Why Data is best for Blue Ocean Shift?

Blue Ocean Shift – In the contemporary business landscape, the “Blue Ocean Shift” uncovers, unearths, and underscores the pervasive presence of data. The companies fervently seek its accumulation and storage, akin to a philosopher’s stone in modern times. Data, changing names from new fuel to new global currency, etc., is now a strategic asset that possesses the latent capability to metamorphose enterprises into lucrative revenue generators or serve as a pivotal driver for core operational engines.

However, the realization of these prospects hinges upon the meticulous, correct, and ethical usage and timely utilization of data, conducted with precision and in alignment with the escalating velocity trends.

BIG DATA – the new Ruler or an Emperor of today’s business

Data – Biggest Hunt of Today’s Time

A sound data opportunity roadmap is the secret ingredient that gives businesses extra bite. Factual information on the table says billions of terabytes of data, i.e., more than 2.7 zettabytes of data, exist in today’s digital universe. This data is projected to grow up to 180 zettabytes by 2025 (source: Open Internet). 80% of today’s data was generated only in the last few years. In a business landscape brimming with copious data, strategic considerations arise: What actionable steps should be taken, when, and with what degree of urgency, to harness the transformative potential embedded within this data-rich environment?

In order to win in these times of recession, companies need to make use of automated analytical tools to take a more scientific approach to decision-making through observation, experimentation, and measurements. For effective machine learning, the data model should be trained on its own with large dynamic data sets, tested, and used for predictive and prescriptive analysis.

“Behavioural biometrics data intelligence can help to learn consumer behaviour by tracking certain patterns.”

Behavioural biometrics data intelligence can help to learn consumer behavior by tracking certain patterns. When behavior changes, it raises an alarm, and it can detect subtle shifts in the underlying data and revise algorithms accordingly.

Big data, or even a fraction of it, simply can’t 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 a variety of information assets that demand innovative platforms for enhanced insights and decision-making.

Data Sources

Data comes from multiple sources—almost everything around us. Some examples of data sources are sensors for climate-related information, posts on social media sites, digital pictures and videos, purchase transaction records, and cell phone GPS signals. The data gets generated even when you walk or drive. How many steps you walk, how much time, how many kilometers, etc. are examples, but the list is endless.

Social media platforms are the biggest inflow data pipes for Global Data Factories (GDF) across the globe. The GDF data is the most complex, full of variety, has a lightning speed of change ability, and its size is just humongous. Interestingly, data formats are not the same for any source.

“Social media platform are the biggest inflow data pipes for Global Data Factories(GDF) across the globe”

The data here could be both structured and unstructured. The data insights driving the process could be manual or automated. Data Science: adopts or develops appropriate methods to transform data into actionable knowledge, to perform predictions, and 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.

Organising data in particular data framework

As a way for businesses to organize and understand their data assets, it is an easy way to understand and make use of them. It’s only recently that companies have 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, Payment Intelligence, AI, Conversational AI, 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”

Its Data Collection Race In Reality

Big data presents a tremendous opportunity for enterprises across multiple industries, especially in the tsunami-like data flow industry of “Payments”. FinTech, InsureTech, and MedTech are major data-generating industries, i.e., massive groups of factories. According to some data from Google, it shows in the face of an increasingly complex reality often characterized by large amounts of data of various types.

Numeric, ordinal, nominal, symbolic, texts, images, data streams, multiway, networks, etc. are a few examples. Also, it comes from disparate sources; it is just a matter of practicing your newly-found skills well enough to become proficient. With practice and monitoring results, innovative and revenue-generating improvements with savings come easily.

The art of data analysis is right here, as Big Data Analysis is about answering questions. It gets generated in millions of Gigabytes. Therefore, the biggest challenge it throws up is How to manage.

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 that are developed to deal with big data solutions, like Hadoop, Spark, and No SQL, are completely different, if not different, but surely separate from small data solutions like SQL or Oracle databases.

AAA

Data – An angle for your Blue Ocean Strategy

Blue Ocean Shift and Strategy: Globally preeminent management thinkers W. Chan Kim and Renée Mauborgne challenge everything you thought you knew about the requirements for strategic success. The Blue Ocean strategy has four action frames: eliminate, reduce, raise, and create.

BES strategically guides enterprises to reconfigure their market presence, effectively sidestepping the conventional competitive fray. By reshaping industry boundaries, BES ignites innovation and unlocks avenues that lie beyond the confines of the saturated market space. This innovative approach transcends the limitations of traditional competition, fostering an environment where differentiation and value creation take center stage.

The Blue Ocean Strategy (BES) offers a structured methodology for rendering competition “insignificant.” Within the framework of BES, a realm of uncontested market space is meticulously crafted, in contrast to the red ocean scenario where competition occurs within pre-existing confines.

Blue Ocean or Red Ocean

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 and connects various areas at a rate never seen before, thus creating a new marketplace. Making the competition irrelevant is the main 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 the red ocean, but in the blue ocean, the 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 “boilerplate” strategy is increasingly unlikely to create profitable growth in the future.

So now what I can comfortably say is that if you understand your Data correctly, then the whole Blue Ocean is yours to sail and grow your business smoothly without any challenge.

Action Frameworks for Blue Ocean Strategy

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 is how data will help: Data science will provide insight and make it easier and faster to know what 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.”

Second, reduce: “Which factors should be reduced well below the industry standard Here, a machine learning algorithm on the collected data can learn and take actions over a period of time to reduce what needs to be reduced. [Please note these are only helicopter views.] So in a nutshell, we have seen how Data can help and become an angle for businesses in 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. The Blue Ocean Shift” 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.

Big Data: The Management Revolution

The data can speak to us by giving us a sneak peak into what we have in mind and then helping us create opportunities from their trends! To capture a quick snapshot of this strategy, certainly, Big Data appears to be a great example of the “Blue Ocean Shift” Strategy.

Based on the limited set of examples presented before, it is obvious that Big Data plays a key role in driving Blue Ocean Strategy or pushing business for Blue Ocean Shift further, and 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 The combination of big data, Data science, and blockchain with the Blue Ocean Strategy is explosive! Blockchain technologies and data can help realize some long-standing dreams of AI and data analysis work and open up several opportunities.

Enterprises and agencies have adeptly mastered the art of data collection, diligently amassing a wealth of information encompassing customer profiles, product intricacies, and competitor landscapes. However, the potential of this data often remains untapped, as analysis and strategic design take a backseat. In numerous instances, the power of big data lies dormant, raising concerns about its effective utilization.

The realm of Big Data analytics possesses the transformative potential to catapult society into uncharted territories, analogous to the “Blue Ocean Shift.” This distinctive reservoir of profound insights, when harnessed within the business context, cuts through the cacophony and paves the way for optimal resource allocation, objective attainment, and a precise evaluation and readiness for impending scenarios.

Revolutionizing Through Blue Ocean Analytics

Breaking free from the confines of the conventional red ocean of competition mandates a redesign of archaic institutions. The “Blue Ocean Shift,” fortified by the dynamic interplay of behavioral science and data analytics, emerges as the compass to navigate the challenges of managing natural resources, burgeoning energy demands, spiraling healthcare expenses, and beyond. This strategic evolution redefines the landscape, replacing stagnation with innovation and redrawing the contours of societal progress.

The era of data proliferation has ushered in unprecedented opportunities for businesses and societies alike. While the collection of vast amounts of data has become commonplace, the true potential lies in the strategic analysis and application of this information. The concept of the “Blue Ocean Shift” serves as a guiding beacon, advocating for a departure from the competitive norms of the past and embracing a new paradigm where data-driven insights fuel innovation and resource optimization.

As we stand at the precipice of a new frontier, armed with behavioral science, data analytics, and a commitment to transformation, the potential for societal progress becomes boundless. By embracing the principles of the “Blue Ocean Shift,” we can chart a course toward a future characterized by innovation, strategic agility, and a harmonious balance between economic prosperity and the responsible stewardship of our shared resources.

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. The journey may be challenging, but the rewards are immense, promising a world where data’s full potential is realized, and new horizons are charted with confidence and purpose.

#MachineLearning #DeepLearning #ArtificialIntelligence #ArtificialNeuralNetworks

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Points to Note

All credits if any remains on the original contributor only. We have covered Importance of Data in Blue Ocean Strategy for business shift. Machine Learning and Data’s high level understanding. In the next upcoming post will talk about Reinforcement machine learning.

Feedback & Further Question

Do you have any questions about Machine Learning or Big Data? Leave a comment or ask your question via email. Will try my best to answer it.

Books + Other readings Referred

• Open Internet – Research Papers and ebooks
• Personal hand on work on data & experience of  @AILabPage members
• Book – Blue Ocean Strategy.

Feedback & Further Question

Do you need more details or have any questions on topics such as technology (including conventional architecture, machine learning, and deep learning), advanced data analysis (such as data science or big data), blockchain, theoretical physics, or photography? Please feel free to ask your question either by leaving a comment or by sending us an email. I will do my utmost to offer a response that meets your needs and expectations.

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