Data strategy template with a worked example

Data strategy template

A data strategy sets out which decisions an organization most needs data to support, and how data will be collected, governed and made available so that people trust it. It is owned by the chief data officer and agreed with the executive team, the business leaders who own each data domain and the head of IT.

Horizon: 3 years4 priorities · 11 outcomes

Use this template

What this data strategy template includes

ContextThe situation, the challenges to solve, and what is deliberately left out.
Business questionsThe decisions data needs to support and where it falls short today.
Current stateThe maturity of each data capability.
GovernanceWho owns data and the standards that apply.
Architecture and platformsWhere data lives and how it moves.
Skills and literacyWho does the work and how the rest of the organization learns to use data.
Priorities and measuresThree to five priorities, each with an owner, outcomes with a baseline and target, and the initiatives behind them.

Data strategy example

A complete data strategy for a fictional organization: regional grocery retailer with 85 supermarkets and a loyalty program of 1.4 million households.

Worked exampleData strategy

Organization	Regional grocery retailer with 85 supermarkets and a loyalty program of 1.4 million households (6,000 staff)
Ambition	Give every buyer and store manager one trusted view of sales, margin, stock and waste by item and store, and use it to cut fresh shrink by a quarter.
Horizon	3 years

Context

Where we are and what we are choosing

The chain turns over $1.9B across 85 stores and 38,000 items, with 71% of sales linked to a loyalty card. Fresh departments lose 6.8% of sales to shrink, about $45M a year, and fresh orders are still written by department managers from memory. Merchandising, finance and store operations each report a different sales and margin figure, and analysts spend 55% of their time reconciling them. The reporting estate holds 1,400 reports, 60% unopened in a year. Item records are wrong or incomplete for 14% of active items, and store stock records match the shelf for 64% of items counted. A study of 600 promotions found 35% lost money.

Challenges

There is no agreed definition of sales, margin or shrink. Vendor funding sits in buyers' spreadsheets, so margin by item is known only at quarter end.
Item and inventory data are wrong at the source. Replenishment and any forecast built on a stock record that is right 64% of the time will order the wrong quantity.
The warehouse loads once a night and waste is recorded weekly by department, so nobody can see yesterday's waste by item for a store.

We will focus on

Three decisions worth the most: how much fresh product to order, which promotions to run again, and what price to set by zone.
Fixing item, vendor funding and inventory data where it is created, with a named owner in the business for each.
One certified set of sales, margin and shrink measures that every report uses.

We will not

One-to-one personalized offers. Marketing wants them, but the loyalty data is the cleanest we have and the larger losses are in fresh and promotions.
Selling shopper insights to suppliers. It could earn several million dollars a year, and it waits until item data and privacy controls would survive a supplier's scrutiny.
Real-time streaming from the tills. Hourly sales loads are enough for ordering and cost a fraction as much to run.

Business questions

Decision	Data needed	Gap today
How much fresh product each store orders each day	Sales and waste by item, store and day; stock on hand; promotions; weather	Waste recorded weekly by department; stock records 64% accurate
Which promotions to repeat, change or drop	Baseline and incremental units, vendor funding, cannibalization within the category	Vendor funding in spreadsheets; no baseline; 5% of promotions evaluated
Shelf price by item and price zone	Elasticity by item and zone, competitor prices, cost changes	Competitor prices checked by hand for 400 items
Which items each store should range	Item sales by store, planogram space, basket and household data	Planograms not linked to the item master
Where on-shelf availability fails	Hourly sales, stock on hand, deliveries, shelf gap scans	Sales arrive at 7am next day; gaps logged on paper
How many labor hours each department needs	Transactions by hour, deliveries, task standards	Held in the workforce system, not joined to sales

Current state

Capability	Maturity	Main issue
Item, vendor and location master data	Low	14% of active items have wrong or missing attributes; no owner
Store inventory data	Low	Stock record matches the shelf for 64% of items counted
Data warehouse and pipelines	Medium-low	Nightly batch on aging on-premises hardware; 31 failed loads last year
Reporting and dashboards	Medium	1,400 reports, three definitions of sales, low trust
Forecasting and advanced analytics	Low	No demand forecast for fresh; one pricing analyst builds models alone
Governance and ownership	Low	No data owners, glossary or quality measures
Customer data and privacy	Medium	Consent recorded, but loyalty data is copied into four systems

Governance

Ownership model	Each data domain has an owner in the business, not in IT: the chief merchant for item, price and promotion data, the head of supply chain for inventory, the finance director for margin and vendor funding, the marketing director for customer data. Each owner appoints a steward who fixes issues at the source. A data council chaired by the chief data officer meets monthly.
Quality standard	Every certified dataset has published definitions and four measured checks: completeness, validity, timeliness and agreement with the financial ledger. Item records must be complete before an item can be ordered. Sales and margin must reconcile to finance within 0.1% each week, and breaches are reported to the owner.
Access and privacy	Household-level data is visible only to the customer analytics team and is masked everywhere else; other users work with aggregated segments. Access is granted by role and reviewed twice a year. Loyalty data is not shared with suppliers at household level, and retention follows the periods stated in the loyalty terms.

Architecture and platforms

Sources	Point of sale in 85 stores, the merchandising and replenishment system, warehouse management, the loyalty platform, the workforce system, e-commerce orders, the finance ledger and external feeds for weather and competitor prices. Waste and markdown scans from handheld devices are added as a new source.
Platform	A cloud data warehouse replaces the on-premises one over 18 months. Sales and stock load hourly; everything else nightly. Data lands raw, is modeled once into conformed item, store, day and household tables, and certified measures are defined in a single semantic layer. Pipelines are tested and monitored, with a status page for users.
Tools for users	One dashboard tool for all staff, with a store scorecard on the handheld devices managers already carry. Analysts get SQL and notebook access to certified tables. Spreadsheet exports remain, but only from certified datasets. Fresh order proposals appear inside the existing ordering screen, not in a separate tool.

Skills and literacy

Data team	A central team that grows from 11 to 24 over three years: data engineers, analytics engineers, two data scientists for forecasting and pricing, a governance lead and a product manager. Six analysts stay in merchandising, supply chain and marketing and follow central standards. The budget rises from $2.6M to $5.2M a year.
Literacy program	Training in three tiers. Store and department managers learn to read the store scorecard and act on waste and availability, in a 90-minute session run in store. Buyers and category managers learn promotion and price evaluation. Analysts learn the warehouse model and the semantic layer.
Community	Data stewards and embedded analysts meet every two weeks to review quality issues and share work. A searchable catalog lists every certified dataset with its owner and definition. Each quarter the data council publishes the decisions that changed because of data, with the money involved.

Priorities and measures

Priority 1Owner: Chief Data Officer

Agree one version of sales, margin and shrink

Three functions report three numbers, and analysts lose more than half their time reconciling them. Nothing else in this strategy is believed until the basic measures agree with finance.

Outcome	Baseline	Target	Due
Executive and store reports built on certified measuresMilestones: 60% by FY27	0%	100%	FY28
Reports in the reporting estateMilestones: 900 reports by FY27 · 500 reports by FY28	1,400 reports	300 reports	FY29
Analyst time spent reconciling and preparing dataMilestones: 45% by FY27 · 33% by FY28	55%	25%	FY29

Initiatives

Define sales, margin, shrink and availability with finance and merchandising and publish them in a glossary
Move vendor funding from buyers' spreadsheets into the merchandising system so margin is known weekly by item
Build the cloud warehouse and semantic layer, and migrate the 300 reports worth keeping
Switch off the legacy reporting tool in FY29, once the last report worth keeping has moved

Targets are illustrative. Adapt them to your baseline before you start.

Priority 2Owner: Head of Supply Chain

Fix item and stock data where it is created

Ordering, forecasting and availability all read the item master and the store stock record. At 86% and 64% accuracy, every downstream system inherits the errors.

Outcome	Baseline	Target	Due
Active items with complete and correct attributesMilestones: 95% by FY27	86%	99%	FY28
Store stock records that match the shelf countMilestones: 70% by FY27 · 78% by FY28	64%	85%	FY29
On-shelf availabilityMilestones: 93.2% by FY27 · 94.4% by FY28	92.5%	95.5%	FY29

Initiatives

Appoint item data stewards in merchandising and block ordering of items with incomplete records
Have suppliers submit new item data through a validated form in place of emailed spreadsheets
Replace annual stock counts with daily cycle counts guided by exception reports
Scan waste, markdowns and shelf gaps by item on store handhelds

Targets are illustrative. Adapt them to your baseline before you start.

Priority 3Owner: VP Fresh Merchandising

Order fresh from a forecast, not from memory

Fresh shrink of 6.8% costs about $45M a year, and orders are written by hand in every store. A store and item forecast is the largest single return available from data.

Outcome	Baseline	Target	Due
Fresh shrink as a share of fresh salesMilestones: 6.6% by FY27 · 5.9% by FY28	6.8%	5.1%	FY29
Fresh items in stock at 5pm in store shelf checksMilestones: 89% by FY27 · 91% by FY28	88%	93%	FY29
Forecast error for fresh items at store and day levelMilestones: 38% by FY27 · 31% by FY28	42%	26%	FY29

Initiatives

Build a daily demand forecast for produce, bakery and meat using sales, promotions, weather and local events
Pilot order proposals in 10 stores, with department managers able to override and give a reason
Review overrides weekly and feed the reasons back into the model
Extend to deli and prepared foods once item-level waste data has a full year of history

Targets are illustrative. Adapt them to your baseline before you start.

Priority 4Owner: Chief Merchant

Measure every promotion and price change

A one-off study found 35% of promotions lost money, but only 5% are ever evaluated. Buyers repeat last year's calendar because they have nothing better to go on.

Outcome	Baseline	Target	Due
Promotions with incremental margin measured within two weeks of endingMilestones: 40% by FY27	5%	90%	FY28
Promotions that lose money after vendor fundingMilestones: 33% by FY27 · 27% by FY28	35%	20%	FY29

Initiatives

Build a baseline sales model so incremental units and cannibalization can be estimated per promotion
Publish a promotion scorecard to each category manager every Monday
Automate competitor price collection for the 3,000 items that drive price perception
Test price changes in matched store groups before applying them to a zone

Targets are illustrative. Adapt them to your baseline before you start.

Blank data strategy template

The same structure with a prompt in every field. Copy it, or download it as a Word or Excel file.

Your planData strategy

Organization	[Describe your organization in a few words, with its size]
Ambition	[One sentence on where you want to be at the end of the plan]
Horizon	[How long the plan runs, for example 12 months]

Context

Where we are and what we are choosing

[Where things stand today: the facts that make this strategy necessary]

Challenges

[A problem the strategy has to solve]
[A problem the strategy has to solve]

We will focus on

[Something you are choosing to concentrate on]
[Something you are choosing to concentrate on]

We will not

[Something you are deliberately leaving out]
[Something you are deliberately leaving out]

Business questions

Decision	Data needed	Gap today
[Decision]	[Data needed]	[Gap today]
[Decision]	[Data needed]	[Gap today]
[Decision]	[Data needed]	[Gap today]

Current state

Capability	Maturity	Main issue
[Capability]	[Maturity]	[Main issue]
[Capability]	[Maturity]	[Main issue]
[Capability]	[Maturity]	[Main issue]

Governance

Ownership model	[Your ownership model]
Quality standard	[Your quality standard]
Access and privacy	[Your access and privacy]

Architecture and platforms

Sources	[Your sources]
Platform	[Your platform]
Tools for users	[Your tools for users]

Skills and literacy

Data team	[Your data team]
Literacy program	[Your literacy program]
Community	[Your community]

Priorities and measures

Priority 1Owner: [Role accountable for this priority]

[Name the priority in a few words]

[Why this priority matters now, in one or two sentences]

Outcome	Baseline	Target	Due
[Metric you will track]	[Where it is today]	[Where it should be]	[When]
[Metric you will track]	[Where it is today]	[Where it should be]	[When]
[Metric you will track]	[Where it is today]	[Where it should be]	[When]

Initiatives

[A project or action that moves the outcomes above]
[A project or action that moves the outcomes above]
[A project or action that moves the outcomes above]
[A project or action that moves the outcomes above]

Priority 2Owner: [Role accountable for this priority]

[Name the priority in a few words]

[Why this priority matters now, in one or two sentences]

Outcome	Baseline	Target	Due
[Metric you will track]	[Where it is today]	[Where it should be]	[When]
[Metric you will track]	[Where it is today]	[Where it should be]	[When]
[Metric you will track]	[Where it is today]	[Where it should be]	[When]

Initiatives

[A project or action that moves the outcomes above]
[A project or action that moves the outcomes above]
[A project or action that moves the outcomes above]
[A project or action that moves the outcomes above]

Priority 3Owner: [Role accountable for this priority]

[Name the priority in a few words]

[Why this priority matters now, in one or two sentences]

Outcome	Baseline	Target	Due
[Metric you will track]	[Where it is today]	[Where it should be]	[When]
[Metric you will track]	[Where it is today]	[Where it should be]	[When]
[Metric you will track]	[Where it is today]	[Where it should be]	[When]

Initiatives

[A project or action that moves the outcomes above]
[A project or action that moves the outcomes above]
[A project or action that moves the outcomes above]
[A project or action that moves the outcomes above]

Priority 4Owner: [Role accountable for this priority]

[Name the priority in a few words]

[Why this priority matters now, in one or two sentences]

Outcome	Baseline	Target	Due
[Metric you will track]	[Where it is today]	[Where it should be]	[When]
[Metric you will track]	[Where it is today]	[Where it should be]	[When]

Initiatives

[A project or action that moves the outcomes above]
[A project or action that moves the outcomes above]
[A project or action that moves the outcomes above]
[A project or action that moves the outcomes above]

How to adapt it

01
List the five or six decisions where better data would be worth the most money, and put a rough value on each. Build the strategy around the top three.
02
For each decision, write down the data it needs and the specific gap today. This table becomes the case for every investment that follows.
03
Assign data owners in the business before choosing technology. A new platform loaded with unowned data reproduces the old problems faster.
04
Measure quality at the source with a few checks per dataset, and publish the results to the owner. Start with the master data everything else depends on.
05
Keep the first platform scope small: the tables needed for the top decisions, modeled once, with certified definitions.
06
Plan training by audience. The people who act on data in stores, branches or plants need something different from analysts.

How to run it

A plan only works if it is reviewed on a rhythm. This is the cadence the example assumes.

Check-in	Outcome owners update progress and confidence weekly.
Review	Priority owners review outcomes and unblock initiatives monthly.
Reset	Leadership revisits priorities and targets quarterly.

Tability keeps this cadence for you. Each priority becomes an objective, each outcome a key result with its baseline and target, and each initiative a tracked task. Owners get a check-in reminder and the plan shows what is on track.

Track this plan in Tability

Still comparing tools? See the 10 best strategy software tools.

Common mistakes

Starting with a platform purchase and looking for uses afterward.
Placing data ownership in IT, which leaves the people who create bad data with no reason to fix it.
Building advanced models on master data nobody has checked, then losing users' trust with the first wrong recommendation.
Migrating every old report to the new platform, which carries the conflicting definitions across with them.

Questions, answered.

What should a data strategy template include?

It should include the business decisions data must support and the gap for each, an assessment of current capabilities, the governance model, the architecture and platforms, and the skills and literacy plan. It should end with measurable outcomes tied to business results, such as waste, margin or time saved, not only to data quality scores.

What is the difference between a data strategy and data governance?

Data governance is one part of a data strategy. The strategy covers what data is for, which decisions it will improve, the platforms and the people. Governance is the set of roles, standards and controls that keep data accurate, secure and consistently defined. A governance program without a strategy tends to produce policies nobody uses.

Who should own data in an organization?

Business leaders should own the data their teams create and depend on, with a chief data officer or equivalent setting standards and running the platforms. For example, merchandising owns item and price data and finance owns margin definitions. IT is the custodian of the systems, not the owner of the content.

How long does a data strategy take to deliver results?

Agreed definitions and a first set of trusted reports can arrive within six to nine months. Results that depend on fixing master data and building forecasts usually take 18 to 36 months to reach every site. Plan a pilot in the first year that proves value on one decision, so funding for the later years is easier to defend.

How do you measure the success of a data strategy?

Measure the business results of the decisions the strategy set out to improve, such as shrink, availability or promotion margin. Support these with a small number of data measures: quality of key datasets, share of reports using certified definitions, and how many people use the tools each week. Report both to the executive team quarterly.

Data strategy template

Get the Word file

What this data strategy template includes

Data strategy example

Context

Business questions

Current state

Governance

Architecture and platforms

Skills and literacy

Agree one version of sales, margin and shrink

Fix item and stock data where it is created

Order fresh from a forecast, not from memory

Measure every promotion and price change

Blank data strategy template

Context

Business questions

Current state

Governance

Architecture and platforms

Skills and literacy

[Name the priority in a few words]

[Name the priority in a few words]

[Name the priority in a few words]

[Name the priority in a few words]

Get the Word file

How to adapt it

How to run it

Common mistakes

Questions, answered.

What this data strategy template includes

Data strategy example

Agree one version of sales, margin and shrink

Fix item and stock data where it is created

Order fresh from a forecast, not from memory

Measure every promotion and price change

Blank data strategy template

[Name the priority in a few words]

[Name the priority in a few words]

[Name the priority in a few words]

[Name the priority in a few words]

How to adapt it

How to run it

Common mistakes

Questions, answered.

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