Data has always been the buzzword amongst enterprises. After all, data is “king.” But being king does not make you rich.

One needs to be able to transform raw data into actionable and meaningful insights, information, and knowledge. So, to benefit yourself from data generated by an enterprise or from an individual, one should always analyze it. There arises a question of how exactly one will analyze data? Well, that’s when “data analytics” makes an entry.

Big data is no longer a developing technology; it is formally mainstream. However, it is the domain of data analytics. Geoffrey Moore, American Management Consultant and Author, said, “Without Big Data Analytics, companies are blind and deaf, wandering out onto the web like deer on a freeway.”

Future of analytics technology

Data analytics is a process of analyzing datasets to get a conclusion regarding the information they contain. The technology refers to the set of qualitative and quantitative tactics for obtaining valuable insights from data. The key process is to extract data and categorize it according to patterns like connections and relations.

Why is data analytics essential?

An enormous amount of data is generated every day. Therefore, businesses must extract useful insights from the generated data. Thus, it plays an important role in developing your business. So, here goes the four major factors that spell out loud the need for Data Analytics:

  • Collect hidden insights
  • Create reports from generated data
  • Perform market analysis
  • Advance business needs

Data analytics – superpower for today’s enterprises!

Data analysis helps organizations to understand their consumers better, create content strategies to develop products, assess their ad campaigns, and customize the content as per client needs. Data analytics is a key hero in the business world; thus, that makes it difficult for your organization to understand how to implement it. So here are some interesting facts of data analytics. Let’s take a look.

  • Technology helps organizations to advance the decision-making process.
  • Offers useful insights for effective marketing.
  • Provides better client service to build a stronger relationship with them.
  • Helps to save money, thus, offers more efficient operations.

The advantages of data are many, but you cannot take benefits until you use data analytics’ tools and processes. Raw data has a lot of ability, but you need data analytics tools to reveal its superpower and expand your business.

Ronald Coase, a great Economist, said, “If you torture the data long enough, it will confess.”

How to use data analytics tools?

There are several techniques and tools used to collect, convert, cleanse, and classify raw data into easily understandable data. In this section, let’s get familiarized with these software tools that adapt to different business needs.

  • Google AdWords: Google Ads offer insights for marketing related data. It helps to find how efficiently advertising is reaching the targeted viewers. It also finds clicks per advertisement and its purchases. In short, Google Analytics show how well your social media and website are performing in terms of usage, traffic, and visibility.
  • Apache Spark: It is one of the largest data processing engines for real-time data analytics that is part of the Hadoop ecosystem. Undoubtedly, it is exclusively used for fast big data processing (cluster computing). However, it comprises built-in capabilities for graph processing, real-time data streaming, machine learning (ML), SQL, and several others.
  • R-programming: It is a leading analytics tool exclusively used by a data scientist for data modeling, graphical applications, and statistical computing. The program runs on various platforms, including Mac OS, UNIX, and Windows. Also, it offers tools to install all packages as per consumer requirement automatically.
  • Python: It is an open-source and one of the most object-oriented programming languages that are easy to write, read, and maintain. It is exclusively used for applications such as ML and offers libraries such as Keras, Scikit-Learn, Pandas, TensorFlow, and Matplotlib. The language can also be assembled on any platform, including JSON, SQL server, or MongoDB database.
  • OpenRefine: This data cleaning software is also termed as GoogleRefine. It is exclusively used for cleaning messy data, translate data from the website, and conversion of data for analysis.
  • Tableau: The most popular business intelligence tool used for business analytics and data visualization.
  • SAS: Enhanced analytical tool which is exclusively used for working with a massive amount of data generated and obtaining valuable insights from it. However, easily accessible and can evaluate data from various available sources.
  • RapidMiner: This analytics tool is mainly used for predictive analytics comprising ML, data mining, and text analytics. This powerful platform can integrate with several data sources such as Sybase, Access, Oracle, Excel, Tera data, and Microsoft SQL.
  • QlikView: A tool exclusively used to compress data to almost 10% of its initial size. Final outcomes are sent quickly to end-users.
  • Konstanz Information Miner (KNIME): It is an open-source data analytics tool that allows us to analyze and model data. However, using visual programming, KNIME offers a platform for reporting and integration of data by using its modular data pipeline concept.

Now that you know all about data analytics, let’s focus on a few quick examples of how you might gather data and evaluate it to help advance business outcomes.

Let’s assume that you are a marketer and promoting the online ad campaign for a new smartphone. Your key aim is to target people who are interested in buying a smartphone laden with the latest features. As your campaign runs, you use data analytics techniques to scrutinize data produced when folks click on the ad. Thus, after analyzing data about these users, you find out that a number of people are interested in photography. This is because your smartphone has a better quality camera than other phone models. Thus, data analytics plays an important role for you to reach the correct audience.

Companies using data analytics

  • Google is the best example of this as it is sitting on the mother lode of all data. It serves trillions of searches every day, making it one of the most data-intensive companies on earth. Therefore, the use of analytical tools is a must for Google.
  • Facebook is another example that uses different data analytics tools to find what services and products its users are interested in. Thus, it works accordingly and makes sure that its ads are up to date in terms of customization and other facets.


Data analytics is one of the important technologies that is boosting some of the best enterprises forward today by bringing concrete changes to their businesses. However, businesses that can transform data into meaningful insights are champions in this hyper competitive world.

So, don’t forget to learn how to enhance your data analysis strategy and be at the top of the competition! Find more such information in our latest whitepapers on data analytics.