Build a Robust and Comprehensive Data Strategy

Build a business-first, not technology-first, data strategy.

Analyst perspective

Build a business-first, technology-second strategy to maximize the value of your data.

Ryan Brunet

Principal Research Director
Info-Tech Research Group

Steve Willis

Principal Research Director
Info-Tech Research Group

Data strategies too often get mired in technical details or remain overly simplistic, failing to connect with the real goals of the organization. A successful strategy should resonate with executive leadership, emphasizing how data initiatives directly support the organization’s mission and critical goals. By clearly articulating the value of data in business terms, data leaders can secure executive sponsorship, ensuring alignment and resources for impactful initiatives.

The data leader must function as the ultimate translator, playing a critical role in aligning data initiatives with the strategic priorities of the organization. This is not just about applying data to meet current needs but about envisioning how data can fuel future growth and innovation through its vast array of applications. By empowering the organization to shape the data journey and creating strategies that are driven by business needs, organizations can ensure that their data initiatives yield meaningful, long-term value.

The future is bright for data leaders who embrace this transformative journey. With the right alignment, data becomes more than a byproduct — it becomes a renewable resource fueling sustainable growth and strategic advantage. Embrace the change and lead with confidence, because the impact of a well-executed data strategy can elevate the entire organization.

Executive summary

Info-Tech Insight: Data strategies often focus on aggregating use cases, but this rarely drives progress toward corporate goals. A strategy should be focused on how data will be used to achieve the corporate mission, not just loosely linked to it. In the rush to prove value, we must ensure that the value aligns with and supports the organization's objectives. A use-case-driven strategy is reactive; a business-focused strategy shapes use cases to fulfill broader business goals.

Your challenge

Data leaders will be required to develop new strategies to meet exponentially growing demand for data and analytical capabilities or risk being left behind.

• The demand for data, and the increasing speed at which it can be processed and made available, will continue to grow exponentially.
• With the advancement of AI technologies, organizations can now tackle more complex problems and build systems that will take advantage of real-time data from multiple sources. Organizations will start collecting new types of data (e.g. data from IoT devices, data generated by physical processes, synthetic data generated to support development of new products and services). These data sources will allow organizations to innovate and solve new, complex problems.
• This exponentially growing demand for data increases the pressure on technology and data leaders to deliver the processes, capabilities, and technologies to service that demand; however, with only a few data leaders considered genuine business value generators by business executives, most of these leaders will be required to develop new tactics to meet these expectations or risk being left behind.

“It is predicted that by the end 2025 around 463 exabytes of data will be generated daily worldwide.”

Source: Edge Delta, 2024

“72% of leading organizations note that managing data is already one of the top challenges preventing them from scaling AI use cases.”

Source: “The Data Dividend," McKinsey, 2023

“OpenAI’s ChatGPT-3 was trained on around 570GB of data sets. Your AI use cases will demand significant quantities of quality data.”

Source: BBC Science Focus, 2023

Common obstacles

These barriers make this challenge difficult to address for many organizations:

• Wasting time and resources on managing all data assets equally and missing business opportunities due to a lack of understanding of the most valuable data assets as the actual differentiator in value generation.
• Inability of data and analytics team to meet business needs to innovate, grow, create differentiators, and stay ahead of the competition. The business views data, analytics, and AI only as a business enabler rather than as a product and driver in achieving the greater outcomes, for example, shaping products and services and driving new business value. IT needs to work with the business to understand how data needs to be managed to meet the business’ need to innovate and grow.
• Organizations are struggling to understand the impact that new AI technologies have across the organization, including the impact on business strategy, data, IT, and AI strategy. Organizations are wasting time and missing opportunities due to the lack of a clear framework that should be used to assess how new technologies should shape the business model.

49.1%

Less than half of organizations report that they are currently managing data as a business asset.

Source: Wavestone, 2024

89%

While 99% of respondents recognize the positive impacts generative AI can have on their organization, 89% of respondents report that their use of generative AI is being slowed.

Source: Elastic, “Global Generative AI Adoption Study,” 2024

The pains are felt across the entire organization

Organizations are not prepared for the exponential speed and scale of AI innovation and the increasing amount of real-time data driven by AI growth, which makes data management and pipeline provisioning more complex and difficult to execute.

Business and IT transformations fail due to a lack of business leadership. Organizations need to build an urgency and trust with business leaders. In most organizations, business stakeholders do not lead data and AI innovation and transformation, which results in the failure of digital transformation efforts. This leads to wasted money, time, and effort.

• The greatest challenges to becoming data-driven are the function of culture, people, process change, and organizational alignment (77.6%) rather than technology limitations (23.4%) (Wavestone, 2024).

69%

69% of respondents stated that employees in their organization struggle to access the data they need when they need it.

Source: Elastic, 2024

Info-Tech’s methodology to Build a Robust and Comprehensive Data Strategy

Insight summary

Overarching insight

Data strategies often focus on aggregating use cases, but this rarely drives progress toward corporate goals. A strategy should be focused on how data will be used to achieve the corporate mission, not just loosely linked to it. In the rush to prove value, we must ensure that the value aligns with and supports the organization's objectives. A use case-driven strategy is reactive; a business-focused strategy shapes use cases to fulfill broader business goals.

Data Isn’t Special

Data, in isolation, is simply raw information with no inherent value. It only becomes meaningful when it is aligned with and actively drives the organization’s strategic goals. By embedding data into decision-making processes, organizations can turn it into actionable insights that lead to measurable outcomes and accelerate your corporate strategic objectives. It is only a strategic asset if you know how to apply the right way.

Do Your Homework

Failing to engage and gather input from key line-of-business stakeholders when developing a data strategy leads to misalignment with business needs and objectives, resulting in a strategy that does not address the actual pain points or priorities of different teams. A lack of stakeholder input often results in low adoption rates, as the tools and strategies developed do not align with the day-to-day realities of the business. Not getting the right people in the room creates an unnecessary and avoidable opportunity cost.

Earn Your Right

Your data strategy is only the starting point of your data and analytics transformation journey. You earn the right to execute and secure funding by clearly articulating the value that your data initiatives will drive. You accomplish this by being the ultimate translator, balancing between commercial guru and technical savant. Marrying the most pressing opportunities with the art of the possible and clearly articulating the value proposition in business language will earn you your right to bring to your strategy to life.

Tactical insight

Be thorough and thoughtful in finding the right cross section of stakeholders to provide input into your strategy development. Missing key stakeholders can derail your effort.

Tactical insight

Don’t overpromise when defining the value horizons for your initiatives; they only need to be directionally correct. You need to provide a rough guideline to secure your funding and support.

Blueprint deliverables

Each step of this blueprint is accompanied by supporting deliverables to help you accomplish your goals:

Business Context Interview Guide

Understand the strategic objectives of your organization so that you can align the right data initiatives.

Data Initiatives & Strategy Ideation Primer

Understand the art of the possible as you ideate through your data initiatives.

Data Strategy Stakeholder Interview Guide and Findings

Understand the line-of-business imperatives for data and analytics applications in your organization.

Data Value Mapping Tool

Establish and rationalize your data initiatives and tactics to support your organizational goals.

Key deliverable

Data Strategy C-Suite Presentation Template

A highly visual and compelling presentation template that enables easy customization and executive-facing content.

Measure the value of this blueprint

Leverage this blueprint’s approach to ensure your data strategy and initiatives align and support your organization’s strategic direction.

Case study

Inside IKEA’s data-led digital transformation strategy

IKEA

INDUSTRY

Retail

SOURCE

Harvard Business Review

Challenge

IKEA, a leading global home furnishings retailer, faced the challenge of adapting its traditional, store-centric business model to meet the demands of a digital world. With shifting customer preferences and the rapid rise of ecommerce, IKEA needed to integrate digital capabilities across its operations while preserving its brand identity and values. The company grappled with managing unprecedented ecommerce growth, aligning its physical stores with digital operations, and creating a seamless omnichannel experience for customers. Additionally, ensuring robust data governance and integration across supply chain, inventory, and customer service functions was crucial to support this transformation.

Solution

To address these challenges, IKEA implemented a comprehensive data-driven transformation that integrated AI and advanced analytics. The company developed a unified data ecosystem across various functions to ensure data accuracy and accessibility, laying a foundation for effective data governance. By embedding AI-driven insights into decision-making, IKEA improved forecasting, inventory management, and personalized engagement, supporting its digital and operational goals. To enhance customer experience, IKEA introduced tools like the “Shop & Go” app, allowing customers to scan and purchase items in-store, and virtual room visualization, enabling customers to view products in their spaces. This transformation aligned with IKEA’s values, fostering cross-team collaboration and developing new digital skills essential for the future.

Results

IKEA’s data strategy and digital transformation led to measurable improvements across various business metrics. Ecommerce sales tripled within three years, with online revenue growing from 7% to 31% of total sales. Operational efficiency increased significantly, as stores were transformed into fulfillment centers, which reduced time-to-insight by 30% and optimized supply chain processes. Through its Customer Data Promise, IKEA also improved data transparency, leading to increased trust and engagement among customers. By aligning its data strategy with its corporate goals and leveraging advanced analytics, IKEA successfully transformed into a data-driven retailer, ready to meet the evolving needs of customers in a digital-first world.

Info-Tech offers various levels of support to best suit your needs

DIY Toolkit

"Our team has already made this critical project a priority, and we have the time and capability, but some guidance along the way would be helpful."

Guided Implementation

"Our team knows that we need to fix a process, but we need assistance to determine where to focus. Some check-ins along the way would help keep us on track."

Workshop

"We need to hit the ground running and get this project kicked off immediately. Our team has the ability to take this over once we get a framework and strategy in place."

Executive & Technical Counseling

"Our team and processes are maturing; however, to expedite the journey we'll need a seasoned practitioner to coach and validate approaches, deliverables, and opportunities."

Consulting

"Our team does not have the time or the knowledge to take this project on. We need assistance through the entirety of this project."

Diagnostics and consistent frameworks are used throughout all five options.

Guided Implementation

What does a typical GI on this topic look like?

A Guided Implementation (GI) is a series of calls with an Info-Tech analyst to help implement our best practices in your organization.

A typical GI is 8 to 12 calls over the course of 2 to 3 months.

Data Strategy – Workshop Overview

Contact your account representative for more information.

workshops@infotech.com
1-888-670-8889

What is a data strategy and why is it needed?

• Your data strategy is the vehicle for ensuring data is poised to support your organization’s strategic objectives.
• For any CDO or equivalent data leader, a robust and comprehensive data strategy is the number one tool in your toolkit for generating measurable business value from data.
• The data strategy will serve as the mechanism for making high-quality, trusted, and well-governed data readily available and accessible to deliver analytical solutions to your business in support of your highest value objectives.

What is driving the need to formulate or refresh your organization’s data strategy?

Who:

This research is designed for:

• Chief Data Officer (CDO) or equivalent
• Head of Data
• Chief Analytics Officer (CAO)
• Head of Digital Transformation
• CIO

Info-Tech Insight: Your data strategy is about determining how to drive value for your organization from your data. Your data strategy is not a data management plan. Data management is about how you execute against your strategy from an operational perspective. Your strategic plan is a prerequisite for your executional plan.

Your data isn’t special

Your data is only as good as your ability to derive value from it.

1. Data is not the new oil

   While the phrase "data is the new oil" is often used to emphasize the value of data in the digital economy, it is a flawed analogy. Unlike oil, which is a finite resource and depleted when used, data is an infinite, renewable resource that can be replicated, reused, and continuously generated without depletion. Oil has intrinsic value once extracted and refined, while data’s value is only realized when it is properly analyzed, contextualized, and leveraged for insights.

2. Data & analytics is a journey, not a destination

   Building a data strategy is a journey, not a destination. It requires organizations to continually evolve and adapt their data practices to ensure they are extracting maximum value from their data. Organizations must focus on building the capability to extract value from their data, whether through analytics, machine learning, or business intelligence tools. A successful data strategy is not just about technical capabilities; it’s about ensuring that data-driven insights are driving the business forward, supporting key goals, and enabling innovation.

3. Misalignment to the business is an opportunity cost

   Without a clear connection to business goals, data initiatives often fail to address the most pressing needs, limiting the strategy’s impact and relevance, presenting a substantial opportunity cost. This misalignment can result in data projects that consume valuable resources but fall short of driving meaningful outcomes, such as improved decision-making, operational efficiency, or customer insights. By not anchoring data efforts to specific business priorities, organizations miss the opportunity to leverage data fully as a strategic asset. As McKinsey advises, “Think business backwards, not data forward.”

Changes in business and technology are altering how organizations use and manage data

The world moves a lot faster today

Businesses of today operate in real time. To maintain a competitive edge, businesses must identify and respond quickly to opportunities and events.

To effectively do this businesses must have accurate and up-to-date data at their fingertips.

To support the new demands around data consumption, data velocity (the pace at which data is captured, organized, and analyzed) must also accelerate.

Data Management Implications

• Strong integration capabilities
• Intelligent and efficient systems
• Embedded data quality management
• Strong transparency into the history of data and its transformation

Studies and projections show a clear case of how data and its usage will grow and evolve.

Zettabyte Era

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