Data Analytics: The Importance of Organizing your Company’s Data

Data analytics has been around since the 1950s and has served as a key method businesses use to figure out market trends, customer preferences, interpret data, and analyze business insights. This has become a standard industry practice, the only difference being the complexity and sheer volume of data has increased since the advent of the Internet in the 90s. Being able to analyze big data, a term that describes hard-to-manage and complex volumes of data, is becoming more and more crucial to the success of modern businesses. 

According to Microstrategy, 59% of organizations around the world use big data analytics so it’s no surprise that more companies are investing more money into data organization and data analytics. Having a good handle on data analytics is becoming more of a necessity in this rapidly developing data-driven climate we live in. If companies want to stay ahead of competition, they must develop reliable strategies and methods for successful data analysis.

Data analysis is the process of collecting, organizing, disseminating, and assessing data to empower businesses to make intelligent business decisions. Businesses utilize a variety of methods to comb through the data and organize it. Some of these methods include employing a team of data analysts, data scientists, data engineers, developing algorithms, and investing in data software.

Methods of Data Organization 

Data transformation is the process of changing the format, structure, accessibility, and values of raw data. For many organizations, data transformation is the first step in data organization because it streamlines the process of data analysis. Data transformation is achieved through the flattening of hierarchical data, indexing, mapping, encryption, filtration, aggregation, and translation. Data transformation benefits companies by creating higher quality data, increases compatibility between applications, and makes the data easier for humans as well as computers to use. 

Predictive Analysis is a method of data analysis that allows businesses to predict and identify future outcomes and customer behavior based on historical data. This helps businesses find patterns and exploit the data to solve complex problems, assess potential risks & rewards, and predict customer behavior. Organizations utilize this method to increase cybersecurity, reduce the chances of fraud, improve operations, and optimize marketing campaigns. 

Diagnostic Analysis is a branch of data analysis that allows businesses to answer questions about specific scenarios, trends, anomalies, and shifts in market trends. This provides crucial information that helps organizations make better informed decisions and provide a full picture of why something happened. A common example is a sudden increase in sales. Diagnostic analysis will provide a data-backed answer and breakdown of what drove the sales, allowing businesses to leverage this into future profits. 

Descriptive Analysis is the most common & widely used method of data analysis that looks at historical data to figure out what happened, measure metrics, and identify trends. Organizations produce reports, metrics, and other data to track operational performance and make key business decisions. This is achieved through digital dashboards, spreadsheets, and visualization such as chart and graphic representations. Descriptive analysis benefits companies by highlighting demands for services, identifies relationships between trends and variables, tracks engagement across the board, and makes it easier for business leaders to scrutinize data for future decisions. 

Prescriptive Analysis is a process of data analysis that provides businesses with the best course of action and forward-thinking suggestions for any scenario. This essentially answers the question of what direction a business should take through the process of algorithms, machine-learning, and artificial intelligence. Organizations usually utilize many of the methods of data analysis above and consider this the final step in the process. This form of analysis improves investment decisions, primes products for success, sharpens content curation, and shows how people can be converted into customers for the long-term. 

Conclusion

According to Microstrategy, 60% of companies around the world use data and analytics to drive process and cost-efficiency. Over the last few decades, data analytics has become a vital part of data management and data organization. Successful businesses need to have a number of systems and processes in place that can effectively analyze patterns to continue flourishing in this day and age. 

Data transformation is the first step in properly organizing data sets and streamlines the entire process of data organization. This is followed by predictive analysis which predicts and identifies future outcomes based off historic trends. Diagnostic analysis provides answers to unexpected scenarios that businesses can use to their advantage. Description analysis measures trends while prescriptive analysis gives businesses action-oriented steps to take moving forward within the organization. 

Harnessing data analytics empowers business leaders to make better decisions, develop more intimate relationships with their customers, reduce security risks, identify opportunities for growth, and increase the chances for profit.  When used correctly, data analytics can give businesses a distinct edge over competition, optimize operational performance, develop key strategies, and allows them to stay one step ahead of market trends.