ESTIMATED READING TIME: 10 minutes
The financial industry may point the way for the financial function when it comes to using the cloud as an analytics powerhouse.
- Although the majority of CFOs have implemented analytics programs and practices within their departments, not many would yet say their approach to finance analytics has been very effective to date.
- By contrast, the financial services industry has made advanced analytics a keystone of their business processes and functions, leveraging cloud environments for corporatewide availability and growth.
- By deploying these cutting-edge capabilities now available in cloud-based analytics platforms, CFOs in other industries are starting to radically transform their function’s benefit to the corporation, moving from (primarily) controlling, measuring and reporting business results to enabling business growth and success.
The de facto COO?
In case you haven’t noticed, many CFOs today are becoming the de facto chief operating officers of their companies. Thanks to the growing interconnectedness of global markets and the increasing volatility tied to events of both human and natural origins, many CEOs have discovered they need a strategic partner in the C-suite with a clear-eyed view into their company’s current state and future risks and opportunities.1
Another, not-unrelated trend we’re seeing: many CFOs today increasingly come into their current jobs from investment banking.2 As a result, and due to competitive pressures, many of the leading CFOs are applying the kinds of analytical tools that their commercial banking and investment have been using to competitive advantage for years.
CFOs add “analytics” to their job descriptions
A recent survey by the American Productivity & Quality Center (APQC) of 200 high-level finance department professionals3 found that more than two-thirds have been conducting analytics since at least 2014 — albeit with mixed views of their results. Only a third said they’re using the most advanced forms of analytics — prescriptive analytics — for most of their financial processes, and only 35% said that external data — industry, competitor, market trends, benchmarks — were used as inputs for their analytics. Of these finance executives surveyed, less than a quarter regarded their current finance analytics approach to be “very effective.” (Most said “effective” or “adequate.”)
There seems to be a disconnect between the CFOs who are able to access and take action upon the majority of key operational, financial and economic data with immediate effect, versus those who are only able to do that with a handful of that same kind of data.
This disconnect may derive from a misapprehension among the more traditionalist CFOs. “Of course investment banks, private equity firms, and born-digital enterprises have robust analytics capabilities!” they may say. “Bespoke, proprietary tools, algorithms and models are central to their business model.”
And they’re not wrong. But what they fail to understand is the extent to which their more strategic colleagues in industries other than investment banking are embracing cloud capabilities particularly to rely on the advanced, custom analytics engines and toolsets available in that cloud environment — because advanced analytics capabilities are now available for every company in every industry.
Roadblocks to bigger and better data
Like other corporate functions working to modernize their operations, many finance departments’ transition into advanced analytics maturity is often hamstrung by inconsistent data standards or silos of data across the wider organization. Further — and from what I’ve seen, far more frequently than their CMO colleagues, for example — CFOs have often been slow to take advantage of the kinds of ecosystem data or the collections of universal data that could provide a more accurate picture on a range of operational and financial issues.
For example, the growing use of online markets dedicated to B2B transactions, alongside or even wholly independent of the better-known B2C e-commerce and online media environments, has created a growing pool of financial and sales data from these new business ecosystems. Similarly, many corporate operations and even startup strategies are being built around the hundreds (soon to be thousands) of publicly available datasets through Google Cloud’s BigQuery platform4 —such as the several genomic databases5 or the U.S. National Oceanic and Atmospheric Administration’s environmental data, to name just two examples.
The experience of CFOs who have built robust data-centric operations for their enteprise’s future, particularly when contrasted with the hesitant or well-meaning-but-naïve attempts of other companies’ finance functions, reveals the first priorities for CFOs seeking to join the ranks of their more successful colleagues: corporate data governance, data standards, and casting as wide as possible a net while improving the signal-to-noise ratio of data inputs is often the first order of business in building a robust analytics-based finance organization.
The finance industry can provide inspiration for the finance function
As already mentioned, many of the CFOs who have been most successful in leveraging new analytics and other cloud-based capabilities for their organizations have come into their positions from the worlds of investment banking, public accounting or consulting, as contrasted with the more traditional corporate-accounting route. Thus, it makes sense to look at success stories from these kinds of companies to understand what is possible for a finance function.
For example, the multinational banking and financial services company ING has used cloud-based AI capabilities to build an “early warning system” (EWS) so its credit risk analysts can make better decisions faster. According to the project lead; “Through machine learning, the EWS scans financial and non-financial information, such as news items from all over the world.” By processing up to 80,000 news articles a day, the EWS alerts risk analysts when a company’s share price drops below a certain percentage (determined by the analyst) or, even prior to that, if news coverage about a company has turned negative, based on sentiment analysis.
Other financial companies are using cloud capabilities to create entirely new offerings for their customers. To create its Smart Stream service, CME Group, the world’s oldest futures exchange (founded in 1848) used Google’s “Cloud Pub/Sub” and its managed streaming data ingestion services for real-time analytics. At launch, Smart Stream became the first derivatives exchange to offer cloud-based real-time futures and options market data, so that customers can access a full day’s history of market activity in less than 20 seconds. As a result, firms can get real-time quotes in an efficient and cost-effective way to gauge their risk exposure in real-time.
CFOs break new ground in cloud analytics
Like their finance industry colleagues, CFOs who see their role as enabling business results rather than (merely) accounting for those results have been exploiting the wide range of analytics capabilities available through cloud platforms. This has allowed them to create industry- and company-specific approaches to real-time insights for better decisions and, as a result, their companies often enjoy an advantage over the competition.
A 2017 study by Oxford Economics6 is telling. It showed that, of the 1,500 CFOs and other finance executives surveyed, only 173 (11.5%) could legitimately claim their finance function drives outstanding corporate performance. But among these more strategic CFOs, nearly all agreed that “cloud-based apps are critical to optimizing working capital; real-time analytics are critical to driving strategic growth initiatives; and predictive analytics are critical to improving efficiency across the organization.” These leaders among CFOs were also almost two times as likely as the rest surveyed to report growing market share.
What these CFOs have discovered is that analytics, cloud capabilities and rethinking the business of a business can leapfrog a company’s operations over the competition’s.
Start at the pain points
The range of advantages and capabilities that derive from cloud-based migrations and transformations are generally undertaken to solve otherwise intractable problems. These problems might have a primarily technological basis or they may pose a business challenge, but in solving them, companies often experience additional benefits, thanks to the digital ecosystems that cloud computing enables.
For example, a typical technology problem might be the increasing cost and limitations of maintaining and upgrading a company’s on-premises computing and storage infrastructure. Consider work we did with KeyBank, a Fortune 500 financial services company headquartered in Cleveland, Ohio. Their existing data warehousing platform was constrained and expanding it would be both costly and time-consuming. Yet even after large capital investments for storage license renewals, and platform upgrades, the on-premises infrastructure of their data warehouse would still have limited ability to scale to meet KeyBank’s future needs.
TCS migrated 6 of the bank’s data marts to Google Cloud’s BigQuery platform for flexible computing and storage capacity that can scale on demand. As a result, data elements from different parts of the company became available on the cloud, which enables each business line to make more accurate predictions using cloud-based analytics, artificial intelligence and machine learning capabilities. Now, KeyBank can unlock value from insights gained from data ranging from enterprise commercial payments, transaction details, mortgage servicing activity, standalone sources, and more, which enables them to deliver financial wellness recommendations and risk monitoring and alerts services to customers.
Tackling a business issue through cloud capabilities can have even greater benefits. A good example of moving from, literally, an agrarian payments model to the digital age would be work that TCS did for an ASEAN-based global distributor of fruit that engaged us to rethink the way it paid growers.
The company’s existing payment system, in use for 20 years, took a long time to complete transactions. Payments were calculated manually multiple times a year based on complicated rules, projections and actual delivery of trays of produce.
Given the high-end global market for its fruit, the company sought to modernize its payment system. TCS created a serverless architecture using the Google Cloud platform to implement a digital solution that transformed the client's payment processing system. The TCS solution used analytics and automation to forecast payments to growers according to their production and cash flow.
Being able to automate the analysis ensured accurate and faster forecasting and payment of all transactions. As a result, the company saw productivity gains with reduced cycle times to pay for produce, incentives and services. And its local fruit growers — a key stakeholder group — experienced greater satisfaction.
Industry leadership begins with departmental leadership
Like the IT department, the finance function touches every other part of the company in one way or another. In our work we have discovered that, for the companies where finance teams embrace their emerging leadership role, moving to the cloud and augmenting their enterprise core with cloud-based analytics and AI capabilities benefits both the finance function and the company at large. But the competition within different industries, and in creating new categories of competition and collaboration, is moving fast.
Any CFO hesitating to engage with their company’s strategy and operational models in a way that positions them for a cloud-based ecosystem of business will end up having to make up for valuable time and effort down the road just to keep their business viable.
Fortunately, cloud migrations have become far less onerous as the technology has matured, lowering some of the barriers to undertaking them in the first place. At the same time, some of the most advanced analytics, automation, artificial intelligence and machine learning capabilities are today only available on platforms like Google Cloud. Given these advantages, and the financial benefits and the room to grow that cloud computing can enable, increasingly the greatest obstacle to a successful cloud-based transformation ends up being only the limits of a CFO’s imagination.
1. Jane Their, “When the CFO departs, who steps up?,” CIO Dive, June 17, 2020: https://www.cfodive.com/news/cfo-departure-transition/579521/
2. Robert Freedman, “3 must-have CFO skills: Operating finance, strategy and communications,” CIO Dive, November 8, 2020: https://www.cfodive.com/news/cfo-skills-operating-finance-strategy-communications/588234/
3. Rachele Collins and Mercy Harper, “In Pursuit of Better Analytics,” CFO, June 25, 2020: https://www.cfo.com/analytics/2020/06/in-pursuit-of-better-analytics/
4. “BigQuery,” Google Cloud: https://cloud.google.com/bigquery
5. “Cloud Life Sciences public datasets,” Google Cloud: https://cloud.google.com/life-sciences/docs/resources/public-datasets
6. “How Finance Leadership Pays Off: Six Ways CFOs Stay Ahead of the Pack,” Oxford Economics, June 2017: https://www.oxfordeconomics.com/publication/open/287111