Leggings have made a “comeback” into high fashion. The modern skin-tight trousers are worn as exercise wear, casual, as well as evening wear. Meggings (men’s legging) and jeggings (jean leggings) have become hot trends in the last couple of years. Interestingly, leggings have been worn by men and women since the thirteenth century. In fact, soldiers used to wear them to protect their ankles and to keep dirt from entering their shoes.
In information technology, big data analytics has become a “hot” topic. With the advance of technology, the volume of data a company collects and stores is exploding. This massive volume of data is useless if business is not able to garner useful business insight. Companies which are able to take advantage of the data could drive efficiency, increase margin, predict customer needs, and create new products and services. Big data analytics is a turbo version of business intelligence, which replaced the business decision support systems from the 1960s. Business intelligence applications became popular in the 1990s. Today, big data technologies offer flexibility in handling various data types and the ability to process vast amounts of data with speed.
Whether you are using a traditional decision support system, a business intelligence solution, or a big data technology, the goal remains the same—extract insightful, fact-based information to support decision making. In order to achieve this goal, you need to:
Articulate the Question You Want To Address
It is the first thing you need to do. You could start with a broad question, and then narrow it down to a specific question. The more specific it is a better handle you would have on the data you need. For example, the smart meters installed to monitor energy consumption in every home and business collect hourly electricity consumption data. Do you want to help customers conserve consumption? Or, do you want to optimize the distribution of electricity?
Make Relevant Assumptions
Start with the question you posed, identify assumptions that form the basis for the question. Using the smart meter example, you might assume that customers want to save money when they understand their consumption behavior. Alternatively, you might assume that distribution optimization is an effective approach to minimize waste. Knowing the assumptions is important to validate the analysis you do.
Identify the Data You Need
The data needed for analyzing individual household or business consumption behaviour is easy to identify, namely hourly electricity consumption. On the other hand, data required for electricity distribution is more complex. You might need data matrices to correlate electricity requirements through the distribution network. Understanding the end goal of the question posed helps to solidify the data needs.
Extract Data and Perform Analysis
This is where technology is put to work. The tool that you use, a business intelligence or big data technology, would gather the data that you need and provide the insight you seek. Do the results address the question you posed? You might need to test the assumptions you have made, and further refine the data needs and analysis.
Initiate Action To help customers save money, you provide information on their consumption pattern and educate them on the electricity rates and ways to shift consumption to save. To help customers conserve consumption, you provide information on their consumption pattern and emphasize the overall electricity supply and demand. To optimize electricity distribution, you assess the dynamics of the network needs and make adjustments in loading the network throughout the day. Action varies with the end goal and focus impacts effectiveness.
Note that the technology only performs one of the five things listed above. Without technology, you would be challenged to process the amount of data. However, you still need to determine what problem you want to solve and what data you need in order to draw the appropriate conclusions for action. It is exciting to learn about new technologies but let’s not lose sight of the real purpose they serve.
Meggings and jeggings certainly got my attention when I first came across the terms. I wouldn’t be surprised if the shoulder pads of the 1980s are back in vogue!