Applying best practices for data analytics in finance seems like a no-brainer. After all, finance teams are under intense pressure to create accurate reporting, forecasting, and intelligence for the business. But the use of data analytics in finance is not as efficient as it should be — Accenture found that 53% of CFOs worry that the finance function is reactive or that data and information-sharing processes are not streamlined.
This blog post will unpack why data analytics in finance is a challenge and how they could be improved to make room for innovation.
Big Data Analytics in Finance Have Changed the Game
It’s no secret that the amount of data across all lines of business has exploded in recent years, making big data analytics in finance the name of the game. But being able to access, interpret, and reconcile all of that data for disparate sources into usable (and accurate) financial models is often fraught with process bottlenecks. From P&L statements, to variance reports, to cash flow or other forecasts, reporting is often more complicated than it should be:
Getting the Data You Need Takes Forever
Getting the data you need means pulling data from a legacy ERP system, then over to spreadsheets to refine. Next, you might need to synthesize that with business data sources, ranging from cloud data sources that you have to separately pull or spreadsheets that you ask a business leader to send every month without fail. Gathering all this data to even begin reporting likely takes up a huge chunk of time, especially if you have to request any minor modifications.
Data Analytics in Finance Relies Heavily on Spreadsheets
You pull manually from a source system, paste the data into a spreadsheet, and run VBA scripts against some sheets to generate further sheets — that are again manually edited. With the rise of big data analytics in finance, maybe you experience huge slowdowns or even crashes with spreadsheets. You refine and revise the data until the very last moment, getting it as close to perfect as possible before showing your reports to stakeholders.
Data Analytics Projects in Finance Aren’t Repeatable
Even though you can imagine a more efficient, transparent approach, you may not have the right systems or bandwidth to make processes repeatable or automated, so you’ll be back at this same point next month, wondering if you can get ahead of these deadlines in the future.
Applying Data Analytics in Finance Faster With the Right Tools
Reporting processes don’t have to be a monthly, quarterly, or annual scramble. With Dataiku you can easily refine figures in a controlled environment to increase process efficiency.
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Dataiku is an analytics and AI platform that enables you to build reporting pipelines and financial models. It empowers your team to deliver valuable business insights faster and more accurately, all while making your standard reporting processes more robust, agile, and explainable. By speeding up data access, incorporating automation, and empowering agility with control, teams can get the most out of regular reporting:
Quickly Connect to Data
Quickly and easily intersect ledger datasets with prior period results as well as non-financial sources like Salesforce, Stripe, Hubspot, and more to enrich data analysis projects in finance. Dataiku’s data connectors allow you to access data no matter where it’s stored (plus maintain that connection in the future).
Automate Data Analysis Projects in Finance
Automate data pulls from key sources for regular reporting plus data preparation steps done manually today — all with no code required and without breaking baseline data pipelines. Applying processes in a way that’s replicable and repeatable month to month with Dataiku saves days of time — on average, two people armed with Dataiku on the FP&A team at Standard Chartered Bank are doing the work of about 70 people limited to spreadsheets.
Gain Transparency, Reusability, and Version Control
Collaborate in Dataiku with your subject matter experts and stakeholders in a shared workflow that shows everything that’s occurred to data along the way, empowering analysts across your team to easily backtrack analysis no matter who conducted the original. Searchable projects organize work, while audit trails and user activity trackers show who has done what, and when, bringing a layer of transparency.
Get to the Next Level With Machine Learning
Allow team members with data science skills to enhance projects by directly incorporating AI and machine learning elements, such as time series forecasting, all in the same tool.
Dataiku has helped finance teams around the world find new efficiencies with finance processes. For example, Standard Chartered Bank’s FP&A team was able to achieve a 30x increase in efficiency, and Mercedes-Benz has Democratized Automated Forecasting. Learn more about how Dataiku can solve your most pressing challenges and change how you think about the use of data analytics in finance.