Part 4 in the “Embedded Analytics” series that explores the many ways that companies across industries are using analytics to support innovation.
Finance is woven into the fabric of almost everything a business does. Take, for example, a typical manufacturing scenario, which involves estimating the cost of production, funding operations, tracking the costs of materials, labor, and equipment. Finance touches it all.
As a result, the volume and complexity of financial data seem to grow unabated. This creates a “needle in the haystack” problem, where it becomes difficult to find exactly the right data for any given task. Analytics, then, becomes difficult – limiting visibility and insight.
Constant business and technological changes only exacerbate this situation. A new business acquisition, for instance, might be a brilliant strategic move. But for finance, it can add untold volumes of data, all of which need to be incorporated into the fold.
It doesn’t help that finance is notorious for “spreadsheet warriors” who do most of their work with Microsoft Excel. Storing important but highly idiosyncratic spreadsheets on the laptops of individuals in finance is hardly a recipe for organization-wide visibility into financial data.
A new kind of finance
The simple fact is that today’s businesses need finance to do more than track and control costs. Leading organizations rely on finance to serve as a strategic partner to the business – one that adds value by delivering business insight and accurately predicting what lies ahead.
But how can finance get there in the face of these challenges? One key enabler is better finance analytics. Here are three keys for moving forward today.
Establish a single source of financial truth
Organizational silos lead to data silos – the enemy of visibility. It is unfeasible, of course, to expect a business to consolidate the various departments that have their own focus and mission. Data silos, however, can be better managed.
A business doesn’t necessarily have to break down these silos. However, with connectors and smart data management, data can be brought together into a single source of financial truth. This sets the foundation for universal data access throughout the organization. All financial data is now available at the fingertips of every authorized stakeholder.
This single source of truth is the final word on data validity. Duplication – and the resulting confusion over data quality – is a thing of the past. Instead of arguing over which set of numbers best reflects reality, the organizations can move forward and get down to the work of analysis.
Embed analytics into processes and make use of machine learning
With an established single source of truth, organizations can then embed analytics directly into processes. For example, leading organizations are using machine learning algorithms to fine-tune processes and detect anomalies. Starting with the logic of past experience, the algorithm can analyze current data on issues such as revenue leakage. Analytics are able to then detect patterns and provide insights for better detection moving forward.
Another example is management by exception. Typically, a controller would log into the finance system and view a dashboard of all open tasks. For those areas where there are no issues, there’s nothing to see. But for areas where the KPIs indicate the need for attention, that’s where the attention goes.
But let’s say there’s a sudden negative cash balance associated with vendor payments. Without the controller even diving into her dashboard, a notification powered by underlying analytics could alert her to the problem for immediate action. Instead of browsing the system to look for work, the controller becomes a problem-solver who manages by exception.
And, in line with expectations from the business, controllers can now evolve to the next level and go beyond understanding today’s financial performance, delivering forward-looking insights, executing scenario simulations, and more with the aim of optimizing future performance.
Move to self-service – or even introduce a whole new user interaction
To continue with our fictional controller, what if she needs to do more analysis to solve her problem? With self-service capabilities, she can drill down on problem areas and generate reports on an ad hoc basis – without depending on IT or moving over to a separate data warehouse.
Innovative companies are also using natural language recognition technology to further simplify the self-service experience. Today, users increasingly expect a consumer-like experience, getting immediate answers to their questions simply by speaking (or typing) their query. Making this happen, however, requires at least three elements to come together: the single source of financial truth, a user interface that requires no education, and digital intelligence to return the insight requested. Today’s technology can deliver on all three.
Many finance organizations that are aspiring to become more data-driven are moving forward with analytics transformation today. Starting small, say with a single division, is a common approach that can serve as an important test case.
With lessons learned and a more concrete notion of the advantages of more powerful analytics, many organizations are taking the next steps toward an enterprise-wide single source of financial truth. This, in turn, leads to the ultimate goal of embedding analytics into core financial processes and empowering users with insight on demand.
For more information, download the March 2019 Forrester Consulting opportunity snapshot, “Optimize Business Intelligence Efforts With Embedded, Application-Driven Analytics,” to examine the challenges, best practices, and recommended actions that can help your finance team turn your business into an intelligent, competitive force.