Data is everywhere, everyone is running after data, collecting and storing it as if data is a philosopher's stone (Paras Stone) which has the potential to turn businesses into revenue factories. However, it will only be possible if the data is used correctly, on time and with good velocity which is on the rise every day. Today, we have billions of terabytes of data available this is equivalent to more than 2.7 zettabytes of data that exists in today's digital universe, and it is projected to grow to 180 zettabytes in 2025 . To analyze and process data models, machine learning is very important. It involves large dynamic datasets to train itself, test and perform predictive and prescriptive analysis.

So, this big data or even fraction of this data simply cannot be handled in a traditional manner and requires non-traditional databases, tools, and techniques. As per Gartner, big data is huge in terms of volume, velocity, and variety with information assets demanding innovative platforms for enhanced insights and decision making. Sources of data are everything and it's up to us like how many steps one takes to reach on foot or how much time and how many kilometers one travel to reach their destination by bus/car etc. We can train and learn the consumer behavior by tracking certain patterns and behavioral biometrics. And this will surely give it flying wings. When behavior changes, it raises an alarm and it can detect subtle shifts in the underlying data, and then we can revise algorithms accordingly.

"Social media platforms are the biggest inflow data pipes for Global Data Factories(GDF) "

Social media platforms are the biggest inflow data pipes for Global Data Factories (GDF) . The GDF data is the most complex data with variety, speed, and the size which is just humongous. Interestingly, data format is not the same as a data source. The data here could be both structured and unstructured and the insights driving process could be manual or automated. Data Science – adopts/develops appropriate methods to transform data into actionable knowledge, to perform predictions as well as to support and validate decisions. The most dramatic advances in AI are coming from a data-rich or we may call data greedy techniques i.e machine learning & deep learning.

Organising data in particular data framework as a way for businesses to understand their data assets is an easy way to make use of it. It's just recently that companies have begun to analyze data to glean insights that can help improve their businesses. That's why more organizations are seeking professionals who can make sense out of the large volume of data. Today's data has the answer for most of the things if not everything and can be quantified and tracked easily. Machine learning requires lots of data to create, test and "train" the AI. So, what is the best direction? The answer lies in the analysis of future technologies developed within the 3GPP framework (For Telecom), FinTech, AI, and AGI, Machine learning & Deep Learning. This means (though list is endless):

•   When someone is likely to get married/divorce or even downfall?

•   When the factory will have a power outage or fire?

•   What will the temperature be next day or week or even on a particular day in future?

•   How my followers' trend may look like in the next three months?

•   How would the health of the person be based on data and environment?

•   What would be the sales  next month?

"To predict patterns from valuable insights and make right decisions"

Big data presents a tremendous opportunity for enterprises across multiple industries especially in the tsunami-like data flow industry of "Payments". FinTech, InsureTech, MedTech are the major data generating industries involving a massive group of factories. According to a Google source, it shows in the face of increasingly complex reality often characterised by large amounts of data of various types (numeric, ordinal, nominal, symbolic, texts, images, data streams, multiway, networks etc) and coming from disparate sources, it is just a matter of practicing your newly-found skills well enough to become proficient.

The art of data analysis right here as big data analysis is about answering questions. It gets generated in millions of gigabytes. Therefore, the biggest challenge it throws is; How to manage it. Data alone is meaningless as it changes fast and comes in different forms that are difficult to manage and process using any relational database management system (SQL or Oracle databases) or any other traditional technologies.  So, the technologies which are developed to deal with big data solutions, like Hadoop, Spark, No SQL are complete if not different, but surely separate from small data solutions like SQL or Oracle databases.

Data – An Angle for your Blue Ocean Strategy

Blue Ocean Shift / Strategy – Globally preeminent management thinkers W. Chan Kim and Renée Mauborgne challenge everything you thought you knew about the requirements for strategic success. Blue Ocean strategy has four action frameworks, ERRC- eliminate, reduce, raise and create. Blue Ocean Strategy provides a systematic approach to making the competition "irrelevant". In Blue Ocean Strategy, uncontested market space gets created as opposed to the red ocean wherein it needs to compete in existing space. Some creative thinkers and consultants argue that tomorrow's leading companies will succeed not by battling competitors, but rather by creating "Blue Oceans" of uncontested market space ripe for growth. Big data promotes innovation, connects various areas at a rate never seen before, thus creating a new marketplace.

Making the competition irrelevant is the key strategy of Blue Ocean. In the past, companies have fought for competitive advantage, battled over market share, and struggled for differentiation with the conception of commerce in Red Ocean, However, in the Blue Ocean, the strategy is to create and capture new demand. Yet, in today's overcrowded industries, competing for results is nothing but a "Red Ocean" of rivals fighting over a dwindling profit pool. This "boiler-plate" strategy is increasingly unlikely to create profitable growth in the future.

So now, what I can comfortably say is if you understand your data correctly then the whole of Blue Ocean is yours to sail and grow your business smoothly without any challenge. Let's take all action frameworks one by one. The first action is to eliminate the factors that the industry takes for granted. Data science will provide insight and make it easier and process faster to know what all attributes should remain and what should be discarded in an automated manner. It involves value innovation, which gives organizations the ability to combine differentiation and low-cost at the same time.

"Big data shows you trends and allows you to build scalability into your operations."

The second action is to know, "Which factors should be reduced below the industry's standard". Here, machine learning algorithms on the collected data can learn and take actions over the period of time to reduce attributes. So in a nutshell, we have seen how data can help and become angle for businesses for their Blue Ocean Shift process. Big data shows you trends and allows you to build scalability into your operations. How to transform big challenges into huge opportunities, that's what big data shows us. This strategy also touches upon the need to strategically sequence in the right direction to align it towards the "big picture" vision, thus creating a new marketplace reaching beyond the current demand.

Data can speak to us by giving a sneak peek into what we have in our mind and prospect, it then helps create opportunities for the trends. To capture a quick snapshot of this strategy, certainly, big data appears to be a great example of Blue Ocean Strategy. Based on a limited set of examples presented before, it is obvious that big data plays a key role to drive Blue Ocean Strategy or push business for Blue Ocean Shift. It also helps on the cost front at the same time. There's no single answer to this without end-to-end architectural analysis. The combination of AI, big data, data science, and blockchain with Blue Ocean Strategy is explosive! Data originating from Blockchain technologies  can help realize some long-standing dreams of AI and data analysis work and open up several opportunities.

Conclusion

To extract the best out of this disruption, there should be a coordinated action through relevant governmental policies, organizational strategies, and educational offerings to navigate the new paradigm in technology. Companies and agencies are actually doing an awesome job by collecting data about their customers (personal, professional and behavioral), products (features, market acceptance, usefulness and etc) and competitors. Sadly most of the time it ends there rather than analyzing that data and designing strategy around it. So, the problem is, in many cases, big data is not used properly. Big data analytics has the ability to transform the society into the new frontier. With the active use of behavioral science and data analytics, it would be possible to address the growing concerns of managing natural resources, energy demand, health care costs etc.