How Big Data Can Make a Difference
The big data platform is a collection of functions that include high-performance processing such as machine learning and data mining of huge data volumes
This is the era of Big Data -Advancements in Information Technology have led organizations, institutions and governments to collect massive volumes of structured, semi-structured and unstructured data. To benefit from all this data, it has become necessary to be able to mine through it for valuable information. That is where Big Data processing comes in, where advanced predictive analytics are used to extract information from large and complex data sets that cannot be processed by traditional data processing applications. Although big data exploded on the scene in the first decade of the 21st century, the practice of advanced analytics is grounded in years of numerical exploration and logical application.It is clearly foreseeable there will be a rise in the volume of data captured by enterprises, and through multimedia, social media, and the Internet of Things. In turn, most companies are now emphasizing on using the new Big Data technologies to gain value from these complex data sets. Investments in research and development are rapidly growing and startups have started building their niche in the market.
The big data platform is a collection of functions that include high-performance processing such as machine learning and data mining of huge data volumes. The platform includes capabilities to integrate, manage, and apply complicated computational processing to the data. Big data is usually characterized by high volume, variety and velocity of data. The most significant objective and potential incentive of Big Data initiatives is the capability to examine varied data sources and new data types, and not just managing very large data sets. Typically, big data platforms include a Hadoop foundation which is an integrated storage and processing environment that is highly scalable to large and complex data volumes. Hadoop was designed and built to optimize difficult manipulation of large amounts of data while enormously exceeding the cost/performance of conventional databases. Many large business executives see Hadoop as a cost effective option for the archival and fast retrieval of large amounts of historical data. Due to its prevalent value execution, a few organizations are actually wagering on Hadoop as a data warehouse replacement.
The technologies and concepts behind big data allow organizations to achieve a variety of objectives. Development in big data analysis offer cost-effective opportunities to improve decision-making in critical areas such as health care, governance, employment, economic productivity, crime, security, natural disaster, complex scientific simulations, and resource management. Business decisions with big data can also include other traditional areas for analytics such as supply chains, risk management, or pricing.Organizations that pursue big data consider the petabytes of storage for structured data is now inexpensively delivered through big data technologies. By deploying big data, a business organization is able to interact with the client in real time, using analytics and information derived from the customer experience. Many of the firms that apply the big data approach are online firms, which have an understandable need to utilize data-based products and services. Companies like Google, eBay, LinkedIn, and Facebook were built around big data from the start; however there are companies that have made huge investments in legacy systems.
No single business model in the last decade has as much potential effect on current IT speculations as big data. As IT innovations advance, businesses and IT companies are stressing on investing in technology solutions to monetize big data. Organizations are not just replacing legacy technologies for open source solutions like Apache Hadoop; they are additionally supplanting exclusive equipment with product hardware, custom-composed applications with packaged solutions, and decades-old business knowledge tools with data visualization.There are cases where a lot of big companies have existing data warehouses that are working well and they cannot justify the complete restoration of these environments. As a result, most of these big companies use a coexistence policy to get the best of both worlds that combines the best of legacy data warehouse and analytics environments with the new power of big data solutions. Many companies continue to rely on incumbent data warehouses for standard Business Intelligence and Analytics reporting, including regional sales reports, customer dashboards, or credit risk history. In this new environment, the data warehouse can continue with its standard workload, using data from legacy operational systems and storing historical data for the provision of traditional business intelligence and analytics results.
Similarly, not every company can afford to build an internal big data setup with a team of professional data scientists. However, they still need to process the masses of unstructured inputs. So the most appropriate solution is to have big data and analytics offered ‘as a service’ through the sharing of big data scientists and resources which can be engaged by companies with different requirements. Startup companies and leading data management organizations are contending to profit from the demand for big data. Notable companies such as IBM, Oracle Corporation, SAP, Microsoft, and HP have spent billions on software firms specializing in data management and analytics.The three major characteristics of big data that attract firms towards it are the lack of structures, the opportunities it presents, and the technologies which are of low cost with time efficiency. Storing huge and varied amounts of data on disk is becoming less expensive as the disk technologies become more commoditized and efficient.
Studies have shown that data intensive sectors are gaining the most from the use of big data such as computer, electronic products, information sectors, finance, insurance and government. Big data is helping companies satisfy their clients by providing relevant customer intelligence based on both structured and unstructured sources of information like customer relationship management (CRM) data and social media.Governments and political parties are adopting big data techniques to gauge public response and outline course of actions. Big data is also being coupled with the Internet of Things (IoT) to improve the efficiency of operations in industries. IoT generates huge volumes of data which is reported by sensors connected to gadgets and machinery. Data is also being collected from equipment through RFID technology. Manufacturers have started using predictive analytics to innovate their operations such as proactive maintenance and after-sales service offerings.
In the coming years, more and more companies will be involved in buying and selling data. Several large organizations are purchasing external data considered to be value addition. More organizations are expected to monetize their new and unique data sources data by selling them. The usage of big data will also be a useful through mobile phones in providing real-time intelligence for quick decision-making. Similarly, company employees can feed in data through their mobile devices and help in maintaining an updated knowledge base. Media (video, audio, image) analytics is expected to triple in the next few year with most large organizations investing in analyzing rich media.
Big Data technology and its services are a multi-billion dollar market filled with opportunities worldwide growing several times faster than the overall information technology market. Big data is transforming the IT future by driving exponential changes in conventional data analysis platforms. Big data is bringing about striking cost reductions, better management decisions, considerable improvements in the time needed to execute a computing task, or new product and service offerings. Like conventional analytics, it can also sustain internal business decisions. Big data is set to progress even more in the near future and there are numerous prospects to be availed by enterprises that invest in big data solutions; startups getting into this business; and for workforce aiming to become big data scientists.With all the evidence backing big data, businesses cannot afford to miss out on its value addition. Without a doubt, organizations that effectively develop big data capabilities can separate themselves a long way from competitors.