2015 Year in “PreReview” in Technology

The summer of 2015 marked the release of the blockbuster Sci-fi movie, “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 

Cloud-based Healthcare Analytics and Decision Support Solutions

The 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.

Big Data Performance Anxiety and Data Grids

In Memory Data Grid (IMGD) is a data structure that is being increasingly cited 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?

2014 Year in PreReview for Big Data Analytics

In the movie “Minority Report,” set in 2054, Tom Cruise plays the captain of the “PreCrime” police force, which uses “precognitive” abilities of mutants to stop crime before it happens. Silicon Valley futurists have sometimes used this reference in the context of the art of the possible with Big Data. We have another 40 years to go to see how analytics can accurately forecast future events based on human behavior. Meanwhile, imagining the future with some level of accuracy is within our reach today.

Value creation in the data economy made headlines in 2014. While Big Data continued to be the buzzword of the year in 2014, solutions that created economic impact were center stage.  Trending terms such as “predictive analytics” and “advanced analytics” approached the levels of “Big Data” on Google Trends during the year. “ROI,” which was vaguely referenced in the last two years, became the most commonly used term with Big Data in 2014. Here is a cross-section of 2014 events.

Apple announces TopsyTV

This is their next-generation TV appliance that integrates social media engagement with the TV watching experience. Earlier in 2013, Apple acquired Topsy Labs, a reseller for Twitter content for $200M. This was followed by a series of less publicized acquisitions of social media data companies. Apple is characteristically tight-lipped about its plans for monetizing this product with advertising, but speculation is rife that Apple is poised to get a piece of the $600 billion that is spent on advertising today.

Blessed are the Mid-Markets, for they shall Scale Big Data

In a parody of Start Trek, Silicon Valley technology companies describe their business goal as “Scale, the final frontier…”.  Mid-market companies, defined as those having 100-2500 employees, may indeed provide an opportunity to emerging technology vendors to scale their business. According to Techaisle, a market research firm, these 800,000 global companies spend $300B on IT and are sought after by technology vendors big and small. In the last decade, technologies such as Cloud, SAAS and Virtualization have reached scale with a large number of mid-market companies as early adopters. Intuit, Salesforce.com, NetSuite and Amazon are just a few examples of companies who have relied upon mid-market companies as a key building block for their business.

What does this mean for Big Data? To find out, Carpe Datum Rx spoke to “SMB Guru”, Anurag Agrawal, CEO of Techaisle and the former Head of Worldwide Research Operations at the Gartner Group. Techaisle recently talked to 3,300 global businesses about their Big Data adoption plans. Here is an excerpt from our discussion.

The SMB Market is considered the Holy Grail for technology vendors because it is hard to penetrate. Does your research show that mid-market companies will adopt Big Data before large enterprises do? Are they the early adopters of this technology?

Big Data needs a good storyteller….like Gary Vaynerchuck

In an episode of Mad Men, Don Draper talks about pitching the Kodak Carousel. “Technology is a glittering lure, but there is the rare occasion when the public can be engaged on a level beyond flash, if they have a sentimental bond with the product….Nostalgia. It’s delicate but potent. Switch it on.”

Combine the storytelling prowess of Don Draper with the high-pitched vitriol of Jim Cramer and add a dose of emotional intelligence to get Gary Vaynerchuck,  social media guru, best-selling author, wine librarian and marketer par excellence of the internet age. Gary Vaynerchuck rose to prominence in social media a few years ago with his video log, wine library tv which he used to grow his family wine store into a mulit-million dollar business. He currently runs VaynerMedia, a social media strategy and production company.

Gary is an avid supporter of the use of quantitative analytics in marketing. Carpe Datum Rx caught up with Gary to ask him a few questions about big data, marketing and technology adoption in the enterprise. Here are his paraphrased comments.

Is Big Data ready for the 99 per cent ?

Email Marketing is a Predictive Analytics Problem

Digital Marketing from 1999 to 2012

In his book Permission Marketing, Seth Godin referred to email marketing as “the most personal advertising medium in history”.  That was 1999.

Where does email marketing stand in 2012 in the age of social media, omni-channel marketing and big data analytics? Here are some interesting data points.

Making Money on Predictive Analytics – Tools, Consulting and Content

Here are just a few examples of analytics at work

• Target predicts customer pregnancy from shopping behavior, thus identifying prospects to contact with offers related to the needs of a newborn’s parents.
• Tesco (UK) annually issues 100 million personalized coupons at grocery cash registers across 13 countries. Predictive analytics increased redemption rates by a factor of 3.6.
• Netflix predicts which movies you will like based on what you watched.
• Life insurance companies can predicts the likelihood an elderly insurance policy holder will die within 18 months in order to trigger end-of-life counseling.
• Con Edison predicts energy distribution cable failure, updating risk levels that are displayed on operators’ screens three times an hour in New York City.

Now you are interested.  So what about your organization. Do you have the right toolset, dataset, skillset and mindset for analytics? Do you want to enable end users to get access to their data without having to go through intermediaries?

The challenge facing managers in every industry is not trivial… how do you effectively derive insights from the deluge of data? How do you structure and execute analytics programs (Infrastructure + Applications + Business Insights) with limited budgets?

Data Scientist Infographic & Managed Analytics

The exploding demand for analytics professionals has exceeded all expectations, and is driven by the Big Data tidal wave.  Big data is a term commonly applied to large data sets where volume, variety, velocity, or multi-structured data complexity are beyond the ability of commonly used software tools to efficiently capture, manage, and process.

To get value from big data, ‘quants’ or data scientists are becoming analytic innovators who create tremendous business value within an organization, quickly exploring and uncovering game-changing insights from vast volumes of data, as opposed to merely accessing transactional data for operational reporting.

This EMC infographic summarizing their Data Scientist study supports my hypothesis – Data is becoming new oil and we need a new category of professionals to handle the downstream and upstream aspects of drilling, refining and distribution. Data is one of the most valuable assets within an organization. With business process automation, the amount of data  being generated, stored and analyzed by organizations is exploding.

Following up on our previous blog post – Are you one of these — Data Scientist, Analytics Guru, Math Geek or Quant Jock? — I am convinced that future jobs are going to be centered around “Raw Data -> Aggregate Data -> Intelligence ->Insight -> Decisions”  data chain.   We are simply industrializing the chain as machines/automation takes over the lower end of the spectrum. Also Web 2.0 and Social Media are creating an interesting data feedback loop – users contribute to the products they use via likes, comments, etc.

CIOs are faced with the daunting task of unlocking the value of their data efficiently in the time-frame required to make accurate decisions. To support the CIOs, companies like IBM are attempting to become a one-stop shop by a rapid-fire $14 Bln plus acquisition strategy:  Cognos,  Netezza, SPSS,  ILog, Solid, CoreMetrics, Algorithmics, Unica, Datacap, OpenPages, Clarity Systems, Emptoris, DemandTec (for retail).  IBM also has other information management assets like Ascential, Filenet, Watson, DB2 etc.  They are building a formidable ecosystem around data. They see this as a $20Bln per year opportunity in managing the data, understanding the data and then acting on the data.

Big Data, Analytics and KPIs in E-commerce and Retail Industry

• How to convert Lookers to Bookers…
• How to create unique and effective Digital Experiences that impact probability of purchase or likelihood of return.
• What offers might result in higher “take rates”

The change in consumer behavior and expectations that e-commerce, mobile and social media are causing is hugely significant – big data and predictive analytics will separate brand/retail winners from losers. This won’t happen overnight but the transformation is for real.

Retail Industry makes up a sizable part of the world economy (6-7%) and covers a large ecosystem –  E-commerce, Apparel, Department Stores, Discount Drugstores, Discount Retailers, Electronics, Home Improvement, Specialty Grocery, Specialty Retailers and Consumer Product Goods suppliers.

Retail is increasingly is looking like a barbell – a brand oriented cluster at the high-end, a very thin middle, and a price sensitive cluster at the low end. The consumerization of technology is putting more downward pricing pressure in an already competitive “middle” retail environment. The squeeze is coming from e-commerce and new “point, scan and analyze” technologies that give shoppers decision making tools — powerful pricing, promotion and product information, often in real-time. Applications in iPhones and Droid, like  Red Laser can scan barcodes and provide immediate price, product and cross-retailer comparisons. They can even point you to the nearest retailer who can give you free shipping (total cost of purchase optimization). This will lead to further margin erosion for retailers that compete based on price (a sizable chunk of the market in the U.S, Europe and Asia).

Data analytics is not new for retailers. Point of sale transactional data obtained from bar-codes first appeared in 1970s. A pack of Wrigley’s chewing gum was the first item scanned using Universal Product Code (UPC) in a Marsh Supermarket in Troy, Ohio in 1974.  Since then, retailers have been applying analytics to get even smarter and speedup the entire industry value chain.

More recent use cases of retail analytics  include: