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Discover the state of Financial Advanced Analytics Readiness

Arvato Financial Solutions survey results reveal overall trends of Financial Advanced Analytics maturity in different industries

Machine Learning and Advanced Analytics are top priorities for many businesses. Managing the digital transformation is an even bigger challenge for an organization, including the finance department. Helping companies tackle this exact challenge has been my passion for many years. “How can we get into Financial Advanced Analytics?” is a question I have been asked multiple times. I have learned that progress is best achieved through answering a slightly different question, which is “Are we ready for Financial Advanced Analytics?” – and if so, to what extent and in which areas?

Since October 2018, we at Arvato Financial Solutions have been offering an online financial analytics maturity self-assessment that allows finance departments to find out where they stand on the analytics journey. Depending on results, participants were classified into three categories (laggards, adopters, or leaders) and could find tailored recommendations to improve the Financial Advanced Analytics Readiness of their organizations in our Whitepaper. Questions and recommendations were designed along with the six key components of a successful Financial Advanced Analytics strategy. The survey results reveal overall trends of Financial Advanced Analytics maturity in different industries that can show you where your business stands in comparison to others – and that it is not too late to get started if you haven’t done so yet.

So, what is the state of Financial Advanced Analytics Readiness?

Strategic Readiness: Getting started

Question: Does your leadership communicate and execute a data & analytics strategy specifically for finance?


Advice:  A clear vision is crucial for the success of any data-related initiative, so defining a dedicated analytics strategy that is aligned with the overall corporate strategy should be at the top of any data & analytics agenda.

Strategic Readiness: Identifying challenges

Question: Have you identified concrete challenges within finance (e.g. revenue lifecycle) that you would like to solve with Advanced Analytics (such as Machine Learning or Artificial Intelligence)?


Advice: Identifying concrete challenges and use cases is a core foundation of any Advanced Analytics project – the answers that data is supposed to provide can only be as good as the questions you ask. If you identify concrete challenges, it will be easier for your business to unlock the potential hidden in big data.

Operational Readiness: People

Question: How do you make sure the expertise for Advanced Analytics is available in your finance department?


Advice: To become a data-driven organization, people with corresponding profiles are essential. The majority of participants have recognized that an educated workforce is needed and have already taken steps towards building teams in which people have data-driven profiles. A combination of in-house talent and external support can be a good option depending on time, budget, and strategic business goals.

Operational Readiness: Tools

Question: What describes best the tools you utilize to perform data analysis in finance?


Advice: Using dedicated tools is a sign of greater maturity on the Advanced Analytics journey and a necessary step towards gaining better and faster insights from data. Exploring and testing different tools is a logical first step on the journey towards leveraging analytics in finance. It is important to bear in mind that the tool will be used by both financial and analytics experts.

Operational Readiness: Data

Question: Are cross-function databases accessible for financial analytics to evaluate the financial impact of operational decisions (such as marketing campaigns or sales initiatives)?


Advice: Data is both the center and source of all Financial Advanced Analytics projects. A centralized data hub to evaluate the financial impact of operational decisions across departments is essential – dissolving data silos is one of the first challenges faced by any organization. Knowing what data is available helps you determine the goals you can reach.

Models and methodology

Question: Does your company currently deploy analytical models for decision-making or predictions (such as fraud prevention and forecasting accounts receivables)?


Advice: Knowing your business, your use cases, and goals, and testing your assumptions in a proof-of-concept will help you build a model for predictive analytics that can be deployed across your entire organization.

Operational model

Question: How do you manage and maintain your in-production models?


Advice: Once the model has been deployed, it needs to be adapted continuously to changing conditions to ensure optimum success and impact of Financial Advanced Analytics projects. The only constant is change – a monitoring framework is, therefore, key to the continuous success of your projects.

Cultural Readiness: Change management

Question: Advanced Analytics projects are supported by all stakeholders and help to achieve mutual goals by transparent communication.


Advice: Implementing Financial Advanced Analytics is not like introducing just another new tool. It’s a project that has the potential to change the company culture and the way everyone works. Therefore, make sure your project isn’t just mapped out, but also communicated across the entire organization. The buy-in of key stakeholders is essential for success.

So, how does your organization rank? If you don’t know the answer, get it here by assessing your own analytics maturity. Now is the best time to join the leaders in this field and get started with your own Financial Advanced Analytics projects – it’s not too late to get started if you want to set your organization apart from competitors.

Are you unsure to what extent and in what areas your business is ready to become a data-driven organization? Download our guide to discover next steps.

Do you need more information? Then email us here.


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