Importance of Dig Data in the Modern Day

Abstract

In the current world of emerging technologies data plays a major role in enhancing products nature and reach to its customers. Information is essential in contributing to that factor. The paper primarily establishes the concepts of big data and importance of big data in the current day that helps in extracting useful information. Furthermore, it goes on to cover the impacts of big data, challenges and how companies handle them and finally concludes the paper.

Big Data

 In the ever-changing, globalized economy, companies have started depending on assessments advanced by their internal processes, business operations, and customers to explore new opportunities for advancement and growth. Such insights present massive intricate set of data which are produced then managed, analyzed, as well as manipulated by experts (Navant Partners, 2012). The collation of such large amount of data is collectively referred to as big data. The numerous peta- and terabytes are presently considered as the big data benchmark. The main characteristics which describe the big data include; volume which refers to the amount of produced and stored data which determines the potential and the value of the insight and if it can truly be assumed as big data or not (Authors, 2012).

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 Secondly, the variety referring to the nature and type of the data to assist the data analyst to successfully use the subsequent insights; Velocity refers to the speed that determines how the data is produced and processes to satisfy the demands as well as challenges that define growth and development. Additionally, the variability refers to the inconsistency found in the data sets that affects processes to manage it (Navant Partners, 2012). The last characteristic is veracity which refers to variances in the quality, integrity, and trustworthiness of the captured data; it affects correct analysis and decision making.

 Data analysts use advanced tools like algorithms and analytics to process the data to determine meaningful information. The data is collected from either internal or external digital and traditional sources since they are good sources for continuous discovery and analysis of data. Such sources can include publicly available data, streaming data, and social media data.

Importance of Big Data

 Companies use big data to adopt new strategies to enhance decision-making, increase performance, and explore opportunities. It allows companies to aggregate data across unconnected systems to enhance decision-making capabilities (Navant Partners, 2012). It can also supplement data warehouse solutions by acting as a safeguard to eliminate rarely accessed and old data or to process new data for addition into the data warehouse. It also gives companies enhanced visibility into their operational concerns hence leading to their improvement (Authors, 2012). Additionally, operational data largely relies on machine data which can consist of GPS devices, meters, or sensors. Big data help analyze client’s decision making processes by letting firms track and evaluate shopping patterns, feedbacks, purchasing behavior among other factors that affect sales.

 Besides, big data assists in fraud detection and cyber-security since the access to real-time data allows companies to improve intelligence and security analysis. The complex data streams which are produced due to the growth in transactions and devices acts as a good competitive advantage and a valuable asset to facilitate decision making and problem-solving (Navant Partners, 2012).  The companies use such a mix of multi-structured and unstructured data that consists of high volume of information. The insights from big data and high-powered analytics help in cost reduction, new product development, smart decision-making, and time reductions. Big data is relied in all sectors for instance, banking, education, government, health care, manufacturing, and retail to provide superior services and products (Authors, 2012).

Application of big data in business environment

Big data involves large quantities of information developed by digitization of the aspects that are consolidated and analyzed through certain technologies (Eastaff, 2016). It has changed how data is analyzed, managed, and leveraged in any organization. One of the areas where the big data has been applied successfully to make significant changes is in healthcare institutions. Healthcare analytics have the capability of reducing treatment costs, predicting epidemic outbreaks, avoiding preventable diseases, and improving the general quality of life. The average lifespan of humans is currently increasing among the world population, thus posing new challenges to the current delivery methods of treatment. Health professionals like any other entrepreneurs in business have the capability of gathering a large amount of data and looking for the best strategies of applying.  In such a case, they have applied big data analytics to ensure successful progress. The big data applied in healthcare is associated with several benefits, obstacles, importance, and future application.

Due to the application of big data analytics, the models of treatment have changed since the data drive most of the changes. Physicians need to understand every aspect of the patient as they can for them to identify some warning symptoms of severe diseases as they emerge. Treatment of disease as soon as it develops is considered to be less expensive and simple. According to healthcare data analytics, to prevent a condition is considered to be much better than to cure it. In such a case managing to take a patient’s comprehensive picture enables the insurances to offer a tailored package to the patient. This is one of the methods that the healthcare industry applies to attempt to handle the severe issues associated with patient data. Patient’s information is gathered little by little as it is archived in healthcare facilities. This aspect has reduced the time and cost associated with data collection compared to the time and expenses applied before the emergence of big data analytics in the healthcare sector.

One of the barriers facing the application of big data in the healthcare sector is the spread of the data across several sources that are under the control of various administrative departments, hospitals, and states. Integration of such data requires the development of a new infrastructure in which every data provider collaborates (Koppad & Kumar, 2016). Failure to implement the new infrastructure becomes a significant challenge to the use of big data in healthcare.

Big data in the healthcare sector is critical due to the aspect of increasing cost in countries such as the United States. As per the report by McKinsey, after over 20 years of a steady increase in costs, the expenses of healthcare are currently 17.6% of GDP which is equivalent to $600 billion over the expected the United States’ benchmarked size and wealth (Sun & Reddy, 2013). This implies that the costs are significantly higher than how they are supposed to be since they have been increasing for over 20 years. Due to such a circumstance, a smart data-driven strategy is necessary for the healthcare sector. Moreover, since the current incentives change on a regular basis, several insurance companies are gradually shifting from fee-for-service plans to patient’s outcome prioritization plans. Fee-for-service plan rewards by use of expensive which is sometimes unnecessary treatments that care for a large number of patients quickly. In such a case, big data analytics should be applied.

Healthcare sector is intending to catch up with other industries that apply big data analytics. Currently, it has shown good progress in the use of electronic health record (EHRs), especially in the United States. The fact that the healthcare sector implemented the adoption of the EHRs until they become successful indicates that the application of big data analytics will also be successful if the effort applied in EHRs will be applied in the big data. There are various big data applications in the healthcare sector that indicates that the strategy will be much more successful in the future. The first application is the predictions of patients for improved staffing (Belle & Najarian, 2015). The second application is the use of electronic health records. The next application is the use of real-time alerting. According to the objectives set by the healthcare sector, big data is expected to cure cancer, which is one of the diseases that have been problematic to find treatments. Such an application indicates that the healthcare sector has a good future in the use of big data.

Big data and IoT are related since billions of internet-connected ‘things’ generate large volume of data. The high-value data is then easily accessible in real time and has bigger footprints which affect the company or its customer base (Atkinson, 2014). Such data can create meaningful change after proper analysis as well as follow-up action. IoT makes products to be smarter especially with the integration of internet-enabled sensors and chips to enhance their computing capacity. The sensors and chips are used for data gathering which give meaningful insights on machine operations and customers’ habits. The IoT leads to a huge increase in data that organizations can manage hence, IoT and big data intersect. Companies are investing in data gathering technologies to analyze the modular streams of data which flows from such embedded sensors as well as social media to analyze trends and patterns (Navint Partners, 2012). IoT has led to an incredible explosion of data; hence, gathering, analyzing, and acting on the information will help organizations prosper.

big data has been pivotal to understanding customer trends, and the consequent development of marketing initiatives based on the big data analysis results (Lee, 2017). Collecting information related to customer behavior in various facilities presents the companies with the opportunity to determine trends in the market and thereby make decisions based on observed changes in consumption behavior. Ultimately, this gives the company a greater advantage at marketing their products, with much more specificity than they would have were they not dependent on big data analysis; a modern form of big data.

Secondly, the modern concept of big data has led to more precise decisions by organizations in policy development and other decision-making processes. In particular, the modern data warehouse provides easy access to information over an extended period, and with a broad range of characteristics (Yoon, Hoogduin, & Zhang, 2015). If a firm is capable of accessing not only the financial data of a particular period, but also potential customer interests based on their efforts to seek out information about a particular product, then the management would have much ease making clear decisions on production processes. It would be easy to make decisions confidently, considering that the customers would have specific interest in particular goods. Ultimately, prototypes introduced in the market from such kind of decisions are bound to perform better than those introduced without an analysis as extensive as one conducted using big data analysis.

Big Data and Data Warehousing

Due to their differences in activity, databases configured for online transaction processing (OLTP) are structurally different from data warehouse. The workload for the two systems is different, the data warehouse which has a greater workload and it is structural designed to accommodate data analysis and ad-hoc queries. This means the data warehouse structure is optimized for query and analytical related operations. OLTP has a smaller work load therefore it is optimized to facilitate specific online processing transactions (Singh, 1998). The schema design of the two different databases is also different. Data warehouse has partially de-normalized schemas, which allow for optimized query and analysis activity. While the online transaction databases have normalized full schemas optimized for consistent performance such as update, insert or delete.

The difference in the structure allows for different support activities by the databases.  The typical operations facilitated by a data warehouse include an allowance of querying a lot of data rows at a single click. Thousands and millions of data rows can be sieved and queried using a data warehouse database system. The data can also be used to make critical analysis and develop specific reports. An online processing system cannot allow such kind of operation; its operations on getting data are limited. Similarly the capacity of the two systems to hold historical data is different. With a data warehouse storage and access of data history can be available for many years or months; this data can be useful for making analysis and reports.  The Online Processing system supports data storage for a limited period of time in terms of hours or days. The basis of the historical data in an OLTP is for functional use, in case of retrieving current transactions; hence the needs for data in OLTP are short term and not long term (Cui & Mei, 2013).

Operational data and decision support data are similar because they both store data; however decision support data summarizes business transaction while Operation data provides a daily account of the business activities, therefore it is more detailed. One of difference in requirement is the data structure of the two, for operational data it needs to be detailed in tables showing normalized daily transaction, however decision support data structure, captures critical points in time, which may be useful for decision making for example end of the month sales, as compared to daily sales. Time span is another critical differing criterion, Operation data shows current daily transaction data, while decision data can have the current data, historical data and even projected data all for comparison and decision making. Data summarization is a necessary requirement for decision support data, while operation data is expected to be in as much detail as possible. Operations done in decision support data is only loading and accessing, while in operations data, data is manipulated, adjusted, entered, updated and changed online.

When a summary of information is required out of a lot of information or data received a decision support system may be used.  For example, in case of analyzing an organization performance, after a financial year, a decision Support system can have data indicating monthly performance for the 12 months and the yearly performance. This summary is easier and faster to understand as compared to daily performance. The second use of databases in decision support is when you need to compare data from the past and the current data. Using a database management can obtain historical data on the organization performance from the past 10 years for example, to obtain trends. These trends can be able to help the organization gauge their performance as well as compared to past years. Third use is availability of accurate and up-to-date information about the organization, which is easily accessible online. For example, decision makers can access data about their employees in terms of age, performance, education level, current position and salaries even within a huge organization, with a click of a button. Employees can automatically upload their details for easy access to their employers; this will always ensure the data is accurate and up-to-date.

A store which uses a data warehouse to store daily transactions can use data mining techniques to estimate buying patterns and trends of their customers. These trends can advise them on popular products and brands, which the store can buy more of, to maximize profits, products which are less popular can also be stocked less, to minimize on loss. For production and manufacturing companies, looking at historical data stored in their data warehouse and data mining, can leading to production trends as compared to product consumption. By comparing the two, the firm can be able to work at optimum levels where they avoid over production or underproduction. This will help save the firm money by working at optimum levels (Hand, Mannila, & Smyth, 2001).

Marketers rely on data from various data warehouse even competitors to predict the markets. They use the data to cluster and create market segments, this are people who have similar characteristic thus require similar products. By obtaining marketing intelligence from the competitors in terms of data, they can be able to compare with their own to compare performances.

Challenges

Irrespective of the positive attitude towards big data, concerns often emerge based on issues such as consumer data security in the case of e-commerce organizations that invest in the infrastructure. In particular, one of the primary drivers of big data has often been the ease of access provided by internet access to the warehouses (Chen &Frolick, 2000). However, this driver presents the primary challenge of the concept as well, especially considering the rise in cases of unauthorized access to information databases for various organizations. Consequently, the security of the consumer data on the data warehouses presents a significant challenge in development of the concept. Various firms may have put up extensive security features, but this approach seldom acts as an ultimate solution to the ever-evolving cases of cyber insecurity.

Nonetheless, such challenges do not imply ultimate doom to success of the data warehouse concept, particularly considering its contribution to the modern economy. In particular, the security measures in various firms may not be perfect in ensuring data security, but they offer proper control and act as a sufficient deterrent to any potential cases of unauthorized access to the companies’ databases. Notably, collaboration between different organizations and involvement of governments in ensuring the safety of the cyber space is one of the primary strengths that guarantees a high chance of success in ensuring that cases of attacks are appropriately avoided. Such levels of cooperation ensure that there are no repeat or duplicate cases of attacks on different organizations due to coordination of oversight activities across various firms.

According to expert opinions, data warehouses will continue to extensively influence the data management sector in world economy (Satel, 2014). John L. Myers of BI Enterprise Management, notes that the data warehouses may experience some form of competition from more modern architecture, but they will continue providing the basic frameworks on which the developments are made. On the other hand, David Gorbert of Mark Logic predicts that the future of data warehouses involves more integration of the data into daily activities of various firms. Consequently, this position reflects a possible increase in ease of access of the data.

Conclusion

In conclusion, the development of the big data concept came as a result of the development of the cyber space and the computer technology. Having a large amount of data based on which the computers store information provides an opportunity for proper decision-making, in response to the needs of the users. In turn, the said user would need to access the data with as much ease as possible, which would in turn led to constant efforts to develop the data warehouse concept to facilitate such improvements. Therefore, the concept of data warehouse is just as necessary as the computer technology is to society. Developments are a necessity, but complete eradication of the systems is far from possible.

References

1. Atkinson, G., & Karimi, K. (2014). What the Internet of Things (IoT) Needs to Become a Reality. Arm Freescale white paper n INTOTHNGSWP Rev, 2. Retrieved from               https://goo.gl/IU6Ors
2. Authors, C. (2012). Big data spectrum. Infosys Limited, Bangalore, India.
3. Belle, A., & Najarian, K. (2015). Big data analytics in healthcare. BioMed research international, 2015.
4. Cui, B., & Mei, H. (2013). Big data: the driver for innovation in databases. Oxford Journals, Pp. 27-30.
5. Eastaff, M. M. S. (2016). Application of Big Data Analytics in Health Care. International Journal of Engineering Research and Applications, 6(12), 01-04.
6. Hand, D. J., Mannila, H., & Smyth, P. (2001). Principles of Data Mining. New York: MIT press.
7. Koppad, S. H., & Kumar, A. (2016, March). Application of big data analytics in the healthcare system to predict COPD. In Circuit, Power and Computing Technologies (ICCPCT), 2016 International Conference on (pp. 1-5). IEEE.
8. Navint Partners (2012). Why is BIG Data Important? A Navint Partners White Paper. Retrieved from https://goo.gl/wSDK4J
9. Singh, H. (1998). Data Warehousing: Concepts, Technologies, Implementations, and Management. New York: Prentice Hall.
10. Sun, J., & Reddy, C. K. (2013, August). Big data analytics for healthcare. In Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 1525-1525). ACM