BIG DATA – the new Ruler / an Emperor of today’s business. In today’s time data is everywhere, every one is running for data, collecting data and just storing data as if data is a philosopher’s stone (Paras Stone) which has potential to turn your business into revenue factory or data as a fuel to run your business core engine. But previous statements can only true if data is used correctly, on time and with correct velocity value which is on rise almost every day. Now when you have so much data; what can be done, how and at what time.
Big Data – Introduction
A sound data opportunities roadmap is the secret ingredient that gives business extra bite. Factual information on table says billions of terabytes of data i.e more than 2.7 zettabytes of data that exists in today’s digital universe, and which is projected to grow to 180 zettabytes in 2025 (Source open google search) 80% of it got generated only in last few years.
Behavioural biometrics data intelligence can help to learn consumer behavior by tracking certain patterns.
For effective involving and due to the nature & process of data working model for effective machine learning the model should get trained if self with large dynamic data sets, test and do the predictive & prescriptive analysis. In order to win in this recession times companies needs to make use of automated analytical tools to take more scientific approach for decision making through observation, experimentation and measurement to improve their business processes.
Behavioural biometrics data intelligence can help to learn consumer behavior by tracking certain patterns. When behavior changes it raise alarm and it can detect subtle shifts in the underlying data then revise algorithms accordingly.
Big Data or even fraction of big data simply cant be handled in a traditional manner and requires non traditional data bases, tools and techniques. As per Gartner – Big data is huge-volume, fast-velocity, and different variety information assets that demand innovative platform for enhanced insights and decision making.
This data comes from multiple sources, data comes from everything around us such as sensors used to gather climate-related information, posts on social media sites, digital pictures and videos, purchase transaction records, cell phone GPS signals. The data gets generated even when you walk for example how many steps you take to reach your destination on foot or how much time and how many kilometers you travel to reach your destination by bus/car etc, the list is endless
“Social media platform are the biggest inflow data pipes for Global Data Factories(GDF) across the globe”
Social media platform are the biggest inflow data pipes for Global Data Factories-GDF across the globe. The GDF data is the most complex, variety, lighting speed change ability and size is just humongous. Interestingly data format is not the same as data source. The data here could be both structured and unstructured and insights driving process could be also manual and 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 data greedy techniques i.e machine learning & deep learning.
Organizing data in a particular data framework as a way for businesses to organize and understand their data assets is an easy way to understand and make use of it. Only recently have companies begun to be analyzed to tease out insights that can help organizations improve their businesses. That’s why more organizations are seeking professionals who can make sense of all the data.
Today’s data has answers for most things, if not everything, and can be quantified and tracked easily. Machine learning requires lots of data to create, test, and “train” the AI. Which is the best direction? The answer lies in the analysis of future technology development within the 3GPP framework (for telecom), FinTech, AI and AGI, machine learning, and deep learning. What this means (though the list is endless):
- When some one 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 particular day in future?
- How my followers trend may look like in next 3 months ?
- How the health of the person would be based on data and environment ?
- How much sales will be in 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 major data generating industries i.e massive group of factories. According to some data from google 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 with their results: innovative and revenue-generating improvements and savings.
The art of data analysis right here as Big Data Analysis is about answering questions. It gets generated in millions of Gigabytes. Therefor the biggest challenge it throws is; How to manage. As Data alone is meaningless as it changes fast and comes in varieties of 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 completely 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 frame work ERRC- eliminate,reduce, raise and create. Blue Ocean Strategy – BES provides a systematic approach to making the competition “irrelevant. In BES uncontested market space gets created as oppose to red ocean where in red 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 main key strategy of blue ocean. In the past companies have fought for competitive advantage, battled over market share, and struggled for differentiation since the conception of commerce in red ocean but in blue ocean strategy is to create and capture new demand. Yet in today’s overcrowded industries, competing head-on results in nothing but a bloody “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 if you understand your Data correct then whole of Blue Ocean is yours to sail and grow your business smoothly without any challenge. Now Lets take all action frameworks one by one. First action Eliminate which says “Which of the factors that the industry takes for granted should be eliminated” here how data will help — 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 automated manner. it involves value innovation, which give 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.”
Second one Reduce — “Which factors should be reduces well below the industry’s standard”, here machine learning algorithm on the collected data can learn and take actions over the period of time to reduce what needs to be reduced. [Please note these are only helicopter views]. So in nut shell we have seen how Data can help and become Angle for business 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.
The data can speak to us by giving a sneak peak into what we have in our mind and prospect then helps creating opportunities from their trends!. To capture a quick snapshot of this strategy, certainly, Big Data appears to be a great example for Blue Ocean Strategy. Based on a limited set of examples presented before, it is obvious that Big Data plays key role to drive Blue Ocean Strategy or push business for Blue Ocean Shift further, it also helps on the cost front at the same time.
There’s no single answer to this without end-to-end architectural analysis. AI Big Data, Data Sceince and blockchain combination with Blue Ocean Strategy is explosive! Blockchain technologies with data can help realize some long-standing dreams of AI and data analysis work, and open up several opportunities.
All most all the Companies and agencies are actual doing awesome job by collecting data. Data about their customers (personal, professional and behavioral), products (features, market acceptance, usefulness and etc) and competitors. Sadly most of the time it ends their rather than analyzing that data and designing strategy around it. So the problem is in many cases, big data is not used well. Big Data analytics has the ability to transform the society into the new frontier. This unique and rich insight, when used in a business context, cuts the noise and fosters better allocation of scarce resources, achievement of objectives, and accurate assessment of and preparation for future scenarios.
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 this new paradigm in technology. BOSS – Blue Ocean Shift Strategy can actually help and create vision to focus on areas such as AI, blockchain for education, health & agriculture, create ecosystems using BigData analytics and IoT. To get out of old red ocean of competition and struggle we need to to redesign its outdated & antiquated institutions. 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.
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