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How Amazon could use Big Data to change the way you shop.

Stephen DiFranco – CEO and Founder IoT Advisory Group writing as a guest writer in Entrepreneur magazine bring a fresh perspective on the recent acquisition of Whole Foods by Amazon by describing this buy as a “playground for disruption of retail”. As this gives Amazon a shot at retail analytics, customer traffic management and in–store customer management, going on to add,  that this may also provide a testing ground for Amazon to explore “presence marketing “  – the layering of online and offline buying analytics while tracking the movements of shoppers.

And according to Stephen DiFranco, this acquisition is all about disrupting the retail with Amazon bringing the on-line analytics to off-line shopping.

With Amazon getting a patent for “Physical store on-line traffic control “ just days before acquiring Whole Foods, Stephen DiFranco is sure that Big Data will be a big presence in the off-line retailing from now on. With store visitors encouraged to get connected to the store Wi- Fi, they will then by tracked to know about their typical route thru’ the store, with pertinent suggestions sent to customers to prompt them to buy complementary products. Another scenario may be reminding the customers about frequently brought items and the directing them to that location.

With other biggies in the retail sectors moving for consolidation, and retail margins being very thin, Amazon buying Whole Foods is a great advantage since food retail is best for collecting brick and mortar customer data thereby allowing them to leverage data to push volumes.

With food retailer being the most frequently visited stores, as shopping for food is done twice a week. And with customers looking for convenience, they will be purchasing the same items again and again, week after week.  And this behavior gives out preferences across product lines and lends itself to behavior analysis giving out clear buying patterns across products and categories.

Amazon using a big data application and massive amounts of data pertaining to a number of visits, product category, minimum purchased, can lead the way in enhancing the customer experience by helping in decision making by delivering notifications, discount offers, and other information at an appropriate time.

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Artificial Intelligence will change how companies in future do business.

Vivian Rosenthal – Founder of Snaps, mobile messaging platform writing on Big Data in the Forbes e- magazine tells us how Hollywood always chooses to portray the future in apocalyptic terms with humans reduced to slaves to newer technologies to AI, and offers a counterview of how businesses are using the same technologies to overcome some of their most critical challenges.

According to her, even as there are much still not convinced about the indispensable role machine learning and other artificial intelligence technologies will play in future enterprises, there are many who have used these technologies to make their enterprises smarter, faster and more creative.

Citing CB insights to bring home the point, she shows that it’s not only big tech companies buying AI firms but traditional businesses are also getting into this race by acquiring AI startups and this together with the fact that 34 AI acquisitions were done by in Q1 of 2017, highest at any point of time, indicating wider acceptance of AI.

AI is riding on the convenience of cloud computing, the pervasiveness of processing power, huge, no-cost storage options, and providing enterprises the opportunity to leverage their data by applying machine learning to deliver best in the class customer experience with optimal resources. Vivian then proceeds to bring out how different technology behemoths are using machine learning to increase customer‘s experience.

Pinterest manages to deliver the right pin at the right moment by leveraging machine learning for content discovery, in this, the content recommendations are algorithmically driven and machine learning is used to determine among billions of user interactions the right pin to deliver at the right time. Similarly, Netflix uses machine learning to understand consumer watching pattern, surfing insights thus bring data –informed decision making to content discovery. Amazon‘s Alexa team is trying to surmount the challenges of Natural Language Processing and Generation as we are moving from typing to talking computers.

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Best 5 tips for successful BI initiative.

While trying to catch-up on what’s happening in the BI space, I ran into this wonderful article on It featured Martin Draper, technology director at a luxury retailer, he had listed his “Best 5 tips for a successful BI initiative”.

Let me start with a quote which appeared in the end, “Great BI is about understanding what you and your colleagues are going to do differently tomorrow because of the insight you have today”, this probably summed up the entire article.

For those who are still there, here are the 5 tips, pretty condensed, you can read the entire article by clicking here.

Tip 1.
BI needs to be seen as an organization-wide cultural approach.
How much ever you might invest in BI technology year-on-year, you need to build an organizational culture which can consume it and develop actionable insight, if not, he suggests, don’t start it. See Tip 5. for reinforcement.

Tip 2.
Ensure the “big guy” in your organization is standing behind your BI initiative.
A simple reason for this being, it can be costly and may take some time to get the BI initiative right. The single most important output of a BI initiative is the single version of truth on which the company can take decisions, you wouldn’t want a skeptic doubting the output which chips away the credibility of the entire initiative.

Tip 3.
Get outside help to win early.
While your own team may require some time to deliver big wins, an external BI expert can help you win early, this is critical to ensure the organization stays the course in the long term.

Tip 4.
Keep checking back with business on what you are building.
BI means different things for different stakeholders, while some think it is key to making quality decisions faster, some might want a neat reporting, for some, it might be just process automation. Hence, keep checking back with business, not doing so would leave a lot of the stakeholders unhappy.

Tip 5.
This is probably the best and the most relevant.
Ensure your people know what to do with the insights.
Investing a whole lot of time, effort and money, building some real good dashboards come to a naught if the team is unable to take the insights generated and take some action based on that. It is like building a great space program without knowing where it is going and what’s supposed to do.


Text Analytics Market set to double by 2022.

A just released ReportsnReports study on Text Analytics forecasts the total Text Analytics market size to reach $8.79 Bn by 2022 as compared to $3.97 as of now (2017).

Here are some of the highlights of the report -

The Text Analytics market is expected to see a phenomenal CAGR of 17.2% from 2017 to 2022.

CEM (Customer Experience Management) expectedly has the largest market share considering how text mining is used to improve customer experience.

Once again, North America is a dominating the Text Analytics market, while the highest growth is expected from Asia. The US, according to the report, has many dominant players with high adoption rate, whereas the usage of Text Analytics to make real-time decisions has just started picking up in Asia.

The increasing usage of social media by customers and the advent of cloud following which the availability of metrics and some customized solutions are the main factors propelling the growth.

Massive volumes of data are being collected from various sources and are stored in the cloud, more and more companies are using Text Analytics solutions to analyze this data for actionable insights.

For more info, check this report here –

Big Data: Separating Hype from Reality.

Brett MacLaren – Vice president, Enterprise analytics of Sharp HealthCare writing in CIOReview documents the complexities involved in the adaption of Big Data in his attempt to separate the hype from reality   created among with executive boardrooms. As according to Brett MacLaren  data  has enormous business value and helps enterprises embark on the digital transformation of their enterprises.

As Brett MacLaren puts it, this journey from analog processes and workflows to digital domain starts quite simply with enterprises using the data created in all its attributes to derive business advantage leading to data- driven decision making and this adaption is successful in a few limited and much published cases where the companies are digital from the start helped by massive investments in enabling technologies and human resources adept in leveraging them.

The challenge for most other enterprises is not only to invest in tools and hardware which vendors claim to be proficient in  ROI but to get people who can leverage these tools and deliver value, and most critically this should happen when the enterprise is focused  the meeting the existing business demands. The compulsion to juggle these two crucial activities becomes too demanding for many of the enterprises. And to realize the promise of Big Data enterprises should work on solutions involving several critical components some technological along with one critical aspect – corporate culture.

When it comes to addressing the role of corporate culture in harnessing the benefits of Big Data, Brett MacLaren suggests creation of a high profile leadership position to lead the enterprises thrust to data reorganization. The mandate for this leadership which many enterprises designate as Chief data Officer is to drive home the business value of data and be the pivotal point between business and IT and helping both to recognize the strategic role of data.

Enterprises hoping to realize the benefits of Big Data must spread the culture of data awareness and data competency across enterprise. Big Data and other related tools and technologies are clearly cutting edge and are kind of starting points for adaption of machine learning, deep learning and other parallel processes that can deliver tremendous value to enterprises by bringing the data to the point of decision making.

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Are we ready for Augmented Intelligence?

The recent article by Hannah Williams in – portal delivering news, reports and analysis on global technology industry, makes for interesting reading,  it reports on how two technologies which many consider to be in their infancy is coming together to create a new class of technology -  Artificial Sensors. This development is a consequence of two technologies namely IoT and Artificial Intelligence spinning off innovations in various industries which in turn is pushing technologists to seek newer horizons.

In this article, Hannah Williams documents the keynote address of Neil Harbisson – artist and cyborg activist at the annual SAS global forum, in this address Neil Harbisson deliberates on the cyborg project which made possible the development of artificial sensors which were then implanted in humans to transmit senses.

This project started with Neil Harbisson himself getting implanted with  antennae in the skull to overcome his inability to identify color, this antennae enabled him to hear the frequencies of light in colors.  As Neil elaborates in his address his aim was not to wear or use technology which propelled him to go for a new sensory organ, a sensory organ to sense color, a third eye for color implanted, but then this would have limited his perception of color to what was in front of him. Observing nature made him to create a antennae limited for color perception which enabled to sense colors both in the front and behind just by moving the antennae. This antennae along with its ability to perceive colors was also developed with a feature to send internet transmissions to Neil Harbisson‘s brain facilitating him to receive color from external devices from around the world.

As Neil tells in his address, he sees this as a use of internet as a sense organ or a sensory extension which he feels  will become more prevalent in 2020′s and  the internet  will not limited to be used only as a tool or   for communication but also as an extension of senses to perceive colors and other bodies.

And as with everything else nowadays, the aspect of security is one big questions for which answers need to be found since according to Gartner study, 25% of security attacks in enterprises will be from IoTs and as another HP study discovered an average IoT device has around 25 security flaws most of which the enterprises and users are unaware of. And with internet connected sensory devices built to be implanted in human bodies the security fears raises to different level altogether.

Hannah William in this article cites another example of attempt to merge technology with human brain, this one coming from Elon Musk of Tesla fame, launching a company – Neuralink, where the focus is on the development of neural lace with embedding of string of small electrodes in human brain. The company is working on the concept of merging human brain with computer technology.

Accompanying these examples of artificial  sensory  devices , is the concern about security , since internet as a stand- alone is not secure and how can one be sure when it is paired with human brain.

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Enterprise Mobility, key to your business strategy.

Tony Storr, an Architect and Leader, IBM Mobile at Scale, writing in Mobile Business Insights talks about the necessary factors a C-suite  executive should focus on if enterprise mobility has to play a key role in their business strategy.

Tony Storr starts off how enterprises in their anxiety to get on the mobility bandwagon encouraged various business units to follow their own roadmap and even though it felt empowering at that time led to some serious issues over time. These issues like varying standards, system fragmentation, security implications and provider volatility resulted in uneven user experience. But, the real business issue was when it was time for digital transformation, these issues being key factors effecting pace of change, posed serious challenges, especially as speed to market was crucial. This scenario is widespread irrespective of the industry and geography.

The challenge for the enterprises is to consolidate and industrialize the mobile applications without encroaching upon the innovation aspect brought in by the business unit. This entails much more than having a single vendor or internal team churning out applications in a consistent manner. Enterprises embarking upon consolidation should not forget that consolidation need to happen holistically and continuously across portfolios of apps and also across mobile app services (design, develop, maintain, support and monitor).

The key characteristics of effective mobile services are, continuous operation, increasing assets and accelerators, increasing productivity, cross app design and architecture, integrated support functions and innovative tooling and techniques.

In conclusion, Tony Storr writes that consolidation and refactoring of all existing applications may not be advisable and suggests this may start with the next generation of employee apps and enterprise mobility may well take off from there.

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How machines learn?

Writing in Datanami, a news portal dedicated to Big Data news, insights and analysis, Fiona McNeill, a SAS global marketer and Dr. Hui Li,  senior staff scientist for SAS, shares light on how machines learn. Starting with examples of various enterprises using machine learning to design personalized offerings to attract customers, they then raise the important point of different vendors jumping on the bandwagon of machine learning with their own approaches and solutions making the whole thing confounding to the user. And thru’ this article in Datanami, the duo from SAS try to unravel machine learning and make it easier for users to understand how exactly machine learning works.

Machine learning models are designed to learn how to perform tasks and with algorithms designed to see relationships and patterns between various factors, these models learn continuously from data.  And to generalize this model for business they are then validated on whole set of new data not used initially for training. These models may be made to learn in different ways like supervised learning, semi-supervised, unsupervised and reinforcement learning.

Machine learning is at the center of many advanced intelligent solutions emerging now, like AI, Neural networks, Natural Language Process and Cognitive Computing.

  • Artificial Intelligence – A discipline enabling the design of machine with problem solving skills to accomplish tasks just as human beings can.
  • Neural Networks and Deep Learning – Neural networks are programs written to learn from observational data and present solution to the problem on hand. These are used in speech and image recognition and are very successful in supervised learning.
  • Natural Language Processing and Cognitive Computing – NLPs are interfaces which enable machines to understand human language and humans to interpret machine output. These are applied in image captioning, text generation and machine translation.

The confluence of Big Data and massive parallel computational environments are driving the machine learning initiatives and the goal is to deliver solutions that can be highly customizable and with human – like cognition features.

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2016, Big Year for Big Data

Writing recently in insideBigData, Linda Gimmeson, a technical writer focusing on Big Data, machine learning and IoT, takes a look back on the year 2016, and tells how Big Data contributed technologically and socially, she then sets out to make a list of six disciplines which has benefited by the application of Big Data.

AI advancement – Big Data is advancing the speed and capacity of Artificial Intelligence and taking it to the next level, for example  Google DeepMind AI beating humans in the game of Go, and becomes unbeatable as the game progresses due to AI and the Big Data applied to its functioning.

Tax Shelters Unveiled – Investigative journalists collaborating across continents and using cloud based data analytics and Big Data were able to pursue effectively and unveil tax shelters now famously known as “Panama papers”.  This is one of the first known instance of the real-world good, Big Data can contribute to bring about.

Human Trafficking – Big Data is lending its helping hand to “Polaris Project” in its fight against human trafficking, even though Polaris project has made tremendous progress over the years  in their fight, Big Data became their strongest tool in the year 2016 to decipher the complex numbers and patterns to give useful insights and help victims of this  horrific crime.

Cancer Research – Intel’s Bryce Olson , himself a cancer survivor lead, Trusted Analytics platform is “ a collection of Big Data tools and data analytics, to help in breaking down of DNA – the complex code of human genetics to give insights into where cancer begins and how it can be controlled”.

HIV Outbreaks – When Centre of Disease Control were struggling to contain the HIV outbreak in 2016, which was taking extensive death toll and seriously damaging the health of survivors, they turned to Big Data for insights to fight the outbreak.

In 2016 we were witness to the fact that Big data in addition to its applications in optimizing business efficiency and effectiveness can also be deployed to harness real- world good, and we are sure to see more such deployments in the coming years.

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6 Ways Business Intelligence is going to change in 2017

Ralph Tkatchuk – a freelance security consultant writing in Dataconomy, a leading portal for news, events and opinion on data driven technology talks about six ways the business intelligence is going to change in 2017.

Writing how decision based on data is more effective than those based on intuition, perception and assumptions, he goes along to tell that businesses driven by data are five times more likely to make faster decisions than their peers. And they are twice more likely to land up in the top quartile of financial management within their industry.

Till very recently only large enterprises with access to sophisticated business intelligence tools and ability to collect vast amount of data were beneficiaries of this data driven strategy, business also had to invest in analytical solutions and data scientist to convert this data into useful information leaving out large chunk of small and medium business out of its sphere of influence, but this is going to change in 2017.

In their bid to be on par with large enterprises on taking advantage of data driven technologies, SMBs are turning to self – sufficient business intelligence tools. With intuitive interfaces, astute data preparation tools and this coming at very low price points allow SMBs to be their own data scientists.

In light of this development, Ralph Tkatchuk expects business intelligence to change in 2017 due to the following,

  • Affordable Access – Complex data analytics becoming more cost effective and hence accessible to SMBs.
  • Smart Integration – BI is becoming available thru’ more integrations like messaging services and IOT, and BI is moving to on – demand service and helping SMBs.
  • Simplified Analytics – With comprehensive solution for back end number crunching and front virtualization, BI is commoditized to be within the reach of SMBs.
  • Cloud based data – The adoption of cloud based data- warehousing is contributing to SMBs BI self-sufficiency.
  • Evolved Visualization – The new self – sufficient BI tools offer data visualization in interactive and real time mode spurning dashboards which one can drill down.
  • Collaboration – With BI becoming accessible, SMBs may employ cross – team collaboration to increase effectiveness and efficiency.

All these above advances are making SMBs adapt to BI extensively in the coming days.

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