All data generated by cloud, IoT and other systems is mostly unstructured data for which big data analytics is utilized in big way. 5V disused above represent 5V paradigm for big data.
Most of data generated by cloud and IoT system is unstructured in nature represented by big data system.Huge data generated by cloud computing infrastructure over a decade only way to understand this is Analytics. Cloud computing is characterized by 5V Huge Volume of data, Variety forms of data (images cloud, sound cloud, video site like youtube), velocity of data (like in social media fueling social media analytics) , value of data, .
It’s easy to conclude Cloud computing is also fuelling demand for Analytics. Not recently Every cloud platform from Microsoft azure and Amazon released its ML machine learning services to recognize this need more and more analytics, data mining, machine learning API/services are being added to cloud platform.
Another foreseeable trend is rise of IoT. IoT or Internet of things will bring those things to internet which were never on internet. It will require huge IP list for which IPv4 based network are soon replaces with IPv6. Since giving an IP to each thing present on earth would be prime requirement.
Now imagine when we are moving from IPv4 billion devices to IPv6 based Trillions it would generate even more data. All this IoT data would have to stored for which all major cloud providers have released IoT data capturing and storage services. Like Amazon released kinesis, Lambda for IoT devices.
These IoT trillion devices on internet would fuel in another round of demand for Analytics which is already put in place by major cloud providers.
Now is billion devices produces this much data that we have to take shelter of analytics how much would trillion IoT devices would fuel requirements in analytics. Now not only cloud based analytics platform but non cloud based Analytics platform would be equally more in demand.
Analytics focused platforms like SAS, SPSS, R will gain huge traction even more then the present day systems.
As data sets are becoming huge so are parameters which needs to analyzed. Think of situation where executive have a report each column showing some dimension to be analyzed and if To-Be analysed column per report is say 100 would it make sense.. with amount of complexity in business on rise and amount of details data can be captured more and more such columns are being added each day to reports. Now for each decision if you have to think 50 possibilities represented by column in report is huge task not impossible. Only Analytics can tell which of those columns are more important than other columns hence focus on those columns/possibility for your reports/business. Rising complexity in business, regulations and rise of more IT driven systems like IT driven ticketing.
New age frauds have given rise to new laws like Sarbanes Oxley law or SOX, etc, new regulators for each sector is trend with rising complexity of sector for government to manage. Each of those regulations and fraud detection mechanism is giving rise to new metrics in management to measure and maintain.
For managing each of those measure or metrics new set of analytics are being probed.Amazing within Analytics world so much is changing like take case of rise of data mining, from neural networks of 80’s to Bayesian systems in 90’s and today deep learning is much in demand.
Systems like IBM Watson which will use AI to search for us become more accessible help to deepen our knowledge on subject areas with quick turnaround will bring in more adoption and appetite for Analytics driven systems.
Another trends as more business is getting digitized like amazon eaten physical book retailers, Airbnb the largest hotel chain, and Uber largest Taxi service in world. To analyze business data is available collected from digitized presence of this large business which is spuring growth of analytics to drive value out of the data. Out of all online business those will create more value and survive which will be able to use its data analytics to proper use.