Introduction
Data is important in financial analysis as it helps gauge the financial health of individuals , companies, organizations, tracks prevailing market trends, and investigates various investment opportunities. Therefore, primary data are original source data, whereas secondary data are obtained from existing recorded documents. Both types present certain advantages and disadvantages on which the analyst must decide and select the one suitable for obtaining correct and relevant information.
Financial Analysis
Financial analysis is a process that evaluates businesses, projects, budgets, and other transactions in finance regarding their performance and desirability. In general, people conduct financial analyses to address questions regarding whether an entity, product, or service is strong, solvent, liquid enough , and profitable enough to accept monetary investment.
What is Primary Data?
Primary data is information gathered for the first time through personal experiences or evidence, especially for research purposes . Raw data or first-hand information is also termed primary data. This mode of information gathering can be costly because the analysis may involve an agency or organization outside and utilize human resources with investment. The investigator directly supervises and controls the data – gathering process .
Examples:
Company internal financial reports and records.
Interview of the stakeholders, surveys and questionnaires of the customers or employees.
Observations from any pilot projects or market experiments.
Benefits
Specificity: Direct application ensures that the data received pertains directly to what has been questioned or presented. It is specific so as to ensure that its appropriateness to objectives relates to the study precisely and with much accuracy that other variables cannot interfere into producing a different result.
Currency: Primary data collection gives one the advantage of currency; that is, they get the freshest information that can be obtained. This is crucial for disciplines in which the data constantly changes; examples include markets, technology, or social contexts. When utilizing fresh data, researchers reach timely conclusions reflective of the current situation and add value and relevance to their research findings immensely.
Control over Data Quality: With primary data collection, the researcher is directly in charge of the quality of the data. The researcher determines the method of data collection, identifies the sample, and enforces quality checks to obtain valid and reliable data. This is because direct researcher involvement allows for the resolution of biases, reduction of errors, and methodologies to be adapted as appropriate to ensure that data obtained is valid and representative of the population being studied.
Drawbacks of Primary Data
Time consuming Process: Much time normally goes into the collection of primary data. From developing the tools and protocols meant for collecting data to directly collecting data and analyzing it, the steps are very painstaking to carry out. For example, in-depth interviews or surveys, or even some very extended observations, require loads of preparation and execution. In some fields, there is an extended timeline being a serious challenge because their timely data may be pivotal.
High Costs: It can be very expensive to collect primary data. At each stage of the process, resources are needed to create materials, gather data, personnel, and to analyze the data. For example, the logistics involved in organizing focus groups or large-scale surveys require compensation for participants and possibly travel. Such expenses can make research impossible or restrict the scope of the study in underfunded projects.
Limited Scope: Primary data collection is generally directed towards answering a research question or understanding a context that may be too narrow and thus not wide-ranging in scope. Though the details in the area of study will be rich and more meaningful, it will not enable one to see the bigger picture about the subject. A case study will give deep data about a particular case but the outcome might not be generalized across other contexts or populations hence restricting the generalizability of research findings.
Secondary Data
All data sets collected by someone other than the user are regarded as secondary data. Secondary data sources are highly valuable . They provide a large, high-quality database to researchers and analysts. Such databases assist them in solving the business problems they may face . Incorporating secondary data into the dataset enhances the quality and accuracy of the analyst’s insights . Most secondary data sources originate from external organizations. Sometimes, however, secondary data comes from an internal source collected by an organization but used for different purposes.purposes.
Examples:
Industry-based research studies and publications.
Stock prices and market analysis reports with historical financial data.
Government agencies and other organizations issuing macroeconomic data; there are instances like the Federal Reserve, Bureau of Economic Analysis, and more.
Benefits
Time and Cost Effective: Secondary data have already been collected, cleaned, and stored; it saves analysts a lot of hard work that goes into the collection of data. In qualitative data, for instance, the very complex processes of deciding how best to record the answers or determining an appropriate research question have been completed. Therefore, this saves data analysts and data scientists from beginning from the scratch.
Generating New Insights: While reviewing data again, especially as observed by another person or a different point of view, new things are discovered. There could be something that was not found in the past by the primary collector of data, which the secondary collection of data can reveal.
Drawbacks of Secondary Data
Irrelevant Data: There have been cases where researchers waste much time searching through an ocean of useless data in a bid to finally land on the one they needed. This is because, first and foremost, data was not collected mainly for the researcher.
There have also been instances where the next available data a researcher would opt to use may not be entirely what he or she desired but had to do it anyway.
Exaggerated Data: Some sources exaggerate the information they provide. This bias could be some to maintain a good public image or due to a paid advert.
Many such online blogs even exaggerate what is not true just to garner internet traffic. For instance, the amount of value processed of a FinTech start-up might be exaggerated simply because one wants to be looked at more.
Outdated information: In some cases, some data sources are as old as the data which does not have any contemporary replacement. For example, a national census is conducted very seldom, say rarely per year.
Therefore, there is change of population in that country since the last counting has taken place. Nevertheless, a data worker working in that country will have to settle for the count as recorded earlier though such a count is outdated.
Conclusion
In financial analysis, primary and secondary data have their value. Primary data is customized and current, but it is costly. Secondary data is inexpensive, but it is too general. Together, both form a complete analysis and allow for the taking of informed and strategic decisions in finance.