Big Data Technology, Growth, Innovation, and Application

Big data technology has been expanding fast and increasing past the unimaginable heights in the last decade. Big data technology is defined in terms of quality, velocity and size, every dimension increasing growing.  The last decade, the largest data space was inform of terabytes. Nowadays, we have petabytes and still fast expanding to zettabytes. Companies are handling massive amount of data in their capacities making it central operation. For instance, Google generated over 54 billion US dollars to the American economy in the year 2009. These significant contributions to the economy show the power of data. As the amount of data available in an organizations is exceeding the amount fit can handle, this has created the emergence of companies specifically for handling big data.

Image result for BIG DATA
Tutor Victor

With the explosion of big data technology and internet usage, data use and application is becoming a crucial part in every company and business. Data size is determined by various parameters, which include its collection procedure, analysis and storage. These factors define the size of the data and determine various technology that it in applies.  In that note, big data is characterized on the above factors and specifically the volume. It is also considered on the form and velocity in which it is analyzed. With data size and usage increasing every day in companies and businesses, it is important to analyze the applicable technology, and the effects it has on business.

Big Data Technology description

There is threshold under which data can be classified as big data. The level is changing with the technology as the data volume pushes the data technology to its limits. In the last decade or two, big data was considered to be in form terabytes. However, this is changing as the capacity of various data from the companies is pushing way behold terabytes. Huge data sizes led to the emergence of the data warehousing to provide the specific function like data storage and analysis for companies and businesses.

Big data technology considers three important aspect that include quality, velocity and variety. For data to be described as big, it should at least meet any of these elements of even all of the elements at times. With the innovation of the internet, data sizes are rising to high volumes never experienced before. These mega data Are crucial for companies in decision amki9ng and require the specific technology for storage and analysis.as the data size increases, the technology to handle it also evolves to be more innovative and to handle the increasing demand. Data technology is changing especially in data sources and data analytics (Davenport, & Dyché, 2013).

The main technology behind generating big data is the internet.as companies seek to capture every activity online and use it for their purposes, these data accumulated to be big data and requires necessary technology to handle it .  The emergence of social media is also the technology behind generation of big data/ face book and twitter have millions of followers’ activities generating mega data that companies need to target their marketing and advance their business. Other technologies in the data sources include the communication industry. Data from the call, messaging and GPS generate huge amounts of data (Davenport, & Dyché, 2013)..

Data analytics is another field that requires technology. Stored data can be of much use to a company and a business unless it is analyzed for it to have value be usable in decision-making and other procedures. Data analytics therefore is a significant part of big data that makes it usable. There are variations variations of data analytics that apply different technologies. Descriptive data includes a detailed description of the data trends focusing on the occurrences. The type of analytics is important for companies in forecasting future sales and scorecards.

The other type of analytic is the predictive analytics. It applies different technologies as well. The main use of this data is to provide companies and with a view of what will happen in future. This type of data is useful in marketing purposes as the company can use to align its structures with what will happen in the future. The difference between the two analytic types is that predictive data suggests what will happen while descriptive type analysis suggests what is to be done. The other type of data analytics is exploratory analytics. This analytic type gives a relationship of the data. this is useful for companies as it creates opportunities for companies. These data analytic types are important to analyze as they provide an insight of various technology used on big data (Pease, n.d). 

The most common form of technology that is quite advancing fast since it was invented is data warehousing. Data warehouse technology is important for data storage and the extraction. It forma platform where data can be extracted for the source though the use of systems like operating systems and ERP’s. Before data is loaded to a warehouse, it is first transformed and integrated to storable formats. The data that is stored in the warehouse can be considered to be clean that the original raw data. The main function odd data ware is storage and analysis of data. This technology involves databases that make it accessibility and retrieval easy. The major companies that offer data warehousing service include IBM oracle and Microsoft( Watson, 2014)..

Data mart appliances are an emerging technology in big data industry and compared to data warehousing. These appliances are integrated in the hardware or software and their role is to offload important data from application in which they are mounted. They can be mounted is various designs according to company’s needs and specifications. They use databases to organize the collected data and form specific functions in data collection.

Analytical Sandboxes are another form of technology in big data. They help is advanced data analytic processes by defining the priority in which data can analyzed depending on its importance. However, they operate independently to manage data analysis systems. Sandboxes are classified as virtual and real and depend on the technology and the software that they Use.

Databases are key and central in big data. Although they are different in technology, they serve the same purpose of analyzing, sorting, regulating data among others. Database use different technologies through which they perform their functions. In-database analytics use the SAS software, which performs data analysis in the database without the necessity of moving it first. This ensures that the data is more accurate and data if arranged into a form that in which h it is more usable. Analytic database technology is mainly developed by oracle and Teradata companies.

Columnar databases use columns and rows to analyze data. They therefore make it easy for data entry, update and recording. They are the oldest type of databases but have since been overtaken by advanced technology. However, they still form a crucial part in big data analysis.

Data streaming is and emerging technology in big data. The information is gathered and sent over the internet by devices for processing. This data is composed of various information and must be analyzed to form meaning to businesses and companies that require using it. Data streaming makes use of various sources to gather information. This data may at times comprise of sensitive information like credit6 card details and therefore handled more carefully (Watson, 2014).

Other form of emerging technology in big data is cloud-based services. These services are offered on the internet as an important part of mainstream computing in the today. There is relevant software that is used in these data services. Cloud computing is of different forms and the where the providers provide various platform where the user can upload the data and analyze it using specific software.

Application

Big data has various applications in business, organizations, governments and individuals. it has provided an investment opportunity for various companies who deal specifically with big data services. the companies use this data in their decision making to improve their performances and restructure their operations where necessary.

Social media has data has become very useful for companies targeting online marketing. As more users turn to use these services, companies get richer platform where they can market their products with less costs and better results.

The government collects big data from its agencies and uses the information to leverage its service provision to its citizens. Through this data, the government is able to highlight on various issues that need to be addressed and improved (Wegener & Sinha,2013)..

Social media sites like LinkedIn use the data gathered form its users to create products through the customer preference and skills. Facebook also uses the data from its customers to direct advertisers on the best audience for their audience. Through this, the company generates profits by analyzing the user’s location and preferences.

Medical professionals require from the data collected from various areas to develop effective and specific drugs for use in specific regions and purposes. It is also possible to match the public requirements in provision of services to ensure that the right devices reach the people that need them most. Crime rate can also be identified with areas and enable security personnel to focus their efforts on such specific areas. Data is important in analyzing areas where crime rate is prevalent and high for it to be effectively mitigated

 Search engines are the emerging tools of data collection in the online sites. The record the customers preferences and activities on line and process it to directs specific adverts to this clients. Companies are also making   the use of they collected data and to for location and preference marketing for their products. For instance, an American food company planning to enter the Indian food industry can research the various products it will require to be selling in in the industry by using collected data from their preferences and needs.

Competing technologies

New big data technologies are emerging that compete with the traditional data methods. The cloud computing is replacing the normal computing and eliminating the use of storage services as the companies can back their services online. The software is also state of the art attracting more business and clients to the new form of computing.

Effect on business

Data has become a very important tool in business.  Businesses that make good use of data report better performance and satisfaction in the results. Without the accurate and efficient analyzed data, business cannot currently meet their needs and demands. Information gives a business the competitive advantage form its competitors as they transform it to business value.

Big data is synonymous with innovations. This encourages business models that effectively enable the business to progress. Sites have also emerged that make use of big data in their operations. For instance, dating sites makes use of the data provided by its users effectively match two people with intentions of dating. New social sites like Instagram use uploaded photos to group similar preferences and suggestions to their users. The chats in the Whatsapp site are instances where businesses have innovated to analyze data and provide their users with relevant content depending on their connections (Johnson 2015).

Improvement

Big data requires to be improved in number of ways. First is security. Various technology are gathering user’s data and sending it online, there more sensitive details that are collected.  It has created as platform for criminals and fraudsters who intend to steal from unsuspecting users. There are several instance of crime reported uses through their use of big data. In addition, personal data is captured by unauthorized sources who sell it online without the consent of innocent individuals.

Apart from security the other improvement that big data might require restriction people who are use with the information.  For instance, companies selling drugs may use finding from this data to identify their easy target. Non-adherence companies use this data to provide wrong information like advertisement on lifestyles and substance abuse. Companies require coming with strategies, which can help them collects, and organize data while controlling who has aces to the information.

The data-warehousing platform could be improved to accommodate much data that can be of high values but ends up lost due to poor storage. Data can be classified and analyzed in real time analysis where it is then used in customers’ needs effectively. For instance, due to the information on customer preferences, Google can direct adverts related to that criteria to that manner that relates to a client’s search preferences. Therefore, such is if data is cheap, more businesses will have the access to the inf3oe4mation and it hence reducing competitions.

Conclusion

In conclusion, in order for data to make it purposeful meaning requires to be structured Ana analyzed for the companies and business seek value from the big data. Various technologies are already pushing data from the way the world knew to different levels.  These technologies have formed the sector of innovation for businesses as they compete in the competitive markets. Big data is becoming part of the real operation of the companies but hanks to emerging technologies. These technologies have brought innovations where companies are now outsourcing data services to continue with their real line of operations.

References:

Davenport, T. H., & Dyché, J. (2013). Big data in big companies.International Institute for Analytics, May.

Johnson Jr, M. P. (2015). Data, Analytics and Community-Based Organizations: Transforming Data to Decisions for Community Development.I/S: A Journal of Law and Policy for the Information Society (to appear).

Watson, H. J. (2014). Tutorial: Big data analytics: Concepts, technologies, and applications. Communications of the Association for Information Systems34(1), 1247-1268.

Pease, G. What Exactly Is Predictive Analytics, and Why Is It Useful?.Optimize Your Greatest Asset-Your People: How to Apply Analytics to Big Data to Improve Your Human Capital Investments, 17-28.

Wegener, R., & Sinha, V. (2013). The value of Big Data: How analytics differentiates winners. Bain & Company.