The CFO guide to data management strategy has been saved
The CFO guide to data management strategy
Modernizing data infrastructure with data management tools
Cutting through the noise
As companies generate more and more data each day, finance teams have seemingly limitless opportunities to glean new insights and boost their value to the business. But if it were easy, everyone would do it. The problem is, the amount of data emanating daily from various sources can be overwhelming. In our Finance 2025 series, we call this the data tsunami. To manage it, businesses need a data management strategy for collecting, processing, and acting upon reams of information.
Transitioning to a cloud-based ERP is one way to tackle the problem. We’ve covered this approach in depth in other Crunch time reports. But many data challenges can be addressed throughout the enterprise with simpler or more targeted solutions—and that’s the focus here.
Getting a handle on your data:
Shift from spreadsheet models and intuition to automated, analytic-based models
Integrate cloud planning systems with data lakes to address combined internal and external data needs
Ensure consistent data categories and federated aggregation processes from the corporate core
Create hierarchies that can handle evolving management, financial, and regulatory reporting
Streamline workflows and automate reconciliations across sources to increase journal entry traceability and audit responsiveness
Leverage advanced analytics using machine learning for exception and risk identification
Clarify data definitions across business units, geographies, and source systems
Unlock insights using a big data or cloud-based data-staging environment so data is accessible anywhere it resides, including the ERP
Create interactive reports that let users drill down through multiple layers of information
Leveraging technology in finance transformation
Advances in digital technology offer CFOs new options in data management—particularly when your current systems are not on speaking terms. Cloud-based architecture can organize and reassemble data on the fly. Advanced analytics tools let you draw conclusions from data points spanning multiple platforms. Machine learning and AI can apply controls and monitor risks—enabling course corrections in real-time.
Here are some ways Finance and IT, across diverse industries, are leveraging technology to extract more value from the data they collect.
|Health care: Linking tabular product and contract data with an invoice and purchase-order data to detect spend and savings patterns||Automotive: Using a tool called CogniSteward™ to create business dashboards based on data from Excel and other sources|
|Consumer products: Using analytics to gain new insights from marketing spend and sales performance data, resulting in more targeted marketing programs||Aerospace: Deploying another tool—CogX—to extract data from engineering drawings and store it in Excel, facilitating new business insights|
|Banking: Applying quantitative techniques to understand correlations between business performance, macroeconomic conditions, and internal results||Energy: Allowing data quality queries to be plugged directly into CogniSteward so the tool could perform consolidated data analysis, match, and merge for future M&A|
|Asset management: Using analytics to enhance all-in profitability analyses across the enterprise, enabling leaders to pinpoint what’s truly driving performance||Cable: Employing sparse matrix algorithms to simulate the effects of data outages from 15 minutes to 12 weeks, producing predictions within 12 to 18 seconds of actual viewership|
|Payment processing: Leveraging machine learning to spot new patterns and formulate rules for automating cash-allocation categorization||Food service: Using CogniSteward to integrate customer data across systems and apply machine learning based clustering algorithms to identify overlapping customers|
In short, digital tools and techniques are fundamentally changing how Finance gathers and consumes data—and enhances its value to the business.
So you’re ready to improve your data. Now what?
If you want to improve the quality of your data and boost Finance’s core capabilities, start with solutions using your existing systems, with an eye toward eventually automating and enhancing how your data is developed, delivered, and consumed. Follow these six steps, and you’ll be on your way.
And yes, it may add to the overall timeline. But the downside pales in comparison to the cost of rolling out a company-wide data management tool that proves wrong for the business.
The last word
For any business, big or small, achieving desired outcomes starts with good data. And new tools using artificial intelligence, machine learning, natural language processing, robotic process automation, and other emerging technologies can automate data management and improve data quality—better and faster than ever before.
You don’t need to spend a fortune to reap the benefits, and you don’t need to tie up your resources for years. Instead, set your priorities, explore your options, and take small steps you can build on over time. With a little poking around, you might be surprised at what’s possible.
Enabling digital finance with Oracle Cloud
The delivery model for next-generation data and analytics architectures