Understanding data analytics

Understanding data analytics

In today’s world, almost every organization is data-driven, which has made it compulsory for them to implement approaches to gather data related to customers and markets. Data analytics helps companies to cope with day-to-day computational and data-handling challenges. This blog focuses on the data analysis tools and techniques employed by businesses for optimized functioning.

What is data analytics?

The set of quantitative and qualitative approaches that are implemented to derive insights from huge volumes of data is referred to as data analytics. Data analytics is considered as the fuel for the digital economy as it allows companies to build customer profiles and offer customized products and services to their customer base. The advent of big data applications has enabled organizations to handle business data, cost-effectively. The Big Data analytics domain comprises the following tools:

  • Python;
  • SAS;
  • Hadoop;
  • SQL;
  • Tableau;
  • Spark;
  • Splunk;
  • R programming.

What are the types of data analytics?

Descriptive Analytics – a form of statistical analysis that employs data aggregation and data mining to transform raw data into a user-friendly form. It allows users to gain insights from past behaviours and predict their impact on future outcomes. Descriptive analytics is introductory and retrospective in nature that can be used to:

  • Depict statistical data such as total stock in an inventory, expenditure per customer and sales fluctuation;
  • Provides insights regarding the company’s production, finances and operations;
  • Summarizes and describes different aspects of a business;
  • Reduces website traffic and increases social media engagement.

Predictive Analytics – this is a form of advanced analytics that utilizes data mining, machine learning and machine modelling for predictive data maintenance and fraud detection. Predictive analytics is primarily based on probabilities that can help analyze data that are currently missing or yet to be gathered. It combines previous data that are stored in ERP, CRM, HR and POS systems to identify data patterns.

Companies rely on this form of analytics as it:

  • Provides companies with actionable data based on the data available;
  • Estimates the likelihood of a future outcome;
  • Applies statistical models and algorithms to understand the connection between various datasets;
  • Identifies trends in sales activities and purchasing patterns;
  • Forecasts demands for inputs from the supply chain, operations and the inventory;
  • Helps produce credit scores to determine the probability of customers making timely future credit payments;
  • Predicts the future course of action of the organization.

Diagnostic Analytics – diagnostic analytics is retrospective in nature and employs data mining techniques to answer business queries. This helps to determine the cause of an unexpected outcome and mainly involves working on a dashboard. It plays an important role in reviewing social media campaigns by analyzing the number of:

  • Mentions for a post;
  • Followers;
  • Page views;
  • Reviews.

Prescriptive Analytics – prescriptive analytics provides advice on possible outcomes, prior to decision-making. It employs a combination of tools and techniques that can be applied to datasets such as:

  • Historical and transactional data;
  • Real-time data feeds;
  • Big data.

Prescriptive analytics is composed of the following tools and techniques:

  • Business rules;
  • Algorithms;
  • Machine learning;
  • Computational modelling procedures.

It is an extremely complex form of data analysis and is generally executed by data scientists having the knowledge of prescriptive models. Larger companies often utilize prescriptive analytics to:

  • Receive advice and guidance related to the daily course of business;
  • Optimize production, scheduling and inventory in the supply chain;
  • Deliver the right products at the proper time and enhance the customer experience;
  • Prescribe a definite analytical path for the organization based on certain rules and recommendations.

Understanding the data analysis process

Data analytics involves several processes that include extracting data, analyzing their patterns, relations and connections, categorizing them and gaining insights from them. It utilizes various types of analytics software that employ processes and technology to extract insights out of datasets. The data analysis process is based on the principle of discerning qualitative data from quantitative data. Qualitative data consists of identifiers (attributes or labels) and is generated in the form of:

  • Texts, documents and notes;
  • Audio and video recordings;
  • Images and symbols;
  • Interview transcripts.

Quantitative data consists of numbers and values and is statistical and non-descriptive, unlike qualitative data. It is conclusive and measurable, which makes it more favourable to be analyzed, and is usually generated in the form of:

  • Texts;
  • Experiments;
  • Surveys;
  • Market research;
  • Metrics.

The process of data analysis also involves the understanding of structured and unstructured data. Similar to quantitative data, structured data is highly-structured, organized and can be easily detected in relational databases. Examples of structured data include names and dates, home and email addresses, identification numbers and transactional information. This form of data is generally preferred over unstructured data as it can be easily processed by computing systems.

A high percentage of the data generated falls into the category of unstructured data. It is difficult to collect and process unstructured data using conventional tools and methods. Examples of unstructured data include emails and SMS, audio and video files, server and weblogs, satellite and surveillance imagery and social media.

Harnessing unstructured data uses modern approaches, such as NoSQL databases.
Let us now look at the five steps that organizations need to follow to execute the data analysis process:

Step 1: Understanding and elaborating the need for data analysis;
Step 2: Collecting data from internal (CRM software, ERP systems and marketing automation tools) and external sources (or open data sources);
Step 3: Sorting through the collected data to discard any duplicates or anomalous data and inconsistencies that might skew the analysis;
Step 4: Analysing the data using business intelligence software or data mining techniques, based on the business requirements and resources;
Step 5: Interpreting the results of data analysis and applying them to resolve business problems stepwise.

How does data analytics help in business?

  • Data analytics comprises of programming languages that help with data analysis and solve business-related problems;
  • Helps determine data collection, processing and the methods required to extract insights;
  • Facilitates careful application of insights depending on the type of business, its industry and unique requirements;
  • Helps organizations to predict problems prior to their occurrence and map out possible solutions;
  • Provides incorporation of better resources and perspectives to the business, leading to the growth of the organization.

If you are interested in developing the required skills to analyze data and communicate it effectively, you should apply to the Diploma in Data Analytics Co-op offered by Toronto School of Management (TSoM). This program can help you enhance your knowledge about data analysis and its practical usages.

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