The scope of Big Data in the world of Analytics is changing dramatically and constantly evolving to redefine itself every day. Big Data offers enormous challenges and also the biggest business decision making opportunities for enterprises today.
With more and more businesses robustly switching from analog ecosystem to digital, both individuals and corporates are generating such large pools of information, that experts predict there would be around 4300% leap in the annual production of data by 2020 and most of this data will be in the cloud; In other words, by 2020 about a third of all data will pass through the cloud.
The epic rise of unstructured data like photos, videos and social media has fostered a new breed of non-relational databases, allowing them to unveil their own structures, patterns and trends. The migration from merely collecting data to connecting it, has aided businesses to draw better inferences and relationships between data-sets; leveraging actionable insights for better enterprise decision making.
The number of industries gearing themselves to accommodate the Big Data practice is on an exponential rise. Whether it is comparing utility meteorological data to spot trends and efficiencies or considering ambulance GPS information with the records of the hospital to identify correlation between response time and survival or hanging a tiny device around your neck that tracks your movement, calories and sleep to get you back on line with your personal wellness, they are all reincarnations of Big Data.
Although it’s difficult to point out a singular reason for the creation of Big Data revolution, it would be fair to say that an array of factors would have developed and caused the phenomenon of Big Data as we know it today. It can be anything from the increasing amount of digital data at disposal to inexpensive data storage options to advanced computers might have pushed Big Data to where it is today.
That being said, having such huge amounts of data with volume, variety and velocity at ones disposal also means it becomes equally or more challenging for enterprises to draw relevant Actionable insights and Business Intelligence (BI) for better business decision making. Storing, searching, comparing, combining, visualizing and refining the data can be big challenges. Another challenge in putting Big Data to use is getting your hands on the right information. Some IT companies like Microsoft works with Hadoop – an open source data platform which helps efficiently manage unstructured data to work around these challenges.
The implications and effects of Big Data spans across different industries. unlocking its infinite potential depends on several factors. Cutting edge enterprise datacenters and their advanced analytics practices are imperative to achieve that potential.
A hospital may use a critical gene sequencing to prevent the outbreak of an antibiotic resistant bacteria, or a university may identify dropping activity levels of a student in lines with drop-out rates, reaching out to assist, or a rail-road enterprise may get a notification from a sensor saying that a preventive fix is on demand, saving cost and time taken to remove the trains from the tracks.
Along with social, mobile and cloud, big data analytics and associated technologies have earned a place in today world as one of the core disruptors of the digital age. 2015 saw big data initiatives moving from test to production and a strong push to leverage new data technologies to power business intelligence.
It is believed by industry experts of Hortonworks that not only Internet of Things but also Internet of Anything (IoAT) will be able to provide insights. Getting value from data will extend beyond devices, sensors and machines and also include all data produced by server logs, geo location and data from the Internet. IoAT will create a new paradigm that will require new thinking and new data management systems, and these solutions will mature and permeate the enterprise in 2016.
The democratization of data, increased concerns around data privacy, new applications for data insights, big data analytics based smart systems management, NoSQL takeover, Apache Spark, expand to add speed by Hadoop will mark the new era of Big Data Analytics in 2016. For this reason, leading cloud and data companies such as Google, Amazon Web Services and Microsoft will bring IoT services to life so the data can move seamlessly to their cloud-based analytics engines.
2016 seems like it will be equally as loud as 2015. The pressure is on to leverage data in new ways for competitive advantage. CIOs need to bestride two different worlds with equal importance– satisfying their existing customer base while moving fast to deliver instant, data-driven services to customers, or they risk losing ground to market upstarts. Not to oversimplify the learning of big data collection and analytic fundamentals for developing countries, there are subjects required to acquire necessary knowledge to work with data, e.g. statistics, behavioral science, social science, criminal justice, physical and life sciences, and computer science. To give a better idea of the impact of how developing countries will benefit from big data and analytics, to “improve” their processes and operations for the welfare of the public, here are just a few examples:
- Predicting where the economy will head with more specific data, what will happen if any of the factors were to change
- Trending crime to control and prevent the spread or increase, and eventually reduce
- Tracking migration of micro-societies for their health and well-being e.g. indigenous
In summary, Big Data makes it personal for us, the residents of this era. It can make smarter cities, greater academic learning, faster medical breakthroughs and more efficient use of enterprise resources. The future of Big Data looks bright. McKinsey reckons that over a million of Big Data related jobs would open up in the next couple of years. However, in order to realize this potential, it would be essential that the tight combination of technology, skill sets, processes, planning and understanding of industry application is used to harness and use Big Data to its fullest potential.