Data Analysis Tools for Your Business

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Technological advancements have led to a dramatic increase in the amount of data in the world over the past decade. According to Accenture, 90% of all data has been generated in the past 10 years, and the amount of data available is expected to increase exponentially over the next several years. To remain competitive, businesses in virtually every industry, from healthcare to hospitality, must learn how to leverage this data.

Data analysis is the process of collecting and examining raw data and using it to inform strategic decision-making. Through data analysis, businesses can make sense of the massive amounts of data at their disposal, like consumer buying habits and market trends. This process allows companies to accomplish many different goals, such as:

According to Forbes, nearly 60% of businesses use data analytics in some capacity. However, many of them lack the ability to truly benefit from it. Only 32% of companies are able to get tangible value out of the data they collect, Accenture reported.

To take advantage of the abundance of data, businesses must employ a variety of data analysis tools. These tools streamline operations, help companies improve products and services, and ultimately grow a business.

An understanding of data analysis tools is vital in today’s economic landscape. An advanced degree such as a master’s in data analytics can help individuals better understand the important role these tools play in the future of business.

Business intelligence takes the concept of data analysis and applies it to the world of business. Companies utilize an array of software and other resources to collect and interpret raw data — everything from sales figures to number of video views — and transform it into actionable information they can use to improve products and services and inform business strategy.

At one time, applications like Microsoft Excel and PowerPoint were the go-to tools of the trade for business analysts. However, these programs have limitations and, as data volume and complexity have grown in recent years, more sophisticated data processing tools are called for.

Business intelligence tools — a subset of data analysis tools — enable companies to truly harness the power of their data. Something as simple as a spreadsheet can be used for business intelligence, along with a number of more sophisticated applications that execute critical business intelligence tactics like data mining and data visualization. These actions are essential to a business’s ability to get the most out of its data.

Data mining is the process of collecting, storing, and analyzing large amounts of raw data. Companies use software programs to look for patterns and connections in the data, which they can use to glean information about their customers to develop better marketing strategies, boost sales, or lower costs.

Supermarkets, for example, often employ data mining tactics to provide their customers a better experience. Through the use of loyalty cards — which give shoppers access to reduced prices — supermarkets can more easily track their customers’ buying and spending habits. After analyzing the data, the store can offer customers coupons targeted to their shopping habits and decide when to put items on sale.

Data mining is also frequently used in fraud detection. Using data mining techniques to analyze large blocks of data, like credit card purchases, allows companies to more easily detect deviations in a consumer’s transaction history and flag them as potential fraud. A notification about an unusual purchase on your credit card is a result of data mining.

The data mining process has five main steps:

This last step represents another key business intelligence tactic: data visualization.

Data visualization is a critical aspect of business intelligence because it presents the information derived from data analysis tools in an easily digestible format. While the process of analyzing and interpreting data is highly technical and often requires the expertise of data analysts or data scientists, one of the main goals of business intelligence is that everyone in the organization should find the end result relatively easy to understand.

Data visualization is a manifestation of the classic principle “Show, don’t tell.” Charts, graphs, maps, and infographics are all examples of data visualization. This visual representation of data makes identifying, understanding, and sharing trends and new insights easier for all stakeholders, which is essential to making the best use of the data to impact business strategy and growth.

A line chart depicting a department store’s Black Friday sales over a 10-year period, for example, can help executives determine what items are most and least popular and which marketing strategies work and which ones don’t. Armed with this information, they can make strategic decisions on how to target their customers and boost sales.

Data visualization allows companies to:

Data specialists can use any of an array of business intelligence tools on the market for data analysis, including software like Tableau and Microsoft Power BI and programming languages such as Python. These tools are critical to business intelligence strategies because they streamline operations and enable scalability as businesses — and the data they collect — grow.

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One of the foremost data analysis tools used by businesses is Tableau. Founded in 2003, Tableau is a self-service analytics platform with a range of features that make it uniquely suited for business intelligence.

Tableau is touted for, among other things, its descriptive analytics capabilities. Descriptive analytics, as the name suggests, describes what has happened over a particular period of time — whether sales are up this month compared to last, for example — and presents it in visualizations like pie charts, line graphs, and tables.

With a mission statement centered on helping people “see and understand data,” Tableau’s primary focus is data visualization. Visualization is a key component of descriptive analytics, which makes Tableau for business analysts an excellent tool for getting an immediate sense of the state of a company.

Tableau software offers a number of visualization capabilities, but two in particular make it stand out from the rest of the pack.

Dashboards are perhaps the archetypal business intelligence tool. A dashboard is a hosted software application that automatically pulls complex data points from large data sets and organizes them into visuals. Unlike a chart or infographic, however, a dashboard shows information in real time, presenting an immediate sense of the state of the business. Dashboards are also interactive, which makes sorting, filtering, or examining different types of data easier.

In the healthcare industry, for instance, Tableau software can be used to create a dashboard depicting patient demographics and trends by department, allowing hospitals to uncover insights in the data, such as the number of frequent short-stay patients versus the number of extended-stay patients in a particular month.

A dashboard is a fairly common feature among business intelligence applications, but Tableau makes creating them significantly easier. Tableau simplifies the process of creating customized dashboards and other visualizations, so users don’t have to spend time learning to code or running to their information technology department for help. Mastery of Tableau does require extensive training, however.

The other feature that makes Tableau one of the more user-friendly data analysis tools on the market is its drag-and-drop functionality. Virtually every data analysis function can be performed with a simple drag-and-drop, allowing users to perform complex tasks with just a few clicks. This feature saves time and makes the process of data analysis more accessible to more people in an organization.

Tableau has additional features that make it a popular choice for business analysts, including:

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Microsoft Power BI is perhaps the most widely used business intelligence application. According to the results of a recent survey by TechRepublic, Microsoft is the preferred vendor/product for more than half of businesses that use analytics.

Data analysts find many advantages to using Power BI for business intelligence. The software offers some of the same functions as other data analysis tools — including dashboards and drag-and-drop functionality — but sets itself apart with other features and data visualization capabilities.

Power BI is developed on the same platform as Microsoft Excel, one of the most widely used software products in the world, and is therefore relatively easy to learn. Its simple user interface doesn’t require any programming experience and has built-in intelligence to help users design reports. Powered by artificial intelligence, the program can recognize conversational language and easily provide answers to a host of business questions.

As a Microsoft product, Power BI is naturally compatible with popular Microsoft Office applications like Excel and Teams. This allows everyone in an organization to easily work together on the same data and share insights and empowers them to make data-driven decisions. Its interoperability is also beneficial from a cost perspective, maximizing the value of the entire suite of Microsoft products.

Power BI offers a number of data visualization features that separate it from some other business intelligence tools, including:

Because of how user-friendly Power BI is, novices can usually start creating interactive dashboards and other visualizations right out of the box. It comes with a wide range of templates and visualization options, with new ones available for free or to buy on Microsoft’s Azure marketplace.

Like Tableau, Microsoft Power BI also has customizable dashboards and a drag-and-drop interface that streamlines various tasks like sorting, comparing, and analyzing data.

Microsoft offers different versions of Power BI, with capabilities that correspond to the size of a business and how much of an investment it wants to make in business intelligence.

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Another widely used data analysis tool, Python is one of the top programming languages for a number of fields, including data science, web development, and system administration.

Python is useful in virtually any industry, including healthcare and finance. In the healthcare industry, for example, machine learning algorithms written with Python are used to prevent and diagnose disease and make hospital operations more efficient.

Many companies also use Python for business analytics to handle the massive amounts of data they collect, as well as for their machine learning capabilities.

For a long time, Excel has been the de facto software application for businesses seeking to manage their data. However, Excel isn’t ideally suited for a world where data sets are massive, real-time information is essential, and collaboration is critical.

Open-source programming languages like Python are much better suited for modern business needs. As a business grows, the amount of data it collects, stores, and analyzes will grow with it, and outdated software simply won’t cut it. Python can help companies capitalize on their data and allow for more seamless scalability. That’s critical for a world where the amount of data is expected to reach 175 trillion GB by 2025, according to Accenture.

Another element of business intelligence that benefits from Python is in the field of predictive analytics. While descriptive analytics focuses on what has happened already, predictive analytics uses past data to make projections about what is likely to happen in the future. For example, descriptive analytics can determine how the weather impacted last year’s restaurant sales. Predictive analytics can project what the weather forecast will do to business this year.

Machine learning is employed in predictive analytics to design statistical algorithms, based on existing data, that can make projections or suggestions for the future. Netflix’s recommendation engine is one example of this.

Python is becoming the go-to programming language for machine learning and is used to create models for Bayesian networks (a type of graph used to model projections), decision trees, and more.

Python also stands out as a valuable data analysis tool for a couple of other reasons:

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Businesses increasingly rely on data analysis to compete and grow. As the amount of available data increases, data analysis tools will be essential for companies to better understand their customers and market trends. Applications like Tableau, Microsoft Power BI, and Python are among the many tools of the business intelligence trade, and knowledge of these resources is vital for aspiring business analysts.

Individuals interested in a career in data analysis can explore Maryville University’s online Master of Science in Data Analytics program to learn more. Courses like Introduction to Data Analytics, Data Mining, and Data Visualization can help students understand the importance of data analysis in business.

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Recommended Reading

Top 4 Data Analysis Techniques That Create Business Value

Data Visualization vs. Data Analytics: What’s the Difference?

The Future of Big Data in Business: Using Data Analytics to Provide Insight

Sources

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Bayes Server, Bayesian Networks — an Introduction

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CIO, “What Is Business Intelligence? Transforming Data into Business Insights”

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Forbes, “The 9 Best Analytics Tools for Data Visualization Available Today”

Forbes, “The Age of Analytics and the Importance of Data Quality”

Grow, “Why Is Data Important for Your Business?”

IBM, Data Visualization Investopedia, “Data Analytics”

Investopedia, “Data Mining”

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ResearchGate, “Data Mining Techniques for Credit Card Fraud Detection”

Tableau, “6 Real-World Examples of Business Intelligence Dashboards”

TechRepublic, “Research: Executive Management Recognizes Business Value of Analytics”

Towards Data Science, “How to Visualize Data Using Power BI?”

Towards Data Science, “Why Python Is Essential for Business Analysts”