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The advantage of automation and AI solutions and tools in data analysis compared to manual analysis

Did you know that only 27% of organizations are able to make full use of their data to generate actionable insights?

List of Contents


What is data analysis


A few words about manual data analysis


What is Artificial Intelligence?


Advantages of AI and automation in data analysis


Who is the winner?

Did you know that only 27% of organizations are able to make full use of their data to generate actionable insights, largely due to the limitations of manual data analysis? The main reasons behind this rather grim statistic are the growing data skills gap and many companies being unable to focus their resources in this area. There is, however, a light at the end of the tunnel – data analysis automation and Artificial Intelligence (AI) powered solutions.

I eat data for breakfast.gif

What is data analysis

First things first, let’s get you settled on the topic by explaining what data analysis exactly is and why it’s so extremely important for modern businesses.

Data analytics is the science of analyzing data sets to find trends and patterns, answer questions and hypotheses, and draw insightful conclusions.

Companies from virtually every industry make use of data analytics (or at least they attempt to) to help them make more informed and data-driven decisions.

A graphic portraying different tasks performed and achieved by data analytics

What types of data analytics are out there?

Data analytics is a term that encompasses several distinctive categories of data analysis methods, which answer different specific questions:

Descriptive analytics – answers the question: “What happened?”. It’s the process of using current and historical data to identify trends and relationships.

Diagnostic analytics is a form of advanced analytics that examines data or content to answer the question: “Why did it happen?”.

Predictive analytics answers "what will happen?". These tools provide insights about likely future outcomes — forecasts, based on descriptive data but with added predictions using data science and often algorithms that make use of multiple data sets.

Prescriptive analytics answers the question “What should/can be done?” by using machine learning, modeling, simulation, heuristics, and other methods to predict outcomes and provide decision options.

A graphic depicting the 4 types of data analytics and what question they answer.

Data science vs data analytics

These two terms sometimes are incorrectly used interchangeably. Data analytics focuses on the examination of data sets to identify and explain trends. Data science looks more at data modeling and production processes, creating algorithms and predictive models.

There is some overlay between the two disciplines, however. The meaning of data science relates to a wider field that focuses on discovering large sets of data. Within that scope there is data analytics, a more focused area that looks at the insights offered by examining existing information.

A graphic depicting the differences and similarities between data science and data analytics.

A few words about manual data analysis

Manual data processing had long ago been the go-to method for sorting, interpreting and utilizing data. Some still swear by it, as they put little faith in the wonders of modern AI and machine learning technologies. But are they correct in this?

Some of the tasks that require systematic data analysis, in terms of a digital business, would be:

  • Analyzing your customers’ opinions on your brand through gathering data on brand mentions on social media and reviews.
  • Dispatching review forms and analyzing customer feedback to evaluate your customers’ User Experience
  • Interpreting survey results, interviews or reviews to understand customer expectations and preferences

If you are running a relatively small business, you may not have any problems with analyzing these types of data manually. But your business will grow, and whether it happens slowly and over time, or you strike a goldmine with that new campaign you have planned, manual data analysis will simply not be feasible at a certain point.

Relying on traditional techniques and manually analyzing large data sets is incredibly time-consuming, not to mention very easily susceptible to human error, which is not going to help you scale your company. So much as we would love to believe it, human intelligence can only do so much when faced with a constant inflow of data.

Thankfully, we are smart enough to make the impossible actually possible – with the help of Artificial Intelligence and Machine Learning.

A graphic depicting the disadvantages of manual data analysis

What is Artificial Intelligence?

Artificial intelligence (AI), machine learning and big data analytics have become an integral part of gathering and processing business intelligence.

Simply put, artificial intelligence (AI) comprises systems or machines that mimic human intelligence to perform tasks, and can successively improve their performance based on the information they collect.

Natural language processing

An important element of Artificial Intelligence is Natural language processing. NLP is a branch of artificial intelligence within computer science that focuses on helping computers understand the way that humans write and speak, and how to interpret the impact and intention behind different intonations, speaking patterns, or writing styles.

Big data

The term big data refers to massive and complex datasets, and serves as the driving power of the evolution of Artificial Intelligence. Big data analytics goal is to combine and analyze massive datasets to identify patterns and develop valuable insights.

Deep learning

Deep learning is a type of machine learning and artificial intelligence (AI) that imitates the way humans gain certain types of knowledge. Deep learning is an important element of data science, which includes statistics and predictive modeling.


Advantages of AI and automation in data analysis

Why should you use AI tools for Data Analysis? With the rapid advancements in AI, the adoption of AI-powered solutions for Data analysis automation is growing. These solutions are becoming more powerful, efficient, and readily available.

AI-powered solutions ensure that your data analysis is more accurate and efficient, and the delivered insights are free of bias and common human error.

In contrast to manual data analysis, there is no limit to the amount of data AI for data analysis automation can handle, ensuring efficient processing regardless of data volume. Whether it's 10, 100, or 1000 data sets, an AI data analysis tool will analyze them uniformly each and every time, no need to employ a team of data scientists.

Besides being able to examine vast amounts of data, AI can pinpoint the exact trends and patterns needed to improve your users' experience, eliminate pain points and spot quick win opportunities.

Using an AI-powered solution also helps you be better organized – no more endless excel files with hard to grasp information. By leveraging data analysis automation and consolidating all your data into one platform, you can gain a more complete overview of your data and track it effortlessly.

A graphic depicting the advantages of AI and automation in data analysis

Who is the winner?

Modern AI tools can help you analyze data deeper than ever before.

Thanks to the technological advancements, brought by AI and machine learning, within the data analytics industry, you can rely on these solutions to give you valuable insights, detect trends and patterns that affect your company, and help you make better data-driven decisions.

You don’t need to be a professional data analyst either, when the relevant insights are being presented right in front of your eyes, saving you from the grueling and time-consuming task of processing the data manually.

To revolutionize your business analytics, embrace data analysis automation by implementing AI-powered and automated solutions as soon as possible.

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