It's been a tough week to say the least.
Internal vs External, VVV(VV), Megabyte, Zetabyte and even Brontobyte, Paradigm shifts, IoT, Datafication, N=All, Life cycles and Data Warehouses, ETL, OLTP, EDW, Relational, Normalized, Data cubes, Balance Scorecards, Twitter, Facebook, Blogger, Hans Rosling, Southwest Airlines, KPI's, Dimensions and Facts, Visualization and Dashboards.... phewwww...so much to remember, so much to understand, and so little time to do it. Big Data is taking over our lives in such a fast pace and we want to understand it better so we can make the best use out of it. So, after all of this introduction, what is Big Data? How do we define it and how do we use it? How does Big Data affect our lives and how can we make it improve our daily operations?
Big Data
Big Data is defined as extremely large sets of information that can be analyzed to reveal patterns, trends, and associations relating to human behavior and interactions. In the past we used to quantify and measure only what was quantifiable (Time, Money, Space) however with improvements in technology and changes in needs and new ways of understanding organizational operations, we find ourselves in need of smarter and better analysis tools. Big Data is often referred to as the 3 V's which is a misconception to say the least. Big Data is indeed a large amount of data (Volume), coming to us at extremely fast speed (Velocity), and from many different sources (Variety) however it is defined by many more important features such as (1) Datafication (digitalization and use of any type of data for deeper analysis e.g Facebook likes, Number of steps per run with running apps, and Sugar levels in food with smart refrigerators), (2) the use of large amounts of data (N=All) and not focusing on small samples, and (3) having the ability to record Time and Location stamps for many activities we do (e.g Sharing our location on Foursquare).
There is a very big demand for Big Data specialists in various mediums and jobs. In this article we can see some numbers behind the importance of Big Data analysts and the current status.
In order to better analyze our data we need to go through the Business Analytics Lifecycle:
Collect the data > Clean and process the data > Create visualization and a descriptive analysis of the data > Create a dashboard that will introduce the data to others > Determine our questions that rise from the data > Build predictive models for future improvements.
Data Warehouse
We collect our raw operational data into an OLTP (Online Transactional Processing) table that shows us transactional information from our operations (e.g Amazon Q in stock, prices etc.). This data is constantly changing and we cannot use it to allocate problems or forecast the future. Using a Balanced Scorecard and looking at our organizational strategies we (1) try to ask the right questions about our operations. If we try to inspect only our financial side, we might miss out on other important measures that contribute to our organizations success/failure. Instead, we must also look at customer, business process, and growth measures in order to make the best decisions and define our most important KPI's (Key Performance Indicators).We then (2) create a Star Schema with Facts and unique ID's (Dimensions) that will help us measure our KPI's and determine what we need to improve. (3) We use our OLTP table data along with our facts and dimensions to create OLAP (Online Analytics Processing) tables that we can later analyze. We can now look at the data and draw some real conclusions out of it, conclusions that might open our eyes to different malfunctioning processes in our organization. Once we are done, we can make recommendations to our shareholders and/or our management for different measures, continuing to collect the data and analyzing it for further understandings and improvements.
Find out more about Big Data secrets here.
In general we conclude that Big Data is an important tool in todays growing world of organizational structure, culture, and operations. An organization can find more interesting aspects about themselves by analyzing more data, in real time, and with less work. People contribute to Big Data collection by simply enjoying different apps, websites, services, events and more. They willingly give out information about themselves as the paradigm shift within organizations is caused by a shift in consumer behavior. People look for more service oriented consumption and more care and details than ever before. In addition, the sheer size of Big Data is allowing us to find information that otherwise would never have been considered to be searched. In this great TED about how Big Data is helping find the right information, we can see that we can make better choices (e.g type of pie we buy) when we have more and new data. With Big Data we usually are able to finally see if our "hunch" about something was right, or we are surprised to learn that we were not! We can also see that the need for Big Data is mostly due to ignorance and to the fact that people underestimate it. In the same TED, Kenneth Cukier is mentioning how people ignore the existence of Big Data because of the hype around it, and that it is important to understand that if with small amounts of data we can transform the world, with so much new and relevant information we could really make an impact.
So we should not ignore Big Data, and we should not assume things are the way we think they are, because underneath there might be some hidden information that could change our perception all together.
Porter's 4 Forces, OUT.
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