With our computational powers growing rather exponentially, the type and quantity of data has also grown exponentially. Every day it’s estimated that more than 2.5 quintillion bytes, that’s 2.5 exa-bytes or 2.5 x 10 to the power of 18 bytes, of data is generated. This figure is estimated to double roughly every 3.5 years.
It’s impossible that this data can be processed by single computers, or by individuals. It requires network computing, and complex software. The availability of these huge data sets, coined Big Data, has changed the field of data analytics. More opportunities, more complex technology and applications, and more specialized expertise are evolving.
Before we delve into how Big Data is involved in data analytics, let’s discuss what Big Data is and what data analytics means.
What is Big Data?
Big Data is a word coined to describe data sets so voluminous that normal computational software cannot process them. The types of data sets involved in Big Data are far too large to be stored on a single computer, and requires special treatment.
Big Data is described by the “5Vs”:
- Volume – it’s incredibly large;
- Variety – it’s incredibly diverse;
- Velocity – it’s captured and processed at high speed;
- Variability – there are huge amounts of factors within the data sets;
- Value – data sets can create immense value for end users.
What is Data Analytics?
Data analytics simply means drawing meaningful conclusions from data.
It’s a procedure of inspecting, cleansing, transforming, and modeling data with the objective of finding helpful information.” A project manager at Loginworks aptly describes.
Raw data alone isn’t very useful to a company. The larger the data set becomes, the less useful and more prone to error raw data becomes.
Data analytics helps clean data and transform it into useful information, using complex strategies like modeling and algorithm development.
Data Analytics can be
- Descriptive – telling a picture of the situation;
- Diagnostic – identifying problems;
- Predictive – model future predictions based on trends;
- Prescriptive – suggest behavior based on possible outcomes.
How Can Big Data Help Data Analytics?
The more data available, the better data analytics can become. When we have a very large data set, it’s easier to draw conclusions.
Modeling and algorithms in particular work far more accurately when there are large data sets to work with.
Companies realizing the trend towards faster, cheaper, and more detailed processing of big data are implementing more capturing processes to become more informed. This in turn leads to more opportunities for data analysis.
Big Data is helping data analytics become more refined, more accurate, and more usable.
How is Big Data Processed in Data Analytics?
Companies are compiling data sets daily, but often the data is lost since they don’t have systems in place to use the data. The problem is most don’t have the expertise to even store the data sets, let alone process them for meaningful information.
To collect and make use of big data, companies rely on data analytics experts. Data processing specialists can easily assist in proper capture, storing, and processing of large data sets.
When expert data analytics services are used, the data resource can be managed properly. After cleaning, and transforming, data is then presented to company management and involved parties in a meaningful way depending on their objectives.
Open Source Processing
The informal growth of Big Data has led to evolvement of high quality open source software applications, which has allowed for more wide spread use, where once Big Data was only for major players. One of the popular and accessible ways Big Data is stored and processed is using an open source framework, called Hadoop, by Apache. The Hadoop framework provides software systems for paralleling of computers and servers to increase computing power and reduce data processing times. Hadoop is modular so entry level costs are far lower than traditional MPP systems.
Another common open source database applications used in Big Data is MongoDB. MongoDB is NoSQL, or non-SQL, meaning non-relational. Non-relational databases are another way Big Data has evolved data analytics. Traditional databases are tabular and relational, whereas the huge quantity of variables in big data required the development of non-relational alternatives. One of the most commonly used languages for data analytics with Big Data is R, an open source language developed by GNU.
What Applications are Possible With Big Data
When data analytics are applied to Big Data, the possibilities are unlimited.
Big Data’s role in data analytics can help drive decision making through:
- Developing new technology;
- Increasing customer service and satisfaction;
- Reducing costs and errors in processes;
- Gaining insight into market trends;
How is Big Data Changing Data Analytics?
Big Data brings huge possibilities in the field of data analytics. Consumer data can be processed quickly and effectively to create valuable information in fields such as healthcare, travel and hospitality, consumer services, retail, government, and research to name a few.
Data mining is becoming a specialized field, where data analytics specialists can help companies collect information from their internal processes, and from external sources.
Complex analysis tools are more accurate with large data sets. Creating data models, algorithms, and machine learning becomes far easier when data analysts have large data sets to work with.
Data analytical tools and specialized software are constantly growing and developing to handle the volume of Big Data sets.
What Are Potential Problems With Big Data in Data Analytics
Larger and larger data sets bring with them the potential for larger and larger problems. This results in more and more specialized services being required for processing meaningful information
Quality control measures need to be applied to Big Data processing to ensure the accuracy of information. With large data sets, the chances of spurious correlations increases. That is to say, trends and relationships can be drawn by computer matching where no relationship actually exists in reality. Again this calls for qualified specialists to process the data, in particularly cleaning the data prior to use.
Big Data requires specialized data analysis skills, equipment, and software, which means data professionals need more specialized skills to process data for meaningful results. Companies need to employ more qualified data analysts specializing in particular fields, or they need to outsource big data analytics.
Big Data has Created Opportunities for Growth in the Data Analytics Sector
Some feel that Big Data has excluded smaller players in the data analytics scene. That is companies that are unable to process large data sets cannot compete, however the contrary is potentially more the truth. Big Data has provided opportunities for growth in the middleman sector. Large companies obtain data, and specialized services process the data. Middleman businesses now package the data for resale to smaller companies. In many cases, this information is available in bite sized packages free for the consumer, with specialized professionals buying upgraded services.
The advent of Big Data has also led to successful business models for technology companies like Google, Amazon, Facebook, LinkedIn, and many others. Technology such as Google Adsense relies on fast processing of big data for their business models.
Big Data has created avenues for data analysts to specialize in large data handling, including software development, mathematical modeling, and industry-specific data review. The growth of the business use of big data has created many more opportunities for data analysts and data management firms.
What is the Future for Big Data
While technology giants and huge multi-nationals have used big data for years, the increasing ease at which it can now be processed is making their tasks easier.
Access to big data is now becoming more available to smaller companies, who are using it to create business opportunities and compete in markets they previously may not have had access to.
Consumers are benefiting from reduced prices, better services, information, and community benefits provided by Big Data.
The trends in Big Data’s role in data analytics are that it will be more versatile, more affordable, and more necessary for all players.
Big Data continues to grow, companies that are not making use of it are missing opportunities and making less informed decisions.
Big Data is a vital part of data analytics today. Its role in data analysis means consumers and businesses have far more detailed and accurate information available at our fingertips, it’s important that we use it correctly.
Big Data requires specialized data analytics skills to store, process, and analyze. Most companies, who are not technology or data specialists, need to outsource this process.
Big Data is responsible for the growing development of more specialized resources, in particular open source products, and more niched experts in the data analytics field.
Data collection and processing will only continue to expand with developing technology, so the scope of big data in data analytics is to bring more refined, more complex processes to mainstream data information science.
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