A Data Strategy is a framework that describes what you should do, and how you should do it, to achieve specific business goals based on a smarter use of data in the organisation.
Using data to drive business performance, goes beyond the data itself. Here is the bullet point version of how you create and execute a Data Strategy:
(1) Define what Business goals should be achieved by a smarter use of data.
E.g; reduced customer churn by X%, increased average customer value by Y%, increased cross-sell between brand A and B by Z % etc.
(2) Specify the Capabilities you believe to be instrumental in order to reach those business goals.
E.g: personalized pricing, personalized recommendations/communication, churn prediction capabilities, specific decision support to different parts of the organisation etc.
(3) Map all data sources you can identify as relevant in creating the capabilities described in (2). Based on this; specify what data you have, what data is missing, what data is incomplete/inconsistent, and how different data sources are, and needs to be, integrated.
E.g; in order to achieve a personalized customer experience across multiple channels, you might need to implement supplementary tags/scripts to collect online behavioural data, integrate this data with offline customer data, supplement existing customer data to build richer customer profiles, eliminate duplicate customer records, validate contact information etc. etc.
(4) Identify how you need to apply analytics to leverage the data in (3), in order to achieve the capabilities described in (2).
E.g; what descriptive, predictive and prescriptive analytics capabilities are needed and what techniques/methodologies are likely to be used, e.g. programming languages, statistical modelling, machine learning, data visualisation etc.
(5) Extend your data source map (3) to include end users/decision makers and automated processes. Visualize the complete flow from data to the point of action. Identify the necessary actions to secure that the outcome of (4) gets distributed to the right person/system, at the right time and in the right format.
(6) Specify the necessary changes in IT-infrastructure (systems/platforms) to support actions derived from step 3-5 above.
Examples could be; a data management platform to enable a single customer view, a marketing automation system to enable timely and personalized communication with customers, a BI-tool that end users trust and understand, customized dashboards to support your sales/marketing/CS team etc.
(7) Decide upon how you are best organised to execute on your data strategy, which complementary skills that are needed, and how responsibilities should be divided in the organization to secure a lasting non-silo approach to data.
– When data is considered a core asset in your organization, and your focus is monetizing on this data, then it is highly unlikely that you will stay organised the same way as before you had this focus.
– In a truly data driven company ”data” is separate from ”system” as the use of data can not be restricted by technical or organisational silos. Therefore a system owner and a data owner will most likely be separate people.
(8) Continuously review your work (1-7) from a legal-/compliance perspective. Formulate a policy that comply with privacy regulations (and be serious about it).
– View regulations as a catalyst for handling data professionally, and thereby get more leverage on your data. Do not view regulations as a hindrance to use data for business development.
Outline the execution of your Data Strategy in a high level Roadmap that stipulates what landmarks to achieve after 3, 6, 12 months etc.
All actions deriving from steps 3-8 should be specified in detail in one or more Action plans. Connect each action to the capability/capabilities (2) it will enable (directly or indirectly).
Organising the process:
– The reason you make a Data Strategy is because you identify business opportunities from using data in a more extensive and smart way. Therefore the primary stakeholders are always from the business side (Marketing, Sales, Customer Support etc).
– IT is a support function in developing and executing your data strategy. Seizing business opportunities by being data driven is NOT an IT-project.
– The CDO (Chief Data Officer) is the bridge between business and IT, and is heading the development and execution of your Data Strategy.
– Buy-in from the CEO is vital. As things get complicated, and they will, the CEO needs to stay committed to the process. A Data Strategy is about making the organisation increasingly data driven. This is a transformation equally profound as transforming into digital.
In upcoming blogposts we will dig deeper into each step described above. Stay tuned.
From Data to Money