In 1999, NASA lost its $125-million Mars Climate Orbiter because of a simple math error—its spacecraft engineers couldn’t convert English to metric measurements when exchanging vital data before launching the craft. The navigation team used the millimetres and meters in its calculations, while the spacecraft designing and building team provided acceleration data in the English system of inches, feet and pounds.
The navigation team engineers mistook acceleration readings measured in pound-seconds for a metric measure of force called newton-seconds.
An inaccuracy in data collection and processing caused the orbiter to miss its intended orbit and disintegrate due to atmospheric stresses, resulting in a loss of $125-million to NASA.
Today, every organization relies on big data to create value, as data has become a true catalyst for business growth.
Imagine this — a company collects an insane amount of data, converts it into valuable information, extracts actionable insights, and uses those insights to make impactful decisions to improve its relationship with customers and vendors.
Later, it identifies that planning, forecasting, and business intelligence failed drastically because of data inaccuracy.
How massively will this realization impact the company’s operations, revenue, and overall financial performance?
Unfortunately, with the growing importance of data, data inaccuracies have also shot up exponentially. Poor quality data diminishes an organization’s competitive advantage and undermines critical business objectives. Moreover, it forces the CFO to make suboptimal decisions that cost time and money.
According to Gartner, organizations believe poor data quality to be responsible for an average of $15 million per year in losses.
To extract more valuable insights from captured data, an organization has to strive for collecting the best and cleanest data possible. Therefore, it should improve its data collection systems and eliminate processes that collect bad data.
But before looking at ways of improving data collection, let’s discuss why data inaccuracy occurs in the first place.
What Are the Reasons for Data Inaccuracy?
- Poor data entry – Manual entry of data is the biggest contributor to data inaccuracy. For instance, a time-crunched sales representative may key-in wrong figures or enter customer details with a typo. Without enough validations in place, an organization is susceptible to repeated poor data entry.
- Non-standardized data practices – Many organizations don’t have a standard process for data collection, formatting, and access. For instance, without data collection standardization processes in place, one software can read 4/5/22 as 4th May 2022 while the other software may interpret it as 5th April 2022.
- Incomplete data – If a software is not prepared to collect enough data, it creeps in assumptions and extrapolations, resulting in further data inaccuracies.
- Outdated systems – Many organizations initially collect data manually in excel spreadsheets and later transfer them to digital systems. An outdated and inefficient system for data collection does more harm than good.
How Can I Ensure Data for Financial Statement Analysis Is Accurate?
An organization can place the following checks and controls to ensure data integrity, accuracy, and reliability.
- Gather data from the right sources: The best way to ensure data accuracy is to collect data from the right and best sources, both internally and externally.
- Reduce data entry workload: By delegating the data entry workload evenly across teams, automating various processes, and setting up real-time data collection software, CFOs can reduce or eliminate incorrect data collection and processing.
- Standardize data collection and accessibility: Organizations can standardize data entry and access methods to reduce the likelihood of duplication or misinterpretation.
- Watch out for outliers or unnatural deviations: When dealing with numbers, an organization must ensure that results are within sensible limits. Hence, it becomes critical to look out for outliers or extreme data points.
- Avoid biased results: Biases can negatively impact data quality. To avoid biases in the analysis, an organization should adopt data analytics which processes an insane amount of data based on different parameters and uncovers hidden patterns and trends.
As mentioned above, the first step to ensure data accuracy is to gather data from the right sources. For ensuring financial data accuracy, it’s critical to know the right sources available for collecting data.
What Are the Three Sources of Data for Financial Analysis?
The three primary sources of data for financial analysis are:
A balance sheet reports an organization’s resources (current assets and fixed assets) and its source of capital (equity and liabilities) at a specific point in time and also highlights the changes in each major account from period to period.
A balance sheet reflects a company’s current financial position at a specific point in time, not its business performance over a period of time.
In contrast to the balance sheet, the income statement highlights a company’s financial performance over a certain time period.
The top line of the income statement belongs to a company’s revenue or gross sales. From this figure, Cost of Goods Sold (COGS) and other operating as well as non-operating expenses are deducted. The bottom line then denotes the company’s net income after all incomes and expenses have been accounted for.
Cash Flow Statement
Michael Dell, the founder and CEO of Dell Technologies, highlighted the importance of cash flow:
“We were always focused on our profit and loss statement. But cash flow was not a regularly discussed topic. It was as if we were driving along, watching only the speedometer, when in fact we were running out of gas.”
Cash flow is the most important component of financial analysis because it enables an organization to meet its existing financial obligations and simultaneously plan for the future.
A cash flow statement reports all cash inflows and outflows happening in the organization during a given time period. It also segregates all major cash flows into three main areas—cash from operations, investing, and financing.
Where Else Can I Get Data for Financial Analysis?
In addition to three primary sources of data mentioned above—balance sheet, income statement, and cash flow statement—analysts extract financial data from footnotes that provide additional information about specific topics, shareholder reports and directors’ reports.
Besides, if a company is listed on the stock market, the following sources might be useful as well:
- Forms 10-Q and 10-K
- Press releases containing key financial information about the company
- Earnings calls in which the management discusses the financial results of the reporting period
What Should a Financial Analysis Include?
Financial analysis is a financial reporting tool that involves the examination of financial information collected from the financial statements and other relevant sources to influence business decisions.
By incorporating data analytics, an organization can strengthen its financial analysis ability to dive deeper into data and extract valuable insights.
Data analytics helps the organization to make data-driven decisions that can increase revenue, improve sales and marketing initiatives, strengthen relationships with customers and vendors, and achieve other key metrics.
Gartner has identified the top four data and analytics technology trends in finance:
- By 2025, data stories will be the main source of consuming analytics, with augmented analytics generating 75% of these stories.
- By 2023, augmented data management will free 20% of financial analysts’ time for collaboration, training, and high-value analytics tasks.
- By 2022, cloud applications will drive 90% of data and analytics innovation.
- By 2023, 95% of Fortune 500 companies will adopt broader data and analytics governance initiatives.
The majority of these analytics trends will be driven by the financial analysis techniques commonly used for driving valuable insights based on financial statements.
What Are the 5 Techniques or Methods of Financial Analysis?
Here are 5 major techniques or methods to assess a company’s financial information and derive key insights:
- Trend analysis:
Trend analysis enables an organization to make better forecasts and predictions by analysing trends based on historical data. Although, trends can be identified based on an analysis of 2 years of data, but the more, the merrier. Data analytics uses machine learning algorithms to uncover hidden trends and patterns.
- Common-size financial analysis: In the common-size analysis method, a company’s balance sheet and income statement are analyzed. All income statement items are expressed as a percentage of sales, while all balance sheet items are expressed as a percentage of a business’s total assets. Through common-size financial analysis, an organization can gauge the impact of each line item on the overall financial performance.
- Financial ratio analysis:
Ratio analysis sets up a more meaningful relationship between two or more different items of financial data from a company’s financial statements. Some key ratios are debt-to-equity ratio, current ratio, liquidity ratio, working capital ratio, gross profit ratio, earnings per share, and return on equity ratio.
- Cost volume profit analysis: This analysis technique sets up a better relationship between sales, costs, and business profit. Organizations use cost volume profit analysis to predict how many units are required to be sold to break even (cover all costs) or reach a certain profit margin.
- Benchmarking analysis: It is one of the best ways to check the financial health and identify competitive advantage by comparing an organization to its competitors or to the industry at large. It also enables the organization to gauge its productivity and efficiency compared to industry standards and make necessary changes or improvements.
Till now, we have talked about data analysis techniques used for analysing financial information collected from financial statements. However, an organization’s financial performance analysis remains incomplete without analysing its cash position because cash is king for any business.
How Do You Analyze Cash Flow?
A company may report profits but still go out of business. It may show a net profit in its income statement but still does not grow.
It is easier for any company’s management to manipulate profit figures since the income statement includes non-cash line items such as depreciation as well.
Hence, it is crucial to look at the cash position of the company to really determine its financial position. Cash to a business is like blood to a human body—a business needs to generate enough cash from its activities to meet expenses and liabilities, scale-up, and create more wealth for shareholders.
Since a cash flow statement reports all cash inflows and outflows, it’s a much better indicator of a company’s financial position.
The best way to analyze a cash flow statement is by looking at patterns and understanding the cash position and status of working capital. Then, based on it, the organization can make better cash flow projections and identify more value-added opportunities.
Accurate data on committed spend and accruals, available from a Procure-to-Pay software like PLANERGY, enables a company to project cash flow accurately and have better cash flow management options available when needed.
Financial planning, forecasting, and analysis are three significant components of critical decision making for any organization,. By ensuring better and real-time data collection and processing, the organization can avoid the pitfalls.
PLANERGY, the most robust Spend Management platform, helps you conduct sound financial analysis on spend data by allowing real-time, role-appropriate access. You can use advanced financial analytics and spend analysis automation to save money, boost efficiency, and make better decisions.