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What Is Data Analytics

What Is Data Analytics

By extracting and cataloguing data, organisations can pinpoint and evaluate relationships, patterns and trends so they can glean insights and draw conclusions based on the data and use these to make informed decisions. Such data can include information on:
- Processes, Products, People, Materials
- Market, Suppliers, Customers, Competitors
- Costs, Prices, Revenue
- ………
It’s an interesting innovative work, to collect and convert irrelative tinny pieces of data into insights and recommendations leading to right decision.
But how do data analysts actually turn raw data into something useful? There are a range of methods and techniques that data analysts use depending on the type of data in question and the kinds of insights they want to uncover.
In this article we summarize the main concepts, tools, and techniques of data analytics.

What is data analysis and why is it important?

Data analysis is, put simply, the process of discovering useful information by evaluating data. This is done through a process of inspecting, cleaning, transforming, and modeling data using analytical and statistical tools, which we will explore in detail further along in this article.
Why is data analysis important? Analyzing data effectively helps organizations make business decisions. Nowadays, data is collected by businesses constantly: through surveys, online tracking, online marketing analytics, collected subscription and registration data (think newsletters), social media monitoring, among other methods.
These data will appear as different structures, including—but not limited to—the following:

Big data
The concept of big data—data that is so large, fast, or complex, that it is difficult or impossible to process using traditional methods—gained momentum in the early 2000s. Then, Doug Laney, an industry analyst, articulated what is now known as the mainstream definition of big data as the three Vs: volume, velocity, and variety. 
Volume:  As mentioned earlier, organizations are collecting data constantly. In the not-too-distant past it would have been a real issue to store, but nowadays storage is cheap and takes up little space.
Velocity: Received data needs to be handled in a timely manner. With the growth of the Internet of Things, this can mean these data are coming in constantly, and at an unprecedented speed.
Variety: The data being collected and stored by organizations comes in many forms, ranging from structured data—that is, more traditional, numerical data—to unstructured data—think emails, videos, audio, and so on. We’ll cover structured and unstructured data a little further on.
This is a form of data that provides information about other data, such as an image. In everyday life you’ll find this by, for example, right-clicking on a file in a folder and selecting “Get Info”, which will show you information such as file size and kind, date of creation, and so on.
Real-time data
This is data that is presented as soon as it is acquired. A good example of this is a stock market ticket, which provides information on the most-active stocks in real time.
Machine data
This is data that is produced wholly by machines, without human instruction. An example of this could be call logs automatically generated by your smartphone.
Quantitative and qualitative data
Quantitative data—otherwise known as structured data— may appear as a “traditional” database—that is, with rows and columns. Qualitative data—otherwise known as unstructured data—are the other types of data that don’t fit into rows and columns, which can include text, images, videos and more. We’ll discuss this further in the next section.

Data analytics vs. data science

Data analytics and data science are closely related. Data analytics is a component of data science, used to understand what an organization’s data looks like. Generally, the output of data analytics are reports and visualizations. Data science takes the output of analytics to study and solve problems.
The difference between data analytics and data science is often seen as one of timescale. Data analytics describes the current or historical state of reality, whereas data science uses that data to predict and/or understand the future.

Data analytics vs. data analysis

While the terms data analytics and data analysis are frequently used interchangeably, data analysis is a subset of data analytics concerned with examining, cleansing, transforming, and modeling data to derive conclusions. Data analytics includes the tools and techniques used to perform data analysis.

Data analytics vs. business analytics

Business analytics is another subset of data analytics. Business analytics uses data analytics techniques, including data mining, statistical analysis, and predictive modeling, to drive better business decisions. Gartner defines business analytics as “solutions used to build analysis models and simulations to create scenarios, understand realities, and predict future states.”

Data analytics methods and techniques

Data analysts use a number of methods and techniques to analyze data. According to Emily Stevens, managing editor at CareerFoundry, seven of the most popular include:

Regression analysis:  Regression analysis is a set of statistical processes used to estimate the relationships between variables to determine how changes to one or more variables might affect another. For example, how might social media spending affect sales?
Monte Carlo simulation:  According to Investopedia, “Monte Carlo simulations are used to model the probability of different outcomes in a process that cannot easily be predicted due to the intervention of random variables.” It is frequently used for risk analysis.
Factor analysis:  Factor analysis is a statistical method for taking a massive data set and reducing it to a smaller, more manageable one. This has the added benefit of often uncovering hidden patterns. In a business setting, factor analysis is often used to explore things like customer loyalty.
Cohort analysis:  Cohort analysis is used to break a dataset down into groups that share common characteristics, or cohorts, for analysis. This is often used to understand customer segments.
Cluster analysis:  StatisticsSolutions defines cluster analysis as “a class of techniques that are used to classify objects or cases into relative groups called clusters.” It can be used to reveal structures in data — insurance firms might use cluster analysis to investigate why certain locations are associated with particular insurance claims, for instance.
Time series analysis:   StatisticsSolutions defines time series analysis as “a statistical technique that deals with time series data, or trend analysis. Time series data means that data is in a series of particular time periods or intervals.” Time series analysis can be used to identify trends and cycles over time, e.g., weekly sales numbers. It is frequently used for economic and sales forecasting.
Sentiment analysis:  Sentiment analysis uses tools such as natural language processing, text analysis, computational linguistics, and so on, to understand the feelings expressed in the data. While the previous six methods seek to analyze quantitative data (data that can be measured), sentiment analysis seeks to interpret and classify qualitative data by organizing it into themes. It is often used to understand how customers feel about a brand, product, or service.

Data analytics tools

Data analysts and others who work with analytics use a range of tools to aid them in their roles. The following are some of the most popular:
Apache Spark:  An open source data science platform for processing big data and creating cluster computing engines
Excel:  Microsoft’s spreadsheet software is perhaps the most widely used analytics tool, especially for mathematical analysis and tabular reporting
Looker:  Google’s data analytics and BI platform
Power BI:  Microsoft’s data visualization and analysis tool for creating and distributing reports and dashboards
Python:  An open source programming language that helps users extract, summarize, and visualize data
Qlik:  A suite of data analytics, data integration, and programming platforms for exploring data and creating data visualizations
QuickSight:  A BI and analytics cloud service from Amazon designed to integrate with cloud data sources
R:  An open source data analytics tool for statistical analysis and graphical modeling
RapidMiner:  A data science platform that includes a visual workflow designer
SAS:  An analytics platform for business intelligence and data mining
Sisense:  A popular self-service business intelligence platform
Tableau:  Data analysis software from Salesforce for creating dashboards, maps, and visualizations from data
Talend:  A platform for big data file transformations and loading used by data engineers, data architects, analysts, and developers

Data Analytics FAQs

1. What is the role of data analytics?
Data Analytics is the process of collecting, cleaning, sorting, and processing raw data to extract relevant and valuable information to help businesses. An in-depth understanding of data can improve customer experience, retention, targeting, reducing operational costs, and problem-solving methods.
2. What are the types of data analytics?
Diagnostic Analysis, Predictive Analysis, Prescriptive Analysis, Text Analysis, and Statistical Analysis are the most commonly used data analytics types. Statistical analysis can be further broken down into Descriptive Analytics and Inferential Analysis.
3. What are the analytical tools used in data analytics?
The top 10 data analytical tools are Sequentum Enterprise, Datapine, Looker, KNIME, Lexalytics, SAS Forecasting, RapidMiner, OpenRefine, Talend, and NodeXL. The tools aid different data analysis processes, from data gathering to data sorting and analysis. 
4. What is the career growth in data analytics?
Starting off as a Data Analysis, you can quickly move into Senior Analyst, then Analytics Manager, Director of Analytics, or even Chief Data Officer (CDO).
5. Why Is Data Analytics Important?
Data Analysis is essential as it helps businesses understand their customers better, improves sales, improves customer targeting, reduces costs, and allows for the creation of better problem-solving strategies. 
6. Who Is Using Data Analytics?
Data Analytics has now been adopted almost across every industry. Regardless of company size or industry popularity, data analytics plays a huge part in helping businesses understand their customer’s needs and then use it to better tweak their products or services. Data Analytics is prominently used across industries such as Healthcare, Travel, Hospitality, and even FMCG products.


Data analytics plays a huge part in helping businesses understand their customer’s needs and then use it to better tweak their products or services.


Ok, Done

Kzeem Fas

Do you offer online courses?

Allen Goates

what about SPSS and Minitabe?


Well said

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