X Ways Big Data Impacts Economic Development
By Monica Rodriguez
Big data is a large amount of diverse data that is processed for various purposes. Keywords are data that is being processed. In turn, data differs from the information in that it is its formalized and, as a rule, structured representation for efficient processing and analysis.
Big data refers to large sets of unstructured, semi-structured, or structured data obtained from numerous sources. Among the sources are customer databases, medical records, business transaction systems, social networks, mobile applications, and scientific experiments.
Today, companies are focusing on overhauling their data architecture, consolidating data, and discarding legacy systems. Big data has a great impact on businesses since it helps companies efficiently manage large volumes of data. The statistics by Grand View Research Inc.. prove that:
Big Data is best understood as an untapped resource that technology finally allows us to exploit. For instance, data on weather, insects, and crop plantings has always existed. But it is now possible to cost-effectively collect those data and use them in an informed manner. We can keep a record of every plant’s history, including sprayings and rainfall. When we drive a combine over the field, equipment can identify every plant as either crop or weed and selectively apply herbicide to just the weeds.
Such new use of data has the capacity to transform every industry in similar ways. A recent OECD report listed some of the ways that more and better data will affect the economy:
- Producing new goods and services, such as the Nest home thermometer or mass customized shoes;
- Optimizing business processes;
- More-targeted marketing that injects customer feedback into product design;
- Better organizational management; and
- Faster innovation through a shorter research and development cycle.
Impact of Big Data on Business
With the help of big data, companies aim at offering improved customer services, which can help increase profit. Enhanced customer experience is the primary goal of most companies. Other goals include better target marketing, cost reduction, and improved efficiency of existing processes.
Big data technologies help companies store large volumes of data while enabling significant cost benefits. Such technologies include cloud-based analytics and Hadoop. They help businesses analyze information and improve decision-making. Furthermore, data breaches pose the need for enhanced security, which technology application can solve.
Big data has the potential to bring social and economic benefits to businesses. Therefore, several government agencies have formulated policies for promoting the development of big data.
Over the years, big data analytics has evolved with the adoption of agile technologies and the increase of focus on advanced analytics. There is no single technology that encompasses big data analytics. Several technologies work together to help companies procure optimum value from the information. Among them are machine learning, artificial intelligence, quantum computing, Hadoop, in-memory analytics, and predictive analytics. These technology trends are likely to spur the demand for big data analytics over the forecast period.
Earlier, big data was mainly deployed by businesses that could afford the technologies and channels used to gather and analyze data. Nowadays, both large and small business enterprises are increasingly relying on big data for intelligent business insights. Thereby, they boost the demand for big data.
How Big Data Is Used in Businesses Across Industries
Financial services, retail, manufacturing, and telecommunication are some of the leading industries using big data solutions. Business owners are increasingly investing in big data solutions to optimize their operations and manage data traffic. Vendors are adopting big data solutions for better supply chain management.
The retail industry gathers a large amount of data through RFID, POS scanners, customer loyalty programs, and so on. The use of big data assists in reducing frauds and enables the timely analysis of inventory.
A large amount of data generated in this industry remains untapped. The industry faces several challenges, such as labor constraints, complex supply chains, and equipment breakdown. The use of big data enables companies to discover new ways to save costs and improve product quality.
Oil and Gas
In the oil and gas sector, big data facilitates decision-making. Companies can make better decisions regarding the location of wells through an in-depth analysis of geometry. Agencies also leverage big data to ensure that their safety measures are up to the mark.
Companies have started to take greater advantage of big data. Taking into account the benefits of big data for business, they turn to analytics and other technologies for managing data efficiently.
Recommendations for CIOS
An unprecedented variety of data arising from a huge number of various transactions and interactions provides an excellent fundamental basis for a business to refine forecasts, assess product development prospects and entire directions, better control costs, evaluate performance - the list is easy to continue for as long as you like. On the other hand, big data poses challenging tasks for any IT department, experts wrote 2020vp.com in 2011. Not only are they of a fundamentally new nature, when solving them, it is important to take into account the budgetary restrictions on capital and current costs.
The CIO who intends to benefit from large structured and unstructured data should be guided by the following technical considerations:
Divide and rule
Moving and integrating data is necessary, but both approaches increase the capital and operating costs of information extraction, transformation and loading (ETL) tools. Therefore, you should not neglect standard relational environments, such as Oracle, and analytical data warehouses, such as Teradata.
Compression and deduplication
Both technologies have gone far ahead, for example, multilevel compression allows you to reduce the amount of raw data by tens of times. However, it is always worth remembering what part of the compressed data may require recovery, and already starting from each specific situation, decide on the use of the same compression.
Not all data is the same
Depending on the specific situation, the range of queries for business intelligence varies widely. Often, to get the necessary information, it is enough to get an answer to the SQL query, but there are also deep analytical queries that require the use of tools endowed with business intelligence and have a full range of dashboard and visualization capabilities. To prevent a sharp increase in operating costs, you need to carefully approach a balanced list of required proprietary technologies in combination with Apache Hadoop open source software.
Scaling and manageability
Organizations are forced to solve the problem of heterogeneity of databases and analytical environments, and in this regard, the ability to scale horizontally and vertically is of fundamental importance. Actually, just the ease of horizontal scaling has become one of the main reasons for the rapid spread of Hadoop. Especially in light of the possibility of parallel processing of information on clusters from ordinary servers (does not require highly specialized skills from employees) and thus saving investment in IT resources.
Asymmetry as the Foundation of the Data-driven Economy
A fundamental point of differentiation of the data-driven economic model from the knowledge-based economy model from which it emerged lies in the assumption that knowledge is implicitly accessible by all, even if it is temporarily excludable by innovating firms. This does not appear to be true of the information extracted from “big data.”1 To the human mind, big data is meaningless noise; to computers, it is an information mine. It is precisely the ability of computers to extract systematic information out of this noise that underpins the value proposition of big data and the algorithms built on it. Accordingly, information asymmetry between human and machine is at the foundation of the data-driven economy and makes it prone to market failure. Given the significant capital investments required to exploit big data, information asymmetry also applies across firms. Given the digital divide, it applies across countries as well. Information asymmetry and the market failure to which it tends to give rise are fundamental to the sources of economic gains opened by the data-driven economy — they constitute, in this sense, its original sin.
The advent of big data is already allowing for better measurement of economic effects and outcomes and is enabling novel research designs across a range of topics. Over time, these data are likely to affect the types of questions economists pose, by allowing for more focus on population variation and the analysis of a broader range of economic activities and interactions. We also expect economists to increasingly adopt the large-data statistical methods that have been developed in neighboring fields and that often may complement traditional econometric techniques.
These data opportunities also raise some important challenges. Perhaps the primary one is developing methods for researchers to access and explore data in ways that respect privacy and confidentiality concerns. This is a major issue in working with both government administrative data and private sector firms. Other challenges include developing the appropriate data management and programming capabilities, as well as designing creative and scalable approaches to summarize, describe, and analyze large-scale and relatively unstructured data sets. These challenges notwithstanding, the next few decades are likely to be a very exciting time for economic research.
The big data market is expected to witness remarkable growth over the forecast years. An important reason is a rapid increase in the amount of structured and unstructured data.
Among other factors are increased technology penetration in all spheres of life and the spread of smartphones. That lead to the generation of larger amounts of data.
The escalating need for analyzing data will lead to the rise of demand for big data over the forecast period. Furthermore, the number of online businesses in the industry is also growing, owing to enhanced profit margins.
Other industries, such as healthcare, utilities, and banking, will widely use online platforms to provide improved services to customers.
All the factors mentioned above are expected to contribute to the global big data market growth.
Mónica is a writer, art historian, and editor at LeadsMarket, a personal loan leads website. She specializes in Art History, Art Conservation, History, Literature, Finance, Tech, Wellness, and Travel. In her free time, she’s usually roaming the halls of the museum or the local bookstore surrounded by stacks of books.
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