We have written a number of articles on Smart Data Collective about the overlap between big data and finance. One of the most important trends we’re seeing is the push for data automation across the banking sector. You can already see how institutions are relying on algorithms to make faster, more accurate decisions. It is changing the way services are delivered and how customer expectations are met.
You might be surprised by how fast investment in this area is growing. Research from Mordor Intelligence shows that the amount of resources banks are investing in big data is growing 23.11% a year over the next decade. There are few other industries experiencing this level of growth in data spending. Keep reading to learn more.
Banking’s Data Boom
You are living in a world where data volumes are climbing at an unprecedented pace. Fabio Duarte of Exploding Topics reports that 402.74 million terabytes of data are created each day. There are massive opportunities for banks to extract meaning from this flood of information. It is especially true for large firms with the infrastructure to analyze customer behavior in near real time.
You should also consider the amount of financial data that global exchanges are processing. Trevir Nath, in an article for Investopedia, pointed out that the New York Stock Exchange alone captures 1 terabyte of data each day. By 2016, there were 18.9 billion network connections worldwide, averaging 2.5 connections per person. It is no surprise that finance is becoming more reliant on real-time analytics to stay competitive.
There are plenty of reasons that data automation is gaining traction. You can spot it in loan underwriting, fraud detection, and customer segmentation. It is making decisions faster and reducing manual tasks that were prone to error. There are also fewer delays when customers need service across digital channels.
You will likely see even more changes as AI and machine learning expand their role in banking. There are signs that automation will soon handle even more advanced tasks, like predictive risk modeling and personalized product recommendations. It is one of the clearest signs that data-driven decisions are no longer optional. You can expect banks that fall behind in this trend to face major disadvantages.
In every company, there are core questions that seem simple, but are surprisingly often hard to answer: Is this supplier real? Is this customer already in our system? Can we trust this bank account?
Every enterprise, no matter how large or small, depends on this thing to function smoothly: clean, reliable, and up-to-date data. Yet, for many companies, managing basic information about suppliers, customers, and business partners remains manual, repeatedly messy, and prone to error. In recent years, however, a quiet revolution has begun – one powered by automation, verified external data, and a new mindset focused on trust.
This is the story of that shift.
The daily frustration of dirty data
Let’s start with the problem.
Most organizations still rely heavily on manual processes to create and maintain their business partner master data. Information is copied from emails or spreadsheets, fields are typed in by hand, checks are often done late in the process, or not at all.
The result? Errors, duplicates, and delays become part of daily operations:
- A supplier’s bank account can’t be verified, so a payment is delayed.
- A duplicate customer record causes confusion in sales or billing.
- A tax ID doesn’t match the government register, triggering compliance risks.
These are not edge cases. They’re everyday occurrences stemming from a foundational flaw: too much of the data flowing into enterprise systems is still subject to human error. And once that flawed data is in, it spreads quickly across invoices, contracts, reports, and customer interactions.
The standard approach? Reactive clean-up, which typically involves manual error fixes, running batch validations, or delaying processes until someone could double-check the details. But as companies scale and move faster, these old ways simply don’t work anymore.
A new approach: trust by design
The turning point doesn’t come from technology alone, but rather from a shift in mindset: what if data could be trusted the moment it enters the system?
And that means more than merely avoiding typos. Trusted data is complete, verified, and traceable. It’s data that has been checked against reliable external sources like official business registers, tax authorities, or sanction and watchlists. It’s accurate by design, not by exception handling.
“When you build trust into the system upfront, everything else gets easier,” notes Kai Hüner, Chief Technology Officer at CDQ. “You’re no longer relying on manual gatekeeping, instead you’re engineering trust directly into the workflows and downstream processes.”
For example, when one Fortune 500 company reexamined their process of onboarding suppliers, they realized loud and clear just how many rounds of checks each new record required: tax ID confirmation, legal status review, a call to confirm bank details. And while the number of roles involved in the process can vary depending on the size and structure of the organization, it is a common scenario in the world of data professionals.
Aside from being obviously time-consuming, this old-school approach is also risky, and definitely far from trustworthy. If anything is missed, the consequences mean missed payments, fraud exposure, or compliance gaps.
By integrating real-time lookups from trusted sources into onboarding, the company was able to move most of these checks upstream. Now, if a supplier’s bank account has a low trust score or their registration number doesn’t match the official record, the system catches it before the record is saved and flags unusual or suspicious entries for manual review. In most cases, no human intervention is required, thanks to the trusted data that now forms the backbone of reliable and, unlike many rushed efforts to automate broken processes, truly meaningful automation.
This approach, backed by trusted data, creates meaningful automation instead of rushing broken processes. It moves companies from reactive fixes to sustainable, agile, and trusted data frameworks that deliver speed, scale, and accuracy.
Automating what can (and should) be automated
The idea is quite simple: if the data is reliable and the process is repeatable, software should handle it.
Instead of manually processing each request for a new business partner, customer, or vendor, companies are setting up workflows that evaluate whether a new entry is valid, unique, and complete. That includes everything from enriching company profiles with up-to-date information, to automatically detecting duplicates, to deciding whether a new or change request needs human approval.
As a natural consequence of smart automation, efficiency grows rapidly.
When one global industrial group introduced automation into its MDM platform, the time required to process new supplier records dropped from 15 minutes per record to under a minute. Another company cut its time from customer inquiry to approved sales quote from one month to a single day. All by removing manual and reactive interventions from the critical path.
The benefits go well beyond just saving time. By automating routine decisions and flagging only the exceptions, businesses can focus on what truly matters: complex cases, edge scenarios, strategic decisions, and opportunities for scale.
These gains are detailed in an MDM automation case study from CDQ and SAP that outlines how enterprise workflows can shift from data correction to data confidence, with real-world metrics from early adopters.
Data sharing: the network effect of trust
Another shift gaining ground and strengthening reliable MDM automation is data sharing. Not just within a company, but across ecosystems.
No single business has perfect data on every customer, supplier, or entity it deals with. But most of companies are in fact dealing with the same records. When organizations share verified business partner data, especially things like legal entity names, tax IDs, and addresses, they create a network effect.
Instead of each company validating the same data within its own four walls, collaborative data networks allow verified records to be reused across participants. This network effect increases the reliability of data for everyone involved. When multiple companies confirm the same supplier address, bank account, or tax ID, the confidence in that record grows. And if something changes, like business status or new address, the update propagates through the network – automatically.
This kind of community-based trust model helps companies reduce duplication, streamline compliance efforts, and respond faster to business partner data changes. It’s also an antidote to data decay, because if someone updates a record in the network, everyone benefits.
Embedding trust into the workflows
For trust and automation to really stick, they can’t be treated as IT add-ons. They need to be embedded in day-to-day business processes. That means:
- Integrating real-time validation into ERP, CRM, and other enterprise systems
- Guiding users to reuse existing records instead of creating duplicates
- Auto-filling fields with verified, country-specific data based on official sources
For instance, when a user creates a new customer or supplier, the system checks if it already exists. If it does, the user is guided to use the existing record. If it doesn’t, the system pulls in trusted data (such as the correct company name, country-specific tax fields, or verified address) so that the new entry starts clean.
This also applies to bulk data operations. During mergers or system consolidations, tens of thousands of records need to be imported. Automating this process ensures that each record is validated, enriched, and de-duplicated before it enters the system. This avoids the trap of importing dirty data and spending months cleaning it later under the pressure of already derailed timelines and serious reputational, financial, and regulatory risks looming in.
A broader business case: horizontal value across the organization
For data teams, the return on trusted and automated MDM is transformative. Instead of being stuck in a reactive, error-fixing mode, they move into a strategic, high-impact role. Key benefits include:
- Fewer firefights: Errors are prevented at the source, reducing the need for constant cleanup and root cause analysis.
- Clear accountability: With rules and validation embedded, data ownership becomes transparent and easier to manage.
- Scalable governance: Data teams can define standards once and apply them consistently across global systems.
- Improved data quality KPIs: Automated checks help teams consistently hit quality thresholds for completeness, accuracy, and timeliness.
- Strategic role elevation: Data stewards and MDM leads move beyond “data janitor” tasks to focus on architecture, analytics readiness, and cross-functional enablement.
But the value of smart MDM automation doesn’t stop with the data teams. Once clean, verified, and automated master data becomes standard, its ripple effects transform the entire organization. When trust and automation are embedded at the core:
- Finance avoids payment errors and fraud thanks to verified bank account data.
- Procurement speeds up supplier onboarding and risk assessment.
- Sales and marketing gain confidence in customer segmentation and outreach.
- Compliance teams reduce regulatory exposure without relying on manual checks.
- Analytics and AI models get better input, leading to better predictions and decisions.
- Executive leadership gets faster, more reliable reporting and confidence in decision-making rooted in accurate, real-time information.
Culture change and caution
Obviously, none of this happens with software alone. It requires a cultural shift. One where data quality is everyone’s business, and where automation is trusted because it’s transparent and meaningful for the entire organization from data teams to business stakeholders.
That means setting clear rules: which sources are considered authoritative? What level of completeness or match is needed to auto-approve a record? What gets flagged, and why?
Building these rules collaboratively across IT, data teams, and the business helps secure buy-in and steadily builds trust: in the data, in the systems, and in the process itself. When people see that automation makes their lives easier without losing control, adoption follows naturally.
Still, there are challenges to watch for. Automating bad processes just makes bad outcomes happen faster. Or in the words of George Westerman, Senior Lecturer and Principal Research Scientist at MIT Sloan School of Management, “When digital transformation is done right, it’s like a caterpillar turning into a butterfly, but when done wrong, all you have is a really fast caterpillar.”
So, the foundation must be strong: starting with clean, verified, and trusted data core and well-defined governance.
The path forward
As more companies move toward digital operating models, the pressure to get enterprise data foundation right will only grow. Whether it’s onboarding a new supplier in Asia, integrating a new acquisition in Europe, or validating a customer in North America, speed and accuracy are both expected. And no longer elusive to combine.
The good news is that the tools, frameworks, and networks to make it happen already exist. What is needed is the will to rethink the role of master data, not just as an asset to manage, but as a capability to automate and scale.
In that future, master data won’t “just” support business. It will empower it.
