10 Best Data Analyzer Tools: How to Choose?

Data Analysis
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The competitive landscape in every modern business sector is densely populated by rival organizations that constantly push themselves, and each other, to the limit every day.

Most executives realize even the smallest competitive edge can translate to dramatically increased profits, which is why using a data analyzer tool has become an essential aspect for most businesses.

In this article, we will cover some of the top analytics tools and data analysis software that companies can use to operate as efficiently and profitably as possible.

Coming Up

What are Data Analyst Tools?

What are the Methods of Data Analysis?

What are the Types of Data Analysis?

What is the Difference Between Data Analysis, Data Mining & Data Modeling?

What are the Benefits of Data Analysis Tools?

What are the Best Data Analyzer Tools?

How to Choose the Best Data Analyzer Tool?

Wrap Up

What are Data Analyst Tools?

Data analyst tools are computer programs that compile and analyze the data a company collects from its internal operations and customer base.

The idea behind data analysis software is that once a company has collected this information, they can use it to uncover trends or innovations that will improve processes, efficiency, and/or increase profits.

In many cases, the companies with the best analytics win the most market share, which makes choosing the right data tools an essential function of corporate leadership.

What are the Methods of Data Analysis?

While different companies use different methods to collect and analyze data, the following two methods represent how data can be assessed to glean insights:

1. Qualitative Analysis

Qualitative analysis is a data tool that relies on using intangible or non-quantifiable factors, such as brand recognition, motivation and workplace culture to analyze the prospect of a company’s success or value.

2. Quantitative Analysis

Quantitative analysis is a data tool that looks at hard numbers, such as profit and loss sheets, sales data, website traffic, and consumer “close” rates to assess the current performance or potential value of a company over a given period of time. Put simply, it is data values that can be counted.

What are the Types of Data Analysis?

Within the framework of the quantitative and qualitative methods of analysis, there are four basic types of analysis that companies will conduct to reach their conclusions.

1. Descriptive Analysis

Descriptive analysis examines past data (e.g., same store sales, average cost per consumer conversion, revenue per user) to paint a picture of what has already occurred in a specific period of time.

2. Diagnostic Analysis

Diagnostic analysis examines data with a goal of discovering why a certain upward or downward trend may have occurred or whether there is any intersectionality between different variables in the data.

3. Predictive Analysis

Predictive analysis combines historical data with advanced statistical modeling, (sometimes generated by artificial intelligence) to predict a future trend or market condition.

4. Prescriptive Analysis

Prescriptive analysis processes information and offers insight as to how a business or a company should react to current market conditions or market conditions it anticipates will exist in the future.

As you know, having access to these types of analytics can be a game changer for finance professionals and stakeholders alike. But understanding which analysis to perform, along with having adequate time to do so can pose a challenge. That’s were automation can help!

A finance automation solution like SolveXia can perform advanced analytics and data storage automatically. Many companies without automated data analysis find manually collecting and cataloging data to be a tedious task that hinders their ability to analyze and react to the data they have collected.

On the other hand, using finance automation solutions provided by SolveXia frees CFO’s and their staff up to do what they do best; analyze data and plot a course forward.

What is the Difference Between Data Analysis, Data Mining & Data Modeling?

Data mining, data analysis and data modeling may sound like different ways of saying the same thing, but they are actually three different functions.

  1. Data analysis is interpreting all the data a company collects (e.g., net sales, net profit per sale, customer demographics) to see what story it tells.
  1. Data mining is the collection of massive amounts of data and finding specific patterns or information in that data by using algorithms or other methods, which in turn generates new data.
  1. Data modeling is a predictive method that uses previously mined and analyzed data to predict future trends or market conditions the company may encounter.

Simply put, data mining is active. Data analysis is reactive. Data modeling is proactive.

What are the Benefits of Data Analysis Tools?

Data analysis tools offer a host of benefits to any company. They can be used to gauge the effectiveness of a particular business strategy or gain a better understanding of customer preferences.

It is also a key aspect of regulatory compliance and reporting for many companies. Considering that all companies will collect mountains of data about their own operations and their clients, it only makes sense to analyze that data to push the business forward, lower risk and increase profits.

What are the Best Data Analyzer Tools?

You will find numerous options in the market when searching for the best data analyzer tool. Keep reading to see our top 10 rated tools:

1. SolveXia

SolveXia is one of the most well-rounded data analytic tools and finance automation software on the market. It offers a fully integrated suite of business data collection and business analytics tools that can be activated with the click of a mouse.

SolveXia’s automation allows business to remove time-consuming manual processes and be able to focus on providing greater insights to the business. All the while, you not only free time and prevent errors, but you also get to improve compliance.

SolveXia’s software is compatible with legacy datasets and can pull information from multiple sources while storing them in a highly secured cloud with bank-level encryption. Users can also easily set up automated reports for shareholders, executives or regulatory authorities.

Since SolveXia is a low/no-code solution, anyone with or without technical or coding acumen can utilize the tool with simplicity given its drag and drop functionality.

With quick analytics and a visual interface, each respective party can gain access to the valuable insights they need to make informed business decisions at any time!

Plus, you can also use SolveXia for expense management, account reconciliation, rebate management, APRA reporting, regulatory reporting, and more.

2. Microsoft Power BI

This data analysis tool combines data science and easy visualization that allows users to create reports, spreadsheets and datasets. It also gives you the option to have your data stored on-site or on the  cloud.

However, it is somewhat limited in the formulas you can use to interpret data and may not always be compatible with the coding in your pre existing systems.

3. SAP BusinessObjects

This is a powerful, flexible data analyzer tool that will allow users to perform a wide range of data-collection and reporting. It is known for being highly functional and also has the advantage of using Excel as its base spreadsheet function.

It is also relatively easy to share information between parties. However, some users note that the UI can be difficult, and there is a steep learning curve when it comes to mastering the program’s full capabilities. This can create issues with creation of reports and integration of information.

4. Sisense

Sisense is a cloud-based platform that allows users to perform advanced analytics across all of a company’s operations. Whether it is workflow-related or focused on customer and employee data you are looking for, Sisense can provide it.

This data analysis tool offers a “highly customizable” experience known as Sisense Fusion, which aims to give users more capabilities that a traditional analytics dashboard. However, some users have raised issues about the cost and the fact that Sisense is going over to a Linux-based platform.

5. RapidMiner

RapidMiner software allows users to collect, interpret, clean and transfer all a company’s data before running their statistical modeling and analytics. It offers users an intuitive dashboard that is easy to use and understand.

Users can also expand RapidMiner’s capability with open source scripts available through R and Python. However, unlocking the full capability of this tool may require the user to purchase additional plug-ins and extensions through the RapidMiner marketplace.  

6. Tableau

Tableau boasts some of the industry’s best data visualization capabilities. CFOs and financial staff using this software will be able to create dashboards and graphics that are much easier to read than traditional spreadsheets.

It also offers strong data collection capabilities and it can work with other methods of coding. However, Tableau falls short in some areas, including the lack of an easy method to auto generate reports or refresh old reports so that they integrate new data.

Another drawback is the inability to make custom data fields, and Tableau requires the purchase of a separate tool to “clean” the data Tableau relies on to create its impressive visualization.

7. Python

Python is a programming language that is famous for its flexibility, as it allows users to draw on a wide range of data libraries for analytics and other advanced business calculations.

Its user-friendliness and popularity comes from the fact that it has access to a system known as PANDAS (Python Data Analysis Library). Users can access this library to crunch numbers, and highly skilled users can even create their own algorithms for custom data collection or analysis.

8. R Project

R-Project is an open-source programming language similar to Python. Users can write detailed analytical programs using R language to collect bespoke data specific to their industry or field of operation.

It is also compatible with existing software such as Sisense, which means R can be integrated with your current analytics structure but expanded to offer even deeper, more predictive analysis. However, it requires highly-skilled programmers to operate.

9. Looker

Looker is a 100% web based data analytic tool that features a very innovative method of data modeling which allows even an employee who is a relative novice in the field of data analytics to access some very powerful information.

It offers cloud storage that uses Google technology, so the technology itself is very scalable. It is highly rated for its visualization capabilities but also relatively expensive to use, especially for smaller businesses.

10. Excel

Excel is a tried and tested software that can be used for data collection and analytics. A skilled user can create highly effective visual presentations and the fact that it’s a Microsoft product means it will be compatible with all PC’s.

However, Excel requires an extremely skilled user to maximize its capabilities (especially when it comes to automation) along with a high level of manual data input. If the person or people responsible for running the program or inputting data are not available, the company could face severe delays in collecting and processing data.

How to Choose the Best Data Analyzer Tool?

Choosing the best data analyzer tool is not too different than choosing tires for your car. There are a lot of tire brands, but when you start with some basic questions about what your car is capable of and what kind of performance you need, the field narrows itself down.

In choosing the best data analyser tool for your business, it’s important to ask yourself the following four questions.

  1. What type of data do you need to analyze?
  2. How much data do I need to analyze?
  3. Who will be handling the data?
  4. What’s your budget and timeframe?

Once you have consulted with the braintrust at your company, and gotten feedback from the people who will be chiefly responsible for using your data analytics tools, the right option for your company should become clear. If you’d like to give a data analytics tool like SolveXia a try, we invite you to book a demo!

Wrap Up

It’s important to remember that no tool is more effective than the user, and this is certainly true for data analytics. You must have a clear idea of what data you need to collect, and be committed to learning the ins and outs of whatever data analytics tool you choose.

That being said, you want a solution that’s easy to use, robust in its solutions, and can grant you accurate insights in a timely manner. A data analyzer tool like SolveXia is ready to help you tackle your data analysis needs.


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