Continued From Part-1
In my last post, I discussed about the basics of Big Data at length. Today I’m going to discuss the need, implications and future predictions concerning it.
Need for Big Data:
Big data describes a holistic information management strategy that includes and integrates many new types of data and data management alongside traditional data.
When dealing with larger datasets, organizations face difficulties in being able to create, manipulate, and manage big data. Big Data is particularly a problem in business analytics because standard tools and procedures are not designed to search and analyze massive datasets.
Big data analytics helps organizations harness their data and use it to identify new opportunities. That, in turn, leads to smarter business moves, more efficient operations, higher profits and happier customers. The 3 reasons for the need of it or benefits that organization derive from it are:
- Cost Reduction: Big data technologies such as Hadoop and cloud-based analytics bring significant cost advantages when it comes to storing large amounts of data – plus they can identify more efficient ways of doing business.
- Faster, Better decision making: With the speed of Hadoop and in-memory analytics, combined with the ability to analyze new sources of data, businesses are able to analyze information immediately – and make decisions based on what they’ve learned.
- New Products and Services: With the ability to gauge customer needs and satisfaction through analytics comes the power to give customers what they want. With big data analytics, more companies are creating new products to meet customers’ needs.
Importance of Big Data:
The importance of big data doesn’t revolve around how much data you have, but what you do with it. You can take data from any source and analyze it to find answers that enable 1) cost reductions, 2) time reductions, 3) new product development and optimized offerings, and 4) smart decision making. When you combine big data with high-powered analytics, you can accomplish business-related tasks such as:
- Determining root causes of failures, issues and defects in near-real time.
- Generating coupons at the point of sale based on the customer’s buying habits.
- Recalculating entire risk portfolios in minutes.
- Detecting fraudulent behavior before it affects your organization.
Different Big Data Analytics Techniques:
- Predictive Analytics
- NoSQL Databases
- Search and Knowledge discovery
- Stream Analytics
- In-memory data fabric
- Distributed file Stores
- Data Virtualization
- Data Integration
- Data Preparation
- Data Quality
Big Data Technologies:
- Apache Spark
- Apache Ranger
- Mongo DB
- Apache Cassandra
In order to predict the future of Big Data technology, it is necessary to first predict at what speed and from which all sources this big data will be generated. Listing some of the sources:
- Social Networks and Social Media Communications
- Blogs and comments
- Personal Documents
- Pictures, Videos and Internet Searches
- Mobile Data Content
- User-generated maps
- Data from Sensors – Home Automation Sensors, Future IoT Sensors
- Mobile Sensors – Person (Mobile Phone Location), Road, Rail, Air, Nautical(Ships)
- Satellite Data
- Data from Computer systems
“It is well known that it took from the dawn of civilization to the year 2003 for the world to generate 1.8 zettabytes of data. In 2011 it took two days on average to generate the same amount of data. Data centers consume up to 1.5 percent of all the electricity in the world.”
We swim in a sea of data … and the sea level is rising rapidly.
Tens of millions of connected people, billions of sensors, trillions of transactions now work to create unimaginable amounts of information. An equivalent amount of data is generated by people simply going about their lives, creating what the McKinsey Global Institute calls “digital exhaust”—data given off as a byproduct of other activities such as their Internet browsing and searching or moving around with their smartphone in their pocket.
There are only two certainties in Big Data today: It won’t look like yesterday’s data infrastructure, and it’ll be very, very fast to cope with the speed of its generation.
We may have much more advanced Big Data Technologies to analyze the data which is being produced in such great volumes, at such high-velocity and from all the varied sources. This data would be of great value in decision-making by the smart future machines.
Some of the future prediction as per Forbes are listed as below:
- Ways to analyze data will improve.
- More tools for analysis not needed the help from analyst will emerge.
- Prescriptive analytics will be built in to business analytics software.
- More developed software for Real-time Streaming insights into data by data winners.
- Privacy issues is a big challenge for big data.
- Separate departments would be made in companies for handling Data with a Chief Data Officer to guide management of the data handling capacity.
- Fast Data and Actionable data will replace Big Data as at the end of the day businesses and companies just analyze data to find answers about their questions.
The future of big data is still unsure, as the big data era is still unfolding, but it is clear that the changes ahead of us will transform organizations and societies. Big data is here to stay and organizations will have to adapt to the new paradigm.