How to Overcome the 3 Biggest Challenges in Business Data Management (2025)

Data’s become one of the most valuable assets a business can have, but only if it’s managed properly. It’s hard to make sense of messy datasets, staying on top of privacy rules, or just figuring out which metrics actually matter, and a lot can go wrong without the right systems in place.

To better understand how businesses can navigate these challenges, I reached out to Rohit Bhateja, Director of Data & Digital division at SunTec India.

In our interview, Rohit shares the biggest data challenges he sees today, how businesses can tackle them, and where data management can head next. From centralization and automation to AI and ethics, there’s a lot to unpack.

Rohit’s experience in the field has given him first-hand insight into the data challenges businesses face every day, and more importantly, how to solve them.

When asked about common challenges businesses encounter when managing and utilizing data, he provided a few examples, followed by practical solutions to approach each one:

• Data Quality Issues
  • Challenge: Rohit mentioned that data quality issues are the most common challenges, as you simply can't build reliable insights on inconsistent or inaccurate data. “It's garbage in, garbage out”, he puts it.
  • Solution: To counter this, he advises using a strong data governance framework, supported by regular audits and data cleansing methods.
• Talent Shortage
  • Challenge: Data scientists are in high demand in the market, but what companies often need are people who understand both the technical aspects and the business context. Finding professionals who can bridge that gap is incredibly difficult.
  • Solution: Partnering with experienced data solution providers can offer specialized knowledge and tools, ensuring effective data management.
• Data Security
  • Challenge: As data volumes grow, so do the risks. Many companies struggle to protect sensitive information across multiple systems and touchpoints. Even a small vulnerability can lead to big consequences, both financially and reputationally.
  • Solution: Complying with data protection regulations is a must, and you should utilize data architectures and cloud services to accommodate data volumes as they grow.

Along with the solutions above, Rohit also makes the case for data centralization:

“That alone solves a lot of operational chaos. Whether through a data lake or a unified dashboard, having everything in one place is a game-changer.”

Other suggestions include automation, especially for repetitive tasks like data entry, cleansing and categorization, along with cross-functional teams.

“When you bring together people who understand the business problems with those who know the technical solutions, things work in a better way,” Rohit adds.

Reaching 96% Improved Efficiency

When asked to share a concrete example of when data solutions drastically helped improve a company’s operations, Rohit referred to a collaboration with a “leading market research and competitive intelligence platform operating in the USA and Canada”.

The company approached Suntec India in 2006, initially seeking assistance with data standardization to enhance the quality of their competitive intelligence reports.

While everything was going smoothly, a major issue arose in 2020.

“A significant challenge arose during the pandemic when disruptions threatened data flow and operational stability.

Our team swiftly adapted by implementing remote work protocols and enhancing collaboration tools, ensuring uninterrupted service delivery without reducing headcount or compromising on quality.”

This initiative has since helped Suntec India’s client achieve the following outcomes:

• 96% improved process efficiency
• 50,000+ data fields managed per day with complete accuracy
• 99% accuracy achieved in data processing

Those metrics show how data-driven solutions can improve businesses’ efficiency, but there are other stats to keep an eye on in 2025.

According to Rohit, executives need to focus less on “vanity metrics” and more on indicators that tie directly to business outcomes.

“Data time-to-value is critical to determine how quickly you can go from collecting data to acting on it.

In today's market, the speed of decision-making can be as important as the quality of decisions. Organizations that can rapidly analyze data and implement changes have a significant competitive advantage.”

Rohit also encourages executives to look at data utilization rates. Many companies collect vast amounts of data but only use a small fraction uses that data.

“Understanding and improving this ratio can yield substantial returns without additional data collection efforts.

When it comes to tracking the performance of data-driven solutions, businesses can focus on metrics like accuracy rates, processing speeds, and system uptime.”

How Data Strategies Are Evolving

The way businesses approach data has been undergoing a major transformation in recent years, and it’s not slowing down anytime soon, according to Rohit.

“The most noticeable shift has been the mainstream adoption of AI and ML, not just for data science teams but across functions like customer service, content moderation, and even HR.

Real-time data processing has also become the norm. Businesses can no longer afford to work with outdated reports, as they want dashboards that reflect what’s happening now.”

He also mentioned a growing push for ethical data practices, acknowledging it as a “positive change.”

“With global privacy laws tightening, businesses are becoming more intentional about how they collect and use data. It forces everyone to be more responsible and thoughtful in their data strategies.”

Looking ahead, several trends are already shaping the next decade, including AI-driven automation across data workflows like annotation, processing, and enrichment.

While AI role will become gradually greater, Rohit believes in the “human-in-the-loop approach.”

“Businesses are realizing that full automation isn’t the goal; it’s smart collaboration between machines and people that drives better results.”

Rohit also believes there will be even more concerns around privacy and data usage, which will become a “strategic focus”, not just a compliance to obey.

“How companies handle consent, transparency, and bias will influence brand trust more than ever.

All of this points to a more mature, thoughtful era of data usage, one where speed, ethics, and collaboration will be just as critical as the tech itself.”

Crafting a Data-Driven Future

This conversation with Rohit makes it clear that data management isn’t just a technical challenge, but also a business one.

From improving quality and security to embracing real-time insights and ethical practices, companies that get their data right are the ones that move faster, make smarter decisions, and earn more trust from customers.

As you just read, there’s no one-size-fits-all solution, but there are solid principles to follow, like staying organized, collaborative, and agile.

Whether you’re just starting to build a data strategy or looking to fine-tune an existing one, those are great foundations to grow from.