How Big Data Is Changing the Face of Modern Business and It

How BIG DATA is Changing the Face of Modern Business and IT

Discussion Questions:

1. Describe How Big Data is changing Modern Business?

Ans: If you have huge amount of data it doesn’t mean that its useful, we need to extract the appropriate information from it. In a nutshell, big data is a term that was coined to describe a rapidly changing growth in the accessibility of structured as well as unstructured data, usually within a business. Big data has come under a lot of analysis presently, probably because it’s as essential to a business – and its surrounding community – as the Internet now is. The reason for this is simple, when you have more data, your analysis can be more accurate. This increase in accuracy in turn leads to better and more confident decision making, by the executive, which leads to higher operational efficiency, reduction in risk, cost-efficacy and increase in profitability as a result.

As far back as 2001, big data was already under the spotlight and was characterized according to the following attributes which are still applicable today:
Volume: The volume of data in an organization increases for many reasons: data from consumer transactions, unstructured data from social platforms, machine-to-machine or sensor data. Given decreasing costs of storage, the most essential aspect, more than storage, is determination of relevance of such large volumes through germane analytical methods.
Velocity: Large organizations, especially, have data flowing in at extremely high speeds, and this data must be dealt with accordingly to prevent systemic clogs
Variety: Data comes in formats of all kinds – structured and unstructured – all of which must be effectively managed, merged and governed as needed for operational efficiency.
Veracity: The accuracy of the Data.

Large amounts of data are now flowing in from various sources, but that’s not the good news. The good news is that this data can now be useful, thanks to improvements in computational and statistical methods and development of algorithmic schema for a number of applications. In addition, new ways of data set linking have been developed, as well as creative data visualization techniques, all essential to data analysis for various uses.
Big data analysis is pervading into virtually every field: science and academia, law, industry, government and even non-profits. Modern data analysis is revolutionizing schemes of thought and previously held notions, providing useful insights from the volumes of data now available in every field. The baseline is this: given sufficient and quantifiable information on any subject, a modern statistical technique will outperform individuals each time.
Big data is no visitor to marketing, where it is used to generate recommendation engines, much like Amazon and Netflix use to suggest purchases a consumer might be interested in based on previous purchases/interests. There are also many applications of big data in the public sector: determination of crime hot-zones, genomic analysis to improve drought-resistance of crops, identification of evolutionary patterns and disease resistance, etc.
Big data has opened many doors for businesses to retrieve and analyze large volumes of data for their benefit. However, internally collected data offers only a limited picture, necessitating a shift in days to come if business processes are to be fully transformed. The following are some predictions regarding how this might occur:

1. 80% of last decade’s enterprise processes and products will be digitized and/or eliminated by 2020

2. Over 30% of data accessible to businesses will be availed by data brokerage services to enable better contextual decision-making by 201.
3. Over 20% of consumer-centric analytical deployments will be directed to offer product tracking to strengthen IoT

It is clear that the world of Big Data carries with it massive potential that will alter the course of life and business as we know it. If its potential is maximized even by just a fraction, millions of lives across the world will be changed for the better, as will businesses and enterprises in every field of operation. It’s introducing a new and exciting realm to everything, and that’s just a tip of the big data iceberg.

1. Describe how Big Data is changing modern IT?

      Ans: Gone are the days when traditional software companies could do two years of research and, only after all the intricate information is collated, would they set about building a product. If they did that today, the product they would end up with would already be out-of-date by the time it hit the market. Today, innovation starts with software development teams being able to understand data at a deep level to predict what may happen in the future. Building new solutions happens in small iterative stages so each new element can be tested for market fit and fine-tuned, usually while the platform is live.
If u have huge amount of data it doesn't mean that its useful, we need to extract the appropriate information from it. In the modern IT, Big Data has taken its form from the following
Descriptive Analytics: Its related to Business Intelligence. It is all about Dashboards and visualization using Tableau.
Predictive Analytics: This is looking at the future. It is to understand the hidden patterns and trends and see what’s going to happen in the future. Using Data Mining we need to look at the desired outcome.
Prescriptive Analytics: This is about finding a best course of action for a given situation. This is related to both Descriptive and Predictive Analytics. Prescriptive analytics optimizes decision making to show companies what actions to take in order to maximize profitable growth, given their business constraints and key objectives. The most advanced in the spectrum of business analytics, prescriptive analytics is able to make the most impact on large scale business objectives, e.g., A Bank messaging you to check, Did you really do this transaction? If Yes, please reply to us by either Email or text or Answer our call.
Cognitive Analytics: It is a field of analytics that tries to mimic the human brain by draw inferences from existing data and patterns, draws conclusions based on existing knowledge bases and then inserts this back into the knowledge base for future inferences - a self-learning feedback loop. The best example for this is IBM Watson, It’s like a question and answer machine. It’s an AI machine, it has built solutions for the industries like Healthcare industry, it scans a MRI and detects cancers, tumors, anomalies better than a doctor.

Say you are offered some data source. The conventional data warehouse guys will turn to their trusty ETL tools (extract, transform, and load) to create relational tables out of the data. Now, this works if the data source was structured in nature, like a banking or billing system. Take those same techniques and apply them to big data – diverse formats from diverse sources – and the same techniques crumble.

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