How Companies Can Turn Data Into Currency for Business Success
Explore the roadmap to a ‘data-rich’ future.
In today’s data-driven world, organizations must strategically leverage vast data volumes to drive positive outcomes. This requires the right strategy to align technology, processes, and a data-centric culture to transform information into actionable insights and competitive advantage, says Satish Jayanthi, CTO at Coalesce
By now, we’re all familiar with the expression that data is the new currency. Data has transitioned from a byproduct of business operations to a strategic asset indispensable for growth and innovation. Organizations that can effectively leverage their data gain a competitive edge by making informed decisions, personalizing customer experiences, optimizing operations, and empowering AI-driven innovation.
One example is how Amazon uses data for spot-on product recommendations that account for up to 35% of its annual sales. And we can all relate to getting pulled into a new TV series thanks to Netflix’s recommendation algorithm, which accounts for 80% of users’ stream time. But using data to achieve positive business outcomes is easier said than done. Many organizations still struggle to democratize data and incorporate analytics across the business.
This is often a “people issue” versus a technology roadblock, usually due to a lack of alignment. Progress isn’t possible if the business and technology sides of the house aren’t aligned on data use cases, what to build, or which technologies to adopt. And other aspects of a company’s culture—like a lack of transparency or an overall resistance to becoming data-driven—can further impede an organization’s ability to get the most from its data.
Let’s explore ways to mitigate common challenges on the journey to becoming “data rich.”
Create a Data-centric Culture That Starts at the Top
Becoming data-driven starts at the top. First and foremost, all leaders at the company must believe that every business decision should be based on—or augmented by—data versus going off a hunch. The success of an organization’s data strategy hinges on having a designated data champion, so one should be selected to lead the charge in bridging the gap between business and technology teams.
Companies sometimes go wrong by appointing a data leader who is deeply technical but lacks communication skills and a grasp of the business side of things. In reality, you need to strike the right balance: a data leader should have technical expertise, excel in communication, and understand the business landscape. Such a leader can facilitate engaging discussions about challenges, processes, priorities, and technologies, aligning teams and sparking innovation. This balanced approach is where the true magic begins in driving a company’s objectives forward.
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Invest in Tech for Immediate Gains and Lasting Solutions
Once a company decides to become more data-driven, the data champion must ensure everyone has the resources they need to be successful. This requires training, data literacy awareness, and the right technology. In the early stages, technology teams must gain business leaders’ confidence by illustrating the value of being data-driven through quick wins. Progress over perfection is an important motto: The architecture doesn’t have to be perfect at first; it just has to show business leaders how data can solve their problems or generate better outcomes.
To accomplish this, they’ll need nimble technologies that help them build solutions quickly. But it’s not just about speed: Ideally, they’ll leverage technologies supporting best practices from the start. Anything they create should have documentation, lineage, and governance built right into it. This way, solutions can be iterated on and improved over time, eventually turning into sustainable architectures that continue to offer value long after the “quick win” stage.
Artificial intelligence (AI) is revolutionizing businesses by enabling them to make smarter decisions, automating complex processes, and empowering employees to work more efficiently. However, organizations should be mindful of adopting rapidly evolving technologies like AI. We’re constantly hearing about the value of AI and large language models (LLMs), but companies must understand and trust these technologies before leveraging them. They need to be crystal-clear on their use cases (more on this in a moment) and how AI can address their pain points. It’s not a silver bullet, and the success of AI-powered solutions like LLMs depends on the quality and accuracy of the data they’re trained with.
Identify the Right Use Cases
Organizations can use data to create innovative data products that provide a better customer experience, make breakthroughs in improving their existing products, and more. It’s important that companies identify the right use cases for the business, and it’s equally important that they have the right data to enable the execution of those use cases. This requires extensive knowledge of the problem and the end users whom it will benefit.
For example, AI won’t be an appropriate solution for every use case. However, it can work in instances that don’t require 100% accuracy, since AI isn’t failproof. Essentially, it’s a productivity booster that will get organizations 80% of the results they seek quickly, and humans will have to follow through on the other 20%. This could look like AI reviewing contract documents and flagging certain sections for humans to review. But it can’t—nor should it—approve the contracts themselves. Similarly, AI can scale the loan qualification process by pre-screening many applicants, allowing loan officers to focus on making informed decisions on eligibility.
Other things to consider when selecting the right use case: Does the organization have the data and talent necessary to build the solution they’re envisioning? Is the data high-quality and accurate? Is it feasible from a cost and performance standpoint? LLMs, for example, require a lot of compute and training and, therefore, won’t be appropriate for every use case. Similarly, solutions that process data in real-time and provide feedback instantly will be considerably more expensive to build and operate than those that don’t have real-time requirements. Organizations must consider these factors carefully to avoid excessive rework and/or squandering resources.
It’s been nearly two decades since British mathematician Clive Humby famously declared, “Data is the new oil.” Back then, no one could’ve predicted the extent to which this statement would prove true. Data is the lifeblood of every modern business, and knowing how to leverage it effectively means the difference between innovating or falling behind. By implementing the tips above, organizations will be well-positioned to become “data rich.”
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