Technology
How the Finance Department Can Make Business Intelligence Investments Count
Published by linker 5
Posted on October 6, 2020
5 min readLast updated: January 21, 2026
Global Banking & Finance Review®
Company
Published by linker 5
Posted on October 6, 2020
5 min readLast updated: January 21, 2026
By Nathaniel Spohn is general manager of EMEA at automated data integration provider, Fivetran
As financial operations require ever faster and more accurate data analytics capabilities, investments in Business Intelligence (BI) tools continue to rise. But with the analysts using these tools unhappy with the amount of time they’re forced to spend on peripheral, non-business critical tasks, these investments often don’t live up to their full potential. In this article, Nathaniel Spohn, general manager, EMEA, Fivetran, explains how disjointed data pipelines are a waste of time and money – and offers an alternative approach to help organisations maximise their BI investments.
While 98% of companies have Business Intelligence solutions today, analysts using these tools waste half their time with arduous data preparation, according to a recent Dimensional Research study. This is particularly evident across finance teams, which depend on reliable data in order to make intelligence-led business decisions. Financial analysts are keen to utilise data in this way. Indeed, according to the study, they are the business division which is most likely to use BI tools next year – yet there are bottlenecks that hamper their ability to use and query this information in a timely fashion.
Simply put, while organisations often have the right people, structures and mindset to make data-driven decisions – they are failing to unlock the full value contained within this data.
This is underpinned by Dimensional Research’s study: BI tools are well-resourced, but they are not being used to their full potential. Indeed, it shows organisations are at risk of wasting their investments, with 68 percent of data analysts saying they have ideas for driving more company profits, but lack the time to implement these more strategic initiatives.
Data analysis can be a struggle
The biggest drain on analysts’ productivity is the time they must spend trying to access disparate data sets. While this might seem like a relatively simple process, it can in fact account for as much as a third of an analyst’s time alone. The study further reveals that nine out of ten analysts have found that the data sources they need to do their jobs are sometimes unavailable, broken or only accessible on an intermittent basis. Unreliable data sources often lead to delays that have a wider impact on the overall business. Any lag in the process means analysts are required to generate reports and suggest decisions based on information that is out of date.
Nathaniel Spohn
86 percent of analysts polled indicated they were sometimes forced to use ‘old’ data and of these respondents, more than 50 percent admitted that their data was, on average, at least two months old. In the current economic climate borne out of the pandemic – characterised by unpredictability and rapid change – it’s counterproductive and risky to use aging financial data as the basis for accurate decisions.
The challenge: Making sense of data from multiple sources
Data analysts today rely on numerous disparate data sources. In the survey, the majority of respondents were juggling 11 or more different data sets, with 96 percent of firms drawing at least some of their information from cloud-based platforms. Another factor contributing to this complexity relates to data schemas – the blueprints that help visualise the way data is constructed and organised. Every data source that a BI tool draws from requires a separate schema, and as businesses continually update their operational focus and re-cut their data in order to uncover new efficiencies and opportunities, data schemas are in perpetual flux. Of the analysts asked, three in five reported that schemas update on a monthly cycle. While this improves the accuracy of reporting and the decision-making process for the business, for the analyst these changes often equal more work and more delays.
Additionally, data analysts are forced to take on tasks outside their job descriptions – and core expertise – in order to keep projects moving at the pace required by business leaders. For example, many find themselves creating financial reports in Excel because they cannot access the information they need via a dedicated dashboard. Others have had to code their own scripts in order to ingest new data streams.
To help finance teams spend more time on analysis and less time on data preparation, organisations should reassess their data pipelines to ensure they can extract data from multiple sources – including cloud-based applications – in real-time. By automating certain processes – such as if a change in business direction necessitates an altered schema – organisations can remove most or all of the hurdles data analysts are facing today. Information can then be replicated, transformed and integrated into one coherent data set to be instantly analysed and leveraged to make data-driven decisions.
Financial data pipelines re-imagined
While the commitment from businesses across the world to take advantage of BI tools is encouraging, disjointed data analytics makes it hard for financial departments to make the most of the BI tools available to them. The Dimensional Research study clearly shows that both the technology and the experts who use it are underutilised.
Boosting the data analyst headcount offers no long-term solution. Instead, the more prudent and impactful plan is to focus on how the multiple sources of data are brought together, accessed and managed. When access to real-time, actionable data is made easy, data analysts can extract the full value of Business Intelligence tools needed to leapfrog competitors and keep customers happy.
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