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Do You Think You Know The A-Z of Data Analytics?

Mike Lampa

Table of Contents:
  1. What is Data Analytics?
  2. Descriptive Analytics
  3. Diagnostic Analytics
  4. Predictive Analytics
  5. Prescriptive Analytics

Living in the 21st century, we know you’ve come across the term “Data Analytics.” And though you may have a good basic understanding of what is what, have you ever considered, “What is Data Analytics really?” Why is Data Analytics important? What are the components that makeup Data Analytics at the macro and micro levels? If you have wondered about these things, and you want to start or extend your journey in Data Analytics, then this is the right place to be.

Data analysts are key to the world of Data Analytics. A data analyst will gather raw data, organize it, and convert it from incomprehensible numbers to clear and concise data that can be used to tell a story or make actionable business moves. The data is processed by the Data Analytics Software, and the outcomes are presented in the form of recommendations for the company’s next steps.

Consider Data Analytics to be a part of Business Intelligence (BI) which is used to resolve complex problems and issues within an organization. It all comes down to finding patterns in a dataset that can tell you something accurate and helpful about a specific aspect of the business. In this first installment of “The A-Z of Data Analytics”, we clarify what Data Analytics is and review some of its parts.

What exactly is Data Analytics?

The practice of studying and analyzing large datasets in order to uncover hidden patterns, discover correlations, and derive valuable data in order to make business projections is referred to as Data Analytics. Many businesses all over the world generate massive amounts of data on a daily basis, in the form of log files, web servers, transactional data, and various customer-related data.

They use a wide variety of modern Data Analytics tools, techniques, and methodologies to perform their work because it increases the effectiveness and speed of their processes.

How does Data Analytics work?

The Process of Data Analytics

Data Analytics is the method of gathering, handling, cleaning, and analyzing large datasets to help organizations integrate their big data. Every organization’s method of data collection is unique. With today’s technology, businesses can collect both structured and unstructured data from a wide range of sources, including cloud storage, mobile apps, in-store IoT sensors, and more. Some data will be stored in data warehouses, where it will be readily available through business intelligence tools and solutions. A data lake can be used to store raw or unstructured data that is simply too diverse or complex for a warehouse.

Once the data has been collected and stored, it must be properly organized in order to generate precise outcomes on analytical queries, especially when the data is large and unstructured. Data availability is expanding at an exponential rate, making data processing complicated for organizations. Batch processing, which analyzes large data blocks over time, is one handling option. When the time between collecting and analyzing data is long, batch processing is useful. Stream processing examines small batches of data at once, minimizing the time between collection and evaluation and enabling quick thinking. Stream processing is trickier and frequently costlier.

To improve data quality and obtain better outcomes, all data must be formatted correctly, and any duplicate or irrelevant data must be removed or accounted for. Dirty data can obscure and misguide, leading to imperfect findings. It takes time to convert big data into useful data. Advanced analytics processes can reshape big data into big insights once it is ready.

By identifying anomalies and creating data clusters, data mining sorts through large datasets to recognize relationships and patterns. Predictive Analytics, on the other hand, uses an organization’s historical data to formulate forecasts for the future, identifying potential dangers and opportunities. Deep learning mimics human learning patterns by layering algorithms and identifying patterns in the most abstract and complex data using machine learning and artificial intelligence.

Now that we’ve learned about Data Analytics and how it works, let’s look at its four categories listed below: Descriptive, Diagnostic, Predictive, and Prescriptive.

4 Areas of Data Analytics

1. Descriptive Analytics

Descriptive Analytics is the foundation of reporting, and answers basic questions like “how many, when, where, and what.” It is further classified into two types: Ad hoc and canned reports. A canned report is one that has already been designed and contains information on a specific topic.

Ad hoc reports are created on the fly when there is a need to answer a specific business question. Both of these methods encapsulate massive datasets and can assist and track both successes and failures by designing Key Performance Indicators (KPIs) to explain results to stakeholders. Metrics such as Return on Investment (ROI) are used in many industries.

Specialized metrics are developed to monitor performance in specific industry sectors. This process necessitates the collection of relevant data, data analysis, market research, and data visualization.

2. Diagnostic Analytics

Diagnostic Analytics is the process of examining data to understand the cause and event or why something happened. Drill down, data discovery, data mining, and correlations are all common techniques. The performance indicators are investigated further to evaluate why they have shifted or begun to decline.

It helps answer why something occurred. Like the other categories, it is also broken down into two more specific categories: query and drill-downs and discover and alerts. To obtain more information from a report, queries and drill-downs are used.

Discover and alert notify of a potential issue before it occurs. These use descriptive analytics research findings to delve deeper into the cause.

3. Predictive Analytics

Predictive Analytics helps predict what will occur in the near future. It is used in business to identify patterns, correlations, and causation. Predictive and statistical modeling are subcategories of this category.

These methods use historical data to identify trends and predict if they will occur again. Predictive Analytics Software offers valuable insights into what could happen in the future, and its techniques include a wide range of statistical and Machine Learning (ML) strategies such as neural networks, decision trees, and regression.

4. Prescriptive Analytics

Prescriptive Analytics is the use of AI and Big Data to predict outcomes and decide what actions should be taken. This category is further divided into optimization and random testing. It aids in determining what needs to be done, and predictive analytics findings can be used to make data-driven choices and to make informed decisions.

Machine Learning strategies are used to identify patterns in large datasets, and the likelihood of different outcomes can be predicted by analyzing previous decisions.

Prescriptive analytics, using advances in machine learning, can assist with answers like “What if we try this?” and “What is the best tactic?” You can test the correct variables and propose new parameters that are more likely to produce a positive result.

These four kinds of Data Analytics offer organizations the data needed to make intelligent decisions. Data Analytics Tools are software and programs that collect and analyze data about a business, its customers, and its competitors in order to improve processes and uncover insights to make data-driven decisions. So the question arises, what are Data Analytics Tools? Let’s find out.

What are Data Analytics Tools?

Data analytics has evolved rapidly in terms of technological capabilities, in addition to a wide range of mathematical and statistical approaches to performing calculations. Data analysts now have a myriad of software tools available to them to help them acquire data, store information, process data, and report findings. Data Analytics has always had a tenuous relationship with spreadsheets and Microsoft Excel. To reshape and manipulate databases, data analysts now commonly interact with raw programming languages. Open-source languages like Python and SQL are frequently used. More specialized Data Analytics Tools, like R, can be used for statistical analysis or graphical modeling. Tableau and Power BI are both data visualization and analysis tools for collecting information, performing data analytics, and disseminating results through reports and dashboards.

Other tools to assist data analysts are also arising. SAS is an analytics platform that can help with data mining, whereas Apache Spark is an open-source platform that has the ability to process massive amounts of data. Data analysts now have a wide range of technological capabilities to improve the value they provide to their organizations. If you’d like to learn more about Data Analytics tools, here are the top 11 Data Analytics Tools of 2023.

Business Advantages of Data Analytics

 The ability to analyze more data much faster can provide significant benefits to an organization, allowing it to use data more efficiently to answer critical questions. Data Analytics is crucial because it allows organizations to use huge amounts of information in various formats from different sources to identify both opportunities and risks, permitting them to move quickly and boost their bottom lines. The following are some of the advantages:


1. Make The Customer Experience More Personalized

Customers’ data is collected by businesses through various channels, including physical retail, e-commerce, and social media. Companies can gain customer insights and deliver a more personalized experience by using data analytics to create comprehensive customer profiles from this data.
Consider a retail clothing store with both a physical and an online presence. The company could analyze its sales data in conjunction with data from its social media pages and then create targeted social media campaigns to boost e-commerce sales for product categories in which customers are already interested. Organizations can further optimize the customer experience by running behavioral analytics models on customer data. A company, for example, could run a predictive model on e-commerce payment data to figure out which products to suggest at checkout in order to boost sales.


2. Streamline Processes

Data Analytics can help companies enhance their operational effectiveness. Gathering and analyzing data about the supply chain can reveal where production delays or bottlenecks occur and help predict where future issues could occur. If a demand forecast indicates that a particular vendor will not be able to cope with the volume required for the holiday season, an enterprise may supplement or replace this vendor to avoid production delays.

Furthermore, many companies, particularly those in retail, struggle to maximize their stock levels. Based on factors such as seasonality, holidays, and secular trends, data analytics can help determine the optimal supply for all of an enterprise’s products.


3. Reduce Risk and Deal With Setbacks

In business, risks are everywhere. Customer or employee theft, uncollected receivables, worker safety, and legal liability are among them. Data Analytics can help a business in understanding dangers and taking preventive action. A retail chain, for example, could use a propensity model—a statistical model that predicts future events or actions —to determine which stores are most prone to theft. The company could then use this data to assess the level of safety needed at the stores, in addition to whether it should divest from any locations.

 Data Analytics can also be used by businesses to cover losses after a setback occurs. If a company overestimates the demand for a product, Data Analytics can be employed to figure out the best price for a clearance sale in order to lower inventory. An organization can even develop statistical models to provide suggestions on how to solve persistent problems.

When employed together, they provide a full picture of a company’s opportunities and needs. If you want to learn more about Data Analytics, join our events  in live, interactive sessions or get in touch with our experts.