How analytics can help in better insights, improved governance and enhance decision making

How analytics can help in better insights, improved governance and enhance decision making

By 2030, the world will enter the 6G era — an intelligently autonomous, sensory, massively distributed but highly networked world that blends our physical, digital, and human systems. Data will be created on an unimaginable scale, changing how we collaborate, convene, create, and contextualize.

We always knew how important transformation was to an organization’s enduring success. For decades, this process of organizations changing their operations to improve performance and drive sustainable growth was episodic. Every few years, shifts in market sentiment or stakeholder demands would push leaders to make incremental changes to adapt, or to reimagine their organization from the ground up.

However, over the last few years, there has been a shift in the nature and speed of transformation. According to 82% of board members and CEOs in the EY 2021 Global Board Risk Survey, market disruptions have become more frequent and impactful. To keep pace, companies have begun to transform more frequently. The need to transform — and to do so continuously in the face of disruption — is now critical for organizations to survive.

This article summarizes how analytics can help in better insights, improved governance and enhance decision making.

Supercharged by emerging technologies, the world is entering an entirely new generation of data and analytics. Not tomorrow, right now 53% of senior executives have identified data and analytics as their top investment priority in the next two years based on a survey conducted by EY Global. This is an increase of 50% since 2020. These next generation analytics would just appear out of the ether. They will be built on a flexible, agile, and fast-evolving combination of technology, ecosystems, and talent.

Data is emerging as the strategic currency of the digital age. It will be generated based on the success of how humans work together across an enterprise, including functions, data and analytics teams and technology, to drive a cultural shift. In fact, using advanced data analytics is no longer a purely voluntary decision. External stakeholders, such as investment funds, analyst firms, consumers, employees and, of course, activists, are using freely accessible external data to analyze companies across various dimensions. This harbors the risk that companies which do not intensively use data analytics can be overrun by external analytics results. This could lead to unwanted public attention with regard to their business activities and change the way customers perceive them. It could also result in better forecasts of its actual market potential by third parties, etc.

Evolved finance function to improve governance

To focus on long-term growth, CFOs need to use forward- looking measures in tandem with backward-looking KPIs like return on invested capital.

Companies today operate in an extremely dynamic environment. The time available to respond to any changes is becoming shorter and shorter. The role of the CFO has taken on a new importance in helping companies navigate through change processes. On the one hand, CFOs are responsible for external reporting and, in many cases, for the preparation, interpretation and management of (financial) information. Accordingly, the finance function’s documentation tasks are being replaced by a strong focus on added value.

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 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 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 influencing the process. Consequently, a control measure based on CLV must not only include the financial ratios, but especially also include external analytics on product, brand, and consumer ratings under the customer loyalty parameter.

Reducing cost of capital

A company’s cost of capital also highlights the significance of analytics for the finance function. While the 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 all over the world over are intensely pressuring 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 analysis in this context may be unpleasantly surprised by external findings about their company and suddenly face a drop in investor interest and the rising cost of capital.

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.

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 stir up scandals with significant public impact, but a reputation risk may creep in.

Deeper business insights

We are entering a new era of data centricity, with over half of senior executives now identifying data and analytics as their top investment priority. This shift is enabled by a sophisticated new tech stack of cloud, Artificial Intelligence (‘AI’) and the Internet of Things – the top technology priorities for transformation leaders. The most successful technology transformations put human – committed leadership and empowered employees – at its center.

The use of data has been of critical importance to companies for many years. The current trend in data analytics is also opening new possibilities and opportunities, thanks to increasing technical sophistication and steadily lower barriers to entry. Companies can establish a competitive advantage over peers by monetizing financial and non-financial data from a wide range of sources. They can also present their contribution to society based on data, or address changes in market conditions earlier and more proactively, as well as derive value-added decisions from them using driver-based management models.

margin or stronger customer loyalty. 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.

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 analysis, 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.

Creating new opportunities

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 the following added value for the company:

Enhancing 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) are no longer relevant. 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.

Glide path for implementation

The use of advanced data analytics as part of the finance function is based on the following 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 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.

How we look at it

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 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.

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 contribute decisively toward achieving this objective and will be a, if not the, cornerstone for valuable contribution by CFOs.

In the dynamic world, advanced data analytics in their finance function is imperative. Data usage contains opportunities and risks for an enterprise, creating a need for corporate data strategy. CFOs can leverage data to gain benefits for their company on an internal as well as external front.