Case Study Of NIT6130 Introduction To Research: Big Data In Business Innovation

Data Collection

Big Data in industries refers to large amount of data collection from web browsing data trails, social network communications, and surveillance data from cloud computing. Big data involves three common factors; High-volumes of data which is got when the big data tracks and observes what happens from a number of sources, Velocity which results from streaming data in high speed compared with time given Variety, it allows data to come in all formats, numerals, structured data, unstructured data, audio, video, emails. We see Big Data in businesses being a multi-billion-dollar industry. Big Data is currently a huge battleground in providing the customers with a superior experience as data is always used in estimating the business success. Companies are able to know about their customers, the technologies together with identifying what systems requires some improvements. Finally, this information is usually brought together with the aid of the web data extraction, the customers and the company gets to benefit from it. On the other side it has some overwhelming nature and they get to be solved with the aid of the technologies in Big Data. Some of the case studies of big data in industries innovation are;

Bank of America: it offers the cash-back offers so as to debit and credit the card Basis: it helps incorporate health changes over time. Lend up: helps in the approval of loans based on the interaction between the user and the website Norfolk Southern: what it does is deploy software that are customized so as to monitor the rail traffic Sears: it reduces time needed to launch the major marketing campaigns. Sprint: it does do improve the quality and the customer experience. Walmart: it does do produces relevant search results by use of machine learning and synonym mining. We are then going to carry out some research on the big data in businesses and list out our findings and conclusions.

This will allow us to be able to make research on questions, to test hypotheses together with evaluating the outcomes. Data source helps in gathering all the technical information we will need to access the data. We have two types of data sources one is the file data source, and two is the machine data source. In our case we will use the file data source as our data will be located on the same computer as the program allowing us to connect to the information and use it repeatedly.

1.1.2. The Data Storage

From the research on the problems, the design experiments, and the sources available I concluded that I would use the file data source method. This data is from all categories of people mainly the managers mostly from all companies in context and the critical employees within data management.

First and foremost we need to know that the big data in business innovation mostly tend to use the big data business innovation program. A program that provides one with the vital tools for gaining a vast strategic view of one’s own data plus the data of others that is other companies. On collection, we start by setting up a team that is around the big data project, and it should entail the business developers, the data analysts plus the technical developers.

This subsection entails the raw data the team collected at the end of the project. The data in the file data source I compiled.

There are factors that we must consider when it comes to implementation; we will start slow. We start off with a proof of concept or rather the pilot project. We identify where to improve the decision making. We then prove our findings valuable and feasible from our business model. We make a good selection of the initial project. We use the right skills and data.

Data pre-processing comes out as a very crucial step as not everybody that gets to participate in the survey and also even if they do participate they do not give one all the answers to the setup questions. This involves data mines. This lists out the processes to perform on our raw data for the next processing procedure. It is a preliminary data mining practice that refines our raw data from the surveys to give a new record.

We then get to select the characteristics from our result then reduce the random data’s number.

Table 3 REDUCTION OF DATA

Whenever we have interaction of physical world then the design should be of the high degree of safety, verification is then done later with redesign problems that may follow. Due to the physical interaction to the world then I will use the hybrid methodology for the design. It involves qualitative and quantitative approaches. I collected the data, I analyzed the data and then made a questionnaire.

This subsection follows after we finish the survey hence essential to list down in terms of records.

Table 5 SURVEY PARTICIPATION PERCENTAGES

We are going to determine our findings and the presentations as we have completed our data collection. Analysis at this point is very vital as it helps us make the work more presentable to the readers. We also have the results that will help the reader understand the discussion.

We have a significant number of industries making use of big data and 92% of executives and the managers are contented with the outcomes. At 89% we have the rating of the big data innovations as a very crucial thing in companies. Researchers have also found out that 89% of those who have deployed the big data projects usually see it as a way of revolutionizing operations of a business and quite a big percentage say that big data in companies tend to change the way business is done positively. Research has shown that 84% of business people believe that big data analytics affects positively the competitive landscape for companies and organizations within a year time. 89% hold to it that the lack of implementing big data in businesses will lead to loss of market share.  From the look of things then companies are willing to invest in the big data innovation hugely. For instance, the IDC firm plans to spend $187 billion on Big Data together with analytics technology by the time we are hitting 2019 and point to note is that this is an increase of 50% from the 2015 investment which was $122 billion. Respondents say that Big Data has led to a drastic change in business practices in terms of sales together with the marketing functions.

Implementation and Design

From the research, we can conclude that big data improves the business’s value mainly by lowering the operations cost and the investments cost. We also have technologies storing vast quantities of data at a fraction of the cost of the data warehouses, but for those running on commodity hardware, for instance, we have the Apache Hadoop. Big data technology changes businesses positively and helps the business leaders to be able to make a data-driven decision. Big data involves three common factors; High-volumes of data which is got when the big data tracks and observes what happens from many sources. Velocity which results from streaming data in high speed compared with the time given. Variety, it allows data to come in all formats, numerals, structured data, unstructured data, audio, video, emails.

In conclusion, big data help in accomplishing; re-development of products, making dialogues with the customers, performing the risk analysis, keeping the data safe, customization of websites in real time, creation of new revenue streams, providing tailored healthcare, reduction of the maintenance cost, making of the cities in terms of being smart and giving enterprise-wide insights. We see also the use of Big Data being of benefit to companies in outperforming their peers. Big data is generally used to make business processes optimizations. This because the retailers can perform optimization to their stock that is grounded on predictive models from data from social media together with the web search trends.

References

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