The CFO guide to data management strategy has been saved
The CFO guide to data management strategy
Modernising data infrastructure with data management tools
Data management tools and techniques are evolving rapidly—and they’re helping finance leaders solve thorny data challenges in a matter of months, not years. While there are no silver bullets, you may be able to apply digital finance capabilities in far less time than you thought possible or were led to believe. So, if data quality is a problem—and you keep hearing “the systems don’t talk to each other”—then it might be time to explore new options.
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 practical way to collect, process, and act 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
Digital technologies are helping to reshape how Finance does business—lowering operating costs, effort, and risk while increasing the analytic value and transparency of financial data. Here are some of the ways finance teams are using these technologies to tackle data challenges.
1. Financial planning
- Shift from spread sheet 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
2. Finance operations
- 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
3. Decision support
- 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 organise 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:
Linking tabular product and contract data with invoice and purchase-order data to detect spend and savings patterns.
Using analytics to gain new insights from marketing spend and sales performance data, resulting in more targeted marketing programs
Applying quantitative techniques to understand correlations between business performance, macroeconomic conditions, and internal results
Using analytics to enhance all-in profitability analyses across the enterprise, enabling leaders to pinpoint what’s truly driving performance
Leveraging machine learning to spot new patterns and formulate rules for automating cash-allocation categorisation
Using a tool called CogniSteward™ to create business dashboards based on data from Excel and other sources
Deploying another tool—CogX—to extract data from engineering drawings and store it in Excel, facilitating new business insights
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
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
Using CogniSteward to integrate customer data across systems and apply machine learning-based clustering algorithms to identify overlapping customers
How is this possible?
You’ve hired a new employee on your data governance team and quickly realised you’ve made a great decision. She’s able to sort through millions of data points in record time, accurately organising and scrubbing intricate data sets. This helps your team draw fresh insights from information that's been building in scale and complexity for years.
She’s also a quick study. After some initial guidance, she’s gained confidence and independence, learning from her mistakes and building proficiency. Even when tasked with supporting the data strategy for a complex divestiture, she rose to the occasion, readily sorting and cataloging both structured and unstructured data while simultaneously flagging confidential information.
You may be thinking that this level of work isn’t humanly possible; and you would be right. Your new hire isn’t human. She’s a data management solution powered by artificial intelligence (AI) and machine learning (ML). These technologies, which we are lumping together in the interest of simplicity, can perform tasks normally requiring human intelligence. They can be used, for instance, to automate historically manual and time-consuming data cleansing and profiling processes while also boosting the accuracy and reliability of output data.
Using AI and ML to optimise data management
To optimise your data infrastructure, you can use AI and ML numerous ways, as shown in these examples.
To prepare for a complex divestiture, the company had to determine what unstructured data to transfer to the separating entity and what to retain in-house.
Used ML to triage unstructured data based on ownership, content, and usage; group data into “leaving” and “staying” buckets; and mark sensitive information for special treatment.
Food services distributor
Following a series of acquisitions, the company was unable to identify overlapping customers and vendors listed in legacy and acquired databases.
Used ML-enhanced clustering algorithms to identify common records across databases. The cleansed data, in turn, provided valuable insights for coordinating go-to-market strategies and margin-improvement initiatives.
Aerospace and defense technology manufacturer
The company needed to optimise its inventory management processes to ensure customers’ aircrafts would be mission-ready on schedule.
Used dynamic algorithms and metrics visualisation to address common data quality issues and data mismatches. Also created ML-based statistical models to improve data output
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.
- Decide what insights you need to run the business.
- Consider the tools available to collect, manipulate, analyse, and deliver necessary information.
- Align your leadership team.
- Build your data ecosystem, working toward enabling automated data feeds, data set integration, true self-service capabilities, and new tools for insight-driven decision-making.
- Equip your workforce. Consider ways to build or buy the talent you’ll need. For more on this topic, see Crunch time: The finance workforce in a digital world.
- If it’s feasible, test different approaches in different markets. This will let you compare results to gauge what’s best for the company long term.
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.
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