How can you get to the underlying financial and operational data that reveals what’s really driving the business? Discover how to overcome common barriers and where to best invest so you can adapt to change as it happens.
Imagine you’re the CFO of an insurance firm. You’re trying to get a handle on where your profit really comes from. Which policies, products, or sales channels are driving profits and losses? Where are the sources of most of your claims, and how are those impacting profit? How can you get to the underlying financial and operational data that reveals what’s really driving the business?
If you can answer those questions, you can make confident, informed decisions about where to invest in technology and people so as to better adapt to changing market conditions. This is essential to becoming a decision-ready organization. If you can’t answer those questions yet, read on.
A big part of becoming decision-ready is harnessing data, but that alone can be challenging. The pace of data growth, for instance, is staggering. Between 2010 and 2020, the amount of data created, captured, and copiedgrew by 5,000%. And it’s showing no signs of slowing down. By 2025, the world’s population will generate an estimated483 exabytes of dataevery day. This accelerating data volume and velocity makes it challenging for companies to manage and make sense of the data they use to run their businesses. And it complicates the ability to define their competitive edge in a rapidly changing, data-saturated world.
Companies struggling to get out from under this data tsunami are slowly sinking. In a 2021 survey of 85Fortune1000 companies, a mere 24%defined their business as being data-drivenin the last year. And even though demand for highly coveted data scientists is on the rise,turnover is noticeably high. The biggest complaint? Companies haven’t laid the groundwork for success.
Unfortunately, simply standing up solutions and tacking them on to an existing environment won’t address these shortcomings. Instead,business and finance leaders looking to alter their trajectoryby harnessing the power of data need to start at the bottom and work up. Clearing the path forward to a future-proof, decision-ready state hinges on having one place to ingest, enrich, and transform data, all connected to the system of record; in other words, it requiresan intelligent data foundation.
A unified, broadly accessible data core is something every successful enterprise must have to succeed if it hopes to modernize finance and the enterprise as a whole. For instance, Deloitte has branded the intelligent data foundation as the“common information model” (CIM). From Deloitte’s perspective, implementing an effective CIM is a prerequisite for organizations aiming to plan, record, report, and measure performance consistently across the enterprise. “A well-developed CIM will create a consistent way to look at data,” notes Katie Glynn, Deloitte senior manager of digital controllership. “And when accountants think about data, we like to think about financial data. But there are also managerial and operational data components that need to be considered to really enable a future vision for finance.”
Deloitte identifies aset of principlesthat help create a sustainable financial transformation. An effective CIM must be:
Granular.The data must be detailed enough to help enable automation, limit reconciliation, draw insight, make decisions, and generate meaningful reports.
Unique.Each data element has a unique and singular purpose. Avoid overlap or multiple use cases to keep from diluting the data.
Flexible.Create a foundation that can adapt to future changes such as reorganizations,acquisitions, and business changes. Pay close attention to reporting, ensuring that it’s fit to meet current and future demands.
Integrated.Ensure any compliance or company mandates are baked into the intelligent data foundation or CIM. Also consider needs outside of FP&A such as business finance, external reporting, local accounting, and taxes.
Consistent.To drive financial consolidation and comparative analysis, establish consistency across all regions, divisions, and subsidiaries.
Governed.Define guardrails to ensure adherence to policies and mandates, and to prevent “drift” overtime.
Once an intelligent data foundation is established and embraced across financial and nonfinancial departments, organizations can tap into a richer dataset that simplifies workflows throughout accounting and planning, and ultimately helps organizations become decision-ready.
As essential as it is, an intelligent data foundation doesn’t work alone. You need an array of capabilities and technologies to leverage it so you can build (and plan) for the future. They include:
In-memory architecture.When large amounts of data are stored in memory, processing time is dramatically reduced, and the need to run batch processes to get your final financial reports becomes a thing of the past.
Real-time data.An increasingly shifting landscape demands real-time data to accurately assess current conditions and constraints, and allows for informed decision-making and nimble course corrections.
Object data model.To take full advantage of the insights gleaned from your data, you need an object data model rather than a traditional chart of accounts ledger structure. With native dimensionality, you can make use of richer analytics and more versatile and granular reporting capabilities.
Connected security model.Drawing insights and action out of your managerial, operational, and financial data depends on your ability to wrangle it in one place and make it securely accessible across applications.
Artificial intelligence (AI) and machine learning (ML).To help you manage risk, surface anomalies, and make better decisions faster, AI and ML should bebaked into your planning solutions.
APIs and integrations.Seamlessly integrate your ecosystem of data sources and enterprise systems for an interconnected environment that works as one.
随着越来越多的金融团队掌舵数字传送格式ion of their accounting and FP&A operations, decision-makers are reaping the benefits of implementing an intelligent data foundation coupled with modern capabilities such as powerful enterprise-wide insight, future-focused planning, and agile decision-making. Many of these teams are looking to Workday to make it happen.
Rather than asking customers to create a comprehensive environment by stitching together disparate technology and middleware (not to mention custom programming, which puts ownership further out of finance’s reach), it’s now possible to enable a decision-ready environment via a single system that ingests, enriches, and transforms data into accounting, and then taps that data for advanced analytics and planning. And it’s all done in a way that keeps finance in control.
Stefan Ball, Workday senior solution marketing manager, notes how Workday customers can blend operational workforce and financial data to create an enterprise data hub that finance owns. “We can create accounting from that operational data while maintaining a connection back to the source details,” explains Ball. “All of that is then connected and secured by the security model that already exists in Workday.”
It’s clear that organizational leaders know the health and longevity of their business depends on becoming decision-ready.
By tapping into this unified data core, finance has the ability to draw meaningful connections, collaborate across departments and business units, and continuously adapt and respond in real time. By adding high volumes of internal and external operational data, planning, forecasting, and analysis become more granular and flexible, complete with a full audit trail. This allows for turning data into useful KPIs and metrics, drawing out richer insights, conducting side-by-side comparative analysis, and identifying drivers, patterns and correlations.
It also helps you look ahead. “We’re building machine learning into this fabric to do things like help you understand anomalous journal entries, help automate processes like ingesting invoices or expense receipts on the employee expense side. By surfacing anomalies before our customers have to find them in their analysis, we’re helping our customers keep their data as clean as possible,” explains Ball.
With thisenterprise data hub, the business gains all kinds of operational insights. Take theinsurance examplementioned earlier. (Remember, you’re an insurance company CFO.) A unified, decision-ready environment would allow your finance team not just to determine macro drivers for profitability but also to combine financial, operational, and people data to identify the actual personnel most responsible for driving revenue and profit. You could analyze aspects such as weather or geospatial data, and even factors influenced by the pandemic. From virtually any perspective (service rep, division, region, and so forth), you can dive into your opportunity pipeline, unpack your capacity and demand, and assess your backlog versus forecast.
It’s clear that organizational leaders know the health and longevity of their business depends on becoming decision-ready, and in large part that means becoming data-driven. But few know how to get there.
For those who understand the criticality of a well-designed intelligent data foundation, the path forward is better defined—and frankly more fruitful. As Ball points out, “It’s one thing to have all that data together in one place. But the end goal here is to actually drive decision-making.”
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