To successfully align your AI, data and analytics strategy with your business strategy, you need the right data architecture in place.

CDOs have a critical role to play in helping the business use data and technology for digital transformation, but this requires an alignment between the data and analytics strategy with the overall business strategy. A recent IBV Chief Data Officer study revealed that 63% the top the CDOs surveyed are aligned with the business strategy in compared to 48% of other surveyed CDOs. To get from data to valuable, actionable insights, you need to understand what drives the business, connecting your data and analytics strategy to business objectives.

Consider the fact that the average surveyed CDO allocates 2.32% of annual revenue to data management and strategy to increase revenue growth by 1%. But the top surveyed CDOs achieve the same result by allocating 2.27%. While it doesn’t seem like a big difference, it can mean millions of dollars in savings, materially improved ROI and better long-term outcomes.

When data strategy and business strategy are aligned you can use data investments to pursue new sources of value, and fuel innovation within your organizations in alignment with the organization’s strategic goals. In fact, almost 9 out of 10 of an elite group chief data officer whose organizations allocate proportionally less of their revenue to data yet generate equal or greater business value use data investments in this way.

Aligning your data strategy and business strategy means merging the various frameworks and guidelines that exist throughout departments for a single unified view of the data landscape that everyone agrees to. To leverage the connections between business users, technical users and overall business outcomes, today’s data leaders need a modern data architecture such as data fabric to simplify their data access and to facilitate self-service data consumption. There are four key ways to use a data fabric architecture to drive chance for internal and external partners that can empower teams across the entire data landscape.

1. Create an actionable data foundation with proper data governance to help ensure data privacy and security across your data landscape.

When asked for the most important characteristics of their data architecture, the majority of CDOs ranked security far above any other areas. A strong data foundation includes defining a data governance policy that guides data quality, privacy, and security practices for the organization that can be reinforced using your data architecture. Yet, fewer than two-thirds of CDO respondents say they are protecting and securing data to the maximum. The challenges that shape this lack of confidence in their current protection and security processes include full compliance with data legislation and standards, feeling their operational data (management, financials, supply chain, inventory, and personnel) is protected and secure, and their customer’s data is secure.

This governance foundation of a data fabric architecture makes it clear what data exists, who should have access to which data sets, and how compliance at scale allows for better data quality and lineage across your data landscape using an augmented data catalog.

It also provides automated metadata generation and a governance layer that ensures data quality, data privacy and security standards are upheld. This ensures compliance with regulations and auditing needs through increased visibility and data masking across all data, analytics and AI initiatives on any cloud.

Learn more by exploring the “Data governance for data leaders” ebook.

2. Streamline workflows and decision-making with a human-centric, principled approach to AI that’s responsible, transparent and explainable.

CDOs have a critical role to play in setting the data foundation that facilitates the creation of AI solutions that drive better decision-making, but also mitigating risks. AI algorithms and models need to operate reliably to be actionable and to drive value, but there also needs to be visibility and accountability for insights. End users need to understand the standards for used to process data, define an ethics framework within which to apply AI and algorithms, and diverse teams involved in ensuring results looks right and avoid unconscious bias. Otherwise technology and organizational change management investments that cultivate a data-driven organization (such as data literacy) are wasted.

A data fabric provides a technical foundation supported by a clear process for benchmarking and evaluating AI models across the end-to-end AI lifecycle, regardless of your stage of AI adoption.

Shaped by a strong, cross-functional team, this combination of technology, people and processes empowers teams to be proactive and infuse governance into their AI initiatives from the onset. This approach also minimizes risk while strengthening your team’s ability to meet ethical principles and government regulations.

Learn more by reading the “AI governance for data leaders” ebook.

3. Simplify model building and deployment with automation.

AI can’t exist without the data science that shapes great AI models, but only 33% of surveyed CDOs point to explainable, comprehensive outputs as an important characteristic of their data architecture. Data science requires a modern data architecture like data fabric that can orchestrate different data types from a variety of sources within a hybrid multicloud environment. This allows for more productive ways of working like MLOps that take advantage of AI capabilities to streamline and automate operational workflows, such as model building.

Learn more by exploring the “Data science and MLOps for data leaders” ebook.

4. Connect data across the IT landscape, regardless of where it resides.

Without connected data sets, your organization is stuck with disparate and complex data that doesn’t tell a clear story for its users. This makes it nearly impossible to create meaningful data products that deliver business value to the organization and accelerate revenue growth.

A data fabric architecture provides a sound multicloud data integration strategy and democratizes data access by delivering data where you need it and frees you up from locking into a specific vendor along the way. It can also synchronize data in real time without disrupting mission-critical data.

The result? Your business and technical data users have access to self-service data that shows a holistic and explainable view of the data landscape. Thus, you can pinpoint exactly what data you have and where it can be used to drive the most business value.

Learn more by exploring the “Data integration for data leaders” ebook.

Unlock the power of data fabric

The actions of the top 8% of surveyed CDOs signify that it’s never really about the data. It’s about the business transformation value of insights from integrated data. The CDO is critical to driving transformation, but it requires the basic foundations of data management and data governance.

A data fabric architecture ultimately provides the interconnectivity of data with the governance required for true business innovation, especially in highly regulated industries like banks. This drives value for the organization and is tied back to business goals, especially when paired with a deliberate data strategy.