Sunday, December 4

The Importance of Data Analytics for Businesses


In today’s world, data is one of the most valuable resources businesses have, and knowing how to refine and apply it can help them realize this value. Businesses rely on the skills of data analytics professionals more than ever before to stay competitive, as data insights can inform everything from process optimization to marketing strategies.

Understanding the value of data analytics and exploring some specific examples can help you understand the importance of putting your data to work. In this post, we’ll discuss why data is the most prized resource in the modern business landscape and explore examples of how data analytics can help you succeed.

What are data analytics?

Data analytics is the science of processing raw data to draw conclusions from it. Trained professionals can analyze and interpret data sets manually or use computer programs that automate data analysis and make information intelligible to people. Businesses can leverage data to improve their performance, optimize processes, make more intelligent and strategic decisions, enhance marketing campaigns, and much more.

Data analysis leads to the extraction of evidence-based insights on consumer behavior or resource management, for example, helping businesses maximize their profits through improved efficiency. There are many data analysis methods, and professionals can apply data analysis techniques to any information gathered by the business. Here are some of the main types of data analytics:

1. Descriptive analytics

This type of data analytics is a traditional aspect of Business Intelligence (BI). It helps professionals examine data sets, typically done manually, to determine what has happened over a given time. The data produced by descriptive analytics is often visualized using pie charts, bar charts, and other easily readable formats. It’s useful for understanding things like sales performance and website traffic, and it helps businesses identify trends based on month-by-month statistics, for example. Descriptive analytics is a relatively basic form of data analytics because it only exposes what is happening and can’t answer why something is happening.

2. Diagnostic analytics

This is an advanced method of data analysis that uses many more variables and includes the hypotheses of data specialists. It seeks to answer questions relating to why things happen through techniques like data drilling and data mining. By identifying things like correlations, diagnostic analytics can help data professionals identify factors that affect other factors, such as how a marketing campaign affects sales, for example. Professionals might apply this form of analytics after descriptive analytics to discover why a trend occurred.

3. Predictive analytics

This type of analysis combines other forms of analysis to make predictions about the future. It may use both descriptive and diagnostic analytics to understand what’s happening, why something’s happening, and what this means for the future based on extrapolation and other techniques. Professionals typically use it to predict outcomes in the near future.

For example, if an increase in ad spending resulted in a 20% increase in sales last year, it may be reasonable to predict a similar outcome this year. Likewise, if a change in temperature caused an uptick in ice cream sales two summers ago, and the same average temperatures are predicted this summer, it may be reasonable to anticipate a similar sales increase this summer.

4. Prescriptive analytics

This is the final form of data analytics for businesses that prescribes action plans based on available intelligence. It includes advanced techniques such as simulation, complex events processing, and graph analysis. For example, if data suggest a potential uptick in ice cream sales due to increased temperatures in the summer, a factory might increase its output to meet the anticipated demand according to a prescriptive analysis. In short, the prescriptive analysis uses past data insights, like trends and statistics, to inform strategies to achieve organizational goals. Businesses use these data analytics methods to measure and optimize different business areas.

How data analytics is used in different industries

Virtually every industry uses data to generate insights and inform decision-making. Here are a few examples:

1. Engineering

Engineers use data for many of the same reasons as businesses in other sectors: it helps them streamline processes, optimize efficiency, and boost productivity in various ways. Engineering managers with data analytics training, such as those who’ve completed an online master’s in engineering management degree at an institution such as the University of Ottawa, can use their expertise to strategically manage employees. This is also known as “people analytics” and involves using data insights to inform hiring decisions, team management, and task assignment. It can also help with things like risk assessment, making engineering projects safer by helping people identify and eliminate threats.

Data analytics is also essential for generating reports and making forecasts, which helps with project optimization and sticking to timelines, among other things. Engineers often report to stakeholders frequently throughout the management of projects and being able to report key data findings to them is essential. These reports can clearly show trends and patterns to stakeholders, who can then make informed decisions about the direction of projects. Forecasts based on data also help engineers and stakeholders take corrective action where necessary, which prevents mistakes and saves money.

2. Retail

Retail businesses use data in increasingly effective ways, such as by personalizing shopping experiences and suggesting the right products to the right people at the right times. Many have noticed the almost eerie ability of advertisers to show the most relevant products to potential customers through online ads. This is because they use huge data sets to predict what kinds of products you may be interested in buying. Based on how users behave, companies can devise effective ways to maintain engagement and boost sales. They can also extract valuable insights into what customers like and dislike, which informs future product designs.

Some big retailers analyze colossal data sets to offer more competitive pricing in real-time. For example, certain retail chains analyze data sets containing thousands of variables to detect when sales drop and adjust pricing to incentivize more sales. Data analytics in retail leads to more effective pricing, more consumer insights, product improvement, better customer experiences, intelligent processes, marketing automation, and much more. All these factors lead to businesses generating more profit and customers receiving better service, products, and experiences.

3. Banking

For a long time, the banking industry has relied on masses of numerical information to drive decision-making and the provision of services, and data analytics allows them to do it in even more sophisticated ways. Banks use insights to create predictive profiles for each of their customers, which helps them offer services their customers want and need based on their financial behaviors. This helps banks offer customized services and products instead of generalized ones, leading to more value for customers and banking institutions. Data can help banks retain their customers over the long term and get the most lifetime value from them.

Data science in banking also helps institutions make intelligent investment and lending decisions, as it can analyze multiple aspects of a client’s financial and credit history and predict their creditworthiness. By using predictive analysis techniques, banks can also forecast the performance of markets and individual stocks, helping them to make profitable investments. Many people use internet banking nowadays, which includes mobile phone apps that allow customers to manage most of their banking needs while on the go. Such apps produce huge amounts of consumer data that banks can use to their advantage in a variety of ways.

4. Agriculture

Data analytics is helping farmers produce and provide food in more efficient, sustainable, and profitable ways, which is increasingly important due to climate change. All countries now rely on data for mass food production. For example, many African countries rely heavily on farming, as agriculture is the largest industry on the continent. Big data and data analytics help farmers predict the value of resources and the probability that they will grow successfully, which also helps financial institutions make more informed lending decisions. Data analytics also helps farmers make use of land more efficiently.

Benefits of data analytics for businesses

The benefits of data analytics are numerous and significant, which is why data is commonly referred to as ‘the new gold’ in many circles. Like gold, data is useless unless properly mined and refined, as this process makes it usable and valuable. Here are some of the main benefits of data analytics for businesses:

  • Enables more personalized customer experiences

Personalization in terms of business offerings and customer experiences is the key to driving sales and engagement for many companies. For this reason, many companies leverage data analytics to tailor offers and interactions to make them more meaningful to consumers. This type of personalization can apply to multiple channels, such as social media, email, e-commerce, and even physical retail, and businesses can generate data from all these sources. For example, an e-commerce business could use its social media data to find out what products people are interested in, categorize them accordingly, and target marketing campaigns to relevant customers.

Big data also enables businesses to automate marketing efforts while also making them highly personalized. For example, businesses can enhance their email marketing by sending email sequences that are personalized based on the customers’ respective positions in the sales funnel. If the customer has just signed up to a mailing list, the first email might be a simple welcome message offering some basic information about the company and its products. If a customer is closer to a purchase decision, signaled by leaving an item in the basket on the company’s website, they might receive a message nudging them to complete their purchase.

  • Facilitates intelligent decision making

The most fundamental use of data and data analytics is to inform decision-making, as it enables businesses to make evidence-based decisions that are more likely to produce desirable and predictable outcomes. For example, descriptive analysis can help businesses identify and visualize what’s going on in each situation, predictive analysis can help them determine what might happen in the future and prescriptive analysis helps them decide how to respond.

All these methods increase the business’s chances of success by making their decisions more intelligent. Businesses can create models and hypotheses and test their ideas to gauge results, allowing them to always run with the best decisions.

  • Improves operational efficiency

Businesses can also increase their profits by improving their operational efficiency, such as by saving money through better waste management or eliminating bottlenecks in their processes. Data analysis on supply chains, for example, can enhance supply chain management, leading to more streamlined production and increased output. Retail businesses often use data analytics to manage their inventory levels and ensure they always have enough supply to meet changing consumer demands. Sharp increases in demand during holiday seasons mean retail stores must increase their inventory to stay competitive, and data analytics helps them predict which items to stock and in what quantities.


Without a doubt, data is the new gold in the modern business world. With the help of qualified data analytics professionals, businesses can harness smart data insights to help them stay ahead of trends and remain competitive in their field.