Data Analytics

Data Analytics

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What is Data Analytics?

Data Analytics is a technique that involves gathering, cleaning, and organizing data. These processes, which usually include data analytics software, that is necessary to prepare the data for business purposes.

Data is available in different structures, formats, and types, including the following:

  • Big Data
  • Structured/Unstructured Data
  • Metadata
  • Real-Time Data
  • Machine Data

Why Data Analytics?

Data analytics is important because it helps businesses optimize their performances. Implementing it into the business model helps companies reduce costs by identifying more efficient ways of doing business and by storing large amounts of data. To maximize opportunities from data, organizations must employ data analytics techniques and make a data analytics strategy that can help overcome the following challenges,

  • Poor data quality
  • Absence of an effective data strategy
  • Difficulty in finding skilled employees

Data Analytics Techniques:

Data analytics methods and techniques are useful for finding insights in data, such as metrics, facts, and figures. The two primary methods for data analytics are qualitative data analytics techniques and quantitative data analytics techniques. These data analytics techniques can be used independently or in combination with the other to help business leaders and decision-makers acquire business insights from different data types.

1. Quantitative data analytics

Quantitative data analytics involves working with numerical variables — including statistics, percentages, calculations, measurements, and other data — as the nature of quantitative data is numerical. Quantitative data analytics techniques typically include working with algorithms, mathematical analytics tools, and software to manipulate data and uncover insights that reveal the business value.

For example, a financial data analyst can change one or more variables on a company’s Excel balance sheet to project their employer’s future financial performance. Quantitative data analytics can also be used to assess market data to help a company set a competitive price for its new product.

2. Qualitative data analytics

Qualitative data describes information that is typically non-numerical. The qualitative data analytics approach involves working with unique identifiers, such as labels and properties, and categorical variables, such as statistics, percentages, and measurements. A data analyst may use firsthand or participant observation approaches, conduct interviews, run focus groups, or review documents and artifacts in qualitative data analytics.

Qualitative data analytics can be used in various business processes. For example, qualitative data analytics techniques are often part of the software development process. Software testers record bugs – ranging from functional errors to spelling mistakes – to determine bug severity on a predetermined scale: from critical to low. When collected, this data provides information that can help improve the final product.

Data Analytics Process:

The Data analytics Process is gathering information by using a proper application or tool which allows you to explore the data and find a pattern in it.

Data Analytics involves the following steps:

  • Data Requirement Gathering
  • Data Collection
  • Data Cleaning
  • Data analytics
  • Data Interpretation
  • Data Visualization

Data Requirement Gathering

Involves the decision making on the type of data analytics to be done. In this phase, you have to decide what to analyze and how to measure it, you have to understand why you are investigating and what measures you have to use to do this analytics.

Data Collection

To collect the data based on requirements. Once the data is collected, data must be processed or organized for Analytics. As the data is collected from various sources, a log must be maintained with the collection date and source of the data.

Data Cleaning

The data collected may not be useful or irrelevant to the purpose of analytics, hence it should be cleaned. The data which is collected may contain duplicate records, white spaces, or errors. The data should be cleaned and made error-free. This phase must be performed before analytics because based on data cleaning, the output of the analytics will be closer to the expected outcome.

Data Analytics

Once the data is collected, cleaned, and processed, it is ready for analytics. During this phase, data analytics tools and softwares are used which will helps to understand, interpret, and derive conclusions based on the requirements.

Data Interpretation

After analyzing the data, it’s finally time to interpret the results. A relevant way is chosen to express or communicate the data analytics results either by use of words or maybe a table or chart. Then the results of the data analytics process are used to decide the best course of action.

Data Visualization

Data visualization often appears in the form of charts and graphs. In other words, data is shown graphically so that it will be easier for the human brain to understand and process it. Data visualization is often used to discover unknown facts and trends. By observing relationships and comparing datasets, we can find a way to gather meaningful information.

mCycloid Data Analytics Tools and Technologies:

1. R Programming:

R is the leading analytics tool in the industry and widely used for statistics and data modeling. It can easily manipulate your data and present in different ways.

2. Tableau Public:

Tableau Public is a free software that connects any data source be it corporate Data Warehouse, Microsoft Excel or web-based data, and creates data visualizations, maps, dashboards etc. with real-time updates presenting on web.

3. Python:

Python is an object-oriented scripting language which is easy to read, write, maintain and is a free open source tool. Python has very good machine learning libraries viz. Scikitlearn, Theano, Tensorflow and Keras.

4. SAS:

Sas is a programming environment and language for data manipulation and a leader in analytics. SAS is easily accessible, manageable and can analyze data from any source. SAS modules are used to predict behaviors, manage, and optimize communications.

5. Apache Spark:

Apache Spark is a fast large-scale data processing engine and executes applications in Hadoop clusters 100 times faster in memory and 10 times faster on disk. Spark is built on data science and its concept makes data science effortless. Spark is also popular for data pipelines and machine learning models development.

6. Excel:

Excel is a basic, popular and widely used analytical tool almost in all industries. Excel has the advance business analytics option which helps in modelling capabilities which have prebuilt options like automatic relationship detection, and time grouping.

7. RapidMiner:

RapidMiner can incorporate with any data source types, including Access, Excel, Microsoft SQL, Tera data, Oracle, Sybase, IBM DB2, Ingres, MySQL, IBM SPSS, Dbase, etc. The tool is very powerful that can generate analytics based on real-life data transformation settings, i.e. you can control the formats and data sets for predictive analysis..


KNIME is leading open source, reporting, and integrated analytics tools that allow you to analyze and model the data through visual programming, it integrates various components for data mining and machine learning via its modular data-pipelining concept.

9. QlikView:

QlikView has many unique features like patented technology and has in-memory data processing, which executes the result very fast to the end users and stores the data in the report itself. Data relationship is visualized using colors – a specific color is given to related data and another color for non-related data.

10. Splunk:

Splunk is a tool that analyzes and search the machine-generated data. Splunk pulls all text-based log data and provides a simple way to search through it, a user can pull in all kind of data, and perform all sort of interesting statistical analysis on it, and present it in different formats.

11. ELK Stack:

The ELK Stack is a collection of three open-source products – Elasticsearch, Logstash, and Kibana. ELK stack provides centralized logging in order to identify problems with servers or applications. It allows you to search all the logs in a single place.

12. Microsoft Power BI:

Microsoft Power BI is a top business intelligence platform with support for dozens of data sources. It allows users to create and share reports, visualizations, and dashboards. Power BI also allows users to build automated machine learning models and integrates with Azure Machine Learning.

13. Sisense:

Sisense data analytics platform aims at helping both technical developers and business analysts process and visualize all of their business data. It boasts a large collection of drag-and-drop tools and provides interactive dashboards for collaboration.

14. Jupyter Notebook:

Jupyter Notebook is a free, open source web application that can be run in a browser or on desktop platforms after installation using the Anaconda platform or Python’s package manager, pip. It allows developers to create reports with data and visualizations from live code. It allows developers to make use of the wide range of Python packages for analytics and visualizations.