Joe DosSantos, Chief Data and Information Officer, Qlik
Digital transformation is no longer an aspiration for tomorrow but a necessity of today. However, amid a rush of post-pandemic digitalization, one essential element is too often swept aside. That is how modernized data strategies can deliver up-to-date financial performance insights that drive real-time decisions and action. How can a Chief Finance Officer (CFO) transform the finance function to provide rich analysis that impacts business performance?
Finance’s ability to support today’s agile, data-driven organizations is imperative. However, according to McKinsey, less than half of CFOs play a leading role in driving enterprise-wide transformation.
As a data-driven organization we knew that finance data modernization was key to our growth ambitions. As Chief Data Officer (CDO), I worked closely with Qlik’s CFO to modernize our data, strategy and architecture, integrating finance and business data to transform Qlik into an agile, data-driven organization. In ensuring finance can harness data and digital to become an essential lever for driving the business forward, our journey has produced learnings that others may find helpful.
People first
Before getting to master data management, analytics and artificial intelligence (AI), it is essential to understand the skill set that finance professionals need to play their role in modernization. Data literacy is vital; having the ability to understand data and translate it into recommendations and actions for the business. Increasingly, finance teams need to understand the principles of master data, data flow and how AI and machine learning can contribute to their decision-making process. Armed with the right data and insights, finance can better interact with broader business stakeholders in this new paradigm.
For example, my first order of business was to request our executive team to dedicate one person from their function as the go-to data champion, to speak on behalf of that group and uncover strategic drivers. Then we embarked upon a value engineering exercise to truly triangulate on the value that each of these different use cases could create, and then to focus on cross-functional issues.
If you want to drive value across the organization, you need to get people working together to become laser focused on strategic priorities. In our case, that was understanding our customer base to support retention and satisfaction, underpinned by financial metrics. By going through this exercise and involving the whole organization, the finance function has gone from number crunching to becoming a true business partner.
Finance’s insatiable appetite for data
From there, we start by going back to what seems like a basic. Just as we are all used to opening up a mobile banking app and seeing our balance, the CFO wants to know the organization’s cash position in real-time. Especially given current global currency fluctuations, it is crucial to have a clear picture to inform decisions made in the moment.
Every day organizations make micro-decisions that affect the customer, and it is no longer viable to make those decisions based on disparate data from weeks or months ago. This requires real-time data within a comprehensive master data strategy.
Finance has an insatiable desire for data, and can draw on myriad sources, including ERP, CRM, supply chain management and other dispersed systems. Data sources are constantly evolving, and finance teams must be able to quickly harness larger and more complex data sets that deliver actionable insights that support the business.
As McKinsey highlights, ‘Owing to its central role, the finance function is uniquely positioned to help define the master data strategy for the enterprise. To support the business – whether through more nuanced financial-scenario planning, insight into how to better manage liquidity, or improved guidance on where to best deploy assets – finance must be able to quickly marshal high-quality, trusted data.’
Modernizing our finance operations started by moving all data to the cloud, using our Qlik SaaS platform to address size and scale requirements. From here, it was crucial to address data usability and reusability. For example, determining how exactly we would calculate EBITDA or recurring revenue so that we had an array of replicable, reusable metrics to deliver in-the-moment insight.
Anchoring sophisticated data science in hard metrics
Formalizing definitions combined with a cloud data strategy set the foundation to be able to answer vital questions quickly. It goes further, however, opening up opportunities to leverage technology to automate, innovate and to monetize, as well as providing the scaffolding for more sophisticated data science approaches, including our use of AI.
For example, we can dissect different data segmentations that give a real picture of current performance, but that also indicate what is needed to improve future performance, whether in terms of months, quarters or years. The more value drivers connected across the business, the more transparency and insight you gain into the impact of one KPI relative to the overall performance, and this can prove incredibly powerful.
Data science helps us understand the behavioral patterns of people, their product usage, buying cycles, for example. But the key to harnessing this insight is making sure it is anchored to financial metrics. Again, this is echoed by McKinsey’s analysis; ‘Leaders can help their teams by ensuring that requests for more information are grounded in a solid understanding of an agreed-upon set of drivers of business financial performance’.
To achieve a high retention rate, we must access the metrics that drive it. That means using data science to understand customer behavior, ensuring the right, accessible analytics to measure the impact on the bottom line of using this insight. In that respect, today’s data modernization strategy uses finance as a lever for the rest of the organization to meet goals, charting a path of clarity that traverses marketing, sales, and customer success.
Future growth rooted in today’s data
Adopting a cloud-based, master-data strategy driven through finance, and supported by collaboration across the organization, we have transformed Qlik to become truly agile. We needed to better understand how to please customers and grow our revenue over time and that understanding is rooted in financial metrics.
By modernizing infrastructure finance teams can work with data in the moment, partnering with other lines of business to drive fact-based decision-making. As a result, finance is now instrumental in ensuring the business uses data to keep their eyes on the future, yet with feet firmly grounded to drive positive change today.
