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Shyamsunder Panchavati
Shyamsunder Panchavati is an Independent Management Consultant Coach Author....... Member Advisory Council.Harvard Business Review....... Member Online Executive Panel of Experts Mckinsey & Co.
Shyamsunder Panchavati is an Independent Management Consultant Coach Author....... Member Advisory Council.Harvard Business Review....... Member Online Executive Panel of Experts Mckinsey & Co.

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Machine learning (ML) is a computer’s way of learning from examples, and it’s one of the most useful tools we have for the construction of artificial intelligence (AI). It begins with the design of an algorithm that learns from collected data, creating machines that in most cases become smarter as data volumes intensify.

We’ve seen a breakthrough in the field of ML in the last five years in part due to the recent wealth of big data streams provided from high-speed internet, cloud computing, and widespread smartphone usage, leading to the birth of the now popular “deep learning” algorithms. Heavily- used applications that have emerged with ML at their core include recommendation systems like those from Netflix and Amazon, face recognition technology as seen in Facebook, email spam filters like those from Google and Microsoft, and speech recognition systems such as Siri.

While the depth of advancement is unknown, what we can say with high certainty is that development in this field in the past five years will be nothing compared to what we’re going to see in the five years to come. Based on machine learning’s current state, here are four predictions of what we could see in the near future:

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As companies struggle to make sense of their increasingly big data, they're laboring to figure out the morass of technologies necessary to become successful. However, many will remain stymied, because they keep trying to fit a necessarily fluid process of asking questions of one's data with outmoded, rigid data infrastructure.
Or as Amazon Web Services (AWS) data science chief Matt Wood tells it, they need the cloud.
While the cloud isn't a panacea, its elasticity may well prove to be the essential ingredient to big data success.
How much cloud do I need?
The problem with trying to run big data projects within a data center revolves around rigidity. As Matt Wood told me in a recent interview, this problem "is not so much about absolute scale of data but rather relative scale of data."
In other words, as a company's data volume takes a step function up or down, enterprise infrastructure can't keep up. In his words, 
"Customers will tool for the scale they're currently experiencing," which is great... until it's not.

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The hospital’s success in using advanced patient analytics can partially be attributed to Singapore’s existing e-health infrastructure. The national e-health record system, which allows each patient to have a single record accessible by any medical centre, was launched in 2009.
Sharing information across healthcare providers can reduce costs for the patient. The basics such as drug allergy and treatment history aside, data can cut the need for repeat clinical tests and ensure ineffective drugs aren’t prescribed multiple times.
But building a central database is just the starting point for healthcare analytics to prosper.
According to Lee Chew Chiat, Executive Director, Consulting and Public Sector Industry Leader, Deloitte Southeast Asia, the potential for data mining technology to benefit the healthcare industry today extends much further. In addition to predicting profiles of frail or elderly patients who are likely to be re-admitted into hospitals, data also allow medical professionals to balance drug efficacy and cost; and determine the locality of disease - dengue fever, for example - for better control.
“Having consistent basic information of a patient or a consumer is critical in healthcare. It is the foundation and since we have the foundation, Singapore can be a good test-bed,” said Lee.

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Are You Ready for Personalized Predictive Analytics?

Predictive analytics have the potential power to "produce remarkable services and longer lives," says James Heskett. But can businesses make bets in this area without first understanding the social consequences? What do YOU think?

In 2002, the film Minority Report introduced many of us to the world of predictive analytics. In it, an innovative technology allows Washington, D.C. to go without a murder for six years by helping Tom Cruise, chief of the Precrime Unit, to identify, arrest, and prosecute killers before they commit their crimes.

This was a case of the movies catching up to the business world. At that time, predictive analytics had been applied to the continuing maintenance of everything from CAT scan machines produced by GE to elevators made by Otis. It enabled these firms to sell "up time" rather than just products, thanks to a number of sensors and the continuing remote surveillance of the performance of these products.

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The case for digital reinvention 03-21

Digital technology, despite its seeming ubiquity, has only begun to penetrate industries. As it continues its advance, the implications for revenues, profits, and opportunities will be dramatic.

As digitization penetrates more fully, it will dampen revenue and profit growth for some, particularly the bottom quartile of companies, according to our research, while the top quartile captures disproportionate gains. Bold, tightly integrated digital strategies will be the biggest differentiator between companies that win and companies that don’t, and the biggest payouts will go to those that initiate digital disruptions. Fast-followers with operational excellence and superior organizational health won’t be far behind.

These findings emerged from a research effort to understand the nature, extent, and top-management implications of the progress of digitization. We tailored our efforts to examine its effects along multiple dimensions: products and services, marketing and distribution channels, business processes, supply chains, and new entrants at the ecosystem level (for details, see sidebar “About the research”). We sought to understand how economic performance will change as digitization continues its advance along these different dimensions. What are the best-performing companies doing in the face of rising pressure? Which approach is more important as digitization progresses: a great strategy with average execution or an average strategy with great execution?

#DigitalReinvention, #DigitalTechnology, #DigitalDisruption, #DigitalMarketing, #Digitisation, 

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What’s Your Data Worth? 03-20

In 2016, Microsoft Corp. acquired the online professional network LinkedIn Corp. for $26.2 billion. Why did Microsoft consider LinkedIn to be so valuable? And how much of the price paid was for LinkedIn’s user data — as opposed to its other assets? Globally, LinkedIn had 433 million registered users and approximately 100 million active users per month prior to the acquisition. Simple arithmetic tells us that Microsoft paid about $260 per monthly active user.

Did Microsoft pay a reasonable price for the LinkedIn user data? Microsoft must have thought so — and LinkedIn agreed. But the deal generated scrutiny from the rating agency Moody’s Investors Service Inc., which conducted a review of Microsoft’s credit rating after the deal was announced. What can be learned from the Microsoft–LinkedIn transaction about the valuation of user data? How can we determine if Microsoft — or any acquirer — paid a reasonable price?

The answers to these questions are not clear. But the subject is growing increasingly relevant as companies collect and analyze ever more data. Indeed, the multibillion-dollar deal between Microsoft and LinkedIn is just one recent example of data valuation coming to the fore. Another example occurred during the Chapter 11 bankruptcy proceedings of Caesars Entertainment Operating Corp.

Image credit: Shyam's Imagination Library

#BigData, #Data Analytics, #BusinesAnalytics, 

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“Culture trumps data, I don’t care how good your model is. If you don’t understand the culture … you’re not going succeed with analytics and deliver success for the business.” Jim Sprigg, director of database marketing and analytics for InterContinental Hotels Group. 

Beijing-based Xiaomi is among the top phone makers in the world. With a market capitalization of $45 billion, the five-year-old company is known for its flash sales of high quality, limited-edition phones, viral marketing campaigns, and razor-thin margins.

The importance of Xiaomi’s use of analytics was revealed in an interview we conducted with co-founder Bin Lin. “We are a data-mining company,” Lin said. “Our business model is based on the data we collect.” Analytics has proven crucial to managing the company’s supply chain, delivering data as a valuable service to apps makers, developing new products, launching in new markets, and protecting a loyal customer base from third parties trying to create a black market for Xiaomi products.

Northwestern Mutual

For technologists pushing for change, that change might not come soon enough. David Pahl, director of analytics for The Northwestern Mutual Life Insurance Company (NM) and a 21-year veteran of the organization, had been trying for a decade to get the company to pay more attention to the value of applying advanced analytics to various business problems.

Despite creating several proofs of concept identifying new ways to generate value, he could not get any traction within the company to leverage more robust analytics. In February 2013, Pahl was presented with an opportunity to start an advanced analytic team at NM from a leader who felt the company was finally ready and would finally embrace it. Initially skeptical, Pahl ultimately agreed to give it a renewed push.

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This is an article on Cytopathology for my followers from the Bio-Technology domain. This article is on cytometry

Unfortunately Cytopathology hasn't evolved as much as Histopathology. Intracellular and even intercellular probing still has to depend on possibilities and probabilities. May be Medical Research needs to evolve further for the cytometry to be more specific and effective.

The article.

"When To NOT Use Isotype Controls"

Antibodies can bind to cells in a specific manner – where the FAB portion of the antibody binds to a high-affinity specific target or the FC portion of the antibody binds to the FcR on the surface of some cells. They can also bind to cells in a nonspecific ...

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The collaboration will combine Cisco's leadership in providing technology solutions for the Internet of Everything (IoE) with IMD's expertise in developing global leaders. According to Gartner Research, by 2020, 75 percent of businesses will be a digital business or will be preparing to become one, yet only 30 percent of these efforts will be successful due to lack of talent and technical expertise. Gartner reveals the number one reason companies fail in digital transformation efforts is a failure to re-imagine and reinvent the business from top to bottom before they begin.

The partnership between Cisco and IMD will combine original research and Cisco Consulting's open innovation approach in order to generate practical insights that executives can apply directly to their businesses. By conducting original research, the Global Center for Digital Business Transformation will work to define digital business transformation, what it means for companies today, and how to stay relevant in today's competitive marketplace.
The Global Center aims to become a world-leading hub of research and innovation, helping executives take advantage of digital opportunities and neutralize digital threats. Employing full-time researchers from both IMD and Cisco, the Center will produce cutting-edge thought leadership and organize learning events. It will also collaborate with Cisco's existing network of innovation centers around the world.

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Transforming an Analog Company into a Digital Company: The Case of BBVA ......................................................
These processes of transformation are all the more far-reaching, swift and radical in information-rich domains, such as the media, culture, and entertainment. Banking has changed, too. But despite being an information-rich activity—the “raw materials” of financial services are money and information—banking has changed a lot less than other industries.
Money is readily digitized: when it takes the form of electronic book entries, it becomes information that can be processed and transferred in an instant. Various reasons have been suggested to explain why banking has changed relatively little.
First, the industry is subject to heavy regulation and government intervention. This discourages potential new entrants, so incumbent banks feel less pressure to change. Another factor often pointed to is average user age, which is higher than that seen in other industries—such as music. What’s more, most people take a conservative approach to their finances.
And it may well be that the rapid growth and high earnings of the financial services industry in the years leading up to the downturn nurtured complacency.
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