According to internet live stats, more than 5 million searches are conducted on Google every minute, that’s a lot of data to be stored, managed, and analyzed in a short period. You probably come across the term BIG data analytics, but have you ever wondered, what does it mean?
What’s Big Data?
If you ask a data engineer: How “Big” is Big Data? the answer would probably be “It depends“, while you would expect an answer like, “if the data is more than 100TB” or another amount.
In reality, the definition of Big Data is a bit foggier than that, but to make it as simple as possible, data starts being “big” when we can no longer process it using traditional tools.
Traditional tools refer to software and data structures like Excel, SQL databases, and flat files on a single computer. When data is so big, adding, deleting, and modifying that data takes unreasonable time, and stocking it on a single machine might become physically impossible. When that happens we say have a Big Data problem.
Characteristics of BIG Data or The 3 Vs
Data experts characterize the properties and dimensions of big data with the 3Vs which describe how to understand and deal with the power data to easily make business strategies and decisions.
Volume
It’s the amount of data generated by users and collected by organizations from a variety of sources, including social media, business transactions, smart (IoT) devices, industrial equipment, streaming videos, and more. Big Data is unbelievably big and practically incomprehensible.
Today, We have passed the age of Giga and Terabytes and talking in Petabytes and Exabytes.
One Exabyte = 36,000 years of HDTV video, or streaming the entire Netflix catalog over 3,000 times.
It’s now easier than ever for your organization to make use of the quantity of data they acquire thanks to the advanced convenient storage technology that brings Big Data extraction and analytics into their reach.
Velocity
When we talk about velocity we don’t mean the average speed at which the data transferred between devices but rather than that we’re talking about the rate at which data grows.
Today, it would take over 5 years to watch the amount of video that crosses global networks every second! That’s more data traffic in one second than were stored in the entire Internet 20 years ago.
The faster your organization can process and analyze data flows that they generate or collect, the faster it can respond compared with its competitors.
Variety
More than 80% of today’s data are unstructured and the ordinary structured databases that housed the majority of business data until now are not well suited to storing and processing today’s Big Data. Big data variety refers to the ability to classify the unstructured amounts of data generated in real-time from different sources and formats into structured categories able to be analyzed.
How can organizations optimize the value of their Big Data?
Setting a Big Data strategy
To get the most of their big data, your organization needs to implement a big data strategy which is a plan that helps them oversee and improve the way they acquire, store, manage, share, and use data within and outside your organization.
Because the primary goal of Big Data is to capture value by leveraging data, the Big Data strategy should be aligned with your corporate business objectives and address key business problems. One way to accomplish this is to align with the enterprise strategic planning process, which is already in place in most organizations, and treat your Big Data like any valuable business asset.
Identifying data sources and integration method
As we mentioned above, companies generate a vast amount of structured and unstructured data from a multitude of sources every day, yet much of that data remains in raw forms such as streaming videos, social media, SMS messages, and emails, publically available data, and other data from cloud services
That’s where data integration takes place. It‘s the process of combining data from various sources into a single, unified view. Ingestion is the first step in the integration process, which includes steps like cleansing, ETL mapping, and transformation.
Due to the need for scalability and high performance for managing both structured and unstructured data. Organizations widely use both data lakes and data warehouses to store big data, but the terms are not interchangeable. A data lake is a large pool of unstructured data with no clear purpose. A data warehouse is a repository for structured, filtered data that has been previously processed for a specific purpose.
Choosing the right analytical tool
“Information is the oil of the 21st century, and analytics is the combustion engine,” Peter Sondergaard, Gartner Research
Your organization can opt to leverage all of their big data for analysis using high-performance tools or identify whether data is important before studying it. In both cases, analytics is how businesses extract value and insights from data.
Choosing the right analytical tool depends on multiple factors depending on the company’s business needs, experience, and the expected performance.
Can Power BI handle Big Data?
Analyzing and extracting insights from Petabytes of data used to take a long time for companies.
But with Power BI and Azure SQL Data Warehouse – a secure fully managed cloud data warehouse with industry-leading performance this problem has been solved. Customers can now perform Petabyte-scale analytics with instant response times, exploring and analyzing trillions of rows of data and interactively extracting insights on the fly.
Azure SQL Data Warehouse was shown to be up to 14x times faster and costing 94% less than other cloud providers according to GigaOm research.
Business analysts can manipulate data stored in Azure Data Lake Storage with ease, using its scalability, performance, and security. Meanwhile, data engineers and data scientists can strengthen their insights with sophisticated analytics and AI from Azure Data Services such as Azure Machine Learning, Azure Databricks, and Azure SQL Data Warehouse.
You can get faster insights, make better business decisions, and progress toward positive revenue growth by converting your raw big data into understandable reports, dashboards, and drawings by using Power BI to analyze your company’s Big Data.
We at DASH work to make your organization more competitive, more productive, and innovative by helping you make sense of your business data.
We design and develop systems and solutions that help you harness your data to its full potential