A Data Strategy provides the unifying vision and actionable plan for an organisation’s ability to realise data value; through related capability, interoperability and digital evolution; to align to the current and future business and to participate in (exogenous) ecosystems.
A Data Strategy shelters and tests subordinate tactical activities such as BI, MDM, Big Data Analytics, data marts, reporting, publication and visualisation.
The Data Strategy frames the work as it comprises the following:
- Kick-off workshop: this would be accompanied by interviews, discussion, consulting and documenting to define, develop and capture the organisational data objectives, operational data use-cases, capability, structures, processes, flows, topologies and ownership, according to policies, standards, risk management and governance requirements.
- Exploring the issues: this involves discussion, situational analysis and agreement regarding the business (forward planning, priorities and data challenges), its technical and infrastructure landscape; the context of the business in its environment and the impact, risks and trends of both endogenous and exogenous forces.
- Living Data Strategy (document): the workshop and exploration determinations should be summarised in a living document, a frame, for ideas and assumptions to be tested against objectives and subject to discovery. This document will be refined according to:
- top management and board feedback;
- insights gained as the work progresses;
- changing needs, objectives or the regulatory environment.
The Data Strategy should be resilient: to equip an organisation for evolution not just the present day and answer the questions: “what data do I have now; what data do I need in the future; what gaps do I have; how will I use, access, manage, curate and govern data in order to fulfil business objectives, operational needs, governance, security and risk management imperatives?”
The topics and reference data that inform the strategy include:
- Investigation of relevant data, technology and IT trends, developments and requirements;
- Review of framing, based on exogenous as well as endogenous considerations (a need to know what is outside, as well as inside, the frame);
- Definition, description and categorisation of types (structured, unstructured, spatial etc.), formats, sources, and standards of data, metadata and information to be managed;
- Definition, description and categorisation of data (or data system) ownership, administrative responsibilities and onward accountabilities (e.g. DPO), access roles, privileges and use cases;
- Describe and map relevant data models, taxonomies, structures, protocols, processes, workflows etc. to systems, datasets, sources, clouds or applications – in overview and in particular – and against policies, governance and risks;
- Statement draft use cases for data assets with a view to protecting, enhancing and reaping data value potential;
- Statement anticipated top-level service-level metrics (data systems, quality and process flow), standards and protocols;
- Statement, timeline and update current or potential subordinate technology activities e.g. MDM, Content Management, BI, data marts, other development, commissions or upgrades;
- List and timeline relevant plans (even blue-sky thinking) that may impact data requirements or design;
- Identify and timeline business (and IT) challenges, blockages and anticipated design decisions.
- Investigate and appraise unstructured and/or dark data and state of analysis/comprehension, with recommendations as to usability, viability and any required remedial actions; and finally…
- Suggested data discovery actions and remediation next steps.