Ecommerce businesses already collect large amounts of data through their enterprise resource planning (ERP) systems and other tools. A common challenge is turning that data into decisions. Some companies face data quality issues like duplicate or incomplete records, while others keep data siloed between systems, making it harder to use across the business
According to IBM, 83% of businesses say data silos hinder innovation, limit their ability to produce real-time analytics, and hinder effective decision-making. Only 26% of respondents said their current data makes their companies ready to implement new technologies like AI.
A data analytics strategy can help teams move from flawed data management to more structured data initiatives that support retail innovation and automation.
This article explains the key components of an effective data analytics strategy and the milestones to include in a roadmap.
What is a data analytics strategy?
A data analytics strategy is how businesses interpret historical data, including transactions and customer segments, to support decisions that create business value.
A comprehensive data strategy covers all the bases. Data and analytics leaders need a strategy that includes these components:
- People: Who owns each part of the data strategy
- Process: How data moves from one system to another
- Technology: Which tools and dashboards manage, model, and display the data
- Governance: Security rules and process protocols for managing the data
- Measurement: Defining key performance indicators (KPIs) and processes for measuring analytics performance
A data management strategy is not limited to dashboards or data platforms. Those tools support and help process raw data, but they’re not the full scope of the strategy.
Who owns the data analytics strategy?
The ownership question is becoming more strategic as businesses add AI to analytics workflows. Gartner’s 2025 “CDAO Agenda Survey” found that 70% of chief data and analytics officers are primarily responsible for building their organization’s AI strategy and operating model, while 36% now report to the CEO, up from 21% in 2024.
The data leader or executive team should set the direction and define business objectives for data analytics. Data engineers can manage the systems that support these objectives. Then, business teams across the company can use analytics to make decisions.
Core elements of a data analytics strategy
Effective data management requires refinement: taking raw data and distilling it into consistent, useful information that drives business decisions. That process takes more than one step, and depends on several connected components.
Business alignment and use-case prioritization
In IBM’s survey of chief data officers, 78% said using their own proprietary data would help their organization stand out. Without clear and specific use cases, however, it can be hard to find measurable business value in the data. Transform proprietary data into data-driven insights by knowing which variables to look for.
A data strategy starts by working backward from business goals. First, define which business objectives the data support. Specific, named goals help determine where to look for patterns in the data.
Tie the company’s data strategy directly to specific business outcomes:
- Revenue
- Retention
- Conversion
- Inventory
- Forecasting
- Margin
Those goals inform which use cases to prioritize. Look for questions the data might be able to answer. For example:
- Revenue/retention: Which marketing channels are driving the highest LTV customers right now?
- Inventory/forecasting: Which products are most likely to go out of stock next month?
- Conversion: Which segments are most likely to convert with a promotional discount?
Working backward from the goals clarifies the use cases. For each one, define the following:
- The decision being made (“What are we prioritizing?”)
- The metric used to evaluate it (“How do we evaluate it?”)
- The data sources required (“Where is this data in our existing systems?”)
- The owner responsible for acting on it (“Which role will lead this specific use case?”)
Data architecture, integration, and access
Analytics initiatives are easier when relevant data is accessible in one place. In practice, data often sits across multiple systems: ecommerce tools, customer relationship management (CRM), ERP, and inventory.
The cost of weak architecture is now showing up in AI projects. In Fivetran’s 2025 “AI and Data Readiness Survey”, 42% of enterprises said more than half of their AI projects had failed, underperformed, or been delayed because of data-readiness issues. That helps explain why 65% said data-integration tools were a primary investment priority for enabling AI.
A data analytics strategy should make existing data assets available to the right teams. This requires some decisions from leadership:
- How should the data move? For example, do two data analytics tools need access to the same data, or is it better if some data remains separate for security reasons?
- How often does the data update? Options include real-time dashboards, hourly or daily updates, or delays of several days if speed isn’t a priority.
- Who can access which data? Can business users self-serve data, or will they have to rely on data published by another team?
A data strategy is less useful if data is hard to use, outdated, or disconnected from the team that needs it.
Governance, privacy, and trust
AI readiness often looks stronger on paper than it proves in practice. The "2026 State of Data Integrity and AI Readiness" report from Precisely and Drexel University’s LeBow College of Business found that 87% of leaders say they’re ready for AI, but 43% say data readiness is their biggest obstacle.
Before teams use a data strategy to generate actionable insights, they need to ensure unauthorized users can’t access sensitive data. This requires data governance rules. By defining clear boundaries, team members can “self-service” data without creating security or process risks.
That starts with operating rules. Establish the following:
- Definitions: Agree on key metrics and what they mean. For example, if the goal is revenue, a business should define whether a returned item’s fee is subtracted from total revenue.
- Ownership: Agree on who is responsible for which data, including any responsibilities for sharing or reporting data across systems.
- Permissions: Agree on which roles can access which data.
- Data quality: Agree on where data needs improvement. For example, identifying missing fields, data duplicates, or outdated data.
Data governance rules can reduce issues like conflicting metrics, resulting in more reliable data. With more reliable data, teams can conduct analyses that connect more directly to business insights.
Talent, operating model, and data literacy
Successful strategy depends on execution. People and operating processes are important for running a data analytics strategy and using it well. This is referred to as data literacy.
Data strategy within a team might look something like this:
- Leadership defines the direction and overall goals of the data analytics strategy.
- Data teams build or manage the data infrastructure necessary to monitor progress.
- Business teams have the data integration and permissions necessary to use it.
This is where technical data analysis connects with business decision-making.
Many companies use a hybrid approach here, referred to as “data democratization.” They share the standards established above, then delegate ownership over the relevant data to each team.
According to AWS and the Harvard Business Review, 42% of business respondents say they are investing in talent development and team restructuring. Business leaders can do the same as part of a data analytics strategy.
Roadmap, KPIs, and measurement
Teams, even as they implement all of the steps above, should measure whether the data analytics strategy is generating real business value. A roadmap can help teams implement the strategy in stages.
Measurement is the weak link. Gartner’s 2025 CDAO survey found that 30% of CDAOs cite measuring the impact of data, analytics, and AI on business outcomes as their top challenge. Only 22% of surveyed organizations had defined, tracked, and communicated business-impact metrics for the majority of their data and analytics use cases.
Get clear about how the team will measure the success of its data analytics strategy. This is different from measuring the success of the business goals. The data strategy is working according to success in these categories::
- Data health: Success here is defined by accurate, up-to-date data, delivered on a consistent basis.
- Adoption:Teams are using the data at a high rate.
- Business outcomes: Key areas flagged in the beginning of the process are showing improvement.
Teams don’t need to implement this all in one step. The roadmap for data might look something like this:
- Fix the basics, like cleaning data, integrating data between systems, and establishing governance.
- Apply relevant data to real use cases that match specific business goals.
- Once teams vet specific use cases, expand the analytics strategy across the business.
How to build a data analytics strategy
Assess the current state of data analytics as it relates to business objectives
Data management depends on the existing data quality. Here’s a checklist for turning a data analytics assessment into a specific step:
- Identify which systems manage existing data, including ecommerce, CRM, and ERP tools.
- Write down where this data currently lives, and whether it has quality issues like duplicates or missing fields.
- Let teams point out what’s not working, such as slow reports or too much manual logging work.
- Identify areas where data strategy or ownership is vague.
- Call out which business objectives are difficult to achieve because of these problems.
Prioritize high-impact use cases
The next step is prioritizing use cases. Teams choose which use cases can drive business impact. Feasibility is an important consideration. The data needed to support each use case should already exist.
For example, let’s say the goal is to improve customer retention. The company might already track purchase history and customer segments, meaning the data might exist to forecast which customers are most likely to churn. This can then drive specific business decisions like creating new campaigns targeted to these customers.
Without those goals in hand, and that data strategy, it will be more difficult to identify those use cases.
Design the architecture and governance layer
Once the use case is clear, teams should have a stronger sense of how the data strategy supports it.
Refer to the previous section on governance. Understand which systems and tools will have to share data to drive meaningful results. Write governance rules that identify the “source of truth” or the system considered authoritative. Then assign who’s responsible for maintaining the quality of that data.
Launch quick wins, then scale
Many data strategy teams begin by testing their approach through focused pilots. One small pilot tied to a specific use case, enabled by the existing data, can help test whether the approach is effective.
It can also identify any problems in the way data moves between teams and disconnected systems. Once that foundation’s in place, it’s possible to expand it to additional teams using the same style of governance.
Data analytics strategy FAQ
How does a data analytics strategy support business initiatives?
A data analytics strategy connects data to specific use cases and end goals: revenue growth, retention, operational efficiency, and more. It can help teams prioritize these initiatives by identifying which decisions matter most, then provide signals for what to do next.
What role does data architecture play in analytics strategies?
Data architecture refers to how data is collected, connected, and stored across systems within a business. Analytics teams need this. When architecture is well defined, teams with the right permissions can look up data, run reports, and make recommendations that support business decisions.
How can a business leader tell if an analytics strategy is ready?
A mature analytics strategy produces consistent, trusted data, giving teams the information they need to make new decisions with confidence. Analytics capabilities show up as timely reports, clearly defined metrics, and clear alignment on who is responsible for tracking which data.
What is data democratization within a data analytics strategy?
Data democratization is a data strategy that makes a company’s data accessible to the specific teams who need it. This is a way to avoid the “silo” problem. By investing in skilled teams and giving them self-service tools that can generate custom reports, businesses make it easier for teams to explore insights in the data without going through rigid processes.
