Data Preparedness Will Define Tomorrow’s Industry Laggards Vs. Leaders

In today’s digital era, business success is built on bits and bytes of information. 

That’s because, typically speaking, more data leads to better products, which attract more users, who generate more data, which further improves the product. 

The dynamic between information economy and software landscape’s growth has only accelerated this flywheel effect.  

For enterprise organizations and upstart firms alike looking to tap into the latest cycle of bleeding edge innovation, it is becoming crucial to have the appropriate technical infrastructure in place to unlock its true potential. 

Financial institutions and payment companies, in particular, are coming to rely heavily on data to drive their operations and make informed decisions. However, merely collecting vast amounts of data is not enough.

Data readiness plays a pivotal role in effectively harnessing real-time data for enhanced decision-making and improved customer experiences.

And taking the appropriate steps to transform legacy workflows will have far-reaching downstream consequences for firms’ compliance and governance controls, as well as employee education and future-fit staffing requirements. 

But it’s important not to put the cart before the horse. Every data-driven decisioning process must support a business use case or growth goal. 

It is the ability to not just capture data but to synthesize and activate it effectively that will be crucial in defining the industry laggards and leaders across tomorrow’s business operating landscape. 

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Solving for Real Challenges in Real Time

By prioritizing data readiness, organizations can enhance risk management practices, improve customer experiences, make data-driven decisions, meet compliance requirements, and leverage emerging technologies effectively to gain greater insights. 

That’s because treasurers and chief financial officers “need data for everything they do…They need it available at their fingertips,” Naveed Anwar, global head of digital and data platforms at Citi Treasury and Trade Solutions, told PYMNTS earlier in July. 

“[But] there are a lot of data-related challenges that decision-makers have,” he added. 

Through advanced analytics, firms may use the massive amount of data collected to gain greater insights into both consumer and customer behavior. These insights can then be used to leverage growth strategies, including plans to increase loyalty, or they can aid in achieving other goals that benefit from detailed information analysis.

Yet traditional systems often fall short in providing the needed level of up-to-date immediacy. 

This disconnect can leave many firms in the lurch and can be compounded by the huge growth in internal data volume and variety over the past few years. 

“If you think about a typical organization, one that’s been in business for 10-plus years, and consider all the data that they have amassed, particularly unstructured data — until recently, this data was generally produced and then shelved,” Taylor Lowe, CEO and co-founder of large language model (LLM) developer platform Metal, told PYMNTS at the beginning of the month.

Lowe went on to explain that while this information can be incredibly useful once unlocked, it still needs to be structured to remove unnecessary complexity.

After all, having all the data in the world doesn’t mean much without the ability to use it.

That’s where data preparedness comes in. 

Read more: Cybersecurity Is Driving Banks to the Cloud

Data at the Heart of Innovation

Investing in robust data governance frameworks, advanced analytics capabilities, and secure data management systems is crucial for organizations to thrive in the digital age. 

PYMNTS research found that organizations relying exclusively on legacy processes and tools may find themselves vulnerable to modern attacks. 

Separately, sticking with monolithic yesteryear workflows and processes increasingly leaves firms vulnerable to modern competition. 

Of course, the ability to gain data-driven benefits depends almost entirely on infrastructure preparedness, as innovative processes may include the implementation of automated software to extract and categorize massive amounts of data and require modernized capabilities to get off the ground, as detailed in 2023 PYMNTS’ research

“The data that exists within companies is at the heart of everything that drives better decision-making. Historically, and very commonly, companies do not have a data strategy or data governance models to harness that data and to be able to layer on the tech that’s available to drive better decision-making,” Emburse CFO Adriana Carpenter said in an interview with PYMNTS. 

That’s why, with the rapid commercialization of artificial intelligence (AI) across today’s operating landscape, it’s becoming more important than ever for enterprises to get their legacy data right to remain competitive and win new customers. 

Broadly speaking, AI can help enhance three core organizational needs: automating back-office and financial business processes and activities; providing leaders with real-time insights through data analysis; and offering new relational touchpoints for engaging with both customers and staff.

However, successful implementation of any AI strategy relies heavily on quality data inputs. Financial institutions and payment companies that have invested in data readiness are better positioned to leverage these technologies for automation, fraud detection, customer service chatbots, and more.