Why CFOs need to become data natives
Anders Liu-Lindberg
Leading advisor to senior Finance and FP&A leaders on creating impact through business partnering | Interim | VP Finance | Business Finance
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How does the CFO develop a winning partnership with technology? Harnessing Artificial Intelligence to improve the finance function exposes uncertainties and concerns about the future of the CFO role. However, the most productive and effective model is for AI and humans to work in partnership.
The volume of new data available to companies is increasing exponentially. The amount of data already held in organizations is already substantial. Processing and interpreting all of this data, extracting meaningful insights and value, is now beyond the scope of the human resources in the finance function. This is a job for AI.
We spoke to Simone Bohnenberger-Rich, Head of Product at Eigen Technologies about their Eigen AI platform and the impact that it is already having on organizations around the world.
Supercharging business
Eigen Technologies was founded by Dr. Lewis Z Liu and Jonathan Feuer in 2015. “We are a team of data scientists, engineers, and product strategists based in New York, London, and Lisbon on a mission to supercharge the way organizations operate by unlocking the value of their qualitative data,” says Simone.
Eigen Technologies’ tools help in extracting data from documents, but they are very clear that this doesn’t mean AI will ‘eat’ accounting. Instead, there is a role and a need for CFOs to become ‘data natives’ to help interpret, analyze and extract maximum value from the data that AI can help to identify.
Their core product, the Eigen platform, is AI-powered and user-friendly enabling non-technical users to extract and classify information from diverse types of documents at scale.
“Our clients use this data to assess risks, find opportunities, automate manual processes, meet regulatory requirements, and ultimately drive down associated costs and timescales,” explains Simone. The results have been startling. Clients have reported cost savings of up to 80% and increases in process efficiency of 75%.
Eigen’s clients include some of the most well-known and respected names in finance, law, insurance, and professional services, including Goldman Sachs, ING, BlackRock, Aviva, and Allen & Overy. Almost half of all global systemically important banks (G-SIBs) use Eigen to overcome their document and data challenges.
Unlocking unstructured data
The problem that the Eigen team set out to solve was that of unusable data. “Experts estimate that approx. 80% of a company’s data is unstructured,” explains Simone. In particular, this includes written text containing dates and numbers. The data is siloed, unstandardized, and therefore difficult to analyze and action.
“Think of all the information that you need to maintain and refer to that’s burrowed in contracts, emails, invoices, lease documents, reports, etc,” says Simone. “Organisations need an easy way to extract value from these data sources and documents that pervade business and our work lives.”
The idea for Eigen’s intelligent automation and document processing technology was born when Lewis Liu, Eigen’s co-founder and CEO, realized that the maths underlying his research in physics at the University of Oxford could create algorithms that could ‘understand’ and precisely extract information from complex documents.
“People are the most important resource for any organization and should be focused on the highest-value work,” says Simone. The human in this partnership should be making informed decisions, rather than compiling and re-keying pages of information. “We developed the Eigen platform to enable any organization to extract value from these sources, straight-through process this information and optimize their data flows to maximize the potential of their people through better use of organizational data.”
A perfect partnership
What is the perfect partnership between CFO and machine? “In finance, the ability to produce, analyze and act upon data has a direct impact on performance, productivity and profitability,” says Simone. “AI-powered intelligent automation tools can eliminate manual document and data processing and provide accounting and finance teams with faster access to the information they need for cash flow, financial planning, taxation and reporting purposes.”
One message that comes through loud and clear is that AI should not be seen as a threat for finance professionals but as a tool to make their lives easier. AI creates opportunities for CFOs to improve their teams’ efficiency, supply more meaningful insights and deliver greater value to the wider organization than previously possible.
Digital and data skills combine
How do we become data natives? Simone explains that there are some very specific skills that we need to develop to seize this opportunity for value creation.
“By the very nature of their role, accountants are data experts, but they must be digital experts too,” she says. “To ensure the financial data needs of their organization are met in digital transformation programs, finance professionals must understand how the latest technology and tools can help them to successfully gather and analyze real-time and historical data from a myriad of new as well as existing sources.”
The new Data Native CFO needs to start with a basic understanding of data extraction, integration, analytics, and business intelligence platforms. “Data normalization processes will give finance professionals an advantage when it comes to getting access to the information they need.”
The mindset of finance has to be focused on the value inherent in data-driven insights, and what is required to unlock it. “The value of organizational data is realized at the point it becomes an insight that powers a decision,” says Simone.
Transformation in finance
We are talking about a real and needed digital transformation in finance. There will be cultural and behavioral challenges to address, and clear steps to take to set up a data extraction AI.
“Before undertaking any data-focused digital transformation, it’s imperative to be clear about how the output will be used, by whom, why, and when,” says Simone. “CFOs are ideally placed to do this as they inherently understand the business context and the implications of getting the data wrong.”
Having this focus enables the new data native CFO to be clear on the requirements for transformation, including which systems need integration and which data sets are within scope. The success criteria, key metrics, and milestones should be agreed upon with internal stakeholders upfront to avoid any confusion or disappointment. And of course, as with any program of change, it’s important to have a project team in place to drive the effort forward and coordinate with the impacted internal teams.
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What does the future hold?
AI-powered solutions are driving cost savings and tangible business benefits. They are here to stay and will become more prevalent as organizations step up to grasp the opportunity of this exciting frontier technology. When deployed and used effectively, AI can automate information gathering and remove the human effort required to find and key data into financial systems. “It’s now become a case of when, not if, your organization needs to find a better way to make data useful and actionable,” says Simone.
To understand the potential financial impact that implementing the Eigen platform can have on your organization, you can download the Forrester Total Economic Impact™ (TEI) study from the Eigen Technologies website.
This was the ninth article in my latest series "Frontier Technologies for Finance". You can read the previous ones below. Remember to click subscribe to be notified about future articles.
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Anders Liu-Lindberg is the co-founder and a partner at the Business Partnering Institute and owner of the largest group dedicated to Finance Business Partnering on LinkedIn with more than 10,000 members. I have ten years of experience as a business partner at the global transport and logistics company Maersk. I am the co-author of the book “Create Value as a Finance Business Partner” and a long-time Finance Blogger on LinkedIn with 70.000+ followers and 150.000+ subscribers to my blog. I am also an advisory board member at Born Capital where I help identify and grow the next big thing in #CFOTech.
SaaS Optical Character Recognition (OCR), Automation, Business Analytics and Finance Business Partner
2yIts all about quality data. Sound decisions are derived from critically analyzed data and presented succinctly as actionable insights. Client's trust and confidence with the business partners are for sure essential. Thanks Anders Liu-Lindberg for sharing.
Writer / Optimist / Servant Leader / Quality / Controlling Specialist / Manufacturing Expertise / Experiences in Sales / Father / Husband / Lover of Life 🙂
2yAnders I Imagine that your company is doing this and employing this? How do you work with the mindset change to have people think more automation? Within a large employer change is slow as the implementation for programming has to rolled out for each location to be most effective, but there are ways you can effect change before those steps I am sure. What would you suggest you could do on your own first in order to begin and prepare people for more AI?
Clear financial guidance for Attorneys & Small Business Owners 📶
2yGreat that you're emphasising this sort of discussion Anders!
Sr. Director of IT Infrastructure
2yIt starts with business partnering but will end if the trust is broken.
Helping FP&A Professionals provide value to their businesses | Founder of The FP&A Guy | Host of 3 popular Finance podcasts | Microsoft MVP
2yThis line spoke to me: The new Data Native CFO needs to start with a basic understanding of data extraction, integration, analytics, and business intelligence platforms. “Data normalization processes will give finance professionals an advantage when it comes to getting access to the information they need.” I wish more finance professionals understood the concept of data normalization because it unlocks your data and allows for value-added insights.