Unlocking Value with a Sound Data Strategy​

A sound data strategy— how data is collected, stored, managed, and analyzed to achieve business goals — is the foundation for any successful AI-driven project, because the real magic and value from AI technology happens when it begins with your own data. Every company has different data sources, needs and objectives, but the common element is that before a star technology like AI can deliver value, you need to set the stage for success.

In fact, a recent survey showed that 35 percent of technology decision makers said data will be their leading activity in 2024, followed by 32 percent who will be prioritizing infrastructure modernization, according to Forrester’s Business And Technology Services Survey, 2023. This snapshot shows that while organizations are adopting the latest trends, they’re also prioritizing the necessary groundwork to implement them.

Every AI Strategy Begins with Data

Just about every technology conversation today includes generative AI. It’s important to remember that the bedrock of generative AI is data, and the effectiveness of any genAI effort rests on the accuracy of, and accessibility to, the data that the AI models will be trained on.

Models cannot produce the right outputs without high-quality data. Improving data quality allows machine learning models to accurately identify underlying patterns, thereby improving predictions. When organizations prioritize data quality through rigorous metrics, assessments, continuous monitoring, and improvement, they not only enhance model reliability but also reduce the risk of biased or erroneous conclusions. This improvement in model accuracy translates into cost savings, improved business outcomes, and a competitive advantage.

Trusting Your Data to Work for You

A system with inaccurate, misleading, or partial data will lead to a failed implementation, so it’s critical to  ensure your data is pristine and trustworthy. Data quality is the responsibility of everybody in an organization. At Hitachi Solutions, we see many customers who overestimate the quality of their data and underestimate the effort it takes to get it into shape.

Ask yourself:

• What data do I have?
• Where is it?
• Is it accurate?
• Can people access the data needed to make the right decisions?
• Is it secure?

If you answer ‘no’ or ‘not sure’ to any of those questions, it’s likely others in your organization are answering the same. In fact, only one in six managers actually trust the data they use every day, according to a Harvard Business Services Review.

A Modern Data Foundation for Analytics and AI

Modernizing your data foundation is the right place to start—but most organizations underestimate how much effort it takes to make data usable.

A modern approach isn’t just about deploying a platform. It’s about ensuring data is:

• Connected across systems
• Governed and secure
• Structured for analytics and AI
• Accessible to the people who need it

Technologies like Microsoft Fabric enable a unified approach to data, bringing together storage, analytics, governance, and AI capabilities into a single environment.

But platforms alone aren’t enough.

Organizations still need to integrate, structure, and operationalize their data before they can realize value—which is where many initiatives stall.

Integrating Disparate Data

GenAI is most effective when it has both structured and unstructured, internal and external data to process. As usage of generative AI increases and companies roll out more conversational experiences for customers and employees, the amount of unstructured data managed by enterprises will double in 2024, according to Forrester. In order to scale, organizations need to double down on their infrastructure, with all-in-one unified data platforms such as lakehouses to manage costs, support multi-structured data analytics, and enable broader use cases and workloads.

In 2024, companies will focus on data platform development to maximize the value of their internal and external data, which includes feeding new AI and analytic workloads, internal and external LLMs, and improving data delivery speed to the business with strong data governance, quality and lineage.

                                                                                                                                                                       

Tech Target

Solutions like Microsoft Fabric provide a powerful platform for unifying data and enabling analytics and AI at scale. By bringing together data engineering, analytics, and business intelligence into a single ecosystem, organizations can reduce complexity and accelerate innovation.

But the biggest challenge isn’t choosing the right platform—it’s getting data ready.

The Hitachi Unified Data Accelerator addresses this gap.

Delivered as a service, it removes the heavy lift required to connect, structure, and operationalize your data. Instead of building from scratch, organizations can start with proven architecture deployed directly in their environment.

With the Hitachi Unified Data Accelerator, organizations can:

• Get analytics- and AI-ready data in days—not months
• Accelerate adoption of platforms like Microsoft Fabric and Databricks
• Improve data quality, governance, and accessibility
• Focus on insights and outcomes—not infrastructure

This combination of platform + accelerator enables organizations to move faster from data strategy to measurable impact.

Why Do So Many Data Initiatives Fail?

Initiatives sometimes fail, not because the technology isn’t right, but because organizations don’t foster a culture where employees are encouraged and are accountable for using data in their decision-making processes, and are provided with the necessary training and resources to do so.

Data modernization programs don’t just float in the realm of IT departments but are tightly integrated with an organization’s talent, resources, and processes. If the people aren’t on board and understand the expectations around how they’re supposed to work with data and pull that data for decision-making analysis, issues will almost certainly arise.

“Where we see our customers often underinvested, is the people side of this roadmap. People, talent, and business units all must be bought into the strategy,” said Greg Gant, Hitachi Solutions Advisory Services Vice President.

With Advisory Services, we help customers define, prioritize, and realize value from every modernization initiative.

Listen to our podcast for tips on how to ensure your data modernization initiatives can be successful for all stakeholders in your organization.

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How to Get Started

Having the right data means you can start leveraging ML and AI today to predict what your customers need tomorrow. But there is no one-size-fits-all solution. Because every organization is unique, it is important to start by asking the right questions early on. Data quality, infrastructure, operational procedures, and change management are just a few factors that will determine success.

Check out our AI Readiness Roadmap offer where we evaluate your existing estate, devise an incremental plan, and execute the move to optimize all services and data points. And we do this using best practices to ensure ideal levels of security, resiliency, and access control.

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From a fully managed data foundation to modern platform architectures, Hitachi Solutions meets you where you are—and helps you move forward faster.

With a combination of Microsoft technologies and the Hitachi Unified Data Accelerator, we help organizations reduce the complexity of data modernization and accelerate the path to analytics and AI outcomes.