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 ?
Data Management, AML, and KYC Analytics
To roadmap Wall Street priorities for 2013, we have been having an interesting set of meetings recently with MDs and leading architects in various banks and investment services firms.
Got the scoop on analytics projects they are investing in — Anti-Money Laundering (AML) monitoring, trade surveillance and Know Your Customer (KYC) analytics. To enable AML and KYC initiatives…the big foundational investments in 2013 are around:
1) Strengthening the Golden Sources – Security Master, Account Master and Customer Master.
2) Various enterprise data management initiatives – Data Quality, Data Lineage, Data Lifecycle Management, Data Maturity and Enterprise Architecture procedures.
3) Reporting improvements via next generation Enterprise Datawarehouses (EDW) — Reporting on top of EDW addresses the core problems faced by Finance, Risk and Compliance when these functions extract their own feeds of data from the product systems through which the business is conducted and use differing platforms of associated reference data in support of their reporting processes. Lot of current investments are in the areas of Finance EDW which delivers common pool of contracts, positions and balances, organized on an enterprise wide basis and completed by anointed “gold” sources of reference data which ensure consistency and integration of information.
Crawl, walk, Run seems to be the execution game-plan as the data complexity is pretty horrendous. Take for instance, Citi alone….has approximately 200 million accounts and business in 160+ countries and jurisdictions.
The type of data challenges banks like Citi are wrestling with include: Read more 
20 must read Infograhics on Big Data
Big Data emphasizes the exponential growth of data volumes worldwide (collectively, >2.5 Exabytes/ day).
Big Data incorporate the following key tenets: diversification, low latency, and ubiquity. In parallel, the emerging field of data science introduces new terms including, predictive modeling, machine learning, parallelized and in-database algorithms, Map Reduce, and data monetization.
A variety of infographics have been published around Big Data, Data Scientists. Here is a compendium of some very interesting ones.
Big Data Big Opportunity
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A Data Scientist Study
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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.
Apple IOS 6 Passbook: Enabling SoLoMoMe + Omni-channel Analytics
At the Analytics Executive Forum, I facilitated a session on Omni-channel analytics. It struck me how every leading consumer facing firm seems convinced that mobile is becoming the dominant B2C interaction channel. Mobile is the gateway to insight based marketing and the “always addressable customer”….
Insight-based interactions – The company knows who you are, what you prefer, and communicates with relevant, timely messages, using the power of analytical intelligence to detect patterns, decode strands of information and create meaningful offers and value.
The “always addressable customer.” This is a consumer who fits the bill on three fronts simultaneously: (1)
- Owns and personally uses at least three connected devices; (2)
Goes online multiple times throughout the day; (3)
- Goes online from at least three different physical locations
The opposite of insight-based is “spray-and-pray” marketing - The company has very limited knowledge about who you are, forgets what you prefer, and tries to reach you with off-target communications that alienate you – based on fragmented data, poor data quality and inadequate integration, resulting in confusing, chaotic interactions. A good example: “I have 2 million frequent flyer miles with your airline and still do not get any recognition, respect or value from this loyalty.”
As companies architect new insight based mobile use cases I suggest that they look at what is coming next. With IOS 6, Apple is delivering several new features. The most interesting is a new integrated time-saver feature called Passbook that I think will be a game changer in terms of user experience and value. Also Apple ditched Google Maps for their own in IOS 6 allows them to integrate user latitude/longitude coordinates combined with maps into a valuable location-aware platform for app development.
Retailers, banks and other customer facing firms/brands better pay attention. 100+ million iPhones are automatically getting this feature with the new OS upgrade making this a mega-disruptor in the coveted target segment everyone is chasing. Read more
Organizing for BI, Analytics and Big Data: CoE, Federated or Departmental
Consider this…eBay’s “Singularity” Teradata warehouse exceeds 40 petabytes. According to eBay, the company’s data volumes are 50+ terabytes per day in new incremental data, processing 50+ petabytes
and tens of millions of queries per day, with 99.98% availability and more than 50 petabytes of online storage.
Data is valuable. Data is plentiful. Data is complex. Data is in flux. Data is fast moving. Capturing and managing data is challenging.
So, if you are a senior leader in a Fortune 2000 company. How do you structure your group to deliver effective BI, Analytics or Big Data projects? Do you have the right structure, toolset, dataset, skillset and mindset for analytics and Big Data?
Organizing for effective BI, Analytics and Big Data is becoming a hot topic in corporations. In 2012, business users are exerting significant influence over BI, Analytics and Big Data decisions, often choosing analytics and visualization platforms and products in addition to/as alternatives to traditional BI platform (reporting and visualization tools).
Omni-channel Conversion Optimization with Social Marketing/Media Analytics
Brooks Brothers is investing in tools & testing to improve the online experience – and ↑sales. In a test involving one product category page: men’s shirts. The retailer using Bazaarvoice Ratings & Reviews software, used customer reviews to sort items on the product page. Items with five ♥♥♥♥♥- the highest rating – appeared on the top of the page. The result: a 9% lift in conversions [Adobe Digital Marketing Symposium]
Are you ready to anticipate and influence your audience in a whole new way? Value migration from traditional marketing to 24×7 digital marketing is happening in leading firms. Real-time marketing and conversion is now becoming possible.
Bloomberg on Business Analytics
Interested in slicing, dicing, measuring, and analyzing data for customer and business insights?
According to a recent survey by Bloomberg, 97% of companies with revenues of more than $100 million are using some form of business analytics, up from 90% just two years ago.
While businesses have embraced the idea of fact-based decision-making, a steep learning curve remains. Only one in four organizations believes its use of business analytics has been “very effective” in helping to make decisions. Data is not just ignored but often discarded in many organizations as the business users can’t figure out how to extract signal from data noise.
Machine Data Analytics: Splunk
Machine data or “data exhaust” analysis is one of the fastest growing segments of “big data”–generated by websites, applications, servers, networks, mobile devices and other sources. The goal is to aggregate, parse and visualize this data – log files, scripts, messages, alerts, changes, IT configurations, tickets, user profiles etc – to spot trends and act.
By monitoring and analyzing data from customer clickstreams, transactions, log files to network activity and call records–and more, there is new breed of startups that are racing to convert “invisible” machine data into useful performance insights. The label for this type of analytics – operational or application performance intelligence.
In this posting we cover a low profile big data company, Splunk which recently went public. Splunk has >3500 customers already. Splunk ended its first day on the stock market with amazing 108.7 percent bump in price from its $17-per-share IPO.
Next Best Offer Design: Solution Architecture
Next best offer, next best action, interaction optimization, and experience optimization typically have similar architecture. Machine learning and multivariate statistical analysis are at the heart of these cutting edge Behavioral Analytics strategies. Typically firms use statistical tools for segmentation models, behavioral propensity modeling, and market basket analysis.
The bleeding edge in next best offer is increasingly around:
- Applying machine learning to find connections between product tastes and different affinity statements
- Developing low-latency algorithms that help show the right product at the right time to a customer
- Developing rich customer affinity profiles through a variety of feedback loops as well as third-party data source (e.g. Facebook user demos and taste graph)
