What's the Big Deal About Big Data?
What is the big deal about Big Data, anyway? Why are businesses falling over one another to implement Big Data technologies into their products and organizations? And what factors have brought about the global boom in Big Data jobs?
Read on to find answers to these and many more questions!
When Is Data Big?
Not too long ago, ‘Big’ and ‘Data’ were just two simple words that were rarely used together in the same context. Today a catch-phrase and among the hottest buzzwords in business, Big Data is a broad term used to describe complex and large data sets that are way too diverse and fast changing for conventional technologies.
It is a term used to describe the exponential growth and availability of structured and unstructured data. Although not referring to any set quantity, the term is used when talking about magnitudes that are measured in petabytes and exabytes of data.
More generally, the term refers to technologies that can store, manage and analyze a large set of data to solve complex problems.
Every day, we are creating close to 2.5 quintillion bytes of data. 90% of the data around the world today, has been created in the last two years. Where doesn’t data come from? Every corner of the world has some data to offer - climate information is gathered from sensors, social media site posts, digital pictures and videos, transaction records, cell phone GPS, etc.
How Does Big Data Help Analytics?
Big Data Analytics is defined as "the process of examining large data sets that contain a variety of data types to uncover unknown correlations, hidden patterns, customer preferences, market trends, and other useful information”.
According to a report from the McKinsey Global Institute (MGI) and McKinsey & Company’s Business Technology Office, the volume of data generated, stored, and mined for insights has become economically relevant to businesses, government, and consumers.
Big Data Analytics is no longer seen as just an experimental tool. Companies have begun to see and achieve real results using this approach, along with expanding their efforts to incorporate more data and models. They have even invested in hiring data scientists with relevant certifications.
What Are The Advantages To Deploying Big Data?
❖Better decision making: Analytics has constantly included endeavors to enhance the decision-making process, and Big Data doesn't change that. Vast associations are looking for both quicker and better choices with huge information, and they're discovering them. There are many organizations that mainly focus on making better choices by analyzing new sources of data.
Take, for example, the health insurance company United Healthcare. They are in the process of using ‘natural language processing tools’ from the SAS to understand customers and what they want, better. The company has already found that the text analysis improves its predictive capability for customer attrition models.
❖Cost reduction: In this data-driven age, businesses are collecting more data than ever before. Data is what drives the world. Sensors and wireless functionalities are embedded into each and every thing. Companies are using this data to understand better how and what their customers want and learn to adapt quickly to the customer's needs. Knowing this helps reduce the amount of resources and energy and material they would generally use. They know the exact result to be delivered.
When comparing big data technology and traditional data warehouses, the difference in costs incurred is tremendous. Instead of processing and storing large quantities of new data in a warehouse, companies have turned to Hadoop technologies to do it for them. Well-established firms like Citi, Wells Fargo, and USAA all have substantial Hadoop projects underway.
❖Newer products and redevelopment of the old: Big Data Analytics is also used to create services and products for customers. Taking after online companies, which have been doing it for years, brick-and-mortar firms have also started implementing Big Data in a big way. Verizon Wireless has devised several new offerings based on their mobile device data. Verizon is selling information in the business unit called Precision Market Insights on how often users are in certain locations, what they do, and their activities. Precision Market Insights, for example, provided information about the Phoenix Sun’s fans’ whereabouts to them.
This is important information for the NBA teams’ advertising and promotional campaigns. Big Data helps companies understand how their products are perceived, which helps in adapting or redeveloping them if something goes wrong. The use of Big Data also allows companies to test the variations of computer aided designs to check how minor changes can influence and affect performance. This helps them effectively raise the efficiency of the production process.
❖Risk Analysis: Big data's predictive analysis helps companies scan and analyze media reports to keep up with the latest developments in the industry, and with market movements and fluctuations. Big Data also gives a detailed health test on company suppliers and customers allowing them to take a stand when one of them are at the risk of defaulting.
❖Collection of Data: Collecting Data isn't enough for a company. Collecting this data, they use it to determine the behavior of the market. Consider, for instance, Facebook ads: Big Data has helped Facebook collect data on and understand exactly what the consumer clicks on, what the consumer likes, what gets shared and what doesn’t, and user-behavior after leaving the page. Based on analysis of this data, Facebook then sends targeted ads to the consumers. Using Big Data, organizations tap into the wide array of public and open data sets.
Is Big Data Worth It?
The results of a global study commissioned by CA Technologies has revealed that the benefits of Big Data clearly outweigh the obstacles in the implementation of Big Data. The percentage of organizations that plan to and already have implemented a Big Data project is 84%. Managers in organizations have rated the improvement of customer experience at 60%, the acquisition of more customers at 54%, and keeping up with competitors at 41% -as the most-important factors in a Big Data project.
The study has also demonstrated a tangible increase in revenue of a Big Data company by 88%, while their improved competitive positioning is increased by 92%, the ability to provide new products and services has increased by 94%, and companies have a 90% more targeted marketing campaign.
Any Examples Of Companies That Have Successfully Implemented Big Data Tech?
- hiQ: is a company that specializes in ‘people analytics’. The company works by gathering public data along with businesses’ internal data and creates predictive and useful models. They also give out mathematical algorithms that give the users more powerful insights. Their reporting tool is flexible and gives insights to the C-suite as well as the work of line managers.
- SumAll: A popular company in the field, SumAll is an NYC startup that uses 42 different platforms to help businesses optimize their social media campaigns with the help of one single chart.
- Splunk: originally a log analysis tool, Splunk partnered with Tableau Software to use their visual analytics software and turned it into what it is today. A machine data analytics business, Splunk lets you use tools which keeps a close eye on end-to-end transactions. The user can also collect important data about the customer’s experience and track real-time trends or have a sentiment analysis performed for social media campaigns.
- Alteryx: this is an American software company. The software they offer combines the structured and unstructured data from a number of sources and stores it in one database. This data is used to conduct spatial, predictive and statistical analysis tasks. The results are then shared with the user.
Preparing for a career in Data Science? Take this test to know where you stand!
Find our Big Data Hadoop and Spark Developer Online Classroom training classes in top cities:
|Big Data Hadoop and Spark Developer||2 Feb -10 Mar 2019, Weekend batch||Your City||View Details|
|Big Data Hadoop and Spark Developer||10 Feb -3 Mar 2019, Weekdays batch||Dallas||View Details|
|Big Data Hadoop and Spark Developer||15 Feb -23 Mar 2019, Weekdays batch||New York City||View Details|
Recommended articles for you
Why Big Data Means Big OpportunitiesArticle
How Big Is Big Data?Article
The Simplilearn Edge: Big Data and AnalyticsArticle