Unlock business growth with data-driven insights: 5 lessons from IT leaders

Maintaining a competitive edge can feel like a constant struggle as IT leaders race to adopt artificial intelligence (AI) to solve their IT challenges and drive innovation. But with the right tools, processes, and strategies, your organization can make the most of your proprietary data and harness the power of data-driven insights and AI to accelerate your business forward.

Using your data in real time at scale is key to driving business value. More than 80% of C-suite executives expect data and AI to improve productivity and revenue, according to a poll of 1,000 IT leaders from Elastic. But to get to those results, it is critical to invest in a strong data foundation that can manage exponentially growing data volumes and uncover insights on your customers, operations, products, and services. 

AI and generative AI (GenAI) can be used to optimize your systems and experiences. But before reaching these next-generation technologies, you should focus on getting access to relevant real-time insights at scale to guide your decision-making. 

Here are five lessons from IT leaders on how to unlock business growth with data and AI.

Lesson 1: Prioritize data-driven insights to accelerate business innovation

Your business runs on vast amounts of data. Everything in your operational environment continuously consumes and creates data from various sources: your applications, systems, services, and infrastructure. A data-driven approach is crucial for solving key business challenges and driving innovation — you can’t create exceptional customer experiences without understanding what your customers expect and want. 

To outmaneuver competitors and truly accelerate business innovation, you need to understand your current state of operations and promising growth opportunities. This is achieved by not only collecting and analyzing your relevant data but also deriving data-driven insights from it. These actionable insights help you improve resilience, increase your productivity, and ultimately accelerate innovation. 

For example, you might get insight into customers abandoning their carts when they add a certain product. You can look into this and discover that the product listing had a bug and wasn’t allowing people to checkout. 

Unless you analyze it, all this useful information can get lost in storage, often leading to lost revenue opportunities or high operational costs. Creating a culture of data-driven, strategic decision-making needs to happen across the organization from every step of the process to uncover and solve existing business challenges and uncover value-creation opportunities that enable new revenue streams, enhance competitive advantage, and boost business growth. “Problems with real-time, scalable data utilization impact business efficiency,” explains one technology decision-maker. 

Adopting a strategy to prioritize a culture of using data-driven insights across your organization lays the foundation for innovation. Transforming your data into actionable insights starts with reducing data silos and enabling data accessibility, which can lead to faster decision-making, increased productivity, and the edge to outperform your competitors.

Lesson 2: Make sure you’re satisfied with your data insights

Now, you may be getting insights from your data, but are you satisfied with those insights? Three out of five C-suite executives and decision-makers are unsatisfied with the data insights available to them. Delivering meaningful and actionable data analytics comes down to defining clear objectives and managing data volume. Too much data results in noise, but not enough data stretched across multiple silos makes connecting the dots very difficult. 

So, how do you make sure you’re satisfied with your data insights? Identify the areas of your organization where you would most benefit from having accurate, real-time insights. Focus first on solving any underlying data challenges in these impact-making areas and then work on refining those insights with accuracy top of mind. The more accurate these insights are, the more helpful and valuable in a business context. Improved data insights can enhance decision-making, reduce risks, and increase operational efficiency.

Lesson 3: Take time to evaluate and enhance your data maturity

The hard truth is that 78% of C-suite leaders and IT decision-makers believe their organization is more advanced in data analytics and intelligence than their peers. In reality, there is a significant disconnect between the perceived and actual data maturity levels across organizations. Data maturity — how well an organization leverages data for business — can be broken down into four stages: capture, analyze, automate, and transform. By identifying where your organization stands in the data maturity framework, you can uncover the best ways to use your data and technology to achieve your business goals.

If you’re facing a similar challenge, it’s likely your organization hasn’t reached the more advanced stages of the data maturity journey. And even if you have, the journey doesn’t end. It’s a constant quest to continuously innovate and operationalize with the power of your data. To begin your organization’s data maturity assessment, look to your data challenges: Are you dealing with excessive or insufficient volumes of data? Is it difficult to find information within your organization? Is your data taxonomy working for you?

By evaluating and advancing through your data maturity journey, you’re building a robust data foundation that aligns with your business goals. Aligning to business objectives is crucial to enable more informed and strategic decision-making and uncover opportunities to use AI.

Lesson 4: Understand that GenAI comes second to good data practices

GenAI comes with the potential to unlock new automation capabilities, enhance your search applications, improve your customer experience, and give your employees time back to focus on strategic activities. It’s impressive and impossible to ignore — so, you’re probably under pressure from your board or leadership to implement new generative AI applications as soon as possible. 

But getting value out of GenAI starts with quality data practices. Your GenAI outputs are dependent upon the data you input. Quality in, quality out. Without the right data and without robust data practices, GenAI won’t help you move the needle, and you won’t see the benefits. The opposite is also true. With rich data inputs and streamlined organizational processes, you’ll glean equally rich insights.

Obtaining quality data begins with making use of your data — across environments, no matter the type of data (structured, unstructured, semi-structured). Building on a foundation of solid data practices, look for a solution that can process all your types of data from across your distributed architecture. Remember: with better data, you will get better AI outputs.

Lesson 5: Embrace GenAI for a competitive advantage

“AI is the future. Without it, we are dinosaurs. GenAI will allow our company to make smarter and more efficient decisions without having to sacrifice anything. GenAI is smart, constantly learning and evolving, and it can tell us what we are missing, where to look, and what to do,” says a technology decision-maker in the manufacturing industry. Recent developments in GenAI have added a whole new wave of dizzying GenAI-powered possibilities, and those who are able to embrace it will gain a host of advantages.

Organizations worldwide feel it: 93% of C-suite executives plan to invest or have already invested in GenAI to improve productivity, operational resilience, customer experience and more.  

Early (relatively speaking) adoption of GenAI can position your business ahead of competitors by creating new opportunities and driving innovation. To stay ahead of the adoption curve you first must have good data ready to go. Then, identify a high-impact use case that can benefit from the value of a large language model (LLM). 

Getting the best results securely requires feeding your proprietary data to a generative AI algorithm using retrieval augmented generation (RAG). This technique contextualizes the output of your organization, resulting in more accurate and relevant results.

Key takeaways from IT leaders

To compete, grow, and innovate, organizations need a solid data foundation to accelerate the adoption of GenAI technologies. Your data and GenAI strategy should empower your customers and employees to make informed, data-driven decisions confidently.

Learn what other IT leaders have had to say about their data and AI strategies.