Depending on who you ask, financial analytics and business intelligence (BI) mean the same thing. Most will agree the two processes fall under the same category. But at Fully Accountable, we understand real change occurs from mastering the details. That’s why we think it’s important to separate these two terms into their respective definitions.
BI software and services uncover current data and translate into easily understood language and visuals. Financial analytics uses tools to analyze that data and predict the future. An outsourced financial analytics team (like the one at Fully Accountable) uncovers data and translates it using BI. Then we analyze the findings and provide predictive analyses. These analyses develop long-term strategies for business’s core functions that improve cash flow and profitability.
In essence, business intelligence mines and refines the data. Financial analytics provide the map to the future. When conceptualizing this, it’s important to remember data analytics skills aren’t a single practice. It consists of a number of techniques. And while it may be tempting to lump these two subjects together, it can have a negative impact on your analytics strategy. Below, we’re discussing the difference so you can create a strategy that fits best with your financial goals.
What Is Business Intelligence?
Business intelligence includes four components: data collection, data mining, descriptive analytics, and visualization and reporting.
Data collection: to analyze any data set, you have to first gather it. Data can come from numerous sources, including IoT devices, apps, spreadsheets, and social media. That then data needs to be consolidated. Companies often use a cloud database to pool all of their data because it streamlines the process.
Data mining: After you consolidate your data in one location (data lake or data warehouse), you have to sort and process it. The advancement of machine learning and AI has greatly aided in this process. These systems sort and recognize patterns and repeatable actions within the data and establish metadata from specific sources. This allows data scientists to focus on the insights rather than the logistical aspects.
Descriptive Analytics: Data scientists analyze the data to uncover what is happening and why it’s happening. This process builds a greater understanding of the story behind the data.
Visualization and Reporting: Visualization and reporting tools break down numbers and models into visual iterations so the layperson can easily comprehend the focus points. This streamlines collaboration and initiatives so the entire organization can uncover new insights.
What Is Financial Analytics?
Financial analytics takes the visual elements generated from BI and creates predictive models for elements such as sales and profitability. Below, we detail some of the more integral analyses financial analytics can uncover.
Predictive Sales Analysis
Sales revenue keeps businesses of all types afloat. Sales projections are essential strategic tools for your organization. Predictive sales analytics creates informed sales forecasts by correlating past trends. Using algorithms and analysis, companies can study their sales wins and losses. Analyzing these metrics gives teams the ability to plan directives for their products and services so they can increase marketing spending and inventory at the ideal times.
Client Profitability Analysis
In the past, many businesses have guessed which clients brought them the most profitability and which required more attention. With financial analytics, companies have the ability to determine their most profitable clients down to a percentage. Profitability for clients typically falls under the 80/20 rule, meaning 20 percent of the clients account for 80 percent of the profit and 20 percent of clients account for 80 percent of the customer-related expenses. Uncovering your most costly and profitable clients is critical to advancing your business.
Understanding your client profitability is critical to generating insight into your division of labor and ensuring your most costly clients get the attention they deserve from the right allocated resources. It also helps you standardize your approach to your most profitable clients so you can give them the attention they deserve without over-allocating company resources.
Misunderstanding your client’s contributions to your business can have detrimental effects on long-term profitability due to labor costs and neglecting clients that need more attention.
Product Profitability Analysis
Organizations trying to stay competitive within their industry must understand where they stand to make money and where they are losing money. Understanding the profitability of your products and services involves an individual analysis of each item you have on offer. Conducting individual analyses establishes profitability insights across the product range as a whole.
Cash Flow Analysis
Cash flow is critical to the continued operation of any business. Cash flow analytics uses real-time data such as the Working Capital Ratio and Cash Conversion cycle to perform predictive and regression analysis. Besides aiding with cash flow management and ensuring you have enough money for day-to-day operations, cash flow analytics helps you uncover various long-term objectives.
Most organizations understand their financial objectives. These types of directives can be formal and listed. However, if they are somewhat nebulous, value-driven analytics can create well-defined objectives and a roadmap to success. For businesses to establish company-wide objectives and clearly communicate them, they need to first identify what those objectives are. After identifying long-term objectives, value-driven analyses uncover the levers you need to pull to achieve those objectives.
Why Is Financial Analytics Important?
Financial analytics is important for companies no matter their industry for the following reasons:
- Businesses require timely data to make data-driven decisions.
- Companies need tools to decide whether their financial planning and forecasting are testable and accurate.
- Financial analytics clearly defines businesses’ financial goals, both short and long-term.
- Financial analytics give you metrics, by which to test your objectives and financial goals. Whether you’re looking to increase profitability, expand your marketing budget, or cut labor costs, financial analytics contains the only testable metrics.
- Financial analytics quantify your business’s tangible assets such as cash and equipment.
- Financial analytics provide thorough insights into your organization’s financial health indicators, such as cash flow, profitability, and overall business value.
What Are the Main Differences Between Business Intelligence and Financial Analytics?
You can recognize the main differences between BI and financial analytics in their aims. While business intelligence is more descriptive, financial analytics is predictive.
Descriptive vs Predictive
The way analysts interpret the data and the insight the data uncovers changes depending whether you incorporate business intelligence or financial analytics. Business intelligence describes events and the reasons those events occurred. Financial analytics, on the other hand, predicts objectives and future points of emphasis. The whys you derive from business intelligence serve as the catalyst for the predictions and goals developed from financial analytics.
Managers vs Analysts
The second differentiating factor between business intelligence and financial analytics is the role of the data analyst. When an analyst uses business intelligence tools, they are typically fulfilling a managerial role over the data. They present the data to marketers, accountants, and managers who don’t have the technical skills to interpret the data. Business intelligence professionals translate data into clear, visual data points.
For financial analysis, professionals require more tangible skills. Business intelligence relies heavily on mathematical models, querying, machine learning, and AI to create projections.
Reporting vs Applying
Business intelligence is a simple way of uncovering the data and reporting it. The data is arranged in a legible way so users can easily uncover the why behind financial occurrences.
But with financial analytics, the data is taken a few steps farther than reporting. Data applications and statistical analysis is done to look further into trends and determine why things are happening. So it’s a scenario of reporting data versus applying data in a new way, creating a vision for the future.
New Analytics Strategy vs Existing Analytics Strategy
Many companies decide to approach financial analytics by implementing business intelligence before trying to incorporate financial analytics. This serves as an advisable initial strategy but it can also take time and prevent you from realizing your financial goals sooner rather than later. Once your business intelligence is in place, you need to hire an in-house team to interpret that data. Data analysts skills vary widely and they are in high demand.
Outsourcing your business intelligence and financial analytics ensures a streamlined approach to your predictive analytics. Additionally, because business intelligence should serve as the foundation for your financial analytics, it helps to have experts familiar with the newest software trends and how to operate that software.
Most companies can learn how to leverage business intelligence. However, most IT managers will tell you advanced financial analytics is the most heavily sought-after service. Implementing that technology isn’t enough, either. You need professionals with the skills to operate financial analytics technology and deliver consistent, actionable insights.
Financial Analytics Use Cases
The first step to utilizing financial analytics is understanding what it is. The next step is learning how you can utilize it in your various departments.
Financial Analytics In Marketing
What emails are customers more likely to respond to? What was your last marketing campaign‘s ROI? If your marketing department wants to understand how their programs affect the company as a whole, financial analytics is the key to uncovering that insight. AI and machine learning provides the powerful analysis used to drive strategic marketing decisions.
Human decisions do employees use to make career decisions? How do HR leaders better understand how the programs they implement affect the overall productivity. Financial analytics can also help understand the optimal recruitment channels.
When is the best time to convert leads into sales? Financial analytics help you break the sales cycle down so you can create a clear picture of what leads to success. All of the contributing factors to your sales, including price, availability, geography, and seasons. Analytics give you the keys to unlock all of those insights.
Financial analytics can help your finances more than any other department, but what improves your finances will inevitably support other departments. Financial analytics bring finances for the future into full-view with predictive modeling, detailed analysis and insights from machine learning and AI.
Fully Accountable- A Trusted Financial Analytics Team a Phone Call Away
As today’s business world becomes more and more intelligent, companies everywhere have an increasing number of software and analytics tools at their disposal. Choosing which ones to use can be extremely difficult, especially if you don’t have professionals on your team trained specifically for BI or financial analytics.
At Fully Accountable, you can trust us to implement the correct BI, utilizing the expertise of data analysts who understand your industry and how to maximize your finances to achieve sustainable success.
Contact us today and start incorporating a mathematical approach to your financial success.