For a data-driven organization
By integrating data scientists into your financial organization, you allow finance to evolve into a more strategic and proactive role. It also helps your business stakeholders to be driven by data and make better decisions every day. This allows the business to grow more profitably. However, beyond data science, a data-driven strategy can only be successful if the entire organization shares a single source of data and metrics. Otherwise (isolated business systems, Excel spreadsheets), we end up with data silos, which prevent us from sharing information and ideas in real time.
A single source of truth for an aligned organization
The finance department must maintain a set of data models related to reserves, revenue, costs, and other sets of financial data. These must be accessible through a centralized data source, shared with other users. Working from the same assumptions and performing their own analyzes, business functions can speak the same language as the financial organization. The result is more effective and collaborative conversations between departments and a stronger alignment around business goals and vision. For example, if marketing generates a lot of demand from a successful advertising campaign, this team can make a real-time decision and ask for funding for a larger budget.
Manage your products and invest in the right data platform
Associating finance data scientists with product management only has advantages. They can model the costs, prices, and monetization associated with launching new features or entering new markets. As for product teams, they should be able to develop strategies and make decisions on their own, based on this financial model and their own analysis. This would lead to more effective discussions and easier decision making during a product review process or strategy development. From a financial perspective, a modern cloud data platform should be the backbone that makes all this possible.
Powerful cloud for near real-time access and fast decision making
First, you need to start with an extremely robust and scalable cloud system that can easily ingest massive amounts of data. You can then perform analytics of this data at scale and apply machine learning models to predict future business. In general, rather than seeing a data platform in the cloud as a cost, it’s important to focus on the benefits of real-time data and the speed of accessing that data. This power leads to stronger information and faster decisions. Many different types of data, structured, semi-structured and even unstructured, need to be processed quickly and analyzed as a whole. Third-party data, such as scores, data sets, and industry data, should complement your internal data and allow for more complete analysis. Again, this data should link perfectly to what you have in the business.
Governance and Security
With all this data in one place, it’s important to know who has access to it and make sure it’s extremely secure. Whenever data is extracted, you need to be able to see who extracted it, when, and have the ability to ask why. Using a single cloud data platform goes hand in hand with my belief that you should limit the number of one-time SaaS solutions used in your enterprise. Everything should work perfectly and all the data should exist in a central repository to have a single true system. Each additional system adds unnecessary headaches. The fewer systems you have, the easier it will be to monitor them from a security perspective, so that you always know what’s going on in your internal environment.
Invest in Data Science to make the most of your data
Companies have an exponential amount of data, not to mention the data they can access from their business partners, in the form of secondary and third-party data. Perhaps one of the biggest challenges is combining ERP data with CRM data. That’s why easily centralizing data on a cloud platform is an essential first step. Advanced CFOs know that it is impossible to harness the power of this data without predicting customer usage patterns and other critical business knowledge. Focusing on real-time data analysis and investing in Data Scientists helps enrich forecasting and predictability.
Whatever the future, financial organizations will always need strong financial accountants and analysts. But they will also need experts who can create predictive models, understand systems, data, and processes to succeed and grow.