6 Best Big Data Analytics Tools: How to Choose?

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A server full of data means nothing if you don’t have the tools to make use of it all. A big data analytics tool can be thought of, in simple terms, as a decoder. It takes the input of raw data and provides the output of insights and understandable information that you can use to better your business.

So, let’s talk about why big data technologies matter so much and see why the big data analytics process is so critical for every business.

Coming Up

What is Big Data Analytics?

Why is Big Data Analytics Important?

What is Big Data?

What are Examples of Big Data Analytics?

What are the Benefits of Big Data Analytics?

What are the Top Big Data Analytics Tools?

What are the Stages of Big Data Analytics?

What are the Types of Big Data Analytics?

What are the Applications of Big Data Analytics?

How to Choose a Data Analytics Tool?

Closing Words

What is Big Data Analytics?

Big data analytics breaks down patterns, customer information, correlations, and trends that would otherwise remain undiscovered behind data. Through the big data analytics process, businesses gain usable insights and advantages.

With this handy, stakeholders and leaders have what they need to make timely decisions, enact policies, update processes, and move forward with confidence.

Why is Big Data Analytics Important?

Historically, big data analytics has been a task for data analysts to handle. By doing so, data analysts provide stakeholders and leaders with the knowledge they need.

In today’s world, everyone can access big data analytics without programming knowledge or technical acumen, thanks to any big data analytics tools (and automation solutions).

Big data analytics is so important because it helps to set up organizations for success by providing answers to improve revenue potential, marketing effectiveness, customer service, and efficiency.

What is Big Data?

When discussing big data analytics tools, the first question you probably have is, “What is big data anyways?”

Big data refers to massive datasets that can’t rely on traditional methods for processing, storage, or analysis. This is because they are so big and heavy! With digitization, the influx and speed of data is always increasing at a rapid rate.

For example, data can come from social media, text, website visits, forms, emails, videos, pictures, and more!

Since data is formatted differently, it has to be structured properly in order to be used for analysis. Big data analytics tools help to transform raw data into understandable and informative content.

What are Examples of Big Data Analytics?

Big data analytics examples can be seen everywhere in businesses of all sizes.

Here’s a look at a few common use cases of big data analytics in action:

  • To understand and improve marketing campaigns
  • To predict future scenarios and potential outcomes
  • To detect fraud and undesirable situations quickly
  • To better know customer behavior to improve the customer experience

However, in order to be able to leverage these benefits of big data analytics, you need the right tools by your side to do so. If everything is stuck in siloed spreadsheets and on disparate desktops, you run the risk of missing data or misusing outdated data.

That’s why it’s so beneficial to have a solution that can centralize, cleanse, and consolidate data.

With all your data formatted for use ,a big data analytics tool can transform it into the insights you seek to achieve whatever business goal that you’re currently focused on.

At the same time, big data analytics can be used to improve your internal processes since you can analyze how your workflows are moving along.

What are the Benefits of Big Data Analytics?

By conducting big data analytics, your business has the knowledge needed to thrive. Take a look at the benefits of big data analytics:

1. Improved Risk Management

Up-to-date data analytics can pinpoint issues within the business such as fraud on an early basis. This way, you can nip problems in the bud before they grow too big to manage.

2. Informed Decision Making

Companies can use data analytics to forecast how a decision today would affect their future. This way, leaders can make smart decisions and test out scenarios before having to invest or move forward one way or the other without any insights.  

3. Better Customer Service

By better understanding how customers feel about your product or service, you can initiate updates, changes, and new offerings to best serve them. In turn, you can enhance the customer experience to generate loyalty and retention.

4. Product Optimization

Last but not least, reviewing how your products are faring or how your processes are flowing with analysis opens the door for product and process improvement.

What are the Top Big Data Analytics Tools?

As you can see, a data analytics tool gives your business a much-needed edge. When comparing the various big data analytics tools out there, it can be hard to decide how to proceed.

We’ve compiled a list of the top big data analytics tools and some details to help you narrow down your choice.

1. R-Programming

R is a statistical data analysis package that is open-source. Given its programming expertise needed, it’s used mostly by data miners and statisticians. Its ecosystem is robust, but it requires knowledge to be able to use it.

2. LUMIFY

LUMIFY is another open-source and free data analytics tool for data integration, visualization, and analytics. It provides 2D and 3D graphical representations of data, but again, requires a lot of skill to use.

3. Apache Hadoop

As a software framework to handle big data and clustered file systems, Apache Hadoop uses a MapReduce programming model to process datasets. It can hold all types of data, which is why so many enterprise companies use this solution, along with the fact that it’s scalable.

4. Rapidminer

Rapidminer is an open-source Java core data analytics solution that integrates with APIs and operates in the cloud. It’s a fairly costly solution that is mostly designed for commercial and small enterprise clients. Rapidminer is used for data science, predictive analytics, and machine learning.

5. Apache Spark

For large-scale data analytics, Apache Spark empowers organizations with data engineering and machine learning on single-node machines or clusters. It can conduct analysis in batches or real-time streaming, perform SQL queries, and scale with your business.

6. Microsoft Azure

Microsoft Azure is a cloud computing platform for computing, networking, analytics, and storage. It helps enterprises run big data workloads in real-time through SQL queries for simple questions and the use of programming languages for complex operations.

Bonus: SolveXia

SolveXia is an out-of-the-box, low-code finance automation solution that can perform complex data analytics without the need of any IT team or highly-trained staff.

With an easy-to-use interface and customizable reports and dashboards, SolveXia not only executes process automation, but it also provides deep insights leveraging real-time data. SolveXia automates rebate management, expense analytics, account reconciliation, reporting, and other crucial complex finance functions.

SolveXia combines, transforms, and securely stores data for any use case, along with the capability of providing deep insights for informed decisionmaking.

With SolveXia, your business gains the answers and support it needs to operate more efficiently with less error, while saving time!

By deploying the finance automation software, you can expect 85x increase in productivity and 90% less errors. With utmost internal control and transparency, SolveXia helps your team work smarter, not harder!

What are the Stages of Big Data Analytics?

With whatever data analytics tool that you decide to deploy, the stages of big data analytics will typically follow the same flow, namely:

1. Evaluate Data

Begin by defining your business use case, or the goal behind running the analysis in the first place.

2. Identify Data

Define the multiple sources of where you’ll pull the data from to achieve the goal set forth above.

3. Filter Data

Remove any corrupt, incomplete, or irrelevant data (this can be automated with the right tool).

4. Extract Data

Any data that isn’t compatible with your tool of choice will need to be transformed to a compatible format.

5. Aggregate Data

Pull data together from multiple sources to be analyzed. There are automation tools that centralize data for you, so you don’t have to worry about all these steps.

6. Analyze Data

The use of statistical tools and analysis is conducted to reap insights.

7. Data Visualization

The insights are transformed into graphs and visual representations for easy understanding.

8. Share Insights

At the final stage, the information can be shared with stakeholders and those who seek it most. Automation tools like SolveXia can be set up to automate processes to create real time reporting and customize dashboards based on those who need access to the information.

What are the Types of Big Data Analytics?

Depending on what you’re trying to accomplish, there are different types of data analytics that can be employed. These are the four main kinds:

1. Descriptive Analytics

Descriptive analytics can be used to put historical data into an easy-to-read representation, such as a report or dashboard for quick understanding.

2. Diagnostic Analytics

This type of analysis is conducted to answer why something happened, or to get to the root cause of a problem in order to resolve it.

3. Predictive Analytics

Predictive analytics looks to the future by using historical and present data to predict what’s yet to happen. It leverages artificial intelligence, data mining, and machine learning to do so.

4. Prescriptive Analytics

Prescriptive analytics help to find a solution to a problem by combining both descriptive and predictive analytics.

What are the Applications of Big Data Analytics?

Big data analytics is used in pretty much every industry you can imagine. For example:

  • Ecommerce: It’s helpful to discover customer trends and better service customers.
  • Media and entertainment: Big data analytics can be used to understand user preferences and provide recommendations that suit the audience.
  • Finance: With access to customer income and spending patterns, finance companies and banks can leverage big data analytics to customize product offerings and personalization.
  • Healthcare: With access to a patient’s medical history, big data analytics can predict future outcomes and health issues to offer preventative solutions.

How to Choose a Data Analytics Tool?

When looking to find the best big data analytics tool for your business’ needs, consider the following factors and ask yourself:

  • The volume, variety, and velocity of your data- can this tool handle it?
  • The timing - does this tool process data in real-time or near real-time?
  • The level - can the tool drill down into the granular details of the data?
  • The interoperability - does the tool integrate with my existing systems?

These key questions can help you narrow down the offerings. At the same time, it’s a good idea to understand the skills and resources of your team to determine if you would rather invest in an out-of-the-box/no-code solution or leverage a team of data analysts instead.

Finance automation solutions offer teams an all-in-one solution to streamline complex processes through automation, provide deep insights and analysis, and clean, connect, and store data for multiple use cases to glean insights.

Closing Words

There’s no reason to be overwhelmed by how to use the data you have available to you. While it can be tricky if performed manually, the right big data analytics tool will manage the heavy lifting for you. This way, you gain insights in real-time and can conduct business with utmost confidence, having answers to your biggest questions.

Automation tools like SolveXia can be used to automate account reconciliation, expense analytics, rebate management, reporting, and additional processes to free your time to be able to accomplish more!

At the same time, you’ll be able to put your data to work to get the insights you need!

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