Evolution and Scope

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Evolution and Scope of Analytics have been used in business since the management exercises were put into place in the late 19th century. But analytics began to secure more attention in the late 1960s when computers were used in decision support systems. Since then, analytics transformed and moulded with the development of enterprise resource planning (ERP) systems, data warehouses, and a large number of other software tools and processes.

In later years the introduction to computers brought the analytics to a whole new level and thus made the possibilities endless.

In the 1980s when Information Technology change projects started to face difficulties Business Analysis was introduced. Prior to that IT change projects could solve only a limited set of problems in a limited way because the only options were to turn paper-based data into electronic data and have simple programs automate the utilization of that data. A few of the limitations were

• Firstly, the storage of the electronic data was expensive
• Secondly the way data was stored was cumbersome (flat files read sequentially in one direction only).
• Next programs were difficult to write in abstract languages
• Subsequently there was only a limited set of functionalities based around mainframe processes
• Then user interfaces were delivered on basic green-screens

Since the 1980s data storage has become cheaper and covers not just paper-based data but audio and visual data too. Other changes that have come about are

• Relational, object orientated and other databases have made access to data easier
• Programming languages have evolved in usability and functionality
• Processing is no longer constrained to mainframes but distributed with increasingly sophisticated user interfaces.

The result of all this change was that there are many more choices to make at each stage of an IT and/or any other type of change project. This increases the chances of choosing the wrong method to analyze the project/business. These wrong choices invalidate the subsequent work based on that wrong choice.

Challenges

Business analytics is dependent on the sufficient volumes of high-quality data. The difficulty in ensuring data quality is integrating and reconciling data across different systems, and then concluding what subsets of data to make available.

A type of after-the-fact method of forecasting consumer behavior was considered by examining the number of units sold in the last quarter or the last year. This type of data warehousing required a lot more storage space. While, now is becoming a tool that can influence the outcome of customer interactions.

Scope

Banks, utilizes data analysis to differentiate among customers based on credit risk, usage and other characteristics and then to match customer characteristics with appropriate product offerings. For instance, It can help you focus on the fundamental objectives of the business and the ways analytics can serve them. A telecoms company that pursues efficient call center usage over customer service might save money.

Relevance

Data analysis is important to businesses. In fact, business’ survival is tough without analyzing available data.

From a decision making view point merely analyzing data isn’t sufficient. How does one illuminate from the analyzed data is more important. Therefore, data analysis is not a decision-making system, but rather a decision supporting system.

Data analysis can offer the following benefits

• Firstly, structuring the findings from survey research or other means of data collection
• Secondly break a macro picture into a micro one
• Next acquiring meaningful insights from the dataset
• Subsequently basing critical decisions from the findings
• Then ruling out human bias through proper statistical treatment

Types of Analytics

• Decisive analytics: this supports human decisions with visual analytics the user models to indicate reasoning.
• Descriptive Analytics: Gain insight from historical data with reporting, scorecards, clustering etc.
• Predictive analytics predictive modeling using statistical and machine learning techniques
• Prescriptive analytics recommend decisions using optimization, simulation etc.

Analytics is the process of transforming raw data into actionable strategic knowledge in order to gain insight into business processes, and thereby to guide decision-making to help businesses run efficiently. An analytics process can be categorized into one of three categories:

• Descriptive Analytics – It looks at an organization’s current and historical performance.
• Predictive Analytics – It forecasts future trends, behavior, and events for decision support.
• Prescriptive Analytics – It determines alternative courses of actions or decisions, given the current and projected situations and a set of objectives, requirements, and constraints.

Levels of Analytics

There are eight levels of “Intelligence through Analytics” which have been outlined by SAS as

Standard Reports – This primary level of the analytical ladder focuses on apprehending what had happened.

Ad-Hoc Report – Now that we understood and captured the events what had happened, secondary questions may surface. Such as, “When did it happen?”, “The number of times it did occur during a particular period of time?”.

Query Drilldowns – This is about online Analytical Processing (OLAP) Diving deeper into the event, answering questions such as, “Where did the event happen?”, “Where exactly was the problem?”.

• Alerts – this is the concluding stage of the first four levels. What are the actions needed when threshold is contravened?

These four levels are common practice in almost all organizations as the foundation of standard business operations. The four levels are known as part of Business Intelligence.

Furthermore, a secondary attribute of Business Intelligence system is that the user knows what they are looking and/or the basic analysis required to produce it. The subsequent levels are where analytical processes kick in.

• Statistical Analysis – In this level we target to go beyond the orbit of ‘what’ and ‘where’, to dive into the hidden gems of the data. In order to understand why, the event had happened. Such knowledge is the substratum of understanding how to identify, prevent, exploit, and so forth.
• Forecasting – Until this stage our focus was entirely on deriving insight from historical data, one of the key elements of analytics is the capability of statistical forecasting. Will this observed trend continue and for how long? Furthermore, this stage is analogous to driving a car – one needs to have a front windscreen in order to know what is likely to happen.
• Predictive Modeling – This stage is focused on uncovering the unknown from the data at hand to surface new insights that may not have been previously known, also to provide the foundation for predicting future events, “What will happen next?” and “Why will it happen?”, such as the likelihood of events occurring.
• Optimisation – Cherry on the cake, optimisation or also referred to as Operation Research (OR), combines all the previous levels to optimize business processes/objectives given operational and other constraints. How to maximize profit, minimize cost? How to optimally allocate resources?

Thus, the above four final levels infer Business Analytics, the uncovering of insights from historical data and the projections into future, using analytical processes in alignment to business requirements.

 

Certified Inventory and Warehouse Analytics Professional

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