There is ample confusion surrounding the topic of Data Analytics. Many are still confused about Data Analytics, especially when it is used together with buzzwords like Big Data and Data Science. As we head towards digitalisation, there is the need to understand the intricacies of the Data Analytics and why it is valuable knowledge for any business leader. Here, we will introduce the basics of Data Analytics.
Quick Links1. The Difference Between Big Data, Data Science and Data Analytics
2. Types of Data Analytics
3. Tools Used for Data Analytics
4. Application of Data Analytics
In today's world, we deal with data every day, everywhere. By 2020, about 1.7 megabytes of data will be generated every second for every human being on the planet. It is crucial then, to handle data efficiently. In a quest to manage data, there are several tools, techniques, strategies, and domain players that can help to extract, filter, process, and present data. However, much of the jargon and terminology used in the analytics world overwhelm and confuse many people. Let us examine the significant differences between some of these.
When companies are grappling with humungous volumes of data that cannot be processed using traditional tools, chances are that they are dealing with Big Data. For a conventional database management system and data storage systems, it is not possible to efficiently manage and process such large amounts of data. Thus, clusters of systems are used to process these volumes of data. Big Data requires tools and frameworks like Hadoop and Apache Spark to process data.
There are multiple data sources and types, including structured, unstructured, and semi-structured. The examples of structured data include a banking transaction database, flight departures and arrivals database, to name a couple. Examples of the unstructured databases include such things as texts from news articles, story books, PDF files and word documents. Semi-structured data includes information contained in an XML or JSON format, for example.
Gartner characterises it as: "Big data is high-volume, and high-velocity and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation".
Data Science is the science of using scientific methods, processes, algorithms and systems to extract insights from data that is structured and unstructured. It deals with 3 core concepts that are namely domain knowledge, mathematics, and programming knowledge. A Data Scientist has domain knowledge, understands mathematical concepts and statistics, and is also adept at high-level programming. Data Science involves data cleansing, preparation and analysis.
In short, Data Science is the science of:
- Processing Big Data
- Passing clean datasets to Data Scientists,
- Who will then apply mathematical models along with programming and business domain knowledge to draw key insights from the data.
As the name suggests, Data Analytics relates to the study and analysis of data. It is the process of extracting useful information from processed raw data. The intent of Data Analytics is to draw useful and accurate insights to support decision-making.
In earlier decades, when manual tools like the logbook and physical spreadsheets were used to record information, human intelligence was sufficient to draw insights. As we have evolved towards computer science, so too have the methods and ways of Data Analytics. There are more sophisticated tools available for accurate analysis beyond the use of human intelligence, such as the Excel spreadsheet, IBM SPSS and Google Charts.
The tools and techniques applied in Data Analytics help to make informed business decisions, increase revenues, and to enhance organisational productivity. Though the application of analytics is quite vast, anything that generates and creates meaningful data is encapsulated by Data Analytics.
- Descriptive Analytics – This classification answers the question "What happened?" For example, based on past years’ air traffic, the number flights that were on time and the number that were delayed can be determined. Simply, descriptive analytics describes everything that has happened in the past.
- Diagnostic Analytics – This is the diagnosis of events that have occurred in the past. It answers the question of "Why it happened?" In this type of analytics, historical data is compared with other datasets to provide a rich diagnosis. For example, diagnosing a particular model of Boeing jet against different air traffic data can provide information for a crash investigation.
- Predictive Analytics – This classification provides the answers to the question "What will likely happen?" This type of analytics clumps the outcomes of descriptive and diagnostic analytics to derive a possible outcome or predict likely trends for the future. An example is the prediction of climate change based on the past 10, 20 or 50-years’ worth of data.
- Prescriptive Analytics – This type of analytics prescribes a probable solution to the problem. It answers the question "What actions should be taken to tackle the problem?" A good example is the prediction of power load surges in certain regions during a certain time of the year, and as importantly, providing possible solutions for that problem.
Find out how these 4 type of analytics can be implemented in your company here.
There are several tools available in the market for Data Analytics. Below are some of the common tools used by many organisations.
- Spreadsheets - They are the simplest and most popular tools among many of the available tools. Spreadsheets are popular due to their many functionalities, such as mathematical functions, scripts, table creation tools, and chart plotting, to name a few.
- Python - Python is one of the best programming languages offering vast features through packages and modules which serve various purposes such as Data Science, Data Analytics and programming for Big Data.
- R - This is yet another programming language which is popular for statistical programming and is quite helpful for statistical analysis.
- IBM SPSS Modeler - This IBM tool is most popularly used for predictive analytics.
- Apache Spark - An open source analytics tool by Apache is quite helpful for in-memory processing of Hadoop tasks.
Find out how you can leverage on free analytics tools in the market here.
Although analytics can be applied to most aspects of life, there are specific industries where Data Analytics has shined.
- Financial fraud detection – This is one industry where Data Analytics is quite useful. By using different analytical tools and analysis trends, financial transactions can detect anomalies and allow for further investigations.
- E-commerce – Based on the study of customer behaviour, different products are recommended to specific customers, enhancing customer experiences.
- Logistics – They are the backbone of every industry and extensively apply analytics to solve various problems involved in the supply chain, especially in terms of efficiency and cost savings.
- Flight ticketing - This field uses analytics to make suggestions to consumers on lowest-cost flights. This industry efficiently deploys several analytical tools and skills.
Data Analytics is a vast subject that requires clear understanding for the digitalisation era we’re thriving in. DataVLT has been working hard at democratising data analytics for businesses of all sizes. Find out how your business can gain invaluable decision-making insights here.
DataVLT is an affordable, on-demand analytics platform secured by blockchain technology. It is designed to simplify the complexity of data science. Backed by artificial intelligence and machine learning capabilities, DataVLT empowers enterprises to make meaningful sense of their big data and scale cost efficiently. Essentially, it is an end-to-end data/information management platform.
Learn more at www.datavlt.com