The World Is Moving Forward, But Some Are Left Behind
Technology adoption refers to the acceptance, integration, and use of new technology in society.
If we look at different organizational departments, we typically see sales, marketing and R&D falling into the categories of early adopters or the early to late majority. On the other hand finance, and particularly the FP&A function, is always a laggard.
While the finance department leads the direction of an organization, the function hasn’t seen much change over the years. Teams today rely heavily on Excel, just as they did some twenty years ago. Instead of leveraging the power of the large datasets available today, data analysis and business insights still depend on individual knowledge. This raises the following questions:
-Why are identical processes in different departments handled differently? -How come when a marketing professional needs to understand his data he can turn to a dedicated system, while the FP&A professional goes to his ERP, exports data manually, and loads it into Excel?
One standout reason is that finance professionals prefer to rely on methods of work they feel they simply can’t live without. However, in a world where data and analytics are increasingly merging with organizational functions, finance has much to gain.
A Sweeping Portrait of FP&A
As part of the FP&A function’s monthly or quarterly efforts to produce income statements, balance sheets, cash flows and basically any monthly reporting package, data is exported from various systems. This can be data from an ERP, CRM, GL, HR data, etc.
After downloading CSV files, data is copied and pasted into spreadsheets. Usually additional data that comes from other spreadsheets that were filled in manually such as forecasts, budgets etc. is added. Lots of time is spent manually consolidating data sources into one dataset, and this process is repeated often. Once all data is consolidated, it is manipulated and prepared for final reporting.
Lastly, the little to no time left is spent on the part with the greatest added value- analyzing data, trying to understand the obstacles standing in the company’s way, and identifying what can help the company excel.
In a world where data and analytics are increasingly merging with organizational functions, finance has much to gain.
A Glance at Other Departments
How do other departments do it?
Marketing: Marketing gets data from sources like Facebook and Google, crosses it with analytics data, combines it with data from their CRM, and finally generates insights.
Sales gets data from their CRM and compares it with forecast spreadsheets to understand where they stand against company goals and so on. It doesn’t take much to realize that these processes are identical to those of the finance department. But, there is one huge, huge difference.
The BI Difference
BI platforms enable workers to take data from different sources, manipulate it, and create sexy dashboards that visually represent data. Unsurprisingly, most of finance’s peer departments are using this tool.
Why does finance continue to rely on Excel and spend so much time tying out all the numbers and making sure they’re accurate, despite the existence of BI tools?
1. BI is a platform, not a solution
The BI product was created as a horizontal platform to deal with data. It was not created as a vertical solution to address a specific need.
While marketing, sales, and other departments require basic data manipulation capabilities, for finance it’s a different game. FX conversions, variance analyses, and intercompany eliminations? Building such capabilities on a standard BI platform requires massive effort from your IT or BI team, which can take months.
Much has been written about why Excel is the past, present and future of finance. Always, the flexibility of Excel is what makes the difference.
BI platforms are able to cope with enormous amounts of data and have stunning dashboards and collaboration features that Excel will never have. But at the end of the day - they don’t offer the flexibility of Excel, and will never allow finance professionals to do all that they need to do in order to validate a specific assumption, or to add just one more validation to make sure reports are correct.
3. Complex Excel Files
Every organization stores a significant portion of their organization’s financial data on spreadsheets, such as forecast reports that are collected from different departments, budget data, P&L etc. These spreadsheets can’t be uploaded into typical BI systems. While they work great with structured excel files (a CSV format if you may), a BI system can’t work with a P&L spreadsheet or forecast data scattered around numerous files with different structures.
Even if you’ve built the perfect financial solution on your BI platform, you won’t be able to incorporate your spreadsheets into it. This forces you to use Excel once again in order to consolidate data, and that just brings you back to square one.
4. Requires IT/BI help
No one wants to be at the mercy of the IT or BI department and their prioritization. And in the BI world, IT is king- they’re the only ones who can help when there’s a missing column or any other issue.
When producing monthly or quarterly reports, finance professionals are under a lot of pressure. A mixture of tight deadlines and changing management requirements don’t leave time for any unplanned technical issues. Since no one wants to miss a deadline or deliver partial reports, finance professionals tend to simply rely on themselves and export data manually instead of turning to the IT or BI team.
Why Are Other Departments Using BI?
If BI has so many downsides, how come every department is using it except finance?
1. Marketing and sales data is much simpler than the data finance deals with.
2. Their core systems are more advanced and have built-in integrations for BI solutions, unlike ERP systems that are often heavy-duty and old-fashioned.
3. Management is usually more willing to invest money to get sales insights and to improve marketing.
That being said, what seems to be the best explanation for what we’re seeing is the existence of department-specific tools.
For example, let's look at the marketing analytics vertical - which has been one of the fastest growing BI verticals in the past few years and consists of platforms such as Supermetrics, funnel.io, and Datorama (acquired by Salesforce). All of these successful companies target one specific department- marketing.
Their advantage lies in the fact that they address the gap between marketing needs and typical, horizontal BI solutions. They created a vertical BI solution that offers out-of-the-box connections to marketing systems and built-in marketing reports and KPIs.
The BI Difference Part II
Unlike CPMs, the idea behind BI is that it’s a solution that is placed on top of your existing solutions and processes and enables you to get business insights without changing your processes. It’s essentially a plug and play concept.
If for whatever reason it fails to work, it doesn’t affect you in any way- you continue to work as usual.
Finance is Overlooked- Unnecessarily.
As previously covered, the marketing analytics solution is an amazing concept that takes the good of BIs and leaves behind the problems that typically come along due to its horizontal approach.
This brings the following questions to mind:
-Why isn’t there such a solution for finance?
-A financial analytics solution that meets the needs of finance departments, much in the same way that marketing analytics meet the needs of marketing? -A platform that connects seamlessly to your systems and allows finance professionals to work with their data, but on their terms? -One with strong financial data preparation capabilities like consolidations and lookups, and with financial manipulation abilities such as FX conversions, intercompany eliminations, and financial adjustments? -A tool that lets you prepare and analyze data with variance analyses, drill-downs, and ad-hoc reporting?
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This article was sponsored by DataRails.
DataRails is an augmented intelligence platform that empowers each finance professional to independently work with data to deliver actionable, data-driven insights. Finally, count on numbers you can trust and reduce inefficiencies without having to change how you work. With DataRails, strengthen the connection between finance and operations to drive better organizational decisions.