a) Informing decision making.


This is about using data both strategically and operationally to help us make better decision Google – google has an ambition that every bit of management decision that takes place has to be informed by data .it states that business strategy should be your data strategy and has to be a closed strategy and this is what google does. Google has identified the biggest business challenges and biggest business questions , they revise question on regular basis . so every question goes to board and has to be answered ,Google assures that the decision making takes place all over the organization not just on very top but across on the entire business
How we give access to data for example say walmart , the largest retailer in world .so they have 150 petabyte data cloud and wants this data to be available to our people ,people working in retails , in headquarters so that they can make better information . So this becomes a big challenge for many organization . To overcome this problem Waymart created cafes , they were actually physical coffee shops where you could turn up grab a cup of coffee and sit with the data analyst and discuss your business challenge . basically the data should be structured so that more people have access to data so that we don’t have all the traditional hierarchy layers that makes it really difficult for people to make data informed decisions. Successful companies create roads like data translator so these would be the people that sit in the data café that helps to bridge the technical and business world .
b) Better understanding of customers to get a good view of them what they are buying or what they will be needing in future
Big data seems for big company but we see another example of local butchers shop so they wanted to know what kind of marketing messages that will attract customer .so here management wants to know how many people just pass away from the shop , how many people looking at the shopping window and how many actually shop here . soinsteade of relying on existing data we can find new data sources and this is for need of more data diversity . so in this case we installed a device that will pick up our mobile data signal in the shopping window of butchers shop . our mobile phones are continuously sending out signals for Bluetooth and wifi connectivity ,so as we usually carry smart phones so this counts how many phones passes by , how many stops and how many shops . so this helps to analyse the footfall data
Another example was of Disney who gave their visitors the wristbands that have RFID sensors and GPS trackers so this allows Disney to have a complete understanding of their visitors of how they use the theme parks so this helps them where the questions are building up and they manage them in real time .it also had camera that have look on the facial expressions of visitors to get the real time feedback .
c) Link to Customers but this time it is about Improving our Customers Value Proposition i.e. Delivering Smarter Product and Smarter Services
Creating smarter product and smarter services for customer .eg like a bank himself alerts the customers that their interest rate is dropping and they are now being able to scan the accounts of their premium customers and tell them about if they are paying an extra amount to a particularly policy . This develops trust that creates transparency in the system
d) Automating some of key business processes is about driving efficiencies across your operations
In cricket pitch , the tracker analyses and tracks data on everything that takes place on cricket pitch so they measure the trajectory of ball using cameras and sensors .Nowadays we ue machine learning algorithm to do the coding

a) Volume:
Volume refers to the incredible amounts of data generated each second from social media, cell phones, cars, credit cards, M2M sensors, photographs, video, etc. The amounts of data have become so large that we can no longer store and analyze data using traditional database technology. Now distributed systems is used where parts of the data is stored in different locations and brought together by software like cloud computing. With just Facebook alone there are 10 billion messages, 4.5 billion times that the “like” button is pressed, and over 350 million new pictures are uploaded every day. Gathering and analyzing this data is definitely an engineering challenge of immensely huge proportions.

b) Variety:
Variety is described as the different types of data we can now use. Data can be structured semi-structured or un-structured . We no longer just have structured data (name, phone number, address, financials, etc) that fits nice and neatly into a data table. We have in fact, 80% of all the world’s data fits into this category of unstructured data, including photos, video sequences, social media updates, etc. Advanced and innovative big data technology allows structured and unstructured data to be harvested, stored, and used simultaneously.

c) Velocity:
Velocity tends to refer the speed at which immense amounts of data are being generated, collected and analyzed. Every day the number of emails, twitter messages, photos, video clips, etc. increases at the speed of light all over the world. Every second of every day data is increasing. Not only must it be analyzed, but the speed of transmission, and access to the data must also remain instantaneous to allow for real-time access to website, credit card verification and instant messaging. Big data technology allows us now to analyze the data while it is being generated, without ever putting it into databases.

d) Veracity:
Veracity refers to the quality or trustworthiness of the data collected. For example, think about all the Twitter posts with hash-tags, abbreviations, types, etc., and the trustworthiness and accuracy of all of the content. This enormously huge data is of no use if the quality or trustworthiness is not accurate. Another good example of this relates to the use of GPS data. Often the GPS will “drift” off course as you peruse through an urban area. Satellite signals are lost as they bounce off tall buildings or other structures. When this happens, location data has to be fused with another data source like road data, or data from an accelerometer to provide accurate data.