Discussion 2 (Chapter 9): What are the common business problems addressed by Big Data analytics? In the era of Big Data, are we about to witness the end of data warehousing? Why?
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Discussion 2 (Chapter 9): What are the common business problems addressed by Big Data analytics? In the era of Big Data, are we about to witness the end of data warehousing? Why?
Introduction
The era of Big Data has brought about many new innovative ways for businesses to analyze their data. This is because there are so many sources of information available now that it can be difficult to filter through them all and extract what you need. In this article, we will discuss how Big Data analytics works with traditional data warehousing techniques by looking at four common problems that arise when dealing with large amounts of data: volume, complexity and time constraints.
The common business problems addressed by Big Data analysis is that there are four main types of problems depending on the data sources being stored.
The common business problems addressed by Big Data analytics are:
Large data volumes
Data complexity
Patterns and trends
These are the four main types of problems depending on the data sources being stored.
First, is large data volumes. The type of problem is trying to analyze the data and generate actionable insights.
The first thing to understand is that Big Data is not just large volumes of data. It’s more than just an increase in the number of data sources, and it’s also not just the amount or volume of unstructured data.
Big Data analytics deals with problems that use large amounts of structured, semi-structured and unstructured data—all types of information that can be stored in many different formats. However, it’s important to note that there are different types of Big Data:
Structured – This category includes structured data such as rows in databases or files on hard drives containing information about customers’ financial transactions (account numbers). It also includes semi-structured information like text messages sent over mobile phones which include both actual words used by users along with metadata about those messages (e.g., who sent them).
Semi-Structured – Semi-structured refers to anything else besides straight lists like tables without columns; instead these items tend towards being very broad categories such as “people born between 1980–2000” or “wine styles.”
Second, is data complexity meaning it is difficult to extract the desired information from it or the data is not available in a format that allows analysis.
Second, is data complexity meaning it is difficult to extract the desired information from it or the data is not available in a format that allows analysis.
Data complexity can be solved by using different techniques such as visualization, clustering, dimensionality reduction and so on. Data complexity can also be solved by performing statistical computations on big data sets where you need to analyze large volumes of structured and unstructured data. In this case, you need sophisticated algorithms to make sense out of your huge amount of unstructured or semi-structured (e.g., text) information which may contain several different formats: HTML pages; emails; images etcetera
Third, applies to patterns and trends that cannot be discovered easily because of large amounts of data.
The third category of data analytics is used to find patterns and trends that cannot be discovered easily because of large amounts of data. Big Data analytics is used to analyze large amounts of unstructured data, such as images and videos, in order to find correlations between them. For example, you can use Big Data analytics to identify suspicious activity or trends by analyzing a number of publicly available videos taken from different cameras around your city (or even your own smartphone).
Big Data analytics will also help you detect fraud more efficiently than traditional methods because it provides detailed information about every customer’s transaction history with your company—so you won’t have any surprises when conducting credit checks on new applicants!
Lastly,is real-time analytics which apply to situations that require an immediate response due to changing conditions.
Real-time analytics is a form of data analysis that is used to make decisions in the present or near-future. It is different from traditional data warehousing techniques because it requires more complex tools and skills, particularly those pertaining to operations research.
Real-time analytics can be used in many industries such as healthcare, finance and manufacturing where the time frames of decisions are shorter than those required by traditional business intelligence (BI) models.
In the era of Big Data, we will not be witnessing an end for data warehousing as Big Data analytics build upon traditional data warehousing techniques.
In the era of Big Data, we will not be witnessing an end for data warehousing as Big Data analytics build upon traditional data warehousing techniques. In fact, they are two different approaches with different goals and objectives.
Traditional data warehousing focuses on organizing and storing large amounts of information in a database so that it can be accessed easily by users at any time. The primary benefit of this approach is that it allows organizations to make better use of their existing IT infrastructure by providing access to historical information about their business activities over time (e.g., sales records).
Big Data uses different techniques than traditional data warehousing techniques for processing different types of data but both work together to support business decisions based on information
Big Data analytics use different techniques than traditional data warehousing techniques for processing different types of data.
Big Data analytics build upon traditional data warehousing techniques.
Big Data analytics are based on the same principles as traditional data warehousing
Conclusion
So, there you have it! Big Data is a great way to solve your problems and make better decisions. It’s time for us all to start looking at data in new ways and embrace this exciting new era of information technology.