# Financial Analytics: what is it & why is it important?

Financial analytics is a subcategory of financial analysis that takes place within a company rather than on Wall Street as an investor.

More specifically, financial analytics seeks to understand 3 elements within a company: 1. its operations, 2. how those operations impact financial statements (FS), and 3. the company’s performance financial performance based on those statements.

A skill often associated with the Financial Planning & Analysis (FP&A) teams, financial analytics looks at topics such as profit per unit, how unit profit reflects on a profit & loss (P&L) statement, and if that P&L statement is as profitable as possible.

We’re going to look at an example of financial analytics, do a quick-and-dirty overview on financial statements, and show how easy it is to bring enormous value to a company with financial analytics.

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## What is financial data analytics?

Again, financial analytics is a type of financial analysis that focuses on the connection between financial metrics in operations and connects them to performance in the financial statements.

For example, imagine you’re an analyst for Apple. If you wanted to run a full financial analytics review of the iPhone (operational), you might start by looking at how much it costs to produce 1 phone.

Let’s say it costs \$500 to obtain all the materials needed (metal, battery, screen, etc). Apple then resells the iPhone for \$1000. This means the unit profit of an iPhone is \$500, since \$1000 – \$500 = \$500. (All number here are fictional and used for educational purposes only!)

Now you want to see how this reflects in the P&L statement. You see the company’s revenues are \$1M in 2020, which means Apple sold 10,000 iPhones in the year. You know your margin is 50% from unit profitability, so the profit is \$500,000.

You now ask if this is enough to pay for all the non-iPhone related costs such as lighting and your water at the headquarters. Lighting and water costs are \$300,000, so you end up with a profit of \$200,000. Not bad.

Your full P&L looks something like the following:

You ask yourself, “is this good enough? How does it compare to other companies in the industry? How can we make this better?” Once you’ve done that, you have gone through a complete financial analytics cycle. This is where the near-tangible value of the financial analyst is.

Obviously the above example is a highly simplified view, but the key point is that data analytics consists of three elements:

1. Operational financial data
2. Operational financial data in the financial statements
3. Company performance based on these financial statements compared to other companies

By definition, financial analytics is a branch of financial analysis, but in reality, this is not the way most people in the industry use these terms. Let’s look more at the difference between the two terms.

## Difference between financial analytics and financial analysis – financial statements

Financial analytics includes analysis on operational, or commercial, data, whereas financial analysis most often refers only to ratios used to analyze the financial statements.

To better understand how ratios work, let’s look very briefly at what the three financial statements are.

### The three financial statements

Financial statements consist of the profit and loss statement (aka P&L), the balance sheet (aka B/S), and the cash flow statement. The P&L shows profitability, but not cash flow. The balance sheet shows assets (iPhones not sold, for example), liabilities (debts), and equity (money contributed by owners).

#### P&L

The P&L’s main components are revenue, cost of sales, operating expenses, depreciation and amortization, and taxes. They all take place in a given period, most often 1 year. It often looks like this:

#### Balance Sheet

The Balance Sheet’s main components are current and non-current assets, current and non-current liabilities, and equity. The balance sheet is a snapshot of the company’s holdings at any given time, NOT a reflection of a current period. That said, we usually analyze it at the end of the P&L’s period so that the two align. It often looks like this:

Let me explain each line. You should always look at the balance sheet as “assets and sources of funds for those assets.” Cash and accounts receivable of \$200 comes from the sales of iPhones. Since we sold them, we made money and don’t have any more in stock, which is \$0.

Accounts receivable is another way of saying “items sold, but not yet paid.” This happens when companies allow customers to use credit, or pay at a later date. As you can see, about 10% of iPhones are paid for at a date after sale.

We list the \$200k earned on the Equity section as retained earnings because the earnings belong to the owner (even if he/she leaves that money in the company), and because we need to balance our balance sheet.

You’ll no doubt see that while we’re waiting to be paid \$10k, it is still considered earned because of the accrual principle, which states that sales are recognized when the good is delivered, not when the good is paid for (learn more about accrual and other principles on the finance page).

We also see that there are \$50,000 computers in stock. We funded them with two sources: \$25k from the owner, and \$25K from Long term debt.

Notice that Equity and Liabilities equal Assets. This is because we can only use two sources to fund Assets: contributing money directly as an owner, or taking on debt from a loaner (aka bank). No matter what happens, the Balance Sheet must always balance as Equity + Liabilities = Assets.

#### Cash Flow Statement

The Cash Flow Statement’s main components are Cash from Operations, Cash from Investing, and Cash from Financing. As you probably guessed, the Cash Flow statement (CFS) shows movement in cash (while the P&L is concerned with profitability, not cash). The CFS takes the results from the P&L and reconciles it with any non-cash items, such as accounts receivable as discussed above. It often looks like this:

For example, we made \$100,000 in profit on the P&L, but in reality, we’re still waiting to be paid \$10k in accounts receivable. Under the Cash Flow from Operations section, we subtract this to show only \$90k in cash inflow.

In this example, the logic is easy because our Balance Sheet had all the current numbers, so Cash there was the same as Cash on the CFS. But it’s not always that easy — most of the time we have B/S numbers accumulated from previous periods, so they won’t reconcile.

## Difference between financial analytics and financial analysis

Now that we have a basic understanding of the financial statements, it’s easier to understand the difference between financial analytics and financial analysis.

In short, financial analysis consists of ratio comparison between multiple companies, whereas financial analytics consists first of operational analysis, then how this operational analysis feeds into the statements, and FINALLY consists of ratio comparison.

But what are financial ratios and ratio comparison? Simply, financial ratios are division calculations of relevant numbers on the financial statements that result in fractions or whole numbers. These fractions or whole numbers can then be compared to other companies. The three most common ratios are net profit margin, debt-to-equity, and cash-flow-to-debt:

• Net profit margin (P&L) = net profit/revenue. Tells us what percentage our net profit is of our revenues, and thus how profitable our company is. In our example of Apple, it would be \$100,000/\$1,000,000, or 10%. Anything above 5% is generally considered good, though you must always compare to similar companies.
• Debt-to-equity (B/S) = debt/equity. Tells us in what proportion we fund our assets with debt vs equity. This is important because we have to pay interest on debt. It’s good in equal proportions, but too much debt is dangerous. In our example of Apple, it would be \$25,000/\$125,000, or 20%.
• Cash-flow-to-debt (CFS and B/S) = net cash flow/debt. Since debt can be dangerous, we want to see how much of our debt we can cover with cash flow. In our example of Apple, this would be \$90,000/\$25,000, or 3.6, or 360%. This is exceptionally high because we only have one P&L period on the B/S — normally it would be a small fraction.

There are loads of financial ratios, each with its own added value. The important thing to note here is that ratio analysis is a high-level exercise, and it’s usually what people refer to when they say “financial analysis.”

However, now you know that ratio analysis is just one piece of the pie. Financial analytics, however, looks at the whole picture, starting from operations and going all the way through ratio analysis.

Let’s turn our attention to operational analysis and how it feeds into the financial statements.

## Financial Analytics: Operational Analysis and Its Reflection in the Financial Statements

Operational analysis in finance is the process of breaking down how the company uses its assets in operations. The way an analyst does so will depend on the industry and business model. That said, we can examine 3 common inquiries in operational analysis: unit profitability, operating expense structure, working capital management.

### Unit profitability

We talked about unit profitability earlier in the article. It’s the gross profit of each individual product sold in the company, or [sale price – the cost of producing it]. The reason we look at unit profitability is to differentiate types of costs.

Costs to produce the product itself are different from costs to maintain the company as a whole. For example, you need cobalt to produce an iPhone, and in another company you need plastic to build toys; however, you don’t need the accountant working at HQ to build the products themselves. The accountant’s salary is a different kind of cost — what we call operating expenses.

Unit profitability focuses on understanding how good the company is at negotiating payment contracts, and how good it is at increasing the price of its products to sell. Over time, a good company will increase its unit price as it conquers the market and builds a brand.

#### Example and how it reflects in the financial statements

Imagine you work as an FP&A Analyst for a company that sells watches, aka “Watchcity.” The company offers two watches: a luxury version and a normal version.

The luxury version sells to department stores for \$500, and the normal version sells for \$20. The cost to produce the luxury watch currently at \$400, while the cost to produce the normal one is \$5. This means that the unit profitabilities are:

While at first glance you would say the Luxury watch generates more money for the company than the normal watch, we see that the normal watch is much better at generating profit! All the company needs to do it sell more Normal watches than Luxury watches.

But how many more?

We know that the Luxury watch generates more absolute profit, so let’s just see how many normal watches it takes to generate the same absolute: 100/15 = 6.6, or 7 Normal watches per 1 Luxury watch. That doesn’t look very good for Normal watches. Intuition says it must be much harder to sell 7 watches than 1. But we don’t know this for sure… let’s investigate.

#### Getting to the financial statements

To get from unit price to the financial statements, we need to determine the financial period for which we would like to project our numbers. Let’s use the default and say it’s a year.

Going from unit to year allows us to add in another variable: volume. We will look at historical data to see how many of each watch sell in a year (volume) and multiply this by the unit price.

In fact, we should always break down revenue by unit price * volume.

We should always break down revenue by Unit Price * Volume.

Consider that the company, on average, sells 25,000 Normal watches for every 1000 Luxury watch in a year. This means Normal watches generate \$500,000 in revenue and Luxury watches generate \$500,000 in revenue.

But remember, Normal watches have a 75% margin while Luxury watches have a 20% margin. So Normal watches generate \$325,000 in profit while Luxury watches generate only \$100,000 in profit!

In other words, the Luxury watch makes more money per unit, the Normal watch generates more money overall due to volume. This is a seriously important insight that only data analytics can provide.

But we still don’t have the complete picture. It’s worth noting that you should understand the accounts receivable impact of both of these watches to understand how they impact working capital (but we’ll talk about this later).

### Operating expense structure

In the above example we focused exclusively on costs associated with gross profit [sales – cost of producing the goods], but now let’s look at cost that come afterwards: operating expenses.

Operating expense are ongoing costs for operating a business other than the costs of the products themselves. Examples include salaries, lighting, water, legal and accounting advice, insurance, office supplies, and building rent.

When we say “operating expense structure,” we refer to all of these costs. A good company sets goals to keep these costs low, then implements controls into operations to ensure they don’t get too high.

These goals should be based on market prices. The FP&A team might look at market data for different costs to see if the company spends more or less than the average. It would then look at why some costs are higher than the average and offer this information to the management for change.

#### Getting to the financial statements

Operational expenses are the easier of the three to reflect in the financial statements. You can use actual numbers provided by accounting to get the monthly expenses, examine them, then include them as costs on the P&L. If there are any accounts payable (like accounts receivable, but its you as the company that waits to pay providers), this must be noted in the balance sheet.

### Working capital management

In the first two examples, we looked at revenues by unit and operating expenses and how to analyze them on the P&L. But there’s a third kind of operational analysis that involves the B/S and CFS: working capital management.

Working capital (WC) concerns current assets (due in less than 1 year) and current liabilities (also due <1 year). Accounts receivable (AR) is a current asset, and accounts payable (AP) is a current liability. As a calculation, working capital is [current assets – current liabilities].

Working capital management aims to have enough current assets (cash, AR, etc.) to cover current liabilities (short term debt, AP, etc.), while at the same time not have too many current assets that could otherwise be used to fund operations (such as marketing or cost of sales).

If current liabilities are 50, you only need 100 in current assets. Anything more should be used to improve the company’s products.

#### Getting to the Financial Statements: Watch Example

Imagine that our Luxury watches are always paid up front, but our Normal watches are always paid within 60 days after delivery. This means that while Normal watches generate more revenue, we have to wait for the cash!

At the same time, we have to pay salaries and insurance premiums, as well as other monthly fees. We push them off for 15 days in accounts payable, but we need to make sure we can cover them.

At its core, working capital management boils down means having a schedule of cash inflow and outflow, which means we must constantly monitor our cash levels. The easiest way to do this is with a predictive waterfall chart. Let’s look at an example of 60 day period in which we start with \$0 cash:

Imagine we purchased and received \$125 in metal for our Normal watches at day zero, which were recorded as accounts payable until day 15. In addition, we sold and delivered \$420 of Normal watches, for which we allow customers to pay up to 60 days.

Even though we have booked more sales than costs, the due date to pay our suppliers comes first. This means we will run out of money and become cash negative. Working capital management, to be more precise, is the process of ensuring this doesn’t happen.

Now, we may have many accounts receivable in current assets and few accounts payable in our current liabilities, which makes our working capital very high, but we need cash to be part of the current assets for it to make a difference. Cash flow management, thus, is part of working capital management.

A good FP&A analyst (and often a treasury analyst), will work to have an excellent cash schedule at the forefront of working capital management.

## Fundamentals of financial analytics: recap

While financial analysis is mainly concerned with ratios on the financial statements, financial analytics is a corporate job that covers operational information and how they reflect into the financial statements, THEN comparing ratios between companies.

## Financial analytics certification

Financial analytics certifications do not exist as such. Instead, most people looking to move into financial analytics seek out the same kinds of educational programs and certifications that FP&A analysts do. The ideal way to get your on-paper certifications is by getting a degree.

A degree in finance or an MBA is great because it gives you a global view of the finance function. As you can tell from this article, there’s a lot to learn. Formal school is a good way to knock it out, BUT, it’s also expensive and very time consuming. So many people shy away from it.

### Non-education certifications

I believe most of the reasons working professionals hesitate to learn about finance is that it either feels overwhelming or too hard. It’s true that you need to spend some time learning the jargon, but the concepts are simple: you sell something, you have costs to do so, and you have to plan your cash well to stay effective.

As the name indicates, financial analytics requires skills in data analysis. You’ll want to demonstrate those skills with some sort of portfolio or certification. If data analysis interests you, read this article on how to become a data analyst.

I’m currently working (as of September 2020) to bring you a host of practical, applicable courses on different data, business, and finance topics, and do so at reasonable prices. All courses will come with certifications detailing exactly what you learned so you can show it to your boss or potential employers.

You shouldn’t have to pay big universities for complicated courses just to get their certificate. At the same time, you shouldn’t be stuck with generalized courses on e-learning platforms that are hit or miss. My goal at AnalystAnsewrs.com is to make finance accessible to everyone, so that everyone can help make their organizations better. You can see more or the home page or finance page.

## Financial analytics jobs

There’s no way around it, the best way to find financial analyst jobs is to hustle. You have two possible approaches:

1. Identifying a location and searching for open positions
2. Doing global searches for positions and narrowing down location

This is highly dependent on if you are willing to relocate or not. The choice is yours. At the same time, you will rarely find a position titled “financial analytics analyst” because it is not a job, it’s a skill. Most people who move into financial analytics jobs look for Financial Planning & Analysis, or FP&A.

So if you want to work on financial analytics, search for jobs with these title:

• Financial Planning and Analysis (FP&A)
• Financial Analyst (in a company, not Wall Street or in a bank)
• Financial Operations Analyst

The key to finding the right job is to always read the job description. This article has outlined what you should expect to see in a job where you will work on financial analytics. Revisit the different sections to compare with job postings.

## Financial analytics tools and software

Financial analytics is indeed about data, and anyone working on financial analytics should be familiar with a few different tools. Here’s a short list:

• Microsoft excel. The go-to tool for anyone working in finance. You should spend time practicing with Excel at every opportunity. There is a great free alternative LibreOffice, which you can use if you don’t have (or want to pay for) Excel.
• Tableau. The go-to data visualization software in finance and data analysis, based on Excel pivot table logic.
• SQL. This is more of a database and data query language, but it’s useful for those working in financial analytics.
• ERP. ERP stands for enterprise resource planning, which often includes accounting software. Being able to work with accounting software in general is key for financial analytics, since actual numbers will come from it. A common ERP is Dynamics 365 from Microsoft.

## Financial analytics companies

In this article we’ve taken a high-level view of data analytics as a corporate job, but a huge part of the process is collecting the data for operational analysis.

Certain companies specialize in providing solutions for this data collection, so that FP&A and other analysts can perform their data analytics. Two companies famous for financial analytics data collection and reporting are:

• DemystData
• SISENSE