Understanding Data Before Creating a Centralized Data Warehouse
Updated: Jun 18, 2018
When it comes to dissecting the aspects of what fosters a successful business, perhaps one of the most common ideas that springs to mind is decision making. Of course, collaboration, customer service, and vision all matter -- but without smart leadership, no organization will reach its fullest potential.
Data is one of the most crucial aspects of decision making in the 21st century, as the sheer quantity of information available to leaders has grown at an incredible rate. Big Data is typically broken down into the three V’s: Volume, Velocity and Variety.
Volume focuses primarily on the amount of data collected through many outlets (social media, customer feedback, day-to-day business transactions, etc)
Velocity centers on how quickly that data needs to be researched and analyzed in order to make the appropriate business decisions.
Variety covers the format in which data is delivered, which can range from standard text documents, videos, audio recordings, and more. With the continuous growth of technology in the 21st century, there’s little doubt that the way companies will collect data in the future is limitless.
Understanding the basics of Big Data also involves knowing which types of Big Data are the most important to pay attention to. Ingram Micro Advisor states the following four types of Big Data as the ones that can really make or break a business: Prescriptive, Predictive, Diagnostic, and Descriptive. Now, what are the recommended ways of successfully using and applying these types of data?
Prescriptive Analytics: This type of data analysis is aimed at solving a specific problem. How can we offset the increased cost of materials? Is a wage increase the best way to solve poor employee retention rates? What can be done to stop productivity from decreasing? These are all good examples of questions that can be answered with prescriptive analytics
Predictive Analytics: As you might have guessed from the name, this type of analysis is aimed at making predictions about the future. A few examples: Will growth continue in the next quarter? How will our competitors fare after we release our newest project? Which new startups are worth investing in? The
Diagnostic Analytics: This type of data analysis tries to answer the question, “why did it happen.” Recycling a couple questions from above in a different perspective, Diagnostic analytics would ask “why did we grow last quarter,” and “why is productivity decreasing.”
Descriptive Analytics: Not a numbers person? Descriptive analytics may be your favorite type of analytics on this list. The point of descriptive analytics is to synthesize and summarize data in a way that shows something about the bigger picture.
Now that you are armed with a bit more information on what kinds of data analysis exist and how it can help your business succeed, why not take data aggregation and organization a bit more seriously? Visit QBIX Analytics online today to learn more about our data solutions.