The Prolifics Guide to Advanced Analytics
Advanced Analytics: Introduction
Advanced Analytics is the study of data using advanced techniques to unearth deeper insights into the market, make predictions on changes to come, and produce appropriate recommendations for the best possible courses of action.
The term “advanced analytics” falls under the umbrella of what we know as Big Data analysis or predictive analytics. The myriad of components used in this type of analysis relies on statistical models and mathematical formulas to analyze large amounts of data.
It is advanced analytics’ ability to forecast future trends and changes that allows it to give businesses a much more profound understanding of the market forces at work.
Some compare business intelligence (BI) to advanced analytics. Typical BI, however, provides nothing more than a screenshot of the business’s performance. Advanced analytics on the other hand offers so much more powerful and valuable information to today’s businesses.
Advanced analytics is fast becoming a key component of corporate strategies of businesses looking to survive and stay ahead of the competition. Businesses rely and survive on the predictive and anticipatory ability of advanced analytics to survive and thrive in today’s fast changing, global marketplace.
A Brief History of Analytics
Analytics is the process of examining data to identify trends and patterns. Analytics is typically considered to be the abbreviated term for data analytics.
Data analytics is based on statistics and one of its earliest recorded uses was to help build the pyramids in Ancient Egypt.
Usage of data analytics skyrocketed in the 19th century when Frederick Taylor conducted time management exercises and when Henry Ford tracked the speed of his assembly lines.
By the 1960s, computers became central components of decision-making systems and accordingly data analytics evolved further into a much more sophisticated science.
Once computers came into the picture, the analytics landscape changed drastically for good.
Suddenly, the tedious tasks of storing and organizing data became much easier. Data analytics became easier to apply and therefore became an even more valuable tool in helping a business gain a deeper insight into the market and customize its response to the conditions.
As the field grew, new forms of data analytics were discovered to include Big Data, predictive, cognitive, prescriptive, augmented analytics and many more.
Data collection and pattern identification have always been somewhat present in the field of business and research. But it was the advent of powerful computing technology and the introduction of fields, such as big data and techniques like data mining and finding patterns and hidden insights, that made analytics evolve into something much more potent and dynamic.
From that point on there was no looking back for analytics and its ability to help businesses improve various aspects of their performance.
The Types Of Analytics
There are many forms of analytics, but the four main types of analytics used in the business world are: descriptive, diagnostic, predictive and prescriptive. Each serves a different purpose from what a person or company needs to know; from what happened, to why it happened, what will happen. The four types of analytics are further defined as:
This is a type of analysis that focuses on simply describing what has already happened. It’s not predictive like many other types of analysis but instead tries to answer the question “What just happened?”
The goal of descriptive analytics is to help you better understand your data so that you can make better decisions in the future. This involves describing existing data using existing business intelligence tools to better understand what is currently happening or has already happened. It is the simplest form of analytics. This analytic is about crunching the sizable data into more manageable information by summarizing the information. Data mining and data aggregation are often used in this analysis technique.
Diagnostic analytics looks at data and breaks it down to identify what happened in the past performance of your business and why that event occurred. Common diagnostics practices include techniques like drill-down analytics, data mining, correlations, and data discovery. However, while this analytics model can inform on why something potentially happened, its results can be limited in providing actionable results and insights.
Predictive analytics focuses on predicting possible outcomes using statistical models and other machine learning techniques. It is important to note that predictive analytics doesn’t predict the guaranteed occurrence of an event but rather it predicts the probability of several possible outcomes in the future. The prediction of future data relies on the existence of current data, which is why it is of the essence to have good quality, reliable data to conduct effective predictive analytics.
This is a form of predictive analysis used to recommend one or more courses of action based on the data analyzed. Prescriptive analytics builds directly on predictive analytics and goes a step further by offering a solution. To be effective, it relies on a strong feedback system and it evolves and updates the relationships between elements as more information comes to light via feedback.
All the four of the previously mentioned analytics tools serve a different purpose, but they are interconnected.
Looking at them you might assume that businesses need to apply them sequentially. However, such is not the case. In fact, usually businesses differ in the analytics they employ.
Many tend to skip on all and go directly to prescriptive analytics while others use it in conjunction with predictive analytics.
Research shows though that prescriptive analytics is still a budding phenomenon and firms have barely tapped its surface when it comes to the potential it has to offer. However, with predictive analytics developing at a rapid pace, it is only a matter of time before prescriptive analytics also develops into a full-fledged analytic tool being used by businesses to respond more effectively to different situations.
Business Analytics Vs. Data Analytics
Some may think that business analytics and data analytics are the same analysis tools. While they are similar and complement each other, they are two separate analytics.
Data analytics is the process of applying data science to find patterns and correlations in business-related data. It uses technology, statistical methods, and big data to identify useful information that can be used for decision making and problem solving. Data analytics helps organizations make better decisions based on reliable information from their own historical records or external sources such as social media channels or online purchases.
Business analytics refers to the branch of knowledge dealing with collecting and analyzing data from an organization’s operations in order to help inform decision-making processes. Business analytics is implemented in a firm to identify weaknesses in its existing procedures and find ways of improving them and adding to the efficiency and growth of the business.
While they have different roles to play in the business environment, they do have the same goals and utilize several similar statistical techniques. Used together, they can help a business discover and apply valuable insights.
Understanding Advanced Analytics
Advanced Analytics is a powerful data analysis technique that helps companies make better decisions based on their past performance.
A good example of how advanced analytics is used is in the evaluation of customer ordering patterns. Advanced analytics analysis goes beyond measuring simple metrics like total sales or number of clicks; instead, this type of analysis focuses on customer behavior patterns (like whether they buy from direct mail marketing, in-store, online, or other sources).
This information can be used by organizations in many ways, helping them decide which products should go into production, deciding where advertising money should be spent, and more.
Advanced analytics predominantly involves the use of numerous tools and techniques for predictive purposes and is designed to offer much more sophisticated insight and customized solutions than basic business intelligence solutions. Some of the types of tasks that can be executed by advanced analytics are:
- Classification of elements based on shared qualities
- Segmentation of items based on demographics, geographic location, niche, etc.
- Establishing correlation by identifying relationships between different elements
- Forecasting future changes in values and patterns
- Establishing an association between two or more elements by identifying the frequency of co-occurrences of the same events
Data is the foundation of advanced analytics and supercomputers have allowed businesses to conduct big data analysis in ways never seen before.
The term “big data” is often thrown around these days, but what does it mean? According to Gartner’s definition of big data, it is the collection of information that has been stored in digital format and cannot be processed by traditional database management tools. For this reason, organizations must rethink their analytics strategy to find new ways to analyze this large amount of information.
This is where advanced analytics practices and tools come into play. Advanced, predictive analytic tactics are the key to managing this amount of information in a way that will not only help your organization meet its goals but also keep up with the increasing speed at which new data is generated and collected.