Running a business is a fast-paced decision-making mill. Wrong or right, decisions are a tool for moving companies forward. To arrive at these decisions, information is relied on. The data inherent in this information must be cleaned, transformed, andmodeled so that business leaders can make sound decisions from it. This process is broadly referred to as data interpretation. It involves using the right tools or applications that allow you to explore data and discern actionable trends or patterns, which inform conclusions and provide a premise for decisions. To learn more about data interpretation examples, read more here.
When information has been translated into a form efficient for movement or processing, it is described as data. Data can be physical in the form of text or numbers. It could be bytes and bits in computer memory. Data is useful in every field of human endeavor, whether it’s finance, sales data, stock prices, governance, in the form of literacy, or unemployment rates, for example. Data can be a single character, Boolean (true or false), textual, numerical, pictorial, auditory, or audio-visual.
Data can be described as qualitative or quantitative. It is qualitative when it represents something, can be quantitative when it is composed of numerical information.
Data interpretation has been defined as reviewing data through predefined processes that will help assign some meaning to the data and arrive at a relevant conclusion. According to the World Economic Forum’s ‘A Day in Data’ report, the accumulated data in the world would reach 44 Zettabytes by 2020. In other words, there is 40 times more data than all the stars in the universe. Therefore, for any business to be successful in today’s data-saturated world, it must possess the capacity to analyze data to derive actionable insights for staying ahead in its market.
Common data interpretation examples are bar and pie charts. Either chart is used to display information and the correlation between at least two variables. For example, a company can discover which demographic is most engaged with their product or service with a bar or pie chart. This can inform a specialized marketing strategy for narrowing down and deriving more value from that group. Data interpretation examples generally involve ordering, manipulating, categorizing, and summarizing raw data collected, with the end goal of turning final results into actionable decisions.
Data interpretation helps to make informed decisions to avoid guesswork or random predictions. A decision is only as good as the quality of the information that underlies it. Leaders who trust data tend to outperform their competitors. Companies are 5% more productive and 6% more profitable when they adhere to data-driven decision-making processes. To achieve this, a goal or problem must be identified which will inform data interpretation.
Companies that collect and analyze data about their operations can spot challenges and problems before they become debilitating and nip them in the bud. This eliminates panic-induced decision-making and ensures a consistent quality of business decisions over time. In addition, these companies can derive insights on customers, view said insights, and work to improve these perceptions.
Data interpretation is cost-efficient. Proper implementation of data interpretation can deliver full cost advantages for businesses. The return on investment for data interpretation drives cost reductions. Unfortunately, this benefit is usually ignored because of the focus on making money rather than saving money. Cost reduction might not be as sexy, but cost reduction opportunities can be implemented without serious exertion of human capital and are low-hanging fruit.
Leveraging data interpretation, Intel was able to save over $3 million in manufacturing costs in 2012. Before 2012, Intel used to carry out over 19,000 manufacturing tests on its chips before market release. After implementing predictive data analytics on its current and historical data, it avoided those 19,000 tests, replacing them with individual-specific tests for chips.
Insights derived by way of data interpretation can be used to set and identify trends for the future. Data interpretation provides knowledge, and knowledge is power. Insights gleaned from market and customer data can be used for market segmentation.
Data interpretation helps to develop policies and processes tailored for business success. There is no hard and fast rule for ensuring workplace productivity. However, by collecting data about your business, you will be better positioned to develop proposals based on unique insights on what can be done to improve your company instead of copying techniques based on external experiences.
The most common data interpretation example involves financial statements such as the balance sheet, income statement, and cash flow statement. These financial statements are prepared from accounting data that has been collected over a specific period. For most people, interpreting these financial statements can be a challenge as each report describes the company’s financial position in a particular area. For example, the balance sheet shows the company’s financial health at a certain point in time, the income statement shows financial performance, while the cash flow statement shows how much money was raised and spent in a period.
Another data interpretation example in finance is layered on the use of financial statements. For instance, vertical analysis involves taking the income statement and its components and dividing them by revenue to achieve a percentage result. The result here can be juxtaposed with that of other companies in the same industry to see the relative performance of the company question. On the other hand, horizontal analysis, used to build financial models, takes years of financial data and tries to determine the growth rate by comparing them across years.
An added layer on this which also qualifies as an example of data interpretation, is financial data visualizations. Financial data visualization involves taking the insights derived from financial statements and analysis and representing them in an interactive format accessible to a wider group of people. Like earlier stated, most people find financial statements challenging. Data visualization in finance helps this group of people understand key concepts in the data such that they can act on it, either for their interests or in the interests of the business.
In all, data interpretation provides businesses and investors with unique insights. Proper interpretation of alternative datasets can lead to exponential financial success when conducted thoroughly and through standardized processes.
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