We live in a modern era where the proliferation of information technology and the internet has given rise to a massive data explosion. According to recent reports, people and organizations are currently producing 2.5 quintillion bytes of data daily, with 90% of the world’s data created in the last couple of years.
This enormous data generation has made analytics highly relevant in the current age and time. Concealed under all this data are insights that can help trigger radical growth in business. The challenge lies in deriving them — this is where analytics comes into play. Information has long been thought of as a great weapon, and analytics is the forge that produces it.
As analytics has emerged as a buzzing terminology in the last decade, we have put forth this article to help guide you in understanding what it means, its types, its purpose, and its applications in various settings, along with other vital information.
What are analytics?
Analytics or data analytics is the scientific process of analyzing vast volumes of raw data to identify hidden patterns and trends, and derive valuable insights and information in order to automate better decisions. Analytics draws from various disciplines, including math, machine learning, statistics, computer programming and operation research to quantify and gain these insights.
Nowadays, practically every organization in the world has transformed itself into a data-driven business, which means that they are employing the use of modern tools and technologies to collect more data related to its clients, business processes and markets. This data is then used to make impactful business decisions and improve performance.
Analytics has the power to change everything, not just in the business world but also in sports, science, healthcare, and other fields where extensive amounts of data are collected. To ensure a robust analysis, analytics leverage various data management techniques, such as data modeling, data mining, data transformation and data cleansing.
Importance of analytics
If used properly, analytics can optimize the performance of any organization. You can better understand its importance and potential through the following points:
- It can help organizations make more informed decisions based on facts instead of simple intuition.
- Using predictive analytics, industries can identify patterns, trends, and opportunities for improvement and plan strategies accordingly. Eventually, this will help them save money and resources.
- With analytics, organizations can understand their customers’ habits, purchasing behaviors, demographics, interests and more. This enables them to identify potential customers and avoid wasting resources on targeting the wrong audience.
- Analytics can assist organizations in understanding risks and taking preventive measures accordingly.
- Organizations can improve their operational efficiency via analytics.
Types of analytics
Analytics can be further divided into four major categories: descriptive analytics, predictive analytics, prescriptive analytics and diagnostic analytics. The details for these types of analytics are given belo
This data analytics uses historical data and key performance indicators (KPIs) from multiple sources to identify and understand trends and patterns. This enables us to keep track of changes and comprehend what happened in the past and what is happening currently by using two major techniques — data aggregation and data mining. The analysts assemble the data, present it in a summarized format (data aggregation), and ‘mine’ the data to uncover trends and patterns.
Many organizations use descriptive analytics to build an understanding of their strengths and weaknesses, as well as their customer base, and how they can devise marketing strategies to target those customers.
Predictive analytics uses statistical analysis models and historical data to identify patterns and address what might happen by predicting and forecasting the future based on probability theory. Instead of offering a single definitive future state, it produces a range of potential future states and the likelihood of each of them. While it might not be 100% accurate, it can eliminate much guesswork from significant business decisions. Ultimately, predictive analytics is used to understand the possibilities and take the most appropriate action accordingly.
Techniques utilized in this type of analytics include a variety of statistical and machine learning approaches, such as forecasting, neural networks, predictive modeling, decision trees, regression analysis, multivariate statistics and pattern matching.
This is a more complex type of analytics that expands on predictive analytics and analyzes past performances and events to recommend the best course of action for handling similar situations in the future, and to increase the probability of a desired future outcome. To put it simply, prescriptive analytics helps you understand how you can take advantage of the results that have been predicted.
During prescriptive analysis, analysts consider a variety of possible scenarios and assess the different actions the company should take to yield the best results. Being a complex type of analytics, prescriptive analytics uses advanced tools and techniques, such as machine learning, graph analysis, algorithms, computational modeling procedures, recommendation engines, simulations, and complex event processing, to deliver the desired outcomes.
Diagnostic analytics is a branch of analytics that uses data, usually generated through descriptive analytics, to identify the reasons why something happened. While descriptive analytics focuses on the ‘what’, diagnostic analysis studies the ‘why’. This allows us to determine the elements that influenced a specific pattern or trend.
When conducting diagnostic analytics, analysts try to identify anomalies within the data — unexpected changes in a metric or market. Analysts try to find the relationships and trends that explain these events and behaviors using data mining, data discovery, regression analysis, data drilldowns, filtering and correlations.
Analytics techniques and methods
Data analysts use different analytical techniques and methods to process and analyze data and extract meaningful information. Some of the most common techniques are listed below:
Time series analysis
Time series analysis is a statistical technique that tracks data over particular time periods or intervals and deals with trend analysis. This analysis method is typically used to identify cyclical trends or project financial forecasts. Moreover, it solidifies the link between the occurrence of a data point and the value of a data point.
Regression analysis is a set of statistical techniques used to assess the relationship between dependent variables to determine how one more variable can bring change in another. For instance, analyzing how social media spending can affect sales.
Factor analysis is a statistical technique where an analyst takes a massive set of data and reduces it to make it smaller and more manageable. The primary use of this method is to discover hidden trends and patterns that would otherwise have been difficult to uncover. In a business setting, factor analysis is typically used to study factors like customer loyalty.
Cohort analysis is a process that breaks down a data set into smaller groups or cohorts of data that share common characteristics, usually for the assessment of customer segments and demographics. So rather than treating all customer data the same, organizations use patterns that relate to specific cohorts. By identifying these trends, industries can produce more targeted products and services.
Monte Carlo simulation
Monte Carlo simulation, also referred to as the Monte Carlo method, is a technique that is used to generate models of the probability of different outcomes. It considers a range of multiple values and variables and calculates how likely it is that each of these outcomes will be realized. This allows analysts to better forecast what might transpire in the future and make decisions accordingly. The Monte Carlo simulation is frequently used to conduct an advanced risk analysis.
Data mining is an analytics strategy used to sift through large datasets to identify trends, patterns, relations, correlations, anomalies, and dependencies that are not initially visible, to generate advanced knowledge and insights. This is done using statistics, database systems and machine learning. Data mining can also draw from other analytics techniques, such as clustering, regression and outlier detection. This is an extremely beneficial method as it leads to better and more efficient decision-making.
For instance, data mining in a business setting can help discover which customers buy certain products at particular times of the year. This data can then be used to target those customers.
This analytics technique is used to identify structures within groups of data. The main objective of cluster analysis is to classify data into relative groups or clusters that are internally homogenous and externally heterogeneous. This means that the data items in one cluster must be similar to each other and dissimilar to the data items available in another cluster. Also, while cluster analysis may uncover hidden structures within datasets, it cannot justify why those structures exist.
Tools used for analytics
Today, there are a broad range of data analysis software tools available on the market, and each has its own functions depending on the type of analysis performed. Analysts use these tools to aid them in acquiring and processing data, storing information and reporting their findings. Here is a list of some of the most popular tools for the analysis of data:
- Python: This is an open-source, object-oriented programming language that offers great flexibility and versatility. Currently, it is being deployed for various applications, such as machine learning, and helps users extract, visualize, and summarize data.
- Apache Spark: Apache Spark is a data analytics engine that is part of the Hadoop ecosystem. It processes data in real time and creates cluster computing engines using SQL queries and machine learning algorithms.
- Tableau: This is one of the most popular data analytics and business intelligence (BI) software. It incorporates advanced natural language processing and unstructured text analysis to compile data and perform data analytics, as well as create dashboards, worksheets, maps, charts, and visualizations to showcase trends and insights.
- Splunk: Splunk is an analytics tool with an intuitive user interface and data visualization It is used for analyzing machine-generated data and deriving meaningful business insights from it.
- SAS: This is an advanced, command-driven analytics software package that offers various statistical methods and algorithms. It is used to perform complex statistical analyses, write SQL queries, visualize data, and create machine learning models to predict behavior and make future predictions.
- R Programming: R is an open-source programming language primarily used for graphical modeling and statistical analysis, such as regression analysis, normal distribution, and cluster classification algorithms. Moreover, it also incorporates concepts of artificial intelligence and machine learning.
- QlikView: This is an analytics tool that offers social data discovery and interactive guided analytics with in-memory storage to assess vast volumes of data and create data visualizations. It can manipulate massive data sets instantly with great accuracy.
- Excel: Microsoft Excel is perhaps the most widely used tool for analytics. This spreadsheet software offers excellent features with additional plugins that enable users to format, organize, and calculate massive amounts of data — used by data analysts to run basic queries and to create pivot tables, graphs and charts.
- RapidMiner: This is an analytics software package that includes a visual workflow designer and is used for text mining, data mining, machine learning and predictive analytics. RapidMiner comes packed with various features, such as automation, data modeling and validation.
- Power BI: Power BI is a business analytics tool by Microsoft that enables users to visualize data and share insights via reports and dashboards across organizations. While it is majorly used for data visualization, it is more of a business intelligence tool.
Applications of analytics in different fields
The inception of analytics has influenced many different fields, industries, and domains, some of which are listed and discussed below:
Analytics in business
Analytics in business incorporates a particular set of methods, procedures, techniques, and competencies to effectively use past and present business history and data. The main purpose of doing this is to acquire insights regarding a business, encourage better decision-making, and improve overall performance and revenue. It can also aid a business in customizing a marketing pitch for a client, along with recognizing and limiting risks.
As mentioned above, you can employ data analytics to improve business. But before you get into that, you will need to acquire a relevant analytics degree. You can easily do so via the online courses offered by St. Bonaventure University. Here, you will learn how to deal with today’s complex business challenges with the help of powerful data methods and technologies.
Analytics in healthcare
The use of analytics in healthcare can help analysts enhance daily operations and improve patient care by channeling vast volumes of data to discover lifesaving diagnoses, treatment choices, and answers for various diseases. It can also reveal preventive measures, track outcomes, and predict deterioration, infection risks and re-admission — lowering expenses and enhancing patient care. Moreover, analytics also helps in the development of new drugs.
Analytics in finance
Analytics in the financial and banking sector is used to monitor movements in the financial market and understand how customers interact with their offerings (e.g., loan application processes). Stock exchanges employ analytics to track illegal trade practices, retail traders, hedge funds, and other factors of the financial markets. This allows financial institutions to understand how they can improve customer experience, mitigate fraudulent activities and transactions, demand enterprise risk management and encourage anti-money laundering.
Analytics in manufacturing
With analytics, the manufacturing sector can discover new cost-efficient opportunities and find solutions to complex supply chain issues in terms of quality, timeliness, cost, breakdown of equipment and labor constraints. It can also help manufacturers understand demand trends to upgrade their warehouse space and prepare them for surges in demand.
Analytics in fraud detection
Most organizations across different industries employ the use of analytics to detect and prevent fraudulent activities. Such sectors include finance, pharmaceutical, retail, banking, tax and security. In terms of tax fraud, analytics is used to analyze the reliability of tax returns for individuals. The Internal Revenue Service (IRS), an agency responsible for overseeing and collecting taxes, primarily uses predictive analytics to predict the possibility of fraudulent activities in the future.
Banks use analytics to detect fraud by analyzing communication with customers. They leverage algorithms to monitor fraudulent activities based on a particular customer’s previous communication data.
Analytics in security
Applying analytics, more specifically predictive analytics, to security has helped minimize crime rates in certain areas. With the help of analytics, security personnel examine data, monitor crime patterns, predict future cases of security breaches, and deliver proactive safety efforts. They can also investigate past or ongoing attacks.
Also, with security analysis, you can examine past and ongoing attacks to analyze how an IT system was breached, other weaknesses, and the behavior of end-users or gadgets involved in the attack.
Analytics in logistics
Different logistics companies such as DHL and FedEx are currently using analytics to collect and examine data while improving their overall efficiency and operations. It allows them to develop new business models and optimize routes. This can help them discover the best shipping routes, ascertain the most cost-efficient means of transport, estimate delivery times, and monitor the real-time status of products using GPS trackers. This has improved industry performance and created a broader customer base.