Data is a necessary resource in business, but it is practically useless in its raw form. With zettabytes of data generated each year, analysts can only make use of it with the help of proper data analytics tools.
Proficient data scientists, armed with the right data analytics software, can take big data, determine what makes it useful, compile the information, and present it to business leaders so that they create a plan of action. Data plays a crucial role in decision-making for businesses. By finding patterns and trends in data, businesses make better choices regarding their resources and pricing options through predictive analysis.
As technology evolves, so does the field of data analytics, and the range of available data analysis tools grows with it. There are copious amounts of tools available in the market that can suit a wide variety of business needs, but choosing the right ones can be challenging.
Understanding Data Analytics
Understanding Data Analytics requires a comprehensive data management strategy. Before businesses can get started with data analytics, they need to understand the basics first, which often involves a data integration platform.
What is Data Analytics?
Data analytics is the process of gathering and analyzing raw data, by applying qualitative and quantitative statistical analysis techniques to transform data into useful information and meaningful insights. This processed information, in turn, plays a big role in informed decision-making, enhancing productivity, and business gains.
Data analysts work with both structured and unstructured data. Structured data comes in standardized, tabular formats, while unstructured data might appear in the form of free text, video, and audio, for example.
Organizations analyze different types of data to make informed decisions that can positively impact their businesses. These include:
- Descriptive analytics: Which leverages data visualization tools to find averages, means, and medians on overall business analytics operations, with the intent of answering the question, “What happened?”.
- Diagnostic analytics: Seeks to pinpoint the driving force behind the results by measuring historical data against newer results to answer the question, “Why?” Analysts drill down the information based on categories to discover dependencies and identify patterns through data manipulation.
- Predictive analytics: Focuses on predicting future outcomes and uses the findings of descriptive and diagnostic analytics to detect tendencies, clusters, and exceptions. Decision-makers use machine learning models to analyze this information and gather information about whether they will be able to accomplish their business goals.
- Prescriptive analytics: Determines how new possible actions can change the results in the future. It uses advanced data analysis tools and machine learning to assess which actions businesses should take to eliminate a future problem or benefit from a promising development.
Assessing different types of data isn’t always straightforward because of missing information, outliers, and noisy data. Luckily, there is a wide variety of popular data analysis tools to simplify the process. These consist of varied software and applications that analysts use to develop and perform analytical processes. They use the results, often visualized through data visualizations, to help companies make business decisions such as decreasing costs and increasing profits.
Companies can take advantage of data analytics to get greater visibility into customer buying behavior. This helps with the design of marketing campaigns to improve sales conversion rates, retarget potential buyers, and reduce the risk of customer churn through data discovery techniques.
The Process of Data Analytics
The Process of Data Analytics starts with data collection, which is often a major component of data integration, followed by processing, analysis, and, finally, decision-making.
Data Collection
The process of gathering info from targeted sources and variables is data collection. The depth of data analysis depends on the collection method. The process starts with primary sources, or internal sources that include data from marketing and CRM tools, as well as marketing automation tools.
These areas contain information concerning finances, customers, sales gaps, and other important aspects of the business. Data analytics tools then further collect data from secondary and external sources.
Data Processing
Not all collected data is useful, and it always requires processing and organizing for analysis. Structuring data for a particular data analysis tool includes removing white spaces, duplicate records, basic errors, anomalous data, and any other inconsistencies that might lead to generating inaccurate results.
Advances in data science and machine learning platforms applied to this part of the processes of data analytics allow for intelligent automation. Not only does it minimize human error, but it also saves time while cleaning data.
Data Analysis
Once data analysts obtain good quality data, they need to analyze it using data analytics tools to help interpret and understand the information before making any conclusions. The type of analysis depends on whether the data is quantitative or qualitative.
Quantitative data handles hard numbers and quantities such as sales numbers, revenues, payroll data, click-through rates (CTR), and other types of countable and measurable data. Qualitative data is subjective and interpretative. It could include information gathered from customer surveys and employee interviews.
To measure quantitative data, analysts can use statistics and simulations such as regression analysis, hypothesis testing, and the Monte Carlo simulation. Qualitative data requires more subjective approaches like content analysis and narrative analysis.
Decision Making
Upon registering the results, data analysts need to interpret them and transform them into courses of action that businesses can understand and implement to gain value. Analysts and business leaders work together during this process and explore different options to test in order to, for instance, reduce production costs without sacrificing quality.
Importance of Data Visualization & Data Analytics
Users generate around 2.5 quintillion bytes worth of data every day, and there are currently over 44 zettabytes of data in the entire digital universe. This is why businesses should implement solid strategies to process large volumes of data and maximize and leverage its useful information through a data integration platform.
According to Statista, 57% of leading organizations currently use data to drive innovation. This value tends to increase, as more companies understand how big data analytics tools can help them detect patterns, identify trends, and obtain insightful data that leads to wise business decisions. In 2021, the value of the big data analytics market was around $241 billion. Experts expect that this value will reach over $655 billion by 2029. This isn’t surprising considering the number of benefits companies obtain by adhering to this practice.
Data analytics programs and tools can help businesses analyze vast amounts of data from several sources in various formats, create effective strategies to improve all departments, and build a deeper understanding of user needs, preferences, and sentiments that could help improve strategies for product development.
Other benefits include extracting significant insights hiding in large datasets, reducing risks by planning for unforeseen circumstances and potential challenges, encouraging the development of innovative business plans, products, and services, and operating in ways that always meet and surpass customer expectations.
The Top Data Analytics Tools
There are many data analytics tools on the market today to help businesses thrive.
Microsoft Power BI
Power BI began as an Excel plug-in before Microsoft redeveloped it into a standalone suite of business data analytics tools. This program allows users to create sophisticated, dynamic, and interactive visual reports and dashboards, with a minimal learning curve.
It makes communicating critical business insights very easy and secure and operates seamlessly with Excel, text field, SQL server, and cloud sources like Google and Facebook analytics.
- Key features – Power BI integrates with Office 365, connecting and protecting data across various Office platforms. It has rich and customizable dashboards, tailored to match any enterprise’s specific needs. Its integration with Azure Machine Learning and Azure Stream Analytics enables predictive intelligence and gives users access to real-time data.
- Pros – This data analysis tool has seamless mobile optimization. It is an easy platform to use, especially for users with Excel knowledge. Users can automate data refresh settings and generate reports, allowing all users to access the most recent data.
- Cons – Using large amounts of data can slow down the platform. It uses rigid formulas and there are data limits in the free version. Power BI only accepts files with a maximum size of 250 Mb.
Tableau
Tableau is a data analytics tool widely used in Business Intelligence services (BI) that allows analysts to visualize, analyze, and understand data without requiring extensive coding expertise. It’s one of the best commercial data analytics tools available on the market for its scalability, efficiency, and ability to handle large amounts of data.
It has a visual drag-and-drop interface that’s fairly simple to use. This tool also possesses Einstein Discovery AI, making it an even more powerful tool for data analytics.
- Key features – Tableau has many built-in custom data connectors and features that make it easy for data preparation and cleaning. It provides efficient data exploration as it handles any type of data from spreadsheets to data on cloud services. Users can engage with and customize their dashboards as they see fit.
- Pros – Tableau incorporates innovations in augmented analytics that improve the power of analysis.Based on scientific research, Tableau makes analysis processes quicker and simpler, which makes it easy to learn. The platform provides enterprise-grade security and offers governance mechanisms to keep data safe.
- Cons – There’s no automated report refreshing in Tableau, so users can’t automatically update their reports with the help of a schedule. There’s a lack of version control that prevents users from accessing earlier data versions once dashboards and reports get published on the server. Tableau also requires some knowledge of SQL, especially when analyzing rich and complex datasets from multiple data sources.
Microsoft Excel
Microsoft Excel is the world’s best-known spreadsheet software that features calculations and graphical functions ideal for data analytics. Excel is a staple in the field, across all industries and specialties.
Even though it has some limitations, users can quickly get around them by taking advantage of the many plug-ins available for this platform. Excel is an important and powerful data analysis tool that makes data analysis simple by manipulating rows and columns and creating quick reports for other users.
- Key features – Data analysts can compare outcomes of several scenarios using What-If Analysis. By adjusting cell values, they can see how they’d affect the output on the worksheet. Users can relate data from a table with an input or LOOKUP value, in vector and array forms. The conditional formatting feature allows analysts to highlight particular cells based on their value and established criteria. This is a great way to find trends and outliers in data.
- Pros – Excel is great for arranging large data sets into structured tables, which makes data analysis more efficient and simple. It is available both online and offline on all major operating systems. Built-in functions make accounting, mathematics, statistics, logic, or any quantitative data calculations simpler in Excel.
- Cons – Excel isn’t scalable. It lacks support for quick autofill options and calculation errors are quite common, with numerous instances of misplacing decimals or percentages, misinterpreted data types, and mixed date or currency formats.
Apache Spark
Apache Spark is a multi-language, open-source, high-performance data processing engine designed to quickly analyze unstructured big data. It works well for batch and streaming data.
This data analytics tool uses RAM rather than local memory which makes it exceptionally fast and great for the development of data-heavy machine learning models. It also has a machine learning library, MLlib, that contains a wide variety of classification, regression, clustering algorithms, and more.
- Key features – Apache Spark can access diverse data sources and run on any platform such as Hadoop, Apache Mesos, standalone, or in the cloud. It includes many libraries like SparkSQL, for SQL and structured data, MLlib, for machine learning, SparkStreaming, for live data stream processing, and GraphX, for graph analytics. Apache can perform tasks in memory, which greatly boosts big data processing.
- Pros – Apache Spark application can process data up to 100 times faster in memory and ten times faster on disc. Users can create applications in various languages as this platform directly supports Java, Python, Scala, and R. Because it is open-source software, there’s no additional licensing fee.
- Cons – Apache Spark is less efficient than other data analytics services or tools because its file management features rely on a separate third-party system. It doesn’t have a system for automatic code optimization, which means that users must manually optimize it.
KNIME
Konstanz Information Miner or KNIME is a free, open-source, cloud-based data analytics tool. Originally built for the pharmaceutical industry, KNIME’s capacity for accumulating data from numerous sources into a single system made it popular across all industries, such as customer analysis, business intelligence, and machine learning.
This platform possesses a drag-and-drop graphical user interface that makes it ideal for visual programming. It is also easy to use and doesn’t require a lot of technical expertise from users to create data workflows.
KNIME supports the full range of data analytics tasks, however, its main strength lies in data mining.
- Key features – KNIME’s user-friendly drag-and-drop interface reduces the platform’s learning curve. It possesses the KNIME Hub, a public repository to engage with other users and access nodes, components, workflows, and extensions. KNIME integrates with many open-source initiatives such as H2O, ScikitLearn, Keras, and other machine learning algorithms.
- Pros – KNIME’s local automation feature gives users more flexibility when creating reusable workflows. Users can keep track of all changes and compare different workflow versions. More the 2,000 modules in KNIME support many data connections for different file formats and large databases.
- Cons – KNIME’s intellectual property and patent issues are complex and there’s always a chance to misuse or illegally distribute code without knowing. Transitioning from a private system to an open source requires a lot of resources, which leads to additional costs.
Choosing the Right Data Analysis Tools
For businesses to choose the right tool, they must first understand their business needs, approximate available costs, and evaluate the overall ease of use and customer service availability.
Understand Your Business Needs
There are many different services and tools that businesses can choose from. The first step is for companies to understand their business needs because different organizations have different data needs.
The right data analysis software should serve both current and future business needs. Business leaders must determine the company’s main objectives and establish a list of desired outcomes. It’s also important to determine who will be using the data analytics tools.
Will data analytics have primary contact with these platforms or will non-technical staff use them more? Companies should try opting for tools with interactive and intuitive interfaces that all sorts of professionals can use.
The most important thing is to select a data analysis tool that can give companies access to data and reporting tools that will help them meet their business goals.
Consider the Cost
Different analytics solutions have different costs and structures. It’s important to understand them before investing in one. Some data mining tools are free, others have free-to-use features but will require licensing for the full product, and others are completely paid. Companies need to have a clear understanding of their needs as well as their budget when choosing the right platform.
Users can also access some tools offered on a subscription or licensing basis. In this case, companies need to consider the number of users required or the potential length of the subscription, in the case of a project-to-project basis.
Look for Easy-to-Use Interfaces
An easy-to-use interface is a crucial factor in choosing the right data analytics tool. User-friendly interfaces can accommodate a larger variety of users, including non-technical ones that must be able to easily build and interpret dashboards and reports with a proper data visualization tool.
Evaluate the Customer Support
Even when using the most user-friendly data analysis tool users will likely encounter some technical difficulties. It’s important to understand how good technical support is before committing to a platform.
Business leaders should evaluate the training and support services a vendor offers which might include 24/7 live chat, online case submissions, webinars, and more. Open-source platforms usually also offer FAQ pages and “how-to” documentation to guide users through their issues.
Conclusion
Data analytics involves gathering, processing, and analyzing data, turning it into understandable information, and conveying it to business leaders so that they can make informed business decisions.
There are many different data analysis tools that companies can use to transform their businesses. The most popular ones include Microsoft Power BI, Tableau, Microsoft Excel, Apache Spark, and KNIME, with some of these serving as effective data visualization tools.
There are many more platforms available on the market, both free and paid. To choose the right one, companies need to take some time to understand their business needs and determine the available budget. They should also look for easy-to-use interfaces and tools that offer good customer support.
FAQ
What is data analytics and why is it important for my business?
Data analytics involves collecting and analyzing raw data to generate insights and guide decisions. Companies can leverage analytics processes and technologies to understand past trends, predict future outcomes, increase performance, and solve problems driven by data.
Analytics enables businesses to base strategies on evidence, grasp customer sentiment, and make informed choices. By converting raw data into meaningful insights, analytics delivers the information companies need to move forward.
What should I look for in a data analytics tool?
When choosing a data analytics tool, companies should look for how easy it is to learn and use, how many different types of graphs and charts they can access, how many plugins it offers, and whether it supports many different devices. Data modeling is also a critical feature to consider.
Depending on their business needs, companies should prefer platforms with advanced analytics that allow them to export data into different formats. It’s also important that the selected tool can connect with existing systems and third-party data sources. Flexibility and scalability, customization, and enterprise-level security are also important things to look for in a data analytics tool.
Is an expensive data analysis tool always better?
Expensive data analysis tools aren’t necessarily better. It all depends on the company’s needs and budget. Oftentimes, the most expensive tools don’t have a wide variety of features, and users shouldn’t ignore the robust free solutions available on the market.
How do I know which data analytics tool is right for my business?
To determine which data analytics tool is right for a particular business, companies must first understand their business needs and have a clear grasp of the available budget. It’s also important to find a platform that’s easy to use and accessible to both technical and non-technical users and that provides stellar customer support.
