What are the Limitations of Finance Organizations in Predictive Analytics?
From Team43
This article contains 4 sections:
The Data Dilemma Facing Financial Leaders
The Pitfalls of Relying on Spreadsheets Alone
The Ambition of every Finance Leader
The Future of Decision-Making: Data-Driven Strategies
The Data Dilemma Facing Financial Leaders
According to a recent article, there are over 750 million business users of Excel worldwide, highlighting its continued prevalence in the business environment despite the rise of alternative solutions. Yet that number is not too much of a surprise by itself. What becomes astounding is the untapped potential locked away in each cell. In today's data-driven market, financial analysts often struggle to derive meaningful insights from their data due to the limitations of these traditional tools.
"[...] While 92% of business people need to manipulate their spreadsheet data to make it understandable, 40% of them often struggle to make sense of the their data in these sheets."
The result is a low return on investment (ROI) from these resources. Although it may appear that productivity is increasing, the manual processes do not translate into tangible, impactful results. This marks an opportunity for those forward-thinking financial leaders to unlock untapped potential. And provides a competitive edge in markets where participants are not data dependent. Moreover, these savvy organizations can expect material gains in system efficiencies and decision-making over time.
Be clear, be confident and don’t overthink it. The beauty of your story is that it’s going to continue to evolve and your site can evolve with it. Your goal should be to make it feel right for right now. Later will take care of itself. It always does.
The Pitfalls of Relying on Spreadsheets Alone
I spent most of my career as an analyst, individual contributor, lead, and now consultant. So, take it from me, I know of the pitfalls finance teams face. From unforeseen restatements because of a business logic change. To shortened reporting periods as we wait for accounting to reconcile the books. There are guaranteed setbacks or roadblocks each reporting period.
While operating in excel, the job does not get any easier when facing these impediments. There have been many a time I have lost work because excel decided now is a wonderful time to crash. Downloaded only partial data from a source system due to its row limitations. And managed many siloed versions of the same workbook. All of which add to losses in productivity or output.
Ultimately, as an organization, you will never be able to scale when every financial analyst is managing data in excel. Furthermore, the organization is missing out on the predictive power within these datasets.
The Ambition of every Finance Leader
In my experience working with various organizations, I have found that every executive team shares similar ambitions. They aim to leverage predictive analytics and enhance profitability through data-driven decision-making. However, despite these ambitions, many fall short of realizing their full potential. What could be the underlying cause?
Here are a couple of my observations:
Skills gap -
The rise of hypespecialization has left many workers on the sidelines. Companies often lack the necessary investments in modern technologies or training. This leads to only a few ambitious workers garnering these needed skills. As the market continues to evolve, the need for these specialized skills becomes increasingly the norm.
According to the 2021 Harvey Nash Group Digital Leadership Report, more than 67 percent of IT leaders say that a lack of specialized talent is preventing them from keeping pace with digital change and innovation.
Excel -
This revisits the analogy of data locked away in cells. Predictive analytics relies on accurate and properly structured data. Although excel provides considerable advantages to manipulating raw data. It is extremely fragile and prone to human error. These inherent issues will always limit an organizations ability to scale. We have also witnessed large corporations experience huge losses when putting their trust solely in spreadsheets.
While all software breaks occasionally, Excel spreadsheets break all the time. But they don’t tell you when they break: they just give you the wrong number.
Fragmented technologies -
When finance organizations adopt technologies they often apply them to back-office operations. Names like NetSuite, Microsoft Dynamics 365, or Workday often come into this discussion for enterprise resource planning (ERP) tools. Yet you rarely hear names like Tableau or more important, Alteryx in finance departments.
The latter ERP tech stack does do a fantastic job of aggregating data. But it does not help your financial analyst make better decisions with it. Finance often perceives these advanced technologies as prohibitively expensive or overly technical. However, avoiding their adoption can result in significant missed opportunities for enhancing efficiency and accuracy.
The Future of Decision-Making: Data-Driven Strategies
There must be a shift in thinking to address these issues. Only then can your organization become true decision-makers using data. The first is to identify the skills gap present at your organization. Then come up with an adoption plan for technologies such as Tableau and Alteryx. This can be done using a few simple use cases. The benefits can then be contrasted with those of traditional spreadsheets. You will likely discover enhanced efficiencies, improved accuracy with robust audit trails, and increased automation capabilities.
The second is investments in community or a center of excellence. Adoption cannot happen in a vacuum. I have found the most success comes from sharing ideas and solutions. Additionally, individuals gain insight into the art of the possible, empowering your organization to cultivate these skills internally and mitigate future skill gaps.
The last is cross-functional partnerships. Pairing analysts with data scientist or data engineers broadens their skillsets. Platforms like Alteryx enable the application of analytics across diverse datasets, fostering cross-pollination of skills that significantly broadens the capabilities of the average financial analyst.
