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Posts from the ‘Data Economy’ Category

10
May

Applied Machine Learning and AI @ FaceBook


“Facebook today cannot exist without AI. Every time you use Facebook or Instagram or Messenger, you may not realize it, but your experiences are being powered by AI.”  —  Joaquin Candela, Facebook’s Applied ML team leader

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“Machine learning is a core transformative way by which we are rethinking everything we are doing. Google Brain is the way we are embedding this in everything we do.”                                               Sundar Pichai (CEO Google)

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Salesforce CEO Marc Benioff said at a recent conference: This [AI, ML] is a huge shift going forward, which is that everybody wants systems that are smarter, everybody wants systems that are more predictive, everybody wants everything scored, everybody wants to uFacebookAInderstand what’s the next best offer, next best opportunity, how to make things a little bit more efficient.”

Machine Learning (ML), Deep Learning and AI powering “Systems that Learn at scale” are at the bleeding edge of data science, deep learning and predictive search today.

Every market leader is jumping on this AI enabled engagement trend in retail, banking and healthcare.

Machine learning is central to Facebook’s future. Creating the extreme personalized experience (“individual equation based on 1000s of attributes – your preferences, predilections, conversations and transactions “) is the killer app.

FaceBook – AI/ML Case Study

Facebook is a bleeding edge case study of where AI/ML are being used to augment user engagement and experiences. I am starting to see many leading firms investing in ML Accelerators and Platforms as part of their data science strategy.

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9
Mar

Omni-channel Retail Paradox: the Curse of Digital


Everyone knows that the retail industry is being transformed by digital, analytics and big data. Winning requires continual data-driven experimentation and transformation.  

Shortened time from idea-to-app is a constant challenge.

Evidence of this “digital disruption” by category are mounting every day. Wal-Mart closes 269 stores as it retools portfolio to compete with online natives like Amazon.com. Macy’s said that it will shutter over 36 stores as store traffic declines faster than expected, and Finish Line said that it would close 150 stores by 2020.  Gap,  J.Crew,  American Apparel, Sears and Kmart are all facing similar headwinds.

Starbucks CEO Howard Schultz laid out his thoughts on the future prospects for retail business, “three years ago we began to envision that there would be a seismic change in consumer behavior, and that seismic change was due in large part to e-commerce, omni-channel and smartphone shopping.”

It’s fascinating to watch retailers trying to shift tech/platform strategies to deal with digital disintermediation, showrooming, asset-light models, physical-to-digital channel integration, mobile shoppers, same-day delivery/fulfillment, programmatic targeting, online native models and now the new buzz.. virtual and augmented reality.

Several retailers have invested in Big Data and Hadoop platforms to mine massive volumes of structured transactional,   operational data and unstructured data—web logs, clickstream data, geo-location data, social interactions and sensor data.

While most retailers understand the mega-shift and seems to know what to do….they are unable to execute consistently or effectively. A talent gap in many cases. A platform gap in others. Others are hindered by legacy IT systems or don’t have the necessary technology capabilities in place.

I think the digital induced pain is going to get worse in 2016 and 2017.  Consumers will continue to diversify their retail activity across channels in search of the best value, forcing retailers to spread out their digital investments.  This puts additional stress on execution and leadership.

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3
Mar

Analytics and ML Use Case – Robo-Advisors in Wealth Management


New data driven FinTech business models built on Hadoop, Spark and Machine Learning are rapidly emerging and disrupting wealth management.  Here is my recent posting from disruptivedigital.wordpress.com about one such use case… Robo-Advisors.

We are in the early stages of a generational shift in wealth management, especially “plain vanilla” investing for the mass affluent and millennial segment.  Until recently, you had only two options when investing:

  • Do-it-yourself (DIY)
  • Hire a registered investment advisor (RIA)

Now there is a third option.  Robo-advisors are new class of personal financial advisors that provides online, algorithm based portfolio management with minimal human intervention. Robo-Advisors going after the low-end of brokerage/RIA business with automated asset allocation using Modern Portfolio Theory.

The Robo-Advisors market leaders who are serving the mass affluent include are:

  • Wealthfront (with over USD 2.6bn in assets under management (AuM) and 20,000 investors);
  • Betterment (with over USD 1.4bn in AuM and 70,000 investors); and
  •  FutureAdvisor (With over $600 million in AUM).

The timing for this market shift coincides with three trends: consumerization, digital tools, and disillusionment with status-quo investment advisors.  The gyrating stock market driven by program trading is increasingly bringing Robo-Advisors, algorithmic portfolio management to the forefront.  Investors are getting disillusioned with traditional investment advisors who simply track the market indices (SPY, QQQ or Russell 2000) by purchasing ETFs at best.

Many banks and brokerage firms over the years have shifted their focus to serve ultra high net worth (UHNW) and high net worth (HNW) investors, leaving an opportunity for firms to target the “mass affluent” investors, or those with less than $1 million in investable assets. Younger investors are increasingly interested in online digital advice (trial-and-error bets), as opposed to hiring an adviser.

wealthfront3 Read more »

2
Jun

The NoSQL and Spark Ecoystem: A C-Level Guide


EvolutionofDBMS

New Technologies | New Possibilities

As a C-level executive, it’s becoming clear to me that NoSQL databases and Machine Learning toolsets like Spark are going to play an increasingly big role in data-driven business models, low-latency architecture & rapid application development (projects that can be done in 8-12 weeks not years).

The best practice firms are making this technology shift as decreasing storage costs have led to an explosion of big data. Commodity cluster software, like Hadoop, has made it 10-20x cheaper to store large datasets.

After spending two days at the leading NoSQL provider  MongoDB World event in NYC, I was pleasantly surprised to see the amount of innovation and size of user community around document centric databases like MongoDB.

Data Driven Insight Economy

It doesn’t take genius to realize that data driven business models, high volume data feeds, mobile first customer engagement, and cloud are creating new distributed database requirements. Today’s modern online and mobile applications need continuous availability, cost effective scalability and high-speed analytics to deliver an engaging customer experience.

We know instinctively that there is value in all the data being captured in the world around out…no question is no longer “if there is value” but “how to extract that value and apply it to the business to make a difference”.

Legacy relational databases fail to meet the requirements of digital and online applications for the following reasons:

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22
Dec

2015 Year in “PreReview” in Technology


The summer of 2015 marked the release of the blockbuster Sci-fi movie, tEREUy1vSfuSu8LzTop3_IMG_2538“Terminator Genisys,” which grossed a record $350 million at the box office and further popularized the notion of time travel. In addition to sequels and prequels, Hollywood has now adopted plots for movies in which the audience can choose among alternate storylines and follow them to their logical conclusion. The future, as we know it, is plural. This year in our PreReview of 2015, we once again present a few alternative scenarios for the future from our vantage point at the end of 2014.

New business models created by emerging technologies and unforeseen partnerships dominated the headlines in 2015.  Trending technologies such as the Internet of Things approached half the level of big data during 2015. Trending terms in the mainstream media such as drones and Bitcoin scored high in Google trends.

Here are three headlines from 2015 that caught our attention.

FedEx launches “parcelopter” service for 50-minute delivery  Read more »

2
Jun

Apple’s HealthKit vs. Google Fit – Wellness Platforms powered by big data and analytics


mobile-applicationsGame on….I think we just witnessed a next generation leap in Healthcare Wellness (powered by Data and Predictive Analytics).  Apple jumped into the health information business on June 2 2014, launching both a new health app (Health) and a cloud-based health information platform with IOS 8 (HealthKit). This was followed by Apple Watch, (Watch launch in September 10, 2014), an intelligent health and fitness companion.

Google followed with Google Fit on June 25. Fit is a set of APIs that will allow developers to sync data across wearables and devices. Google Fit is the equivalent of Apple’s HealthKit.  Google didn’t announce an equivalent of Apple Health app.  It is expecting its ecosystem of Android partners to innovate with apps. Google also might be taking a different approach with Fit aligned with Android Wear SDK which extends the Android platform to a new generation of wearable devices.

The connected health and wearable devices market has a multitude of participants, including specialized consumer electronics companies, such as Fitbit, Garmin, Jawbone, and Misfit, and traditional health and fitness companies, such as adidas, Nike and Under Armour. In addition, many large, broad-based consumer electronics companies either compete in fitness market or adjacent markets, including LG, Microsoft, and Samsung. Read more »

13
May

Cloud-based Healthcare Analytics and Decision Support Solutions


CostTransparencyThe old playbook no longer works. Everyone acknowledges that U.S healthcare is broken.

Technology (preventative apps like Apple Health and HealthKit; EHR, claims and reimbursement analytics; Physician Practice management etc.)  will reinvent healthcare as we know it.  I expect the  healthcare transformation to start incrementally and develop slowly in sophistication.  Though the early changes will appear clumsy and underwhelming, by 2030 they will seem obvious, inevitable and well beyond the changes we might envision today.

Why change? Consider this:

  • Honeywell, a Fortune 100 technology and manufacturing company, needed to manage the ever-escalating cost of insuring its 130,000 employees and their dependents. Honeywell has reported that health care costs were growing approximately 8-10% per year.
  • Self-insured employers like Wal-Mart want to make health care cost and quality information available to their 1.2 Million employees.  Useful information that can be used by employees to select physicians based on how their rank, or how much they cost, resulting in savings for both the employee and the employer. Decision support enabler.

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10
Mar

Fan Engagement and Wearables: Disney MyMagic+


MagicBandA satisfying experience is the driver of any business’s revenue growth. Disney Theme Parks is no exception. Disney is executing a guest (and fan) personalization strategy leveraging wearables (and analytics) to track, measure and improve the overall park experience. The goal is increase sales, return visits, word of mouth recommendations, loyalty and brand engagement across channels, activities, and time.

Wearables are the next big thing.  The new crop of gadgets — mostly worn on the wrist or as eyewear — will become a “fifth screen,” after TVs, PCs, smartphones, and tablets.

Wearables are already being used to monitoring vital signs, wellness and health. Devices like Fitbit, UP, Fuelband, Gear2 track activity, sleep quality, steps taken during the day. Consumers of all sorts — fitness buffs, dieters, and the elderly — have come to rely on them to capture and aggregate biometric data.

What most people don’t understand is how powerful wearables (coupled with  analytics) can be in designing new user experiences.  Businesses thrive when they engage customers by creating a longitudinal predictive view of each customer’s behavior. To understand the wearables use cases and potential we did a deep dive into a real-world application at Disney Theme Parks.

Wearable Computing at Disney: MyMagic+

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4
Mar

Big Data Performance Anxiety and Data Grids


In Memory Data Grid (IMGD) is a data structure that is being increasingly The Gridcited as a solution to the problem of scaling big data applications. Unlike in-memory applications, IMGDs distribute only the data across RAM over multiple servers.  With memory prices continuing to fall and the volume of data for an application continuing to rise, solutions based on memory are looking more attractive to manage the performance bottlenecks of applications using Big Data. Should IMGD be on your radar screen for a Big Data application?

In order to understand this and other questions on IMGDs, Carpe Datum Rx spoke to Miko Matsumura, VP of Marketing and Developer Relations at Hazelcast, who has seen recent adoption of this technology in banks, telcos and technology companies. Here is an extract from our discussion.

Why is it so important to distribute data in a data grid? Why should it be In-memory?

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24
Feb

Security Analytics – Big Data Use Case


Another day, another data breach.  Just received another “We’re sorry you got hacked”…letter.  

This is the fifth letter I have received in the past 3 months:  Forbes.com, Target, Neiman Marcus, credit card company and a previous employer.  What is going on?

Why aren’t firms investing in beefing up their predictive ability to spot the cyber-security intrusion threats? What’s taking them so long to identify?  Why is the attack signature – sophisticated, self-concealing  malware – so difficult to spot?   Do firms need to invest in NSA PRISM type threat monitoring capabilities?

The three impediments to discovering and following up on attacks are:

  • Volume, velocity and variety – Not collecting appropriate security data
  • Immaturity and not identifying relevent event context (event correlation)
  • lack of system awareness and vulnerability awareness

Obviously… where there is pain…there is opportunity for entrepreneurs see below – data from IBM).  There is a growing focus on big data use case for security analytics after all the breaches we are seeing.  General Electric announced it had completed a deal to buy Wurldtech, a Vancouver-based cyber-security firm that protects big industrial sites like refineries and power plants from cyber attacks.

securityanalytics3

 

Here are three recent examples that I was personally affected by – Forbes,  Target, Neiman Marcus.  

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