Expanding their focus from business intelligence alone, today’s data leaders strive for real-time decisioning and predictive models that help keep the organization ahead.
But to get there, your data strategy must define the right approach that makes sense of data, aligns to business strategy and builds AI and data analytics solutions that span the entire organization. You’ve got to empower people and define use cases that meet business needs, from traditional analytics and data science to operational analytics, digital, IoT sensor data, data visualization, AI initiatives and new product development. A clear data strategy is the essential first step to scaling AI.
But fully realizing the potential of data and AI requires creative decision-making, persuasive storytelling and cross functional support. This six-step framework—infused with insights from industry data leaders—will help you design and implement a data strategy that makes the most of your teams, talents and strengths as an organization.
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Connect your data and AI strategies with the business strategy
With any good data strategy, buy-in matters. To align business and data priorities, you need a clear understanding of the aims of the organization and senior leadership. Meeting with C-suite and business stakeholders is the first step in helping your organization reach its objectives by embracing data as a true competitive advantage. “It really all starts and ends with, what business problem are you trying to tackle?” says Dr. Rania Khalaf, Chief Information and Data Officer at Inari.
Eventually, connecting business and data strategies will mean merging the frameworks and guidelines that exist throughout departments for a single unified view of the data landscape that everyone (ideally) agrees to.
In fact according to Gartner®, CDOs who link data and analytics to prioritized, quantified business outcomes and metrics will be more successful than their peers who do not.¹
But as you get started, be realistic, says Srinivasan Sankar, Enterprise Data and Analytics Leader in the insurance industry. To help leadership see the strategic merits of data and AI initiatives, first make sure priorities are clarified and agreed upon as your collaborative, data-driven environment begins to take shape.
Identify the most compelling use cases
If you had better access to high quality data, where in your organization could you solve problems? “As you meet with stakeholders, identify data needs across multiple business objectives within or across lines of business to show the value of data as a strategic asset,” says Jo Ramos, who specializes in designing and implementing data strategies for IBM clients.
Scan the data landscape in every direction. What if you could lower supply chain costs by updating antiquated apps? Or maybe you could automate risk and compliance with AI for faster and improved insights? By better understanding the quality of your data and how it flows (or doesn’t) between areas of the organization like finance, sales and marketing, you get a more holistic view of operations and uncover new opportunities to grow your top line, increase profitability and reduce your risk.
Know the tools in your toolkit
Work hand in hand with IT to take your data strategy to the next level by leveraging existing infrastructure and technologies, as well as new and leading-edge technologies. Understanding your organization’s current tech ecosystem and strategies (and sub- and sub-sub-strategies, too) helps you plot a definitive and achievable course of action for using data, AI and applications to help achieve business outcomes. That knowledge is crucial—taking advantage of planned and funded initiatives helps ensure that you can deliver on your data strategy.
Familiarize yourself with your organization's digital transformation and AI strategies
Ramos points out that updating applications and innovating old systems doesn’t bring value unless you consider your company’s current data environment first. “A lot of organizations are talking about application modernization and bringing apps to the cloud, but they're losing sight of the data itself,” he says. “When it comes to integrating data and doing analytics, it's not about moving all the applications to the cloud—it’s figuring out how the data is going to live in new modern architecture.”
Unpack pain points to reveal blockers and gaps
Now that you know the end goals and have leaders on board (you do have them on board, right?), it’s time to look across your ecosystem and assess what’s working and what’s not. What are the barriers to building a true data-first experience?
Organizational silos often underlie challenges with data integration, data management and workflows. In fact, 82% of enterprises are inhibited by data silos.² To be their most productive, employees need self-service data and AI-powered apps or solutions with the right controls in place. Simply having access should never be the blocker.
You want your users to be able to access data and use it for great outcomes. They should not have to think about where it resides, whether there is governance applied or compliance of the metadata behind it. They should be able to use the data they need with confidence.
Examine data to uncover what you have and what you need
A data topology reveals the curves and contours of information much the way a topographic map shows mountains, hills and valleys. It can classify, cluster and manage data scenarios that embrace the competing priorities and needs of any organization. When you understand your company’s data topology, you can identify constraints and pinpoint outdated data architecture, such as technologies that don’t align with business strategy. You can also identify areas for logical upgrades, such as opportunities to adopt more robust AI and automation technologies, and red flags that hamper data integration.
Take inventory to know who’s on board and what they bring
No matter how brilliant and talented you are, you can’t engineer massive data changes on your own. Make sure your team—and, yes, that includes you—has the specific skills and ongoing training needed to keep up with the rapid pace of the IT industry, especially when it comes to AI. More than half of organizations are upskilling internal staff to expand their data literacy and expertise, while one in five are hiring graduates and training them.³ Get smart, stay smart.
Prioritize critical data elements for governance
Keeping a handle on critical and regulated data elements—such as names, addresses, social security numbers and more—is essential to running various business systems without duplication errors, unreliable searches or privacy breaches. Strike a delicate balance between securing data and fostering innovation. Consider who currently owns, manages and defines policies related to data, and whether that governance affects security, privacy or compliance. Make sure the right people within your organization have the decision rights, accountability framework and external resources to ensure the appropriate behavior in the valuation, creation, consumption and control of data and analytics. Don’t forget governance of any AI technologies you’re using at this stage, either.
To learn more, check out the IBM course on data topology
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Define your data’s target state
Outline your comprehensive vision so that data strategy conversations, and the resulting business process changes, are as meaningful to app engineers and business analysts as to HR and sales. “Many data environments are now dated and rarely have the flexibility to evolve in today’s digital environment,” says Tony Giordano, who leads data strategy, consulting and transformation engagements for IBM.
“But digital requires real-time decisioning capabilities, and the predictive models that provide these real-time decisioning capabilities require data science environments. Increasingly, operational data is now a critical part of your data ecosystem. A modern data architecture requires an integrated data ecosystem with capabilities that need to be managed, governed, and secured to ensure consistent data quality and the flexibility to evolve as the digital channels evolve.”
This level of detail makes changing business processes a little less grueling, since you’re ready to meet concerns with a detailed explanation as to how solutions will make a particular user’s life easier. And that’s a big deal—37% of respondents in a recent survey said data security was their number one challenge, followed by data privacy concerns and managing data pipelines.⁴
Be specific about where application modernization, automation and AI can take your strategy to the next level
The more you learn from your digital transformation and IT strategy, the better your data strategy is. Such insights help drive efficiency, increase revenue growth and mitigate risk, especially when amplified using app modernization, automation and AI.
Lufthansa worked with an IBM team to pilot new AI-based business ideas and services that enhanced customer experience. Previously disparate data sources are now searchable in natural language and aviation terms to more easily address close to 100,000 customer queries annually. “For Lufthansa, AI is so critical because it actually opens up the world of the data that we’re sitting on,” says Mirco Bharpalania, Senior Director, Cross Domain Solutions at Lufthansa Group. “It actually helps us to unlock all the potential that we somehow or somewhere in our databases already have.”
Measure progress toward your goals
We understand what you’re up against. As a data leader, you’re often expected to deliver and quantify major results on three competing fronts: revenue growth, operational efficiency, and mitigating security and privacy risks. Use data for the win to contribute directly to the growth of the company. By establishing metrics of success you prioritize efforts based on what matters most in this moment for your organization.
Don’t forget to look back on your notes from those initial meetings with stakeholders to see how they defined key performance indicators and goals—and how those stack up with current data architecture and AI strategies. Are your metrics delivering on the bold plans that were laid out at the time? If not, it’s time to reconnect and realign. “The CDO role is often very short-lived. The reason is not setting expectations. Make sure you set those expectations and deliver outcomes as you go,” Sankar says.
Capture your data strategy highlights—and share them
At this point, you should be crystal clear on your organization’s priorities and how to use data and AI to deliver and accelerate business value. What are your next gaps to close? A look at the big picture—where you are and what’s ahead—gives you strategic context to make actionable plans for delivery and scale. As you do, include the outcomes, objectives and measures that will keep you on track so you can share them with your enterprise as the journey unfolds. Here’s some of what to include in your data strategy overview:
- Observations, challenges and recommendations
- Objectives, outcomes and measures
- Cross-functional data needs to support multiple use cases
- Data privacy and security needs
Remember: strategy is not just a paper exercise—it is a living and evolving approach. So be creative. Review and optimize frequently based on changing business objectives and goals, and always ensure that your strategy allows for flexibility, agility and human innovation.
Download the six step data strategy framework
Map—and navigate—real-world scenarios
Whether it means innovating tired systems, jettisoning old products, delegating to data-savvy partners or applying artificial intelligence across the business spectrum, your task is to focus on your data objectives with as little sidetracking as possible. You have your insights from your data users. Consider the best ways to accelerate their business value using AI. Implementing the data topology you created in the strategy phase helps you keep tabs on use cases and monitor various controls across multiple lines of business.
Outline a data governance policy based on quality, privacy and security of data and AI
As part of a modern data management approach, a robust governance and privacy capability helps organizations thrive in the midst of growing data volume. A metadata and governance layer for all data, analytics and AI initiatives increases visibility and collaboration across your organization, regardless of where data resides. Your data governance policy will shape behavior around data quality, privacy, security and management, and show where AI is streamlining those regulation efforts. Whatever policy you’re enforcing should help standardize terminology for both structured and unstructured data so everyone in the organization can speak the same language. All of it should be backed by apps designated for specific environments, aligned with security and regulatory requirements, and platformed in a hybrid multicloud approach to ensure optimal protection.
Get the ebook: Data governance for data leaders →
Identify your data advocates
The people across your organization who you identify as allies in data strategy and advocacy are your partners for success. Figure out who’s most passionate about the impact data can have on their work and get them involved in regular meetings and maintaining standards. “I kind of started small by identifying product champions,” Sankar says. “It would start with one business unit and once that became successful, it's contagious.”
As a data-first enterprise, IBM has a team of data advocates dedicated exclusively to helping the organization adopt better, more pervasive use of data at every level.
These data advocates are fully empowered within IBM in the sense that if they find a like-minded group in accounts receivable or supply chain, for example, and want to move ahead with data and AI capabilities, they can push forward without having to come back for permission or funding.
Standardize your nomenclature
By 2024, organizations that make effective use of active metadata will reduce time to integrated data delivery by half and improve the productivity of data teams by 20%.⁵
To use metadata to help standardize your nomenclature, many implement a knowledge catalog. A knowledge catalog lets users access, curate, categorize and share data, knowledge assets and compliance information, like a cross-organizational common glossary. The goal is making sure everyone is on the same page, quite literally, about governance, data quality and compliance.
Set your sprint cycles
For a data and AI strategy to take hold, organizations often need to re-engineer their entire culture around new concepts and environments. That sounds daunting, but it’s hardly impossible.
Start by thinking about what you can achieve that’s valuable and viable in a short amount of time. Assemble your cross-functional team against clear objectives. Then, set short sprint cycles with actionable milestones that will help prove results. One approach is to follow this simple, repeatable process used by IBM data experts:
- Plan for one to two weeks with discovery workshops and data strategy planning sessions that include a data topology mapping exercise.
- Prove over six weeks with a customer-driven use case set with actionable and learnable milestones.
- Adopt and scale with a test product tracked across internal stakeholders to ensure conversion.
That last part is critical. To promote clear understanding of the benefits of any strategy, make sure the C-suite, tech teams and business users all have the same finish line in their sights.
Collect small wins in the form of MVPs
Sometimes, you get the most from the least amount of investment. The IT team at Experian didn’t know there was a place for data analytics in their back office; they only knew they were drowning in information. Assembling a single credit report in less than one second requires no less than 3,000 sources of data, 200 million records updating constantly each month, and billions of rows of additional data tracking archived historical data and derived data sets.
Working with IBM, Experian implemented an MVP that lets users contemplate and test new ideas with the least amount of investment and features. In many cases, it’s the quickest, cost-effective way to test hypotheses and figure out if continued investment makes sense. In this case it absolutely did. “Within 90 days, we had the proof of concept, the results of which had demonstrated that we could improve our coverage by 500% and lower our costs by 80%,” says Joni Rolenaitis, Chief Data Officer at Experian.
By integrating emerging technologies and systems, organizations become more automated, data-driven, risk-tolerant and secure. It’s also how they work smarter, not harder—after all, insights from AI-driven workflows can lead to new efficiencies and more profitable revenue streams. Consider how outdated data ecosystems and management practices impact an employee’s ability to make decisions. Research shows that up to 68% of data isn't analyzed in most organizations.⁶ With head-spinning advances in computing capacity, smarter algorithms and affordable storage, weaving together data is the foundation of future-facing organizations.
Create a central catalog to find—and share—insights
You’ll want to take advantage of a central catalog to store—and share—insights, allowing for simplified data consumption. Within the catalog, data is augmented in original and curated forms with purpose-fit storage allowing for publication and subscription of data across the organization. Data-access tools look beyond individual apps or processes to consider how your data is being consumed and what knowledge is emerging. This level of detail enables users to make real-time decisions that take into account data for lines of business, as well as for analysts, data scientists, and regulatory and federal agencies.
Encourage adoption from all directions by empowering data consumers
This isn’t just about being heads down in data. You can use your new data management framework to encourage adoption of the organization’s data and AI strategies from all directions—not just top down. In this way, you’ll be influencing how your business communicates, improving key workflows, optimizing security and unlocking new business models, market opportunities, and operational efficiencies.
Communicate results for maximum visibility
Let people know how much your efforts are paying off. “Build credibility with business process and data connection, and by telling a compelling story with your data,” Sankar says. Do that across the enterprise (up, down, sideways, diagonally) with quick updates and regular reports that measure how your new strategies are driving revenue and making work more enjoyable for everyone, especially when it comes to AI.
Hire (and reskill) talent to stay agile
The talent shortage is real, but most organizations don’t know what to do about it. Closing the skills gap means looking beyond traditional hiring and training strategies. As companies scramble to meet their talent needs, many are making adjustments to their education and experience requirements just to fill roles. What can you do when training and hiring are not enough? Consider these tips from the IBM enterprise guide to closing the skills gap and explore ways to supplement skills gaps with AI and automation.
Foster data literacy—all the time
Gartner expects that by 2023, data literacy will become an essential and necessary driver of business value, demonstrated by its formal inclusion in over 80% of data and analytics strategies and change management programs.⁷ But keeping up with data literacy shouldn’t be an annual or quarterly endeavor—it should be an ongoing part of your corporate strategy. “If you’re trying to get to a data-driven culture and you don’t empower people, that is in a sense an oxymoron,” Bhandari says. “If it’s a data-driven culture, then people should be looking at the data.”
Build strong partnerships across the organization
On the most basic level, your job as a data leader is to help your organization make the wisest decisions about data collection, management and use. As you build and strengthen partnerships at every level, be open to feedback and collaboration, and expect the unexpected. Because something fascinating happens as you grow a data-first organization. The more your vision threads into the organization’s DNA, the more you can “let go” by simply supporting a culture in which people are motivated to learn and take on new roles. Through it all, continue to communicate purpose and goals with clarity and an eye on the future.
Gartner expects that by 2023, data literacy will become an explicit and necessary driver of business value, demonstrated by its formal inclusion in over 80% of data and analytics strategies and change management programs.⁷
Your organization, inspired by your data strategy, is rallied behind you. As you augment existing technologies and introduce new ones to simplify data access at every organizational level, remember that you’re doing more than creating efficiencies and driving new insights—you’re building a culture of people with a passion for using data to its full potential.
Footnotes
1 “CDO Agenda 2022: Pull Ahead By Focusing on Value, Talent and Culture,” Gartner, 2021.
2 “The Total Economic Impact Of IBM Garage,” a commissioned study conducted by Forrester Consulting, October 2020
3 “Tableau Boosts its Data Literacy Initiatives to Address Data Skills Gap, Expand Market,” IDC doc #EUR148573521, IDC, December 2021
4 “Diving into the data lake—Highlights from VotE: Data & Analytics, Data Platforms 2021,” 451 Research, part of S&P Global Market Intelligence, 2021
5 “The Impacts of Emerging Cloud Data Ecosystems: An Architectural Perspective,” Gartner, September 9, 2021
6 “Rethink Data: Put More of Your Business Data to Work – From Edge to Cloud,” Seagate Technology, July 2020
7 “A Data and Analytics Leader's Guide to Data Literacy,” Gartner, 2021
GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved.