Empower Your AI Efforts with Data Governance

In the last four years, the data science space has transformed with dizzying speed. In 2022, Artificial General Intelligence (AGI) seemed like a distant reality; experts were divided on whether we would see it come to fruition in one year or one hundred years. Then ChatGPT made its disruptive entry. More recently DeepSeek AI released an open-source large language model (LLM) with impressive performance—even though it was developed with relatively minimal resources. Though not perfect, these powerful LLMs have been able to provide intriguing value to use cases across many industries.

Unfortunately, too many organizations are flocking to fancy AI tools without ensuring the data fueling them is secure, accurate, available, connected, and understood. The most valuable way for humans to work with AI is to leverage it as a tool rather than delegate all decisions toward it. Humans should be empowered by AI but keep control of its decision-making.

Embedding complicated AI tools into systems without fully realizing their risks, rewards, and basic operations could turn into a wasted effort when a smaller effort would have been more valuable. Before implementing some of the most advanced technology in the world, take the time to get your data in order and understand the best ways to leverage AI for your goals.

With AI, as with data science, the first step is solid data governance.

Most organizations aren’t lacking data. Exploding Topics reports that around 400 million terabytes of data are created every day. But in data science, it’s more about data quality than quantity. The more quality data you have, the more accurately you can build your model and the more trustworthy its results will be.

As a business leader, it’s important to think about the questions you’re most interested in answering. Then examine your data: Which elements are relevant, where is it coming from, and what’s missing that would be valuable to collect? What’s contained in the data is more important than its volume.

Takeaways:

• Gather a lot of relevant data.
• Assess your data considering business priorities.
• Know where your data is coming from.

Managing an ocean of data is no small feat, but organizations that do it well reap immediate rewards. It’s about storing data efficiently, easing data understanding and access for teams, and ensuring data security. Let’s break down some of these points.

Data Storage

Outdated tools (hard drives and local computers) can’t handle the massive volumes of data being created. But cloud computing enables teams to store at scale. Myriad cloud tools allow for scalable storage but select a cost-effective option that also prioritizes data transformation features (to refine your data), connectivity to other tools, and data security.

Takeaways

• Select a tool with flexible data transformation capabilities.
• Estimate the overall scale and costs of your data needs.
• Choose cloud tools with scalable storage, current system integration, and effective data security.

Data Discoverability

Too often teams will say, “We don’t know where valuable company data exists or if it’s usable.” Analytics projects can go under the radar simply because teams can’t find them. And even when they do find the projects, they are faced with access and ownership questions that can take time to understand before data work can begin.

For data opportunities to be uncovered, teams should ensure they share valuable datasets across the organization. Data governance strategies create that awareness.

Takeaways

• Data is only valuable when those who can use it know it exists.
• Bring stewards together to explore enterprise-wide data opportunities.
• Build common understanding of company data across all domains.

Connectivity

Many organizations face the challenge of silos—teams doing good work but disconnected from each other. These silos lead to tools and ecosystems that struggle to integrate with each other, ultimately slowing data initiatives. To prevent this, teams first need to understand their technical requirements. Then they should collaborate with other teams to ensure their tools are compatible across the organization. If not, valuable business insights between teams may go undiscovered.

It’s wise to find tools that meet specific needs of teams, but it’s also important to understand how well those tools meet organizational needs.

Takeaways

• Connect teams quickly and easily with SDKs and APIs.
• Maintain connectivity documentation for tools in your ecosystem.
• Understand integration requirements before purchasing new tools.

Governance

The word “governance” can provoke fear that everything teams do will be monitored or regulated, but instead, recognize governance as the organizer empowering and aligning teams to do their best work and enhance innovation. It starts with giving all parts of the business a voice in data governance best practices. Together, teams can organize data products in ways that don’t duplicate efforts or disrupt current processes.

Once aligned, teams can start the valuable work to automate data transformation and storage.

Takeaways

• Governance is about alignment and empowerment, not regulation.
• Every stakeholder should have a voice on data alignment requirements.
• Alignment leads to automation.

Protection and Security

Your organization’s data can be your secret sauce; it’s often more important than any other asset in the modern world. Data security isn’t easy, but it’s well worth the investment. Cyberattacks cost businesses billions of dollars a year, and cybercriminals often use social engineering to infiltrate digital ecosystems. Even if your organization has strong digital defensive measures, your people still need to be prepared for phishing scams, sketchy links, and unusual messaging. Invest in training employees on the value of data and the techniques scammers use.

Takeaways

• Data breaches cost organizations billions a year.
• Invest in educating your workforce on data security.
• Source the best solutions and tools on the market.

Data Culture

Data conversations can intimidate folks who don’t often work with numbers. When organizations announce they’re going through a “digital transformation to be more data driven,” it can cause anxiety. That’s normal, and what we want to do is turn that anxiety into excitement. Fortunately, the benefits of organized data empower everyone.

With an organized data infrastructure, business leaders can more easily and quickly spin up analytics efforts. Individual departments can ask informed questions about the data they work with. Companies with great data culture have employees thinking about how they can work alongside data professionals to tackle business challenges together.

Takeaways

• Foster a data culture by encouraging everyday data thinking.
• Pair data professionals with team leaders to co-create solutions.

Analytics

Analytics involves a wide spectrum of work related to data. Most organizations have been working to implement the most advanced tools and techniques like ML and GenAI. These tools can be extremely beneficial, but we at Elder Research often notice some of the most basic analytics approaches are more impactful. No matter where you fall on the analytics maturity spectrum, organized data and good data governance efforts produce great benefits.

You don’t always need fancy AI models; teams devoted to business intelligence and fundamental data science can accomplish much. And achieving immediate impact from initiatives becomes tremendously smoother with an organized data infrastructure.

In our next article on data strategy, we’ll share a technical architecture for making your organization analytics ready.

Takeaways

• Analytics readiness starts with how organized your data is.
• Good data strategy leads to valuable analytics projects.
• To find value in analytics, you can likely start small.