Wait a second!
More handpicked essays just for you.
More handpicked essays just for you.
Importance of big data essay
Importance of big data essay
Advantages of big data
Don’t take our word for it - see why 10 million students trust us with their essay needs.
Recommended: Importance of big data essay
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.
Although big data promises better margin’s, more revenue and improvised operations it also brings new challenges to the It infrastructure which is “extreme data management” .At the same time these companies should also need to look at workload automation and make sure it is robust enough to make to handle the needs that the big data is associated to as well as the needs of the business intelligence it there to serve.
File transfers requires to be scheduled for data to be moved to a central database or data warehouse. This by far involves an Extract, Transform and Load (ETL) workflow as data is usually gathered from different types of database. Once the data is brought to a central database, to determine patterns in the data queries are scheduled from a variety of users using various applications. The frequency of the queries varies from business to business – it can be continuous, once a day or hourly. And of course, as data gets added to the database and moved to new databases, there is the routine task of database management that needs to occur.
Now we can say that an enterprise data warehouse could be used to manage the big data and the extreme workloads but we would find that often it is more efficient to preprocess the data before storing it in the warehouse. Let’s consider an example even data from hardware sensors have a lar...
... middle of paper ...
...ns. Thus optimized systems to meet the challenges of extreme workloads are required by organizations because of all this. To offer packaged hardware and software solutions that are optimized for analytical processing has been the response given by the industries.
One more important factor is to satisfy the business agility requirement which is an important factor in supporting analytical processing. In today’s business environment which is fast paced decisions required to be made faster and this is very important and plays a key role for the business success. For example in a fraud detection application the real time action is required. The ability to make decisions in real time to be more precise in few seconds or minutes rather than hours or days can be very important both financially as well as competitively although this is not true in case of all companies.
Likewise, being analytical is critical in this field, because it will help me during the job when I have to manage the warehouse or improve the production
Big Data is a term used to refer to extremely large and complex data sets that have grown beyond the ability to manage and analyse them with traditional data processing tools. However, Big Data contains a lot of valuable information which if extracted successfully, it will help a lot for business, scientific research, to predict the upcoming epidemic and even determining traffic conditions in real time. Therefore, these data must be collected, organized, storage, search, sharing in a different way than usual. In this article, invite you and learn about Big Data, methods people use to exploit it and how it helps our life.
Ans: Data integration consist of three processes: data access( accessing and extracting data from various sources), data federation ( combining different views of data) and capturing change. All these three process when correctly implemented provides data to ETL tool, which in turn apply its three processes and makes data available to the analysis tool of the data warehouse.
Attracting focus from firms in all industries, Big Data offers many benefits to those companies with the ability to harness its full potential. Firms using small data derive all of the data’s worth from its primary use, the purpose for which the data was initially collected. With Big Data, “data’s value shifts from its primary use towards its potential future uses” (Mayer-Schonberger & Cukier, 2013, p.99) thus leading to considerable increases in business efficiency. Employing Big Data analytics allows firms to increase their innovative capacity, and realize substantial cost reductions and time reductions. Moreover, Big Data techniques can be applied to support internal business decisions by identifying complex relationships within data. However, it is also important to recognize that much of Big Data’s value is “largely predicated on the public’s continued willingness to give data about themselves freely” (Brough, n.d., para. 11). As previously discussed, much of the content of Big Data is unstructured data from social media sites etc., and so if such data were to no longer be publically available due to regulation etc. the value of Big Data would be significantly diminished.
If auditors can look at a complete population, they may not have a great defense if they missed a “smoking gun” since they looked at all the data (Alles and Glen). However, this data may not be valid which raises the importance of the auditor understanding where the data came from and how reliable it is. Not only this, it will be interesting to see how standards consider big data evidence. While it most likely will not be as reliable as confirmations, it would be a challenge to figure out how much the auditors could rely on it. Furthermore, higher education would most likely play a role in helping their graduates understand data and how to use technology to be not only more efficient but also ensure they are able to use sound professional judgement while using big data.
The key strategy implementation efforts at Amazon all surround the use of “big data”. Big data is the growth and availability of large volumes of structured/unstructured data. The use of big data has allowed decision making based upon data and analysis instead of past experience and intuition. Big data has directed organizational change in allowing Amazon to expand from an online book store to an internet giant. Revolutionary application of big data has allowed Amazon to create superior service quality while motivating employees by providing real time information to solve customer issues. Big data has strengthened Amazon’s competitive capabilities by pioneering the application of big data and charging a monthly fee to smaller businesses
System performance is one of the most critical issues faced by companies dealing with vast amounts of data. Companies use database systems and their applications to store, retrieve and handle this data.
In today’s society, technology has become more advanced than the human’s mind. Companies want to make sure that their information systems stay up-to-date with the rapidly growing technology. It is very important to senior-level executives and board of directions of companies that their systems can produce the right and best information for their company to result in a greater outcome and new organizational capabilities. Big data and data analytics are one of those important factors that contribute to a successful company and their updated software and information systems.
First of all, business intelligence analysis requires the capturing of information and storing in a single location for effective data analysis. Currently, data analysis is supported by transactional systems, business specific data marts, and other ad-hoc processes. Information is distributed making it difficult and time-consuming to access. Business teams have adapted to this environment by creating user maintained databases and manual “work-arounds” to support new types of reporting and analysis. This has resulted in inconsistent data, redundant data storage, significant resource use for maintenance, and inefficient response to changing business needs.
In business, making good decisions requires the effective use of information. Business Decision Making provides the opportunity of learning a variety of sources and develops techniques in four aspects of information: data gathering, data storage, tools available to create useful information and presenting. Moreover, using appropriate IT software and spreadsheets for data analysis and the preparation of information provides the advantages of using information systems which is currently used at all levels in every organization.
While operational databases maintain state information, data warehouses typically maintain historical information. Although there are several forms of schema, e.g., star schema and snowflake schema, in the design of a data warehouse, the fact tables and dimension tables are its essential components. Users typically view the fact tables as multidimensional data cubes. The attributes of a dimension table may be organized as one or more concept hierarchies.
Data encryption refers to the process of transforming electronic information into a scrambled form that can only be read by someone who knows how to translate the code. In nowadays business world, it’s the easiest and most practical way to secure the information that we stored and processed, and it’s significant for our sensitive information. For example, as electronic commerce is popular now, the vendors and retailers must protect the customers’ personal information from hackers or competitors. They also have many business files or contracts that need to be strictly protected. Without data encryption, these important information may fall into wrong hands and be misused by others. Besides, data encryption may be used to secure sensitive information that exists on company networks, or create digital signatures, and help to authorize in business. No one should underestimate the importance of encryption. A little mistake in encryption may make sensitive information revealing, or even result in illegal and criminal accuse.
For the past couple of decades the majority of businesses have wanted to construct a data-driven organization or company. Furthermore, companies around the world are considering harnessing data as a basis of competitive advantage over other companies. As a result, business intelligence and data science use are popular in many organizations today. The increase in adoption of these data systems is in response to the heavy rise in communications abilities the world over. Which, in turn ,has increased the need for data products. Indeed, the Data Scientist profession is emerging to be one of the better-paying professions due to the urgent need of their labor. This paper is going to discuss what business intelligence is all about and explain data science that is usually confused to be similar to business intelligence. I will tackle a brief overview of data scientists and their role in organizations.
Big data is a concept that has been misunderstood therefore I will be writing this paper with the intentions of thoroughly discussing this technological concept and all its dimensions with regard to what constitutes big data and how the term came about. The rapid innovations in Information Technology have brought about the realisation of big data. The concept of big data is complex and has different connotations but I intend to clarify its functions. Big data refers to the concept of a collection of large and complex amounts of data that are found extremely difficult to notate or even process by most on-hand devices and database technologies.
Data center, in the context of big data, is not only for data storage but it plays significant role to acquire, manage and organize the big data. Big data has uncompromising requirement for storage and processing capacity. Hence the data center development should be the focus for effective and rapid processing capacity. With the increasing scale of data centers, the operational cost should be reduced for the development of data centers. Today’s data centers are application-centric, powering the many business applications, standalone websites and e-commerce offerings on the web. Tomorrow’s data centers need to be data centric: storage and infrastructure capacity must be expanded to support IoT/Big Data-generated information. This also affects future bandwidth in data centers as resources will be mostly consumed by IoT sensors and machines, as opposed to user activity and behaviour.