Big data analytics can be used by company to make informed business decision by examining large amount of varied data to get customer preferences, market trends and other useful information. Company can use it to explore new revenue opportunities, improved operational efficiency, better customer service and competitive advantages over rivals.
“Big data is a popular term used to describe the exponential growth and availability of data, both structured and unstructured. And big data may be as important to business – and society – as the Internet has become. Why? More data may lead to more accurate analyses.” (SAS, 1)
The research proposal can be developed from the topic of big data to match up the demands of enormous data flow in the dynamic world. Data visualization tools at significant cost can help up in analytics of big data and can form an innovative research proposal for analysis and extensive research. The real time data and the use of techniques of big data in this domain can form an excellent topic of research calling for formulating methodologies and strategies to tap the untapped potential of this field and to experiment more in the field of research.
Big Data is an expansive phrase for data sets so called big, large or complex that they are very difficult to process using traditional data processing applications. Challenges include analysis, capture, curation, search, sharing, storage, transfer, visualization, and information privacy. In common usage, the term big data has largely come to refer simply to the use of predictive analytics. Big data is a set of techniques and technologies that need or require new forms of integration to expose large invisible values from large datasets that are diverse, complex, and of a massive scale. When big data is effectively and efficiently captured, processed, and analyzed, companies
The emergence of big data has provided different avenues for organizations to use data to improve different aspects of their respective operations. Be it customer service, research and development, or market position, Big Data has the potential to be a significant driving force in all these areas. However, there’s still a significant gap between the ability of Big Data to produce insightful analytical information based on real-time data and the ability of organizations to capture and utilize this readily available tool. This is, in part, due to the fact that the systems and processes necessary to fully maximize the usefulness of Big Data is currently lacking in most organizations. This lack of a conducive habitat for Big Data is further magnified in new organizations without any knowledge of Big Data. For organizations that have that have little to no knowledge of Big Data, there must be a thorough assessment of the benefits of big data and how they could improve the organizations overall place in the market. There also needs to be steps taken towards the design of frameworks that will enable the organization to better capture and utilize Big Data.
Business thrive when they have the most accurate, up-to-date, and relevant information at their disposal. This information can be used for a plethora of pertinent markers in small and large businesses, relating to accounting, investments, consumer activity, and much more. Big data is a term used to describe the extremely large amounts of data that floods a business every day. For decades, big data has been a growing field, facing controversy on many levels, but as of late, it has been a major innovator in the challenge of making businesses more sustainable. Big data is often scrutinized for its over-generalization and inability to display meaningful results at times. When applied correctly, data analysis can bring earth-altering information to the table.
What is big data? Big data is structured and unstructured data that is difficult to process using traditional database and software techniques. This is because of its extensive size. Big data ranges “from a few dozen terabytes to many petabytes of data in a single data set – and are constantly growing” (Hopp). A terabyte is equal to 1,024 gigabytes, while a petabyte is equal to 1,024 terabytes. A regular iPhone has 16 gigabytes, so a terabyte contains the same amount of digital storage as 64 iPhones, while a petabyte contains the same amount of digital storage as 65,536 iPhones! Structured data is in a fixed field within a record or file (usually databases or spreadsheets). Unstructured data is unorganized and hard to interpret by traditional databases or data models (like photos, webpages and emails). Structured data is a lot easier to work with and can be easily classified, so it is preferred in big data over unstructured data.
Today, the data consumption rate is tremendously expanding, the amount of data generated and stored is nearly imperceivable and highly growing. Big data that is nothing but a large volume of unstructured or structured data that runs in and out in to a business on daily basis. This big data is analyzed in order to achieve prominent business growth and improved business strategies . Every year there is at least 40% increase in the amount of data growth on global level, leading to which companies have started adopting new data analytic techniques and tools and also have stepped ahead moving their data towards the cloud for their big data analytic requirements and for better analysis. In big data analysis it is not the amount of data that is essential but how efficiently we handle, process and analyze it is the key factor. Big data analysis doesn’t revolve around how much data we occupy, it deals with how well you make use
Big Data has taken the business world by storm. By 2020, it is expected that the amount of digital information in existence will have grown from 3.2 zettabytes in 2014 to 40 zettabytes. Companies are doing all they can to capture this digital information and turn it into actionable insights. Currently, the total amount of data being captured and stored by industry is doubling every 1.2 years. Therefore, companies must find increasingly efficient solutions to store and analyze this incredible amount of data.
The emergence of new technologies, applications and network systems makes it hard to run the current business models and huge data types, and thus emerged various types of analytic tools like Big Data, which make this work easier by way of proper organization of data. Big Data is all about analyzing different forms of data (Structured, Semi-structured and Un-structured) and it is not about the procedure, creation or consumption of data.
‘Big Data’ is a new innovation. Processing big data and converting it into useful information can result in many benefits to a company such as increase its profitability, improve their business practice. Companies do spend lots of resources to harness this new technology. “Google Spent $7.3 Billion on its Data Centers in 2013. (Miller, 2013)” Big Data is regularly being generated by our activities such as surfing internet, using social media, blogs etc. Every digital process leaves a trail of information about consumers and their behaviour. These consumer behaviour and purchasing patterns can be used to visualize information. The big companies use big data to improve their customer’s
Big Data has gained massive importance in IT and Business today. A report recently published state that use of big data by a retailer could increase its operating margin by more than 60 percent and it also states that US health care sector could make more than $300 billion profit with the use of big data. There are many other sectors that could profit largely by proper analysis and usage of big data.
Big Data embraces structured, semi-structured and unstructured information. It can be demographic or psychographic information about the customers, their opinions, product reviews etc gathered from variety of sources such as tweets, blogs, other social media content, technical devices like sensors and stream of data from mobile devices. Businesses have started using Big Data to get right information to identify right markets and right customers at the right time in order to make right strategic decisions. The Big Data market is estimated to grow to $32.1 bn by 2015 and $54.1 bn by 2017. According to the report “Be Careful or Big Data Could Bury Your Bank”, the world creates 2.5 quintillion bytes of data daily and last 2 years have contributed to almost 90% of the data which exists today. It empowers institutions to learn more, create more and do more using the data available with them.
The proliferation of data, process and system integration technologies, combined with the rapid advances made in analytics, Big Data, customer management and supply chain applications are power catalysts of disruptive change in enterprise IT. Given the fact that many legacy, 3rd party and previously disparate, disconnected systems are for the first time being integrated together, the amount of data available for analysis and decision making has never been greater. Add to this the torrent of data being generated daily through an enterprise's sales cycles, social networks subscribed to, and customer interactions, and the amount of data available can becoming quickly overwhelming. All of these dynamics taken together form the area of analytics and enterprise software called Big Data. As tempting it is for the analytically-minded to dive into these terabytes and explore for insights and previously-unknown associations in the data, to get the most value from the investments in BigData, analytics, and enterprise applications, governance-based frameworks need to be defined that align these systems to specific strategic objectives (McKendrick, 2012). The advent of Hadoop, H-Base, MapReduce and other data analysis and aggregation platforms and applications only become relevant in the context of strategic goals and their accomplishment (Rogers, 2011). That is why more
Big Data has seen exponential growth in recent years due to the large volumes of social-networking data that is now being produced. While it may be easier for large enterprises to adopt the use of Big Data it is unclear if many small and medium sized enterprises are firstly aware of Big Data and its uses and secondly whether it will actually be beneficial to the business. The project will look at the use, or lack thereof, of Big Data in SMEs and provide a comparison when looking at larger enterprises.