Achieving profitability, much less significant profitability, is no easy feat — especially in some of the industries with notoriously tight margins. such as:
- Telecommunications (0.79 percent)
- Grocery/food retail (1.44 percent)
- Biotechnology and drugs (-0.84 percent)
- Healthcare support services (1.78 percent)
As Investopedia points out, profitability involves a couple of key components working in tandem — like how efficiently an enterprise is able to utilize its resources and how much revenue its operations generate.
There are a few ways to look at this duality. One point of view is there’s an ongoing challenge of balancing expense versus income. On the bright side, though, it also means there are multiple ways to go about bettering profitability: reducing operational inefficiencies and driving revenue, with both tactics capable of contributing to the bottom line in a positive way.
Knowledge is certainly power when it comes to making decisions designed to maximize profit margins, making data analytics a key part of any financial strategy worth its weight in gold.
Answering Questions to Drive Business Outcomes
One significant way in which modern data analytics has improved upon older iterations of these systems is democratizing data. The outcome? Insights can now be accessed by a wide user base rather than gatekept by special teams. Furthermore, the insights are available on an ad hoc basis rather than requiring more formalized reports.
The faster and more accessible the financial analytics within an enterprise, the better decision-makers throughout organizations can get their questions answered — and act upon those findings in a way that drives actual, measurable business outcomes.
Going beyond static, descriptive analytics is a huge step toward tying data to profitability. Descriptive metrics are a start, but they only really tell employees “what” is happening. Without the ability to drill down into data and keep asking relevant questions, users would unfortunately find themselves missing out on much of the “why.”
Here are just a few examples of the types of questions users would be able to ask as a means of exploring profitability, according to Accounting Today:
- What motivates customers to purchase or not purchase your products/services? What is most important to your customer base: marketing, brand loyalty, price competitiveness, etc.?
- Which vendors are most cost-effective with which to do business?
- Which of your products or services yield the most returns and/or customer service complaints?
Empowering the people who make decisions to go beyond cut-and-dry descriptive profitability metrics can help them understand cause-and-effect and explore possible data-driven avenues for increasing margins. Accessibility and speed to insight are key factors in making this happen.
Discovering Trends Hidden Within Data Depositories
Although employees undoubtedly have many interesting and useful financial questions to ask of your analytics platform, some stones will inevitably still remain unturned – simply because there are a finite number of hours in the day and a massive volume of available data that’s only getting bigger by the minute.
However, some actionable insights capable of impacting profitability are likely hiding within this data. While human analysts can certainly work on mining them, this tends to be a cumbersome and resource-consuming task that draws these experts away from other meaningful pursuits.
Artificial intelligence-driven financial analytics, however, can use algorithms to automatically and quickly detect trends and outliers residing within data, bringing them to the surface for decision-makers to use or not based on their discretion. Ultimately, harnessing AI-driven analytics serves to give decision-makers “a better view of the past, present, and future” by pulling insights from diverse sources quickly and automatically.
Companies throughout every sector imaginable are using both self-service and AI financial analytic tools with the goal of driving profitability through data-driven decision making.