Data Analytics vs Data Analysis: What’s The Difference?

Data analysis Data analytics, data analysis. Two different terms to describe the same thing? Different, but similar terms?

It’s a popular mistake to think of the notion that analysis using data and analytics are one and the same. The most widely accepted distinction is:

  • Data analytics encompasses the entire discipline of using tools and data to help businesses make better decisions.
  • Data analysis, which is a subset of data analytics, refers specific actions.

To help explain this confusion, and try to make it clearer, we’ll examine both the words, examples and tools.

Data analytics: What exactly is it?

Data analytics is broad term used to describe the idea and the practice (or maybe the science or art) of all the activities that are that are related to data. The goal is to allow experts in data, such as engineers, data scientists and analysts to allow everyone else in the company to understand and access the results of these studies.

Raw data, in its present state, is of no use. However, it’s the way you do with the data that adds benefit. Data analytics covers all of the steps you have to undertake, both human and machine-driven, to identify visual, interpret, and communicate the patterns that you observe in your data to inform your the strategy of your business and its results.

A well-run data analytics program will provide a better plan for the direction your company can take. If it is done right the use of data analytics can benefit you:

  • Discover trends
  • Find opportunities
  • The software can predict triggers, actions or even events
  • Make decisions

Like every real-world method the data analytics process is structured, involving several management and computing processes. Experts emphasize the term “systematic”. It is essential to be systematic because data analytics involves a variety of processes and draws upon every kind and size of sources for data.

Many subjects are subject to data analytics, which includes sciences of the data, machine-learning, as well as applied statistics. One tangible consequence of a practice in data analytics is likely to be well-planned and well-organized reports that utilize visualization of data to tell the main details so that the other members of the company–who’re not data experts, can understand, design and modify their strategies.

Consider the numerous ways that data analytics can reveal opportunities to your business:

  • Utilizing data, not guesses in order to know the way your customers interact could require you to alter your marketing or sales processes. The bakery may utilize its data to determine that the demand for bread bowls rises in winter. This means that you don’t have to lower prices when the demand is high.
  • An increase in cyberattacks could suggest that you must adopt proactive measures to prevent cyberattacks.
  • The data from various sensors in a particular location, such as the server area, power station, or even a warehouse, could tell you if you’re ensuring the security and security you require with the least expensive price possible.

Data analytics processes

The practice of data analytics includes a variety of distinct processes which may comprise the following information pipeline:

  • Ingesting and collecting the information
  • Categorizing the data into structured/unstructured forms, which might also define next actions
  • The management of data generally in data lakes, databases or data warehouses.
  • Storage of the data within hot cold, warm, or warm storage
  • ETL ( extract, transform and load)
  • Examining the information to identify patterns as well as trends and other insights.
  • Data sharing to users in the business or consumers, typically in a dashboard , or through particular storage

What is the purpose of data analysis?

Consider data analysis as a part of the data analytics pie. Data analysis involves cleansing transform, modeling, and asking questions about data to uncover valuable data. (It’s generally agreed that the other slices can be classified as other types of activities including storage, collection to visualisation.)

The process of data analysis is typically restricted to one data set, which is already created. You’ll examine, arrange and analyze the data. In the present, and in the 2020s the software (or “machine” usually does a initial round of analysis, typically directly within some of the databases you use or software. However, this is supplemented by a person who studies and analyzes the data in more details.

Once you’ve completed your analysis of your data, you’ll switch to other activities related to data analysis to:

  • Allow others to access the information
  • Display the data (ideally by using the use of storytelling or data visualization)
  • Provide suggestions for actions you can take based upon the information

The most important aspect to consider when conducting data analyses is it already records data, which means that the data is from the past.

Analysis of the type of data

There are a variety of data analysis methods. The most well-known are:

  • Analysis of text. This is also known by the name of Data Mining. This technique identifies patterns in large-scale datasets using databases, or other tools for data mining.
  • Analysis of statistical data. The analysis answers “What happened?” by using data from the past in the form of dashboards. Statistic analysis is the process of collecting and analysis of, interpretation, presentation and application of the data.
  • Diagnostic analysis. This type of analysis can answer “Why did it happen?” by determining the reason through the findings uncovered during statistical analysis. This kind of analysis is useful for finding patterns in the behavior of data.
  • Predictive analysis. This analysis predicts what’s likely to happen using data from the past. This prediction analysis provides predictions for future outcomes based upon the information.
  • Prescriptive analysis. This kind of analysis blends the information from text with diagnostic, statistical or predictive analytics to decide which action(s) to do in order to fix the current issue or to influence the outcome of a decision.

Combining these various methods based on the needs of your business and the decision-making process. Pieter Van Iperen, Managing Partner of PWV Consultants employs an example of web traffic that your company likely monitors. There are tools available to gather and track the individual metrics of the web, including:

  • Location
  • Activity based on the time of day
  • Mobile vs PC
  • Browsers in use

Each of these data points are only a tiny part of the total analysis. Humans perform additional analysis to figure out the best way to optimize your website’s performance to:

  • Enhance the sales opportunities
  • Reduce lead time for sales
  • Revenue growth

Analytical data that can be repeated frequently be transformed into a different metric within your analytical platform.

Which one is the best?

Brack Nelson Director of Marketing for Incrementors SEO Services believes that the results that data analytics produces is more comprehensive and more beneficial than the result of data analysis by itself.

Take note of the differences between

  • An analyst sends a business user an excel spreadsheet rather than creating a dashboard to allow users to interact with detailed data.
  • A business user getting an update that shows the value in real time of a campaign instead of developing a web-based application that shows the forecast as well as allows users to engage using predictive analytics.

The most important thing, Brack says, is making a product which makes predictions based on data and connects to an API of another system to take action. That’s data analytics applied.

Tools for data analytics

Analytics software is a tool that can assist humans and machines in the research that allows us to make crucial business-related decisions.

Common tools for analyses of data and for general analytics are:

  • Microsoft Excel
  • Microsoft Power BI
  • Tableau
  • R analytics
  • Python
  • Google Analytics

(Check for BMC’s Guides for tutorials on a variety of big data tools and visualization tools. )

What is the reason for this confusion?

It is interesting to note that these terms are often misunderstood by data scientists as well as data analysts!

A survey of a range of individuals from across the universe of information revealed the gap. Many were in agreement that analytics are the larger field, in that data analytics is a essential function, while other people had different views. The lack of understanding demonstrates that perhaps the issue isn’t the difference between data analytics and data analysis but the extent to which you’re doing them as efficiently as you could.

A few people have said they’re not worried if we not experts in data use the terms in a similar way. Therefore, if you try to confuse data analytics and analysis at your next meeting, the majority people will not be more knowledgeable.

Leave a Reply

Your email address will not be published. Required fields are marked *