Data Analytics is the discipline of deriving insights from data. It involves collecting, organizing, and making sense of large datasets to generate new or better ideas, products, and services to help organizations increase their efficiency and stay ahead of their competition – all this done using the latest technology tools that help us make decisions as business leaders.
Data analytics incorporates a vast array of quantitative and qualitative methods and processes that can render it a difficult concept to define accurately. Towards that effort, we provide examples of data analysis types including statistical modeling and modeling, reporting, visualization, and AI. Data analytics is the science of gathering and analyzing data to examine, understand and predict patterns in human behavior. By employing various data management techniques, including machine learning and business intelligence tools, data analytics teams are able to make better-informed decisions based on their understanding of how individuals interact with websites, applications, or other digital services.
Types of data analytics:
Essential Data Analytics Capabilities:
Business Intelligence and Reporting- Data analytics is a large part of where business intelligence starts. Data analysis reports are actionable and up-to-date to provide the people in your business with the information they need to make decisions quickly. Monitor the status of your organization, resolve issues quickly and efficiently, improve sales and marketing strategies, optimize inventory management, measure performance, and collect trend data over time to help make informed future decisions.
Data Wrangling/Data Preparation- A good data analytics solution should include the ability to bring together data from multiple sources and clean it in order to make it easily mashable. This process is called self-service data wrangling, and the self-service part refers to the fact that users can control their own destiny by accessing tools that can quickly bring together all the relevant information needed for analysis.
Data Visualization- Data visualization helps solve the problem of volume and speed by overlaying past performance on a map and correlating that information to your current location. A powerful tool for any explorer, it provides you with context about places that might otherwise be unfamiliar.
Geospatial and Location Analytics- Data visualization helps solve the problem of volume and speed by overlaying past performance on a map and correlating that information to your current location. Use this tool to learn about places you might be unfamiliar with, gain more context about your real-time adventures, and become a better traveler. A powerful tool for any explorer, it provides you with context about places that might otherwise be unfamiliar.
Predictive Analytics- Predictive analytics uses historical data to create a model of future events. This model is then used to predict future outcomes and drive actions. In retail, predictive analytics can help companies determine how much inventory is needed at a particular store at a given time. One major use of predictive analytics is predicting when a machine will fail or how much inventory is needed at a particular store at a particular time.
Machine Learning- Computerized analytical models can be programmed to find new patterns and insights in big data. Look for Big Data products that offer natural language search, image analytics, and augmented analytics to allow computers to recognize key patterns and make predictions about real-world activities based on the analysis of raw data sets. With machine learning, computers can be programmed to look for new patterns in your data without explicit programming. This automation will put more of your machines to work finding new insights, which can ultimately provide value to your company.
Streaming Analytics- Real-time analytics is the ability to act on events in real-time. This is an essential capability of today’s top analytics solutions. Pulling data from IoT streaming devices, video sources, audio sources, and social media platforms in real-time allow us to deliver critical insights to our customers on events as they happen. Real-time analytic solutions enable you to analyze live time data from numerous sources, including devices and sensors, social media sites, IoT devices and video sources. It allows you to act on real-time events instantaneously as they happen.
Data analytics methods and techniques
Regression analysis: Regression analysis is a statistical method that uses linear regression to estimate the relationships between variables. One of the primary uses of regression analysis is to model and forecast trends, though several other types of modeling are also possible.
Monte Carlo simulation: 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, taking into account uncertainties and variability associated with specific outcomes such as interest rates, commodity prices, or performance.
Factor analysis: Factor analysis is a statistical method that reduces a large data set to a more manageable one, while at the same time revealing hidden patterns. While this approach can sometimes uncover unexpected consumer behavior, it can also be used by businesses to better understand their customers.
Cohort analysis: Cohort analysis is used to understand customer segments. This data helps us better understand how people interact with our products, which can help us identify growth opportunities and prioritize features or services.
Cluster analysis: statistics solutions define cluster analysis as “a class of techniques that are used to classify objects or cases into relative groups called clusters.” Cluster analysis is a technique for discovering and classifying subtle, otherwise unnoticeable patterns in data.
Time series analysis: statistics solutions define time series analysis as “a statistical technique that deals with time-series data, or trend analysis. Time series data is a sequence of measurements taken at regular intervals of time. Examples include product sales, stock prices, inflation, and theunemployment rate. Time series analysis is used to find patterns in the data and predict future values. For example, if sales have been increasing over the last few years, use forecasting techniques to predict what they might look like in the next few years.
Sentiment analysis: Sentiment analysis is a method of evaluating how one or more persons, or groups of people, feel about something. Sentiment analysis leverages tools such as Natural Language Processing, text analytics, computational linguistics, and so on, to understand the feelings expressed in a given piece of text. Sentiment analysis is the application of natural language processing to gauge the general mood and feeling of a text. It can be used to monitor customer satisfaction and identify consumer trends, as well as provide insights into topics such as seasonality across channels.
Benefits of Data Analytics for your Business:
1. Personalize the customer experience- Data analytics can help businesses better understand customer behavior and provide a more personalized experience. By collecting customer data from many different channels, such as physical retail, e-commerce, and social media, businesses can create comprehensive customer profiles that provide insights into customer behavior. In today's business world, companies that want to remain competitive with their online presence need to make sure that their web presence is as strong as possible. One key way to accomplish this is creating and maintaining a website that is built from the ground up with SEO in mind. Behavioral analytics can be used to identify customers that are likely to churn or leave and understand the reasons why. For example, if a new user doesn’t return after signing up, researchers may look for correlations between their behavior and those of other high-risk customers, who might have left because of poor customer service or a website update.
2. Inform business decision-making- Businesses utilize data analytics in order to assist with decision-making or to understand the needs of the customer. Predictive and prescriptive analytics are both helpful for businesses as they may help with predicting what will happen in response to a change in business and how the business should react to this, respectively. Data analytics is often used to optimize business processes and improve quality. For example, data analytics can be used to model changes to pricing or product offerings to determine how those changes would affect customer demand. After collecting sales data on the changed products, enterprises can use data analytics tools to determine the success of the changes and visualize the results so they can choose whether or not to roll out their new products across the organization.
3. Streamline operations- Companies can improve their operational efficiency by collecting data about their supply chain and analyzing it. With accurate demand forecasts, enterprises can predict where future problems may arise and take steps to prevent them. If a holiday season demand estimate indicates that a specific vendor won't be able to handle the volume required from that enterprise, an alternative vendor can be found to supplement or replace this supplier. Data analytics can help determine the optimal supply for all of an enterprise's products based on factors such as seasonality, holidays, and secular trends.
4. Mitigate risk and handle setbacks- Risks are everywhere in business. The applications of Data Analytics are wide-ranging, with applications in business and government, and across multiple sectors. Some examples include customer or employee theft, uncollected receivables, employee safety, and legal liability. Risks to business include everything from employee theft to uncollected receivables, legal liability, and more. Data analytics can help you determine which areas of your business are at the highest risk for theft and take the appropriate preventative measures. Data analytics can also be used to limit losses after a setback. If a business overestimates demand for a product, it can use data analytics to determine the optimal price for a clearance sale to reduce inventory. An enterprise can even create statistical models to automatically make recommendations on how to resolve recurrent problems.
5. Enhance security- All businesses face data security threats. Making use of data analytics, organizations can build models that help them predict the probability of a breach based on previous attacks. For instance, if your company’s main product is personal health information, an IT employee can use data analytics to determine how many individuals are falling victim to attacks. He or she can then use the information to update patching policies and educate employees about what to avoid. IT staff can use statistical models to detect abnormal access behavior and prevent future attacks. This is done by analyzing historical access data with the goal of discovering reliable indicators which represent specific threats. These indicators are then used in conjunction with monitoring and alerting systems for a more robust security posture for on-premises data centers and cloud environments.
