you know your customers, you can service them better.
Scalability. A good data strategy means you will hit fewer data related roadblocks. Buying new technology rarely fixes a lack of an underlying data strategy.
Multichannel co-ordination. Consumers expect a seamless user experience. Data underpins the ability to service customers via the channels they may choose to use.
Media attribution. More channels make customer journeys more complex. Deciding which channels then deserve the credit for a sale requires an understanding of buying behaviour across those channels.
Break down data silos. Legacy systems produce data silos which get in the way of understanding customers. This is a big challenge and without a strategy it is next to impossible.
Culture. For a business to be customer centric a data-driven culture is necessary. Developing a data driven culture does not happen by itself, it needs a plan.
What should a good data strategy include?
1. Data collection and cleansing.
What data do you need to run your organization? What data is necessary to meet your business goals? How frequently does it need to be updated? What data archiving and summarisation requirements are there?
2. Data architecture and integration.
How should data be shared between the various data silos? This is often where things go wrong. The need to join the dots means data connections are becoming as important as data collections.
3. Data storage and technology.
How to access, share and manage data? Some data may need to be available in real-time whereas other data may not be as time critical.
Should it be a SaaS or in-house solution? Each has their pros and cons and which way to go should be a business rather than an IT or marketing decision.
4. Data insight and analysis.
You can’t manage what you can’t measure. What KPIs need to be measured? How should you facilitate data exploration? What analytical resources exist? How should you foster collaboration on data throughout the organization?
5. Data governance, privacy and security.
Larger businesses need to define roles and responsibilities, approvals and workflows.
For more on this topic, read Econsultancy’s Organisational Structures and Resourcing Best Practice Guide.
Key questions to ask
These are some of the most important questions that businesses need to ask when developing a data strategy:
What are you trying to achieve? The data strategy must compliment the business strategy.
What data do you need where and when? Today and in the future? What level of data quality is practical? Map the customer journey to understand and prioritise where the ball is being dropped.
Where is your database of record for different data and do you require a single customer view?
Where do you need real-time data? What specific data should be real-time? With today’s technology it is rarely practical or necessary to have all data available in real-time in one place.
How do you integrate data silos? What data transfers are necessary and how frequently?
How do you track cross channel? What tags are you using and do you need to standardise?
What data skills and knowledge are in the organisation and business partners? Are there any organisational silos getting in the way? For example, between different channels or marketing and IT.
How much is your data worth? For example, what is the value of an email address? What incremental revenue can you expect? Putting a value on data helps to make data management a continuous improvement process. This way you can be in control of your data optimisation rather than fire-fighting.
Other than start-ups, very few organisations have a perfect data set up.
Data strategy is continuously evolving as requirements change. Data perfection is normally too expensive. At the other end of the scale a lack of strategy can be equally costly.
Try to strike the right balance with incremental evolution of the imperfect world we inhabit.
A data strategy, when properly understood and implemented, focuses the business on the right things and gives you a framework to prioritize limited resources.
You need a data strategy which develops over time in the same way as the business or IT plan does.