Using this Document
This document provides a generic overview of the core components of a Corporate Data Strategy. It is designed to give you a foundational understanding of the areas we cover, the best practices we recommend, and the key definitions we use when creating or evaluating a data strategy.
Important Note: This is a generalised framework. A true data strategy must be tailored to your organisation’s unique goals, challenges, and culture. A bespoke proposal for your organisation should be requested to translate this guidance into a concrete, actionable plan.
1. Introducing Data Strategy
To be successful in the modern economy, organisations must fundamentally evolve how they think about and use their data. Moving from a reactive, siloed approach to a proactive, integrated one requires a clear and deliberate plan: a Data Strategy.
In today’s environment, where organisational agility is paramount, the focus has shifted from rigid, multi-year projects to informed, iterative change. While individual projects might seem successful on their own, it is critical to ensure they all contribute to the organisation’s overarching objectives. The data collected, created, or accessed in these projects should be a unified enabler of efficiency and insight, not a byproduct.
This is the primary role of a data strategy. It acts as an enabler for the corporate strategy, ensuring that data initiatives directly support business goals. It is important to recognise that creating a data strategy is not an IT-centric exercise; the initial stages are overwhelmingly focused on business processes, needs, and culture.
A key principle: Your data strategy must focus on how best to serve the customer or end-user. Internal data siloes (where data is trapped within one department and inaccessible to others) should not become your customers’ problem, leading to fragmented experiences and inconsistent information.
2. Best Practice Data Strategy
SRS will support you in reviewing your existing Data Strategy or creating a new one that incorporates all essential best-practice components.
2.1 Data Strategy Vision
SRS will collaborate with you to create a Data Strategy that provides the foundation for achieving your vision. This strategy will define:
The relationship between your data and your business context.
The specific business outcomes you aim to achieve (e.g., increased customer retention, improved operational efficiency, new revenue streams).
The capabilities and culture you need to develop to realise these outcomes.
This vision will be delivered through four key pillars:
Delivering Data Governance: Establishing the decision-making rights, accountability, and policy framework for your data.
Defining Your Data Architecture: Creating the blueprint for how data is structured, stored, integrated, and used.
Implementing Data Management: The practical execution of policies and procedures to manage the data lifecycle.
Implementing Business Intelligence (BI): Providing the tools and processes to turn data into actionable insights for decision-making.
SRS will not only help create the strategy but will also support the definition of an implementation programme, including a compelling business case and the necessary IT infrastructure design.
2.2 Defining Data Architecture
Data Architecture is the blueprint that aligns your data assets with your business strategy. It provides a consistent view of your organisation’s data landscape. SRS will collaborate with you to develop this blueprint by:
Developing a Consolidated Data Model: Using industry standards, we create a visual and structural representation of your data entities (e.g., “Customer,” “Product,” “Order”) and their relationships, ensuring consistency across the organisation.
Establishing a Common Vocabulary: We define a standard set of business terms and data definitions (e.g., What exactly do we mean by “Active Customer”?) to eliminate confusion and create a “single version of the truth.”
This architecture is built on principles of best-practice data management:
Information Lifecycle Management (ILM):
Explanation: ILM is a comprehensive approach to managing the flow of an information system’s data from creation and initial storage to the time when it becomes obsolete and is deleted. It involves defining policies based on the changing value of data over time.
Process: We classify data into phases (e.g., Active, Archive, Destroy) and define policies for each. For example, customer transaction data might be kept in a high-performance database for 90 days (active), moved to cheaper, slower storage for 7 years (archive) for compliance, and then automatically deleted.
Master Data Management (MDM):
Explanation: MDM is the technology, tools, and processes that ensure an organisation’s critical “master” data is uniform, accurate, and consistent across all systems. “Master data” is the core data about key business entities, such as customers, products, employees, and suppliers.
Process: We identify master data sources (e.g., CRM, ERP), clean and de-duplicate the records (e.g., merging “J. Smith,” “John Smith,” and “Jon Smith” into one golden record), and establish a process to maintain this clean, master list as the primary source for all systems.
Measuring and Improving Data Quality:
Explanation: This involves defining what “good” data means (e.g., accurate, complete, timely, valid) and implementing processes to continuously measure and improve it.
Process: We establish data quality dimensions and metrics (e.g., “The customer email field must be 98% complete and valid”). We then use automated tools to profile data, identify issues (like null values or invalid formats), and create workflows to correct them at the source.
These capabilities are realised through a combination of policies (the rules), processes (the workflows), and automated tools (the technology).
2.3 Data Strategy Principles
These principles are the foundational beliefs that guide all data-related decisions and actions.
2.3.1 Data is a Valued Asset
Explanation: Data should be recognised on the balance sheet as a strategic asset, just like physical equipment or intellectual property.
Bullet Point Processes:
Valuation & Management: Conduct audits to inventory data assets and classify them based on their business value and sensitivity. High-value data receives higher levels of protection and investment.
Lifecycle Governance: Appoint Information Owners/Stewards who are responsible for managing data through its entire lifecycle—from creation and use to archiving and secure destruction. This includes defining retention periods.
Culture & Skills: Invest in training and hiring to ensure all staff handling data understand their responsibilities and are equipped with the necessary skills.
2.3.2 Data is Fit for Purpose
Explanation: Data must be of sufficient quality not only for its original intended use but also for potential future uses, such as analytics or machine learning.
Bullet Point Processes:
Quality Definition: For each critical data element, define and measure quality dimensions like accuracy, validity, reliability, timeliness, relevance, and completeness.
Standardisation: Enforce data standards (e.g., date formats, country codes) for both structured data (in databases) and unstructured data (in documents, using metadata—data that describes other data). This makes data linkable and comparable.
2.3.3 Data is Reused
Explanation: Maximising the return on investment in data collection by enabling its use across multiple business processes and analytics projects.
Bullet Point Processes:
Proactive Stewardship: Data Stewards should actively catalogue and share data assets through an internal data catalogue or marketplace.
Design for Reuse: When creating new systems, architects must design with reuse in mind, ensuring data is easily accessible and well-documented for other teams.
2.3.4 Data is Accessible
Explanation: This goes beyond legal compliance (like GDPR). It promotes a culture of transparency and ease of access, empowering both internal users and external data subjects.
Bullet Point Processes:
Self-Service Portals: Implement secure portals where individuals can easily access their own data without formal, lengthy requests.
Internal Access Controls: Balance accessibility with security by implementing role-based access controls, ensuring employees can easily find and use the data they are authorised to see.
3. Defining Success
It is crucial to define ‘What Good Looks Like’ from the outset. Success is not merely the completion of technical projects; it is the realisation of the business benefits outlined in the strategy.
Success is dependent on the effective alignment of:
Data Governance & Management: The rules and processes.
Technology & IT Infrastructure: The tools and platforms.
Articulation of Business Needs: The clear definition of goals and outcomes.
Key Performance Indicators (KPIs) might include reduced time to insight, improved data quality scores, increased usage of self-service analytics, or tangible business outcomes like increased revenue from data-driven products.
4. Defining Data Governance
Data Governance is the exercise of authority and control over the management of data assets. It is the framework that makes the data strategy actionable.
SRS implements a standardised approach that integrates into your organisation’s existing structures. This approach provides:
A Defined Accountability Framework: Clearly defined roles and responsibilities.
Senior Information Risk Owner (SIRO): A C-level executive accountable for the overall data risk.
Head of Data Governance: The individual responsible for running the data governance program.
Data Steward: A business subject-matter expert responsible for the quality and fitness of a specific data domain (e.g., “Customer Data Steward”).
Policies and Procedures: The formal documents that state the organisation’s rules for data.
Clear Ownership: Data assets are assigned to the business area best placed to make decisions about them, from collection to disposal.
5. Implementing Business Intelligence (BI)
Business Intelligence (BI) is a set of methodologies, processes, architectures, and technologies that transform raw data into meaningful and useful information for business analysis. It is distinct from standard data management as it is focused on analysis and reporting.
SRS will help you introduce a BI environment that enables:
Operational Monitoring & Reporting: Real-time dashboards showing key business metrics.
Impact Assessment: Analysing the effect of business decisions or market changes.
Evaluation of Change Requests: Using data to assess the potential value and impact of proposed changes.
This often involves implementing tools like Tableau, Power BI, or Qlik and building a centralised data warehouse or data mart to feed them.
6. Why is a Data Strategy So Important?
Your data strategy is the essential framework that allows you to prioritise initiatives. It ensures that every data-related project, regardless of its individual merit, collectively moves the organisation toward its corporate objectives.
A critical component of the strategy is the roadmap—a phased plan of key initiatives that considers priorities (what is most important to the business) and dependencies (what needs to be built first). For example, you cannot build a sophisticated BI dashboard (dependent) until you have a solid data governance foundation in place (priority).
SRS helps you formulate a data strategy that firmly positions your data as a primary contributor to business success, turning it from a passive resource into a dynamic engine for growth and efficiency.
If you would like to learn more, contact SRS to talk through the specific situation at your organisation.
ion and explore what we can do for you today.
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