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Turning Big Data into Business Insights

The Reviews Editor at ZDNet UK, Charles McLellan writing recently in a special feature in the ZDNet magazine highlighted how the mere collection of data, which enterprises are doing increasingly efficiently, will not fetch many benefits to enterprises till necessary efforts to turn these into useful business insights are not carried out.

As Charles McLellan writes, the data collection is all pervasive and pervades enterprises of all sizes and definitions and the most important question today is about the how little control an individual as on his personal information, meanwhile astute enterprises with their Big data driven initiatives using large amounts of user data is targeting very specific groups with their services or in case of non-commercial enterprises seeking support to a particular goal or cause.

As he points out further in this article, enterprises to gain business insights from this deluge of information should have in place right planning, budget, tools, and expertise. This will enable them to analyze and get actionable data and help enterprises to implement revenue –generating innovations in a pertinent segment of the business to gain competitive advantage.

Charles McLellan quotes reports from IDC and EMC to project the total data generated globally by 2020 to around 44ZB (zettabytes), and another by Seagate’s Data Age 2025 to around 163ZB, and for enterprises with vast amounts of data coming in it will be easy to get overwhelmed with the possibilities. And with data coming in different types like structured, unstructured and real time, it should be understood that all data is not conducive to analysis. And according to IDC by 2025, 20% of the data will be critical (Data necessary for user daily life) and around 15 % will be hyper critical (Data with direct and immediate impact on user’s life).

With Artificial Intelligence and Machine Learning being the foremost technologies deployed to understand Big Data, the data available for use will be further restricted. In fact, according to IDC by 2025, only 15% will be tagged and so useful for AI /ML analysis and further in this only 3% will be suitable to be analyzed by cognitive systems.

The coming trends for the Big Data industry according to this ZDNet special report by Charles McLellan is  “ AI, Machine Learning, Automation and Cognitive systems”, along with “ data driven business applications “

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Behavioral Analytics is the next generation of business intelligence

Writing an opinion piece in the CIO magazine, Den Steiner – adviser CIO explains how behavioral analytics is turning out to be the new hope for business leaders in their quest for cost – effective mechanisms to analyze business data to gain competitive advantage.

Beginning how today enterprises collect extensive data in course of their business transactions, Den Steiner goes on to tell us this data can be utilized to gain a competitive advantage in the market place when it is streamlined and formatted in an efficient way to lend actionable information.

But with enterprises encountering the challenges of the need for powerful architecture, robust data – warehousing and separate data visualization, this process most times lead to perplexing workflow and foggy analysis resulting in a more or less a very low efficient system.

In their bid to overcome these lacunae, business leaders tend to concentrate their resources on a single channel say mobile activity or website visitors and their page clicks to focus on activity levels and conversion rates. This even though useful to a certain extent they would have made use of a very small set of data.

Enterprises with deep pockets tend to face challenges of data sitting in different silos requiring the services of a development team to get a factual understanding of different data sets as the present data integration process is complex.

In the above scenario, the advent of behavioral analytics offers a solution which unifies the entire of digital activity, making it easy for enterprises to trace the customer’s journey from start to finish.

As is the practice, few industries are faster off the starting block in adopting behavioral analytics than others, on –line merchants in their quest technology innovation are in the fore front but many other industries like cyber – security, gaming and fin –tech can benefit equally from investing in this business intelligence.

And Den Steiner makes a strong case for enterprises to make behavior and its analysis the center in their need to organize data. And is of the firm view that behavioral analytics is the next generation business intelligence.

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How AI helps financial institutions perform customer due diligence.

David McLaughlin, CEO, and founder of QuantaVerse – a provider of Data Science and AI for identifying financial crimes writing recently in the Upside – a new blog from TDWI dedicated to providing information on extracting actionable information from data tells us how AI is helping the financial institutions do due diligence on customers.

He starts off by informing us how financial institutions are forced to rely on expensive and cumbersome KYC (Know Your Customer) initiatives to conduct verification of account opening details and to get additional customer information and documentation. And even when efficiently run this processes fail to uncover the underlying motive of the account owners.

With Financial Crime Enforcement  Network (FinCEN) having come out with new Anti –Money Laundering provisions which in its new Fifth pillar requirement mandates account specific transactional review and analysis from May 11th, 2018, Until now the financial institutions had a client –centric approach to their customer relations and avoided review of individual accounts. But with the Fifth pillar provision coming into effect, Financial Institutions in order to improve their risk management proficiency  are now required to ascertain the accounting objective, account history along with volumes and value of transactions particular to each account.

And going further, financial institutions to mitigate risk also need to monitor, investigate suspicious client activity which involves constant maintenance and up gradation of client information.

These fifth pillar requirements may be addressed effectively by the adoption of new technologies like Artificial Intelligence (AI), machine learning and Big Data. Financial institutions can reduce money laundering risks by deploying solutions to analyze massive amounts of both structured and unstructured data to extract precise information to analyze and review a specific transaction.

Financial Institutions deploying appropriate Data science and AI technologies may address new requirements like Link analysis, Transactional analysis and it may also seek to undertake help from Outside Investigative Sources and Unsupervised Machine Learning Techniques to help meet the growing regulatory demands of state, federal and international regulators.

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Faster Data Management and Analytics? Here are the 8 rules.

Big Data Quarterly, popularly known as BDQ, dedicated to bringing to the fore the various trends and analysis on subjects related to Big Data, features an article by Joe McKendrick in which the author unravels the new rules governing the delivery of information to enterprises and this may be achieved by effective data management and analysis.

Joe McKendrick begins by telling us about the rising expectations of users either employee or customers when visiting a website. With fast galloping technology coupled with increasing bandwidth speeds, the users of connected networks and devices expect information in nanoseconds.

With intense competition driving the enterprises to focus on user experience to increase business revenues, and for this enterprises encourage data and development teams to delivers applications with recommendation engines based on customer insights, which in turn requires not only data but fast and intelligent data in real time or near real time.

With the emergence of in–memory databases, machine learning … and other Gen Next technologies, a typical data eco-system which previously was tasked to deliver static reports based on historical data is today designed to be a  real-time or near time application to help deliver operational intelligence to decision makers on an instantaneous  basis.

Enterprises accustomed to batch mode of data up-gradation usually on 24-hour cycle recognize the benefits accruing to their organizations by keeping the data refreshed on a constant basis. And hence task their data managers to build, maintain and support a fast or streaming data eco-system to support highly interactive and intelligent applications.

Lastly, Joe McKendrick suggested few key elements necessary for fast and intelligent data eco- system.

  •  Mind data storage 
  • Alternate databases
  • Employ analytics close to data
  • In–Memory Options
  • Machine Learning and another real time approach
  • Cloud
  • Enhance skill base
  • Look at Life–cycle data management.

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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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