Data analytics tools are software and programs that collect and analyze data about a business, its customers, and its competition in order to improve processes and help uncover insights to make data-driven decisions. Also, the best data analytics tools help companies draw insights from customer data, and find trends and patterns to make better business findings. Thus, there are a vast number of free data analytics tools you can make use of, whether you want to perform basic or more advanced data analysis. Read on to gain more insights.
Overview
Data analytics tools help businesses in several ways as they can track and analyze data, permitting them to create reports. Having insight into these tools as a business owner assists you to make better decisions about where to put your resources and how to place a price on goods and services.
Equally important, the best data analytics tools help to identify trends and a better understanding of the customer base. Also, the best type of data analytics for a company depends on its stage of development but it typically only affords insights to make reactive, not proactive, business decisions.
Types of Data Analytics Tools
Below are the various types.
#1. Diagnostic Analytics
Diagnostic data analytics is the process of examining data to understand the cause and event or why something happened. Just like descriptive analytics, it uses historical data to answer a question. But instead of focusing on “the what”, diagnostic analytics addresses the critical question of why an occurrence or anomaly occurred within your data. This type of analytics helps companies answer questions such as:
- Why did our company sales drop in the previous quarter?
- Why are we seeing an upsurge in customer churn?
- Why are a specific basket of products vastly exceeding the prior year’s sales figures?
Diagnostic analytics tends to be more accessible and fit a wider range of use cases than machine learning/predictive analytics. You might even find that it solves some business problems you earmarked for predictive analytics use cases.
#2. Prescriptive Data Analytics
Prescriptive analytics is where AI and big data combine to help forecast outcomes and identify what actions to take. This category of analytics can be further broken down into optimization and random testing. Using advancements in ML, prescriptive analytics can help answer questions such as “What if we try this?” and “What is the best action?” You can test the correct variables and even suggest new variables that offer a higher chance of generating a positive outcome.
#3. Descriptive Data Analytics
Descriptive analytics is the backbone of reporting because it’s impossible to have business intelligence (BI) tools and dashboards without it. Thus, descriptive analytics answers the question, “What happened?”. This type of analytics is by far the most commonly used by customers, providing reporting and analysis centered on past events. It helps companies understand things such as:
- How much did we sell as a company?
- What was our overall productivity?
- How many customers churned in the last quarter?
Descriptive analytics is used to understand the overall performance at an aggregate level and is by far the easiest place for a company to start as data tends to be readily available to build reports and applications.
#4. Predictive Data Analytics
Predictive analytics is a form of advanced analytics that determines what is likely to happen based on historical data using machine learning. Historical data that comprises the bulk of descriptive and diagnostic analytics is used as the basis for building predictive analytics models.
For example, an advertising campaign for male wears on Instagram could apply predictive analytics to determine how closely the conversion rate relates to a target audience’s geographic area, income bracket, and interests to analyze the statistics for two (or more) target audiences and provide possible revenue values for each demographic
How to Choose an Effective Data Analytics Tool
Choosing the right data analytics tool can be a challenge. If you are struggling to select an effective tool from the list above then answers to these questions will guide you to finding the best data analytics tool to fit your needs:
#1. What Type of Data Do I Need to Analyze?
Before choosing a data analysis tool, you must decide the data type you want to analyze. Is it quantitative or qualitative data? Because numerical data stored in spreadsheets and databases is easy to transform into visual insights using data in Excel, and BI tools like Tableau. But making sense of qualitative data such as open-ended survey responses, emails, and social media conversations, often calls for AI data analysis software.
#2. Skills Needed to Use Tools
Most companies have refined data science teams that handle SQL queries and tools. Professionals are not needed to make a data analytic tool work but if necessary fill in the gap for training or hire a person with the right skills to teach your teams. Instead, use a tool that is easy to use and grant access to all team members.
#3. How Much Data Do I Need to Analyze?
If you receive a few hundred data points a month, you might be able to handle this data without advanced tools that automate data collection and analysis processes. However, if your data runs into the thousands, you’ll want to implement AI tools to avoid wasting time on manual and tedious tasks. Analyzing vast amounts of text data by hand, for example, is not only repetitive but it’s likely to lead to poor results.
Why? Because humans are subjective. If you need to hire many hands to sort and tag your data, each human tagger will label your data points differently.AI tools are trained using one set of criteria and are often customizable, so you can ensure that your data delivers accurate and actionable insights.
#4. What’s my Budget
also need to consider the costs of infrastructure. Will you need to invest in a data warehouse or cloud-based data storage, and data pipelines? Again, you’ll need to think about your current team structure and if you have the budget to hire a team of experts if you opt for more complex data analysis tools. Finally, price and time-to-implement data analysis tools will also influence your decision. When comparing data analytics tools, you might want to check whether you can pay for what you use or if there’s a fixed monthly fee.
#5. Time
Finally, how long does it take to implement data analysis tools? Do you need a solution that’s up and running in 4 weeks or 4 months? If you’re building your own data analysis tools, you’ll need to factor in the costs of unproductive waiting time. price and licensing. Some data analysis tools will be offered on a subscription or licensing basis. In this case, you may need to consider the number of users required or if you’re looking on solely a project-to-project basis the potential length of the subscription.
Once you’ve answered all the above questions, you’ll have a better understanding of which tools are right for you.
Best Data Analytics Tools
The best data Analytic tools support businesses in improving their products and services to boost client satisfaction. In essence, data analysis is gathering and arranging large amounts of data to extract useful information that aids in making key business choices. In general, Data Analytics examines data and generates predictions to extract useful information. Let us take a look at the best data analytic tools below,
#1.Thoughtspot
Unlike traditional Data Analytics Tools, ThoughtSpot works on natural language understanding. To visualize your data, you only have to ask the right question using natural language. ThoughtSpot automatically showcases visualizations and other unique insights, which you can use to create reports or dashboards.
#2. Microsoft Excel
The world’s best-known spreadsheet software. What’s more, it features calculations and graphing functions that are ideal for data analysis. Whatever your specialism, and no matter what other software you might need, Excel is a staple in the field. Its invaluable built-in features include pivot tables and creation tools.
#3. Python
A programming language with a wide range of uses, Python is a must-have for any data analyst. Unlike more complex languages, it focuses on readability, and its general popularity in the tech field means many programmers are already familiar with it. Python is also extremely versatile; it has a huge range of resource libraries suited to a variety of different data analytics tasks
#4. Qlikview
Qlikview is recommended as the best tool for data visualization. It is faster, easy, and unique in nature. There is a community in QlikView that has discussion forums, blogs, and a library. Community helps to solve most of your queries. It shows the relationship between data using different colors. Qlikview helps users to make the right decisions from their different approaches to data visualization.
#5. R [CRAN]
It is commonly used to create statistical/data analysis software. R’s syntax is more complex than Python and the learning curve is steeper. However, it was built specifically to deal with heavy statistical computing tasks and is very popular for data visualization. A bit like Python, R also has a network of freely available code, called CRAN (the Comprehensive R Archive Network), which offers 10,000+ packages.
Free Data Analytics Tools
Free data analytics tools are used to analyze data and create meaningful insights out of the data set. These are a set of tools that helps businesses to create a data-driven decision-making process.
Free data analytics tools are applications and software used by data analysts to develop and perform the necessary analytical processes that help companies make better, more informed business decisions while lowering costs and increasing profits. Below are examples of free data analytics tools
#1. Looker
Cloud-based Looker provides an intuitive drag-and-drop interface that’s easy to use. It offers free data analytics tools and management, business intelligence, and advanced visualization capabilities. The tool’s multi-cloud strategy supports the use of various data sources and deployment methods. Looker also easily connects with an array of databases, including Snowflake and Amazon Redshift.
#2. Orange
is an open-source data mining and machine learning tool that has existed for more than 20 years as a project from the University of Ljubljana. orange toolkit can be used as simple data visualization to complicated machine learning algorithms provided it is open source. It can also be used with the Python library. It is like a canvas where the user places the widgets and workflow is created. All the data functionalities are done in widgets canvas. Users can explore various visualization techniques available in the tool.
#3. Domo
provides more than 1,000 built-in integrations called connectors that allow users to transfer data to and from on-premises and cloud external systems. Domo also supports building custom apps that integrate with the platform, which allows developers to extend the system with immediate access to the connectors and visualization tools. Domo comes as a single platform that includes a data warehouse and ETL software, so businesses that already have their own data warehouse and data pipeline set up may want to look elsewhere.
#4. Metabase
This is a free, open-source analytics and business intelligence tool. Metabase allows users to “ask questions” about data, which is a way for non-technical users to use a point-and-click interface for query construction. This works well for simple filtering and aggregations; more technical users can go straight to raw SQL for more complex analysis. Metabase also has the ability to push analytics results to external systems like Slack.
#5. Tableau
A free analytics tool for data visualization from simple data to complex data. It is kind of interactive and we can suggest labels, tools, the size of the column, and almost anything we can customize. The drag-and-drop interface is really helpful in this software and calculations can also be done in Tableau. Anyone who doesn’t have any idea of analytics can see and understand data from the Tableau platform.
Benefits of Data Analytics Tools
By instigating data analytics tools, organizations can expedite decision-making, gain new customers, enhance customer experience, and become future-proof. Some of the benefits of data analytics tools are:
#1. Expedite Decision-Making
Since data analytics tools can empower every professional with self-service analytics, organizations can expedite decision-making with data literacy. Users can ask different questions and unearth insights that can revolutionize business operations and sales performances.
#2. Gain Customers
By analyzing data from public domains, including social media, organizations can identify changing needs of users. This will allow companies to stay abreast of the changing requirements by adding new products/services to keep acquiring new uses.
#3. Enhance Customer Experience
Data analytics tools help you assess shortcomings in the delivery of products/services and allow you to find what users dislike. With Data Analytics Tools, you can perform advanced analyses like sentimental analysis to discover how customers feel about your products, allowing you to serve users better.
#4. Become Future-Proof
With insights, organizations make informed decisions to obtain better results in the future. For years, companies relied on the intuition of decision-makers, but with data analysis, they can eliminate inaccurate assumptions and work with clarity.
Conclusion
Data analytics can be done easily with a bit of practice. All the tools will not help equally. It is good to select one tool and become a master in that tool. Understanding data is essential to know where we really are in terms of data analytics. Programming is not really important in visualizing and analyzing data. But some tools make you closer to programming.
References
- Educba
- .stitchdata.
- .forbes.
- Careerfoundry
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