Design your data strategy in six steps

With the rise of AI, a clear and actionable data strategy has never been more important.

Nuance is necessary; each AI use case has its own data needs. Getting the most from generative AI, for example, requires well-managed unstructured data.

Whatever your goal, a successful data strategy begins with making sense of your data landscape: your data assets, data infrastructure and enterprise data usage. You’ll also need to imbue a culture of data literacy, data democratization and AI know-how that empowers teams throughout your organization. 

The following six-part framework will help you design a data strategy to cultivate AI that scales across your business and helps you achieve your business goals.

Key stakeholder questions

1. Which business initiatives should be prioritized?
2. Are there data issues that could slow AI adoption?
3. What challenges threaten priority objectives?
4. Which areas can be improved with better access to high-quality data?
5. How is success measured in the organization?

“As you meet with your stakeholders, identify data needs across the business to show the value of data as a strategic asset.”
 

Jo Ramos
Data and AI solution Engineering Leader

IBM 

Identify the most compelling use cases
 
Aligning the right data with your business objectives “starts and ends with the question, what business problem are you trying to tackle?” says Tony Giordano, who leads data strategy, consulting and transformation engagements for IBM.
 
As you search for a compelling use case, keep clear, attainable outcomes in mind that align with business priorities.¹

Protect your investments


Leverage your existing infrastructure, technology and skills to determine where and how your data can help achieve business outcomes. When you truly understand your data, you can pinpoint outdated data architecture, take better advantage of funded initiatives and identify areas for improvement.

Identify barriers and gaps

Once you’ve defined your goals and gained leadership support, identify the barriers to building a true data-first experience. Silos often prevent data integration, data management and workflow efficiency. In fact, 81% of IT leaders say that data silos are hindering their digital transformation efforts.²

Ensure easy data access

Users should have seamless access to the data that yields great outcomes. They shouldn’t be worrying about where data lives or whether it’s governed and compliant.  

Apply design thinking to data strategy


A design thinking approach helps uncover organizational pain points, which brings strategic value across multiple use cases, lines of business and individual teams. It helps generate achievable resolutions through a continuous cycle of observation, reflection and iteration.

Evaluate talent and skills


Make sure your organization provides ongoing training to keep up with AI and IT advancements. An IBM IBV survey found that 85% of leading CDOs are expanding training, 77% are reskilling internal staff, and 70% are acquiring new talent to increase data literacy across their organizations.³

Prioritize governance


Staying on top of critical, regulated data elements is essential to running your systems without duplication errors, unreliable searches or privacy breaches. Consider who owns, manages and defines your data policies, and whether that governance affects security, privacy or compliance. Ensure that the appropriate parties have the necessary decision-making rights, an accountability framework and external resources to manage data effectively.

Define your data’s target state


“Many data environments are outdated and rarely have the flexibility to evolve in today’s digital environment,” says Giordano. A modern data architecture needs to be managed, governed and secured to ensure consistent data quality. It requires the flexibility to evolve alongside your digital channels.


Measure progress toward your goals


Data leaders are expected to drive long-term transformation, but are often measured by short-term business results. A survey by AWS found that 74% of CDOs say their success is judged by business outcomes or a mix of business and technology goals, while only 3% say their success is measured solely on technical achievements.⁴

“From a board of directors that expects ‘magic’ early in a CDO’s tenure, to CEOs that think a large enterprise can become completely data driven in six months or less, pressures on CDOs are as never before.”
 

IBV CDO Study (2023)³

Focus on your data objectives. Utilize insights from data users to find the best ways to accelerate business value through AI. 


Outline a data governance policy 


A robust governance framework will foster quality, privacy and security. A metadata and governance layer will improve visibility and collaboration across your organization, no matter where your data is stored. In addition, your data governance policy will guide how data is managed, secured and kept private, while also helping you track how AI supports compliance efforts. 

Identify data advocates 


Find people passionate about using data to improve their work. These success partners can help standardize data practices and promote good data habits. Look for advocates in data teams, such as data engineers, architects or scientists building AI models. Business leaders whose teams rely on data analysis are also great candidates. 

Set your sprint cycles

To embed a data and AI strategy, start by setting clear, achievable goals. Assemble a cross-functional team around these objectives and run short sprint cycles with actionable milestones to demonstrate progress. Ensure the C-suite, technology teams and business users share the same vision.

Collect small wins


Focus on simple, impactful use cases to quickly show the value of your data and AI investments. Avoid tackling the hardest problems at first. Invest in pilot programs during the initial stages of AI adoption to gain the experience you need for larger deliverables down the line.

Create a central data catalog


A central catalog stores and shares data in both original and curated forms, making it easier to access and use. It tracks how data is being consumed and what insights are emerging, enabling users to make informed decisions across the organization. 

Empower data consumers to adopt


Encourage enterprise-wide adoption of the new data framework. This improves communication, streamlines workflows, optimizes security, and unlocks new business models, market opportunities and operational efficiencies.

Show and tell


Your use cases are a powerful way to demonstrate impact. As an insightful article from Harvard Business Review points out, CDOs and AI leaders see greater success when they “make data everyone’s business.”⁵

Use cases can span data science, operational analytics, digital transformation, business intelligence, and gen AI initiatives, and more, giving multiple teams the opportunity to leverage data for real business impact. 

Hire and reskill talent


Closing the skills gap means looking beyond traditional hiring and training strategies. As companies scramble to meet their talent needs, many are adjusting their education and experience requirements just to fill roles. When training and hiring are not enough, consider how AI and automation can help address labor shortages and skill mismatches.

Build strong partnerships

Your role as a data leader is to help your organization make wise decisions about data collection, management and usage. As you build and strengthen partnerships at every level, be open to feedback and collaboration. A data-first culture thrives when people are motivated to learn, take ownership and embrace new roles.

Make data your differentiator

As you enhance existing technologies and introduce new solutions to simplify data access, remember you’re doing more than creating efficiencies and driving new insights. You’re actually building a culture that’s passionate about using data to its fullest potential.