5 recommended data analytics tools for your business

Actionable insights in the modern business landscape make data analytics tools essential – sophisticated software or solutions adept at gathering, processing, and dissecting data with precision. Undoubtedly, these invaluable data analytic tools have emerged as a cornerstone of decision-making within the corporate sphere.

Empowered by data analytics software, organizations can seamlessly turn raw data repositories into strategic insights that drive business growth. The ability to navigate intricate datasets, uncover nuanced patterns, and discern trends can be greatly enhanced.

What are data analytic tools?

Data analytics tools are software that businesses equip themselves for data analyses, including the process of collecting and processing data to gain valid insights into a particular subject. It is a crucial step in making strategic business decisions.

Since the data analysis process yields a wealth of information, it offers an objective and informed vantage point for decision-makers, ensuring that the next steps are rooted in concrete facts and that the pitfalls of unfounded assumptions can be avoided.

However, selecting the appropriate data analytics tools mandates a comprehensive understanding of both the underlying business requirements and the intricacies of data analysis itself.

Why data analytic tools are important

Enterprises seek data analytic tools that seamlessly blend user-friendliness, effectiveness, and precision. Yet, the appeal of these tools goes beyond mere utility. Here are several compelling reasons why businesses should use data analysis applications:

1. Empowering decision-making

Data analytic tools are the compass guiding organizations through the sea of data. They expertly navigate the intricacies of data collection, processing, and analysis, ensuring that decisions are anchored in concrete insights, not unreliable assumptions.

2. Strategic blueprinting

Business data analytic tools lay the groundwork for strategic planning by unveiling historical trends. By scrutinizing past trends stored in databases, businesses glean invaluable insights that inform their strategic visions for the future.

3. Customer insights

To survive in the competitive arena of modern business, it is vital to understand customer behavior and preferences. Data analytics tools will help unveil the hidden patterns within customer data and allow businesses to improve customer experience with innovative product and service development.

4. Market trendspotting

Market dynamics shift with a swiftness that defies prediction. Business data analytic tools, however, offer a dynamic lens through which companies can keep a vigilant eye on market trends. Armed with these insights, enterprises can proactively adjust strategies to stay relevant.

5. Performance metrics

The path to progress is illuminated by regular performance monitoring. Real-time data analytic tools offer the flashlight by which businesses gauge the performance of products or services so that each department can chart their course and head in the right direction.

6. Cultivating innovation

Innovation isn't guesswork, but instead, the result of keen insights and opportunity recognition. Tools for data analytics can uncover these gems by identifying emerging trends and unmet market demands.

How to analyze data for your business

Before raw data transforms into easily comprehensible information, it undergoes several essential steps. These steps are fundamental, as the choice of data analysis techniques depends on factors like data type, analysis goals, and the volume of data collected.

Here are the basic steps for data analysis:

1. Problem formulation

You need to define the purpose of your data analysis. What will be the use of the analysis results? At this stage, you also formulate the problem in the form of questions.

2. Data collection

The first step involves gathering the data to be analyzed. To obtain high-quality data, data collection must be carried out with precision.

You can choose to collect quantitative or qualitative data or a combination of both for a higher accuracy:

3. Data cleaning

During data cleaning, you remove duplicate and irrelevant data. Subsequently, you structure the data and fill gaps with the necessary information.

4. Data analysis

Once the collected data is organized, you can begin the analysis process. In the data analysis phase, there are four techniques:

• Descriptive analytics: to identify what has occurred

• Diagnostic analytics: understanding the reasons behind certain events or phenomena

• Predictive analytics: to forecast future trends based on past data

• Prescriptive analytics: combines results from other techniques to make recommendations for the future based on past performance and trends

5 recommended data analytic tools

Now that you have grasped the reasons why business data analytic tools are required, the next step is to determine the best data analytic tools according to your business needs. Tools for data analytics that align with your business requirements will provide invaluable insights into your customers, market trends, and your products or services.

Here are the 5 best data analytics recommendations: 

1. SleekFlow 

SleekFlow represents the apex of data management and customer engagement. By seamlessly integrating social messaging channels and e-commerce websites, it becomes a potent source of customer data. With smart features like CRM integration, businesses can orchestrate and customize customer journeys on preferred messaging platforms, such as WhatsApp. If you need data analytics automation tools, this platform even facilitates A/B testing with a flow builder for you to set up automated flows that will help discern customer preferences and accumulate insights for in-depth analyses.

SleekFlow's plan ranges from US $79 to $299 or can be customized according to your enterprise needs.

2. Tableau

Tableau shines as a premier data analytics software with visualized interactive data. Its prowess lies not only in its ability to represent complex data but also in its user-friendly interface, allowing for the creation of interactive dashboards without the need for intricate coding. 

Tableau’s plans range from US $15 to $75 per user per month.

3. SQL

SQL, or Structured Query Language, as an open-source programming language, is one of the free data analytic tools that stands as the cornerstone of data manipulation for businesses operating within relational databases. It can be embedded with Java to build data processing apps for crafting and executing queries for rapid and efficient data processing. 

You can find free SQL databases such as MySQL, PostgreSQL, and SQLite. Alternatively, you can access them on Amazon Web Services or Microsoft Azure on a free tier or paid solutions depending on your usage.

4. Python

Python, the high-level programming language, is also one of the formidable forces when you’re looking at business data analytic tools. Widely adopted in web applications, software development, and machine learning, Python's open-source nature has democratized the programming landscape. It works well in managing data structures and manipulating complex data sets using simple syntax. However, its potency truly unfolds in the hands of those already well-versed in the art of data analytics.

5. R 

R is another data anaytics tool of choice for dealing with vast datasets. Renowned for its advanced statistical analysis capabilities, R is tailor-made for the rigors of academic statistical exploration. Like Python, it tends to find favor among individuals possessing advanced analytical skills.

Undoubtedly, data analytics tools are software applications that streamline the process of collecting, processing, and analyzing data. Businesses rely on real-time data analytic tools to make objective and valid decisions. With accurate data analytic results, you would be able to identify trends, devise more effective strategies, and uncover opportunities for innovation.