This document from our strategy consulting team outlines an achievable step by step approach to defining your own data strategy that will power your efforts to use data effectively across your business.
What is strategy consulting?
Strategy consulting looks to solve complex business problems with effective strategies, solutions and plans that help the business deliver on their goals. When it comes to using data analytics effectively strategy consulting plays an important role in identifying the needs of the business and framing these into an actionable plan called the data strategy.
Benefits of a data strategy
The benefits of a data strategy are simple. First and foremost it enables the creation of a shared vision and plan for data in the business that will drive collaboration across teams and lead to the effective use of data.
Critically this helps to mitigate against probably the biggest risk with data which is the creation of silos with the business.
Once business units and disparate groups start to develop their own approach to data many of the potential benefits are easily lost as the economies created through shared development of solutions that can be reused across the business do not materialise and in their place come confusion with multiple versions of the truth.
The data strategy should be seen as a key enablement to putting data to work.
How to define your data strategy
It is an important first step to define what your data strategy will be and set the right expectations with the business.
Essentially, we believe a data strategy should act as a common document or reference point that details the vision, architecture, methods, tools, policies and processes for using data science effectively and putting your data to work across the business.
The business should understand that a data strategy is not a detailed solution document and the process will not provide all the answers.
What it will do however is provide a pivot point for the business to align the executive sponsorship and governance behind the plan and help ensure that the strategy lives and evolves in line with the corporate objectives.
What to include in your data strategy?
The Discovery and Requirements Gathering Phase
This important first phase is about discovering what capabilities and needs the business has from data. It also serves as an important step in sharing the art of the possible from using data science data effectively and beginning the collaboration journey with the different stakeholder groups in the business.
Capabilities Assessment Findings and Gap Analysis
The capabilities assessment defines how data is being used today, maps out current systems, data and tools and should consist of the following activities:
Technology Review - map of existing tools and systems in use assessment of current strengths and limitations .
In-flight Initiatives Review - Review of any significant in-flight data initiatives that need to be considered and aligned with the data strategy.
Data Audit and Analysis – this can be a desk-based review of the current data, its accessibility, availability and a quality and suitability review of source data and current data stores.
Data Map of what is available and what it contains, how it is used today, storage etc.
Data prioritisation – current deployed use cases, value of benefits, cost to manage.
Data quality assessment – qualitative assessment of how it meets current needs.
Define Business Wide Data and Analytics Requirements
Gathering together the requirements for data from the business should be carried out and is best achieved through a structured interview process covering off the following:
Summary of the Data as-is
This summary should look to identify both the successes and challenges with current use cases to provide a clear picture of the current state of play that can be referred back to later as part of reviewing progress.
Business wide analytics requirements – a summary of the key analytical requirements and benefits linked to the core business activities.
E.g. better understanding of the customer and their purchasing habits will support our marketing ROI.
Business function based requirements/ use cases – a description of the key practical use cases for analytics for each individual business function as identified throughout the interview process.
Including incorporation of any other requirements not identified through interviews and existing work.
Summary overview and assessment of how analytics is perceived and being used across the business today, including highlighting of any successes/pockets of best practice for analytics within the business.
Summary of current state of play (culture/people, processes, technology) including the main perceived challenges for analytics within the business.
Gap analysis – side by side analysis of requirements/use cases (and any existing activities) to highlight any gaps (data availability, quality, tools, capability etc.) and where possible identify the changes that will be required/ to close these gaps.
Setting the Vision
Setting a vision for the data strategy is an effective way to engage stakeholders in helping to really define what success will look like for them as well as a great tool for communicating the outcomes you are looking for from the data strategy.
In defining the vision for data and analytics you need to be thinking about:
What is the purpose and role of analytics?
How will it operate and function across the business?
How will this play out over the next 2-3 years?
What are the pillars that will support this?
Investment Rationale and Business Case
The vision needs to be accompanied by tangible financial benefits or goals such as increased profit, customer satisfaction and staff engagement. The investment rationale and business case should present the outputs from high level scenario analyses that estimates the potential business upsides from deploying data analysis: e.g. impact of increasing customer retention, driving up basket size, increasing product attachments etc., the purpose being to demonstrate the financial impact of pulling various levers that can directly be related back to data driven activities.
Alongside this business case should feature a shortlist of possible insight/analytics use cases and their likely cost, potential benefits where possible, backed up with data based metrics driven from the business. This will provide tangible first steps for the business to take to set off on their data journey.
Building the Roadmap and Implementation Plan
With the core of the strategy in place a level of detail will then be required across the main workstreams identified to implement the programme. These do not need to be definitive to start with but carry sufficient detail to assist the business in identifying the level of time and effort required and set expectations effectively from the outset.
The key workstreams we typically start with in our strategy consulting team are:
Building the Customer & Business Priority Led Plan
Core Insights Solutions
Adoption & change management
Measurement & Reporting
Data & Tools
Plan for 6,12 & 24 months
Customer and Business Priority Led Plan
This stream needs to relate to the overall business objectives and map out the plan for the major data use cases that the business wishes to focus on. For each major use cases you should aim to develop a level of detail as follows:
Background – description of the use case
Business objective - Clearly define the goal, what's needed to overcome the problem and how the project will support the business
Benefits and limitations – financial and non-financial benefits, e.g. Decrease expenses; Find new innovation avenues; Launch new products/services; Add revenue; Increase the speed of current efforts; Transform business for the future; Establish a data driven culture
Option identification and selection – review of the potential range of solutions and an initial view on the likely candidate
Scope, impact, and interdependencies - describes the work needed to deliver the business objective, the scope and identifies the business functions affected by the project. To cover what is included and what is excluded plus the key dependencies with other initiatives.
Outline plan - summary of the main activities and overall likely timescale to implement
Data & Tools
This workstream needs to outline the principles around the proposed data environment, data governance, collection and maintenance.
It should cover off the top level technical requirements such as the high level architecture required to collect the data, perform the analysis and report on the results. The main tools proposed to support these activities should also be listed.
It is not necessary for this section to be definitive. Using the initial use cases to try out different tools and test certain solutions can be an effective way to road test the initial thinking and refine before committing to larger investment.
Core Insight Solutions
The core insight solutions that will power the use of data to support decision making and taking action such as customer segmentations and models should be identified and mapped against the use case priorities.
There will be much cross over between these and with forward planning significant efficiencies can be achieved.
Measurement and Reporting
Measurement and reporting will be a cornerstone to the success of your data strategy. This will become part of a new language for the business as it familiarises itself with new measures and KPIS on customer performance.
This section should start to outline these key measures and KPIs and align them with the Vision.
It should also begin the process identifying the core user groups and requirements and responsibilities for reporting moving forward.
Adoption and Change Management
Data science used effectively always leads to change in the ways things work and operate and this requires careful planning and consideration. This section should outline the processes and plan around how the change will be managed effectively and new initiatives put into play covering off:
Adoption and change management
Business Awareness and Training
KPI ownership and integration
Integration with Business processes
Implications for organisational design
Plan for 6 months, 1 year and 2 years
The final section should be a high level plan and road map setting out the major workstreams within the strategy, with a description of key tasks required, what success looks like and a suggested timeline approach for the short to medium term.
If you would like to discover more about implementing a successful data strategy within your business, contact our team of experts directly or read our strategy consultants published insights including a guide on the Key Components of data strategy.