October 15, 2024
In this blog, we introduce the concept of big data and data analytics solutions and discuss the challenges of working with big data (large data sets) that must rapidly produce meaningful insights. We also introduce you to the five challenges (the 5 Vs) of data analysis. Lastly, we define what you need to know to plan your data analysis solution.
big data simply refers to extremely large data sets. This size, combined with the complexity and evolving nature of these data sets, has enabled them to surpass the capabilities of traditional data management tools. This way, data warehouses, and data lakes have emerged as the go-to solutions to handle big data, far surpassing the power of traditional databases.
Data Analytics is the process of analyzing data in order to extract meaningful data from a given data set. These analytics techniques and methods are carried out on big data in most cases, though they certainly can be applied to any data set.
Data analytics is vital to large and small businesses. Data analytic solutions help businesses decide where and when to launch new products, when to offer discounts, and when to market in new areas. Without the information provided by data analytics, many decision-makers would base their decisions on intuition and pure luck.
As businesses begin to implement data analysis solutions, challenges arise. These challenges are based on the characteristics of the data and analytics required for their use case. In the past, these challenges have been defined as “big data” challenges. However, in today’s cloud-based environment, these challenges can apply to small or slow data sets nearly as often as very large, fast data sets.
This course will show you how to identify the data analysis solution that best fits your requirements and how to plan and carry out a strategy for implementing it.
Organizations spend millions of dollars on data storage. The problem isn’t finding the data — the problem is failing to do anything with it.
Big data is an industry term that has changed in recent years. Big data solutions are often part of data analysis solutions.
Data is generated in many ways. The big question is where to put it all and how to use it to create value or generate competitive advantages. The challenges identified in many data analysis solutions can be summarized by five key challenges: volume, velocity, variety, veracity, and value.
Not all organizations experience challenges in every area. Some organizations struggle with ingesting large volumes of data rapidly. Others struggle with processing massive volumes of data to produce new predictive insights. Still, others have users that need to perform detailed data analysis on the fly over enormous data sets.
A data analysis solution has many components. The analytics performed in each of these components may require different services and different approaches.
Collecting the data from transactions, logs, and IOT devices is a challenge. A good data analysis solution allows developers to ingest a wide variety of data at any speed, from batch to streaming.
A good data analysis solution should provide secure, scalable, and durable storage. This storage should include data stores that can house structured, semi-structured, and unstructured data.
Data Process means sorting, aggregating, joining and applying business logic to produce meaningful analytical data sets. The final step is to load this analytical data set into a new storage location, like a data lake, database, or data warehouse.
You can consume data: by querying or by using business intelligence (BI) tools. Querying produces results that are great for quick analysis. BI tools produce results that are grouped into reports and dashboards.
Due to the increasing volume, velocity, variety, veracity, and value of data, some data management challenges cannot be solved with traditional database and processing solutions. That’s where data analysis solutions come in.
A brief definition of the five challenges will help you understand each one before you move on.
Volume means the amount of data that will be ingested by the solution — the total size of the data coming in. Solutions must work efficiently across distributed systems and be easily expandable in order to accommodate spikes in traffic.
Velocity means the speed of data entering a solution. Many organizations now require near real-time ingestion and processing of data. The high velocity of data results in a shorter time to analyze than traditional data processing can provide. Solutions must be able to manage this velocity efficiently. Processing systems must be able to return results within an acceptable time frame.
Data can come from many different sources. Variety means the number of different sources and the types of sources — that the solution will use. Solutions need to be sophisticated enough to manage all the different types of data while providing accurate analysis of the data
Veracity is the degree to which data is accurate, precise, and trusted. It is contingent on the integrity and trustworthiness of the data. Solutions should be able to identify the common flaws in the data and fix them before the data is stored. This is known as data cleansing.
Value is the ability of a solution to extract meaningful information from the data that has been stored and analyzed. Solutions must be able to produce the right form of analytical results to inform business decision-makers and stakeholders of insights using trusted reports and dashboards.
Data analysis solutions incorporate many forms of analytics to store, process, and visualize data. Planning a data analysis solution begins with knowing what you need out of that solution
There are many different solutions available for processing your data. There is no one-size-fits-all approach. You must carefully evaluate your business needs and match them to the services that will combine to provide you with the required results.
You must be prepared to learn from your data, work with internal teams to optimize efforts, and be willing to experiment.
It is vital to spot trends, make correlations, and run more efficient and profitable businesses. It’s time to put your data to work.
In this lesson, we introduced the concept of big data, data analytics and data analysis solutions. We discussed the challenges that can come from working with large data sets that must rapidly produce meaningful insights. We also introduced you to the five Vs of data analysis and outlined some questions to explore when you start planning your data analysis solution.
