solved Learning Engagement # 2Â What is “big data”? Â What

Learning Engagement # 2 
What is “big data”?  What are the most important drivers of big data? What could happen if companies focus solely on the quantitative (i.e., “numbers”) aspects of big data?  Explain.
PROFESSOR’S GUIDANCE FOR THIS WEEK’S LE:
Big Data is no longer just a buzzword; it is a proven phenomenon and not likely to die away soon. A recent IDC report predicts that the digital universe will be 44 times bigger in 2020 than it was in 2009, totaling a staggering 35 zettabytes.
Two factors have combined to make Big Data especially appealing
now. One is that so many potentially valuable data resources have come into existence. These sources include the telemetry generated by today’s smart devices, the digital footprints left by people who are increasingly living their lives online, and the rich sources of information commercially available from specialized data vendors. Add to this the tremendous wealth of data — structured and unstructured, historical and real-time — that have come to reside in diverse systems across the enterprise, and it is clear that Big Data offers hugely appealing opportunities to those who can unlock its secrets.
The other factor contributing to Big Data’s appeal is the emergence of powerful technologies for effectively exploiting it. IT organizations can now take advantage of tools such as Hadoop, NoSQL, and Gephi to rationalize, analyze and visualize Big Data in ways that enable them to quickly separate the actionable insight from the massive chaff of raw input. As an added bonus, many of these tools are available free under open source licensing. This promises to help keep the cost of Big Data implementation under control.
Post 1
salem
what is big data?
big data from recent releases that have emerged as a result of the recent trends in large
flow process knows the important data at the moment as it is working to produce
huge amount of digital data and data from social media and various puzzles and
tools in which we work or record or store it as many financial managers and providing
high-quality data services, modern organizations are trying to keep and store decision
(power,2016)
big data
big data has been defined as a large variety of big data that has a great deal of diversity and speed generating the need to develop large-scale and fast-moving cell and big data to help make decisions in the possible valuable extraction and knowledge of a wide range of institutions
(Elgendy,n.and elragal,A,2014)
-the most important drivers of big data engines
1- leadership: the organization must have the ability and capabilities to manage and use data properly
2- technical: where there must be multiple devices amount of data and speed of
completion and of the most famous programs dealing with big data
3- people with high skills have the ability to handle huge amounts of big data and
present it in a scientific and documented way to decision making
( mc Afee and Brynjolfson,2012)
what are the important drivers of big data?
-for quality and credibility: so that high-quality information and data can be used and
utilized in serving the organization making the right decision the company in improving its work
– fast-growing: so that it is greatly inflated as a result of active interaction with the
situations by individuals customers and beneficiaries must be quickly responded
to in the service the organization and achieve its goals
-data variable value: meaning that the same information and data can change several things and benefit them in multiple things
(AL Shehhi,2017)
-there are catalysts and drivers of large information depends on its continuity and
development
-building information strategies on analysis platforms
-ensure the quality and suitability of data with digital analysis systems
– development of work quality measurement mechanisms
-review and evaluate data collection systems and make recommendations for
their development in alife that suits the data and is it engine
(provost&fawcett,2013)
-what could happen if companies focus quantitative data and aspects of big data
-the heavy reliance on quantitative data only big data conflicts with real numbers are misleading and not heavily relied upon large amount data only unregulated and incorrect data spending a lot of money and inaccurate results
-https://www. wireless-world.com terminology advantages and disadvantages of big data
references
-power,d,j (2016) data science supporting decision-making
journal of decision system
-Elgendy, big data analytics in support of decision-making process,2014
-Al Shehhi, Hafez (2017) online course: introduction of big data science 11/11/2017
-mc Afee, A, Bryn Jolfsson & davenport, T, H (2012) big data the management revolution
Harvard business review
-provost,f, Fawcett, T (2013) data science and relationship to big-data-driven decision making big data
– https://www.wireless-world.com terminology advantages and disadvantages of big data 

Post 2
On the topic of Big Data, Oracle (n.d) had previously stated that, “The definition of big data is data that contains greater variety, arriving in increasing volumes and with more velocity. This is also known as the three Vs. Put simply, big data is larger, more complex data sets, especially from new data sources. These data sets are so voluminous that traditional data processing software just can’t manage them. But these massive volumes of data can be used to address business problems you wouldn’t have been able to tackle before”.
I personally am not surprised by the fact that experts have calculated and predict that the digital world is just going to become more and more popular amongst many companies and the general public. We can see it in our daily lives how important technology has become to us, more and more of our life revolves around technology and it is only possible that eventually life is going to revolve strictly around technology.
Smith T. (2018) had previously stated, “The most important element is getting the data from the application to a place to process it. This drove the adoption of the data lake but that didn’t solve the entire problem. Customers have many different data sources – it’s hard to maintain master data references and you end up with a lot of duplication. The master data management (MDM) problem is a context that you can apply to the rest of the data. It’s the building block to AI/ML with big data. Bridge the gaps of sifting through data to make scientists and analysts more effective”.
I personally believe that if companies solely put their concentration and focus on quantitative data then the companies will have a more accurate idea of the all that is going on in their company from marketing to sales to income and expenses but the only problem is that this practice can be very hard if you do not have someone who has an extensive statistical background.
References

T. Smith (2018, November 15). Big Data Success Drivers – dzone big data.
https://dzone.com/articles/big-data-success-drivers
Oracle (n.d) What is big data?
https://www.oracle.com/big-data/what-is-big-data/.