Advanced data analytics: an entrepreneurial imperative?
Companies should not overlook the changes data analytics has brought into their business environment – as it contains huge opportunities.
Advanced data analytics is regarded as the new gold. In the past, it was difficult to identify and quantify the specific added value and actual results of effective data management. However, as the costs and accessibility of artificial intelligence (AI), cloud services and other analytical instruments are steadily declining, and there is greater clarity about key analytical areas, the targeted use of data analytics is increasingly becoming a focal point for many companies. Today, no company can afford to ignore the potential advantages of data analytics for business decision-making processes, for example regarding an improved profit margin or stronger customer loyalty. This would be a considerable competitive handicap. The prime objective of data analytics is to obtain more detailed information and draw conclusions to flow into ongoing operations in a timely and targeted manner. Clear responsibilities and governance structures in the various functions and business segments are key to successfully unlocking the value potential of data.
While converting data into insights is a technical task, the translation of insights into added value requires a sound understanding of the business and financial impact. The finance function has always been responsible for translating financials into business activity, and already works in a cross-functional manner by monetizing each of the company’s actions, options and activities. Ultimately, it is in the unique position of being able to collect data from every area of the company as well as to process and present this data for management decisions. CFOs today are expected, from an internal management perspective, to provide real-time insights from data of all types, also increasing beyond the scope of purely financial insights. To this end, the finance function requires access to structured financial data as well as other structured company data and unstructured market data. Only by doing this can the finance function use analytics to map complex value relationships, influence their impact on financial ratios, simulate potential scenarios and reliably predict relevant market developments.
On the other hand, CFOs are under immense pressure to prepare information for a host of different external stakeholders, ranging from customers through to social influencers. As a rule, more transparency has a measurable effect on a company’s market position, image and value. Strengthening the finance function so that it can prepare a factual basis for decision-making, namely through the optimal use of data analytics, is therefore vital. Multiple factors determine the extent to which companies proactively explore the possibilities of data analytics or adopt a wait-and-see approach. These include the willingness to change or the pressure felt by companies from external stakeholders and their analyses.
CFOs are gradually taking on the role of Chief Value Officers - they are becoming a pivotal constituent of a data-based business strategy and drivers of corporate added value.
Competitive advantages and risks from advanced data analytics
Leveraging advanced data analytics can open a world of possibilities, whereas limiting data analytics could bring competitive disadvantages.
Advanced data analytics is often perceived as an opportunity to address business challenges with the aid of new technology. It can also be used in the finance function to create added value for the company.
1. Customer lifetime value
Customer lifetime value (CLV) is a key management ratio to measure the contribution margin that customers or customer groups realize during the entire course of their “lifetime.” A significant factor that affects CLV is the customer’s trust in a brand or company. The traditional parameters for calculating CLV (customer loyalty, product mix, product margin, age, purchasing power) no longer fit: data analytics used by consumers increasingly determine the customer loyalty parameter. Only a few years ago, customer loyalty primarily depended on direct product experiences and marketing. Now customers can use analytics solutions to gather information on every conceivable aspect of a product and promptly change their assessment.
One example of such an analytics solution is Codecheck, a free service that provides information on harmful ingredients in foodstuffs and cosmetics. The app scans the barcode of products and instantly provides a rating regarding the origin, the ingredients used and how healthy or unhealthy they are. Codecheck receives the required information for these assessments from institutions such as the EU Commission or the California Department of Public Health. This guides purchasing decisions without companies being able to influence the process. Consequently, a control measure based on CLV must not only include the financial ratios, but especially also external analytics on product, brand and consumer ratings under the customer loyalty parameter.
2. Cost of capital
A company’s cost of capital also highlights the significance of analytics for the finance function. While cost of capital mainly depended on a company’s creditworthiness and economic perspectives in the past, the increasing importance of sustainability and other social factors are causing the spotlight to shift to “green funding.” Institutional investors the world over are placing intense pressure on companies to operate ethically and sustainably. Analytics allow investors to gain extensive insights into a company’s environment, social and governance (ESG) footprint using data freely available on the market. Companies that fail to perform adequate data analyses in this context may be unpleasantly surprised by external findings about their company and suddenly face a drop in investor interest and rising cost of capital.
Investor decisions based on ESG67%
of investors in the 2020 EY Climate Change and Sustainability Services Institutional Investor survey make a “significant use” of ESG disclosures.
For example, the Norwegian sovereign fund sold several large investments in international companies due to breaches of maximum environmental emissions and other metrics. Its active investment management policy provides explicit limits for measurement ratios in the area of sustainability.
Moreover, ESG scorecards and ratings are gaining increasing weight in the context of available capital. This is already affecting access to and the cost of capital to a large extent.
of investors state that non-financial performance plays a pivotal role for their decision-taking.
Evaluation of non-financial disclosure72%
of investors surveyed say they conduct a structured, methodical evaluation of non-financial disclosure.
3. Management of reputation risks
Classic reputation risks often arise as a result of global-scale scandals, which prompt customers to reassess a company. But advanced data analytics increase the possibility that even smaller scandals can be aggregated to a larger overall picture. Or analytics can allow external parties to question or refute assertions in companies’ sustainability reports. This may not necessarily stir up scandals with significant public impact, but a reputation risk may creep in nonetheless.
A key objective of the finance function will therefore be to align quality assertions in the financial statements with the data available on the market. In contrast to external analyses, the CFO must always be in a position to gain deeper insight, access better information and make more accurate forecasts than external parties. The establishment of a management and analytics model is necessary to allow for prompt reporting on key value drivers.
4. Market access
In addition to customers, investment companies and activists, companies are also facing another stakeholder group that intends to increase its use of data analytics: governments, authorities and other institutions. An increasing reporting burden is being placed on the finance function, especially in the regulatory environment, which cannot be managed adequately without analytics and flexible reporting solutions. Another lever will be the rating and assessment of company information by authorities, as is the case already with taxes.
The Chinese Corporate Social Credit System (CSCS), scheduled for implementation in 2021, will force the trend significantly further by recording and analyzing publicly accessible corporate information across various parameters. To achieve this, China plans to use data analytics and AI to collect the digital data footprint of companies and analyze the data in real time. The company rating from the analysis will likely have a direct impact on the company and could include sanctions such as higher tax rates, exclusion from project tender procedures or blacklisting. The positive effects are fewer customs inspections, swifter processing of applications and much more.
Even if the finance function does not necessarily bear the responsibility of monitoring the data analyses planned in the CSCS, its integration within the finance function should be endorsed. On the one hand the finance function is also responsible for other ratings, while on the other, embedding the main driver of CSCS in the internal control model alone already leads to operationalization and use of all relevant data in day-to-day decision-making processes.
Success factors for the use of advanced data analytics
Utilize data analytics to the best effect to benefit your enterprise.
In a survey conducted by Accounting Today in 2019, 92% of the respondents, primarily senior executives, indicated they planned to invest in data analytics in the subsequent year.1 However, only 28% were of the opinion that their company had a suitable database to use data analytics effectively.
According to the EY survey DNA of the CFO 2020, only one third of companies currently estimate that they have adequate capabilities in advanced data analytics or AI. Perhaps even more importantly, only a small contingent of the CFOs surveyed were of the opinion that their companies had made good progress with regard to data access and its use. A lot of companies would first have to set up an internal infrastructure designed to handle analytics. However, there are ways and means to make this first step considerably easier.
The use of advanced data analytics as part of the finance function is based on three critical success factors.
Firstly, a clear strategic direction must be developed for the use of advanced data analytics. Together with an integrated change management initiative, necessary change processes can then be implemented. A clear, future-oriented target state is necessary and must combine the strategic and technological components of a company-wide uniform data utilization system. This can be closely monitored by management with strong leadership support.
Secondly, companies will need a flexible culture and work methodology with interdisciplinary teaming. The ideal team will have a broad range of core competencies, from data scientists, programmers and business analysts through to on-call experts. These skillsets will be able to address specific questions and drive the project forward with new approaches, value-creating innovations and data-based decisions. The right team composition thus forms the link between the strategy and the operational challenges of the company as a whole, including IT and the finance function.
The third success factor is the right infrastructure and architecture for analytics tools and methods. Strategically valuable information can be extracted from a pool of structured and unstructured internal and external data using statistical models, algorithms and cloud-based tools and solutions. Using specific mathematical methods and toolsets enable companies to model future scenarios and automate decision-making aids. Machine learning, AI, data and process mining as well as sentinel and semantic analysis make it possible to integrate internal data with enriched external data. This combination can produce reliable new findings that can be used to derive KPIs and create company-wide added value.
Cloud-based infrastructure provides access to significant and scalable computing power, making calculations of the most complex data models and solutions possible. More and more plug-and-play solutions are also available, which employees can use without specific programming experience. One of the greatest hurdles in the implementation of advanced data analytics is the high initial cost to implement the infrastructure and architecture. However, this can be reduced thanks to scalable cloud options, thereby lowering the initial start-up threshold compared to just a few years ago – provided the company has the ideal team and a robust strategy in place. The ultimate challenge facing CFOs in this decade is combining all three elements in an appropriate time period.
All you need is a dependable strategy.
Now more than ever, CFOs have the means of contributing to the success of their companies in a sustainable manner thanks to solid data analysis. In this way, the finance function’s key focus will shift from a unit primarily providing internal transparency and transactional services to that of a business partner providing added value.
Business partnering will be decisive for companies using advanced data analytics, allowing them to increasingly strengthen their market position, by making advanced data analytics an integral part of their DNA. Largely basing their decisions, forecasts and communication on data analytics means they can become the kind of company that stakeholders expect them to be. Not only does the company benefit internally from better and fact-based decisions, but it is regarded as a reliable partner on the market by consumers, investors, employees and other stakeholders.
Reliable information, accurate forecasts and transparency vis-à-vis various stakeholder groups allow for informed purchasing decisions, investments and regulatory clarity. The companies consequently benefit from the trust placed in them over other companies.
In a world shaped by constant change, it is critical that CFOs collaborate on the corporate strategy and help communicate it to stakeholders on the basis of well-balanced information. Data analytics can make a decisive contribution toward achieving this objective and will be a, if not the key, cornerstone for valuable contribution by CFOs.
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- G. Silverman, "How the CFO can build a data-driven company," Accounting Today, accountingtoday.com, 18 March 2019, https://www.accountingtoday.com/opinion/how-the-cfo-can-build-a-data-driven-company, accessed 29 March 2021.
In today’s dynamic world, businesses can gain a competitive advantage by introducing advanced data analytics in their finance function. Intensive usage of data by diverse stakeholder groups contains opportunities and risks for an enterprise, and creates the need for a reliable corporate data strategy. Once CFOs leverage data appropriately they can unfold immense benefits for their company on internal as well as external front.