5 Steps to Build an Enterprise Data Strategy, Straight From an Expert

Data is the lifeblood of the modern enterprise. But without a clear strategy to harness it, data can be more of a liability than an asset. In fact, less than 30% of organizations say they have a well-articulated data strategy that is widely understood across the business, according to a 2022 NewVantage Partners survey.

As a data strategy consultant, I‘ve seen firsthand how difficult it can be for companies to wrangle their data into a strategic advantage. The good news is, by following a proven framework, any organization can develop a robust data strategy that drives real business value.

In this guide, I‘ll walk you through the five essential steps to build an effective enterprise data strategy, complete with expert tips, real-world examples, and actionable advice you can implement today.

Step 1: Assess Your Data Maturity Level

The first step in creating your data strategy is to honestly assess where your organization stands in terms of data maturity. This will be your baseline to identify gaps and set realistic goals.

I recommend using Gartner‘s Data and Analytics Maturity Model, which evaluates capabilities across five key dimensions:

Dimension Basic Opportunistic Systematic Differentiating Transformational
Strategy Undefined Developing Defined Quantified Optimized
People Unaware Aware Engaged Empowered Innovating
Governance Ad Hoc Defined Managed Enhanced Optimized
Technology Siloed Integrated Scaled Automated Continuously Improving
Processes Manual Repeatable Standardized Measured Optimized

Source: Gartner

To determine your maturity level:

  1. Get input from stakeholders across business and IT
  2. Evaluate your current state for each dimension
  3. Identify your desired future state
  4. Analyze the gaps between current and future state
  5. Prioritize areas for improvement

For example, say you‘re a midsize retailer and determine your current maturity to be:

  • Strategy: Opportunistic
  • People: Aware
  • Governance: Ad Hoc
  • Technology: Siloed
  • Processes: Manual

You‘ve defined some data standards but they aren‘t enforced. You have pockets of analytical talent but lack data-driven decision making. Data still lives in silos and governance is minimal.

Compare that to your goal state:

  • Strategy: Systematic
  • People: Engaged
  • Governance: Managed
  • Technology: Integrated
  • Processes: Standardized

This reveals you have work to do to develop your data ops processes, better integrate data, improve literacy, and formalize governance. Prioritize these gaps in your data strategy.

Step 2: Understand Internal and External Factors

With a realistic view of your data maturity, turn your focus outward. Several factors will shape your data strategy, including:

  • Industry – Your industry‘s competitive landscape and regulatory requirements have a huge impact. Industries like healthcare and financial services have strict data compliance needs. Retail and tech are more focused on personalization and innovation. Understand industry best practices and evolve them.

  • Business Model – A B2C company has different data needs than a B2B company. Product-led growth companies rely on product usage data. Services firms need 360° client views. Align your data strategy to your business model and growth levers.

  • Culture – Is your company‘s culture data-driven or intuition-led today? Centralized or federated? Risk-averse or agile? Your data strategy must work within your current culture while enabling desired behaviors. If you‘re a 100-year-old incumbent trying to be more like a startup, your data strategy will be a key enabler.

  • Technology – What does your current data systems landscape look like and what are your future technology goals? An effective data strategy aligns with your overall enterprise architecture and cloud strategy. You may need to modernize legacy tech while accounting for new investments.

  • Talent – Do you have the right data talent and skillsets you need? What gaps must be filled? How can you upskill current employees? Data literacy and fluency are must-haves for a data-driven org. A robust data strategy considers both tech and people.

  • Strategic Priorities – Ultimately, your data strategy must connect to business priorities. If M&A is on the roadmap, master data management should be a focus area. Launching a new digital business? Invest in data mesh to support it.

For instance, imagine you‘re a global consumer packaged goods brand. You‘re in a highly competitive industry being disrupted by digitally native brands. Consumers expect personalized experiences. Retailers demand granular sales data.

At the same time, your legacy ERP and point-of-sale systems can‘t deliver real-time insights. Marketing and product data live in silos. Data science talent is scarce. But recent strategic plans call for boosting ecommerce to 30% of sales.

All these factors must be accounted for in your data strategy. You‘ll need to modernize data ingestion from store to HQ, integrate marketing and sales data to enable personalization, and partner with HR on analytics upskilling programs, for starters.

Step 3: Envision Your Future Data State

Once you‘re grounded in internal and external realities, paint a picture of your ideal future data state 3-5 years down the road. Consider questions like:

  • What key business initiatives will data enable?
  • How will our culture evolve to be more data-driven?
  • What data products will we build to drive growth?
  • How will data be democratized for self-service insights?
  • What new data talent and skills will we acquire?
  • How will we measure the value of our data?

For instance, your future state may include:

  • A unified customer data platform powering 1:1 marketing
  • Real-time sales and inventory analytics for dynamic pricing
  • AI-enabled predictive quality control in manufacturing
  • A self-service insights hub for >80% of business users
  • Dedicated analytics centers of excellence for each BU
  • Value measurement framework capturing ROI

Engage a diverse group of stakeholders in envisioning the future – senior leaders, lines of business, IT, legal, HR. Document the vision and socialize it broadly to secure buy-in.

Step 4: Design Your Data Strategy Roadmap

With your current and future state defined, it‘s time to build the roadmap to get there. Break your strategy down into workstreams such as:

  1. Data Architecture – Define end state data architecture and phased modernization plan. To support the future vision, you may need to re-platform legacy data warehouses to the cloud, shift to a data mesh architecture, establish event streaming, and/or implement a data fabric layer.

  2. Data Management – Design your target operating model and data lifecycle management approach. Identify tooling required for data cataloging, metadata management, master data management, data quality, and more.

  3. Data Governance – Ensure your strategy balances offensive (value-generating) and defensive (risk-mitigating) data governance practices. Offensive practices include data democratization, self-service, and monetization. Defensive include privacy, security, compliance, and quality. Design governance that is people-centric.

  4. Data Literacy – Define personas (data engineers, scientists, analysts, etc.) and skill requirements. Build an L&D program that upskills the org on data. Don‘t forget soft skills like data storytelling. Consider a data literacy platform. Measure and incentivize data-driven behaviors.

  5. Data Products – Identify and prioritize the data products and use cases needed to activate your strategy. Build templates for ideation, MVP development, and ongoing product iteration. Establish product managers who ensure the usefulness and adoption of data products.

  6. Change Management – Develop a change management plan to drive the cultural and process shifts needed for data transformation. Communicate the "why," partner with HR, and facilitate workshops on new ways of working.

Within each workstream, identify initiatives and sequence them across a 3+ year roadmap. Consider effort, impact, and dependencies to prioritize. Assign owners and KPIs for each initiative.

Step 5: Execute, Iterate and Evangelize

Treat your data strategy as a living, breathing plan. Regularly reassess based on progress, value delivered, and feedback. Course correct when needed. And relentlessly evangelize the strategy.

Best-in-class data orgs report on strategy progress to leadership monthly. They build data strategy review into quarterly business planning. They have a steering committee of data champions advocating for the strategy.

Quick wins are critical to keeping momentum. Celebrate milestones and showcase the value being created. Identify enthusiastic early adopters and share their success stories. Build a flywheel of ever-growing data believers and beneficiaries.

Remember, it‘s not a sprint, it‘s a marathon. Only 24% of companies say they have created a data-driven organization in 2023, according to NewVantage Partners. So if you‘re feeling behind, you‘re in good company.

But the time to act is now. By 2026, 65% of global GDP will be digitalized, according to IDC. Data will be the currency of the digital economy. Your data strategy is your plan to harness that currency for growth – one smart, deliberate step at a time.

Is your data strategy future-ready? Apply this framework to find out – and pave your path to data domination.