For the first point, drivers of marketing effectiveness, Ron basically states that whilst big data analytics enables companies to gain customer insights, unless you know how to use that to achieve marketing effectiveness, it’s worthless. That is an argument demonstrating that it is necessary, but not sufficient to use big analytics to understand and address customer needs.

It is for this reason that big data tends to be sold not just as a product but also packaged with professional services to ensure that firms are guided in how to gain value from their investment. This model is true of most enterprise technology acquisitions that you can mention.

On the challenges of data integration, big data technologies are specifically designed to address the issue of integration of data across storage silos. By accessing disparate internal data stores, combining them with a broad ranges of external and remote sources and consolidating them into a coherent, centralized repository, big data solutions form vast pools of information that typically could not be analyzed using traditional tools. Data is one of the few resources that has increasing returns to scale, becoming more valuable per bit as its volume grows.

Concerning the causes of data scientist shortages, yes, Ron is correct that there are already people working in FSOs to understand trends from statistical analysis and data flows, but they tend to be working on datasets within structured data silos without the ability to analyse larger volumes of unstructured data of different types.

Big data is a technology, not a job role. Arguing that an organization already has access to the necessary skills to benefit from the technology that this is a negative point for the promise of big data seems illogical. Surely, if a technology can enable existing staff to be more effective, that must be a good thing. It enables these employees to gain a broader understanding of the environment by consolidating disparate data stores becoming a target for analysis.

Additionally, as the second wave of big data products come online, the objective is to reduce the requirements for data scientists and bring these analytics frameworks to the level of operational managers, rather than relying on the specific competencies of statisticians, by enhancing the simplicity of the visualization tools and improving usability.

Having said all that, I agree on Ron’s final point. Big data is a management fad. At Tarmin, where we develop the GridBank Data Management Platform for big data, rather than getting swept up in the hype, we counsel our clients that, as with all technology solution acquisitions, they should ensure that they have clear objectives when considering the solution and that they know how, whether through internal competencies or professional services, they will implement big data to add value to their business.

Database marketers (who have been dealing with large data sets for years) have been approached, over the past 20 years, by countless number of vendors selling “data” that they promise will improve marketing performance.

The first thing smart marketers do is run a test to see if a new type of data or data source really outperforms the existing data and, very importantly, if the benefit of acquiring the data outweighs the cost.

Big Data proponents are making unsubstantiated claims that all this new data that’s being captured will improve marketing performance. Show me the proof.

The consequences are significant, because — if big data proponents are correct — financial institutions currently lack the resources to handle (1) the massive streams of new data, (2) coming at them from all these various sources, (3) at a ferocious speed. In a nutshell, the basic argument is that “petabytes are the new reality, and FIs simply aren’t prepared to handle it.” So there’s lots of banks and credit unions out there hearing the “big data” story who are scratching their heads wondering if they need to invest in new, super-fancy über-servers and hire a bunch of PhDs. There’s more involved here than simply looking at data analytics in a new way.

Jim cites examples of data analytics that are innovative and/or unusual. Brent cites examples where “alternative data” is used. But just because new and/or alternative data is being tapped, does that constitute “big data?” Where’s the scale? How is it “unstructured?” And where does “velocity” fit in?

BofA’s reward scheme is a simple if/then search routine algorithm: If a customer bought from Brand-1 in the past and is within X miles of a Brand-1 or Brand-2 location, then present offer Y (from either Brand-1 or Brand-2). It’s a smart way to manage offers, but it seems like an example of geolocational marketing — not “big data.”

And while combining data into a single system like Terradata’s “Relationship Manager” is certainly a smart thing for FIs to do, it strikes me as a classic CRM solution having little to do with “big data.” How/where are “vast seas of data” being analyzed?

Wonga seems to have the model that most closely meets the academic, technical standards for defining “big data.”

“[Wonga uses] a lot of social media and other tools on the internet you don’t even think about. That’s where the magic is. The crux of the algorithm is less about the individual pieces of data — your postcode, the colour of your car, how large your mortgage is — but how these pieces of information relate to one another. The data points are stacked against the other pieces of information gleaned from past Wonga clients. By 2009, Wonga had issued 100,000 loans. That’s 100,000 data sets contributing to an ever growing net of information, and each comprising 6,000-8,000 pieces of information about a borrower.”

– William Shaw writing for Wired Magazine

“It’s going to take a great deal of resources to provide a Big Data solution that will be proprietary, including technology such as search engines with in-memory analytics and a data collection and crunching capability. You need a big investment in enabling technology.”

– Rodney Nelsestuen, Sr. Research Director, CEB Towergroup.

But what Wonga is doing is not just a “big data initiative.” They are completely retooling the entire lending model — starting from the ground up. Taking on a data analytics project of this scale is simply not something 95% of most financial institutions can even remotely consider. It is smarter and safer to wait for their core data processing provider (e.g., Fiserv) to update the lending module.

As suggested previously, the real promise of big data seems to center around lending and risk management — not marketing. Underwriting, fraud, defaults, credit worthiness… these are all issues with little- to no impact on marketers.

Will financial institutions find some value in “big data?” Perhaps. Will it be the marketing department that finds that value? Almost certainly not. Will it be the marketing department that uses the insights from “big data?” Maybe, but how so and in what ways is very unclear at this time.

Zest Finance, founded by Google’s former CIO, uses alternative data sets to “extend credit to 25% more americans and increase repayment by 20%.” http://www.zestfinance.com/pdf/ZestCashHollerREV.pdf

Wonga.com, not my favorite because it borders on predatory, used underwriting based on alternative data sets to issue over 100,000 loans, worth £20 million, earning about £15 million by in astronomical interest. http://www.wired.co.uk/magazine/archive/2011/06/features/wonga?page=all

In 2010, MIT researchers used machine learning algorithms to predict credit risk and “Using conservative assumptions for the costs and benefits of cutting credit lines based on machine-learning forecasts, we estimate the cost savings to range from 6% to 25% of total losses.” http://www.argentumlux.org/documents/CRisk_final.pdf

We’re looking into it at Filene, and early early findings suggest we can use our hybrid scoring model to approve 1% more loans while reducing costs through default by 15%.

. . .

On the other examples:

////Narrative Science’s article-writing robots’ use of big data –

“There is no shortage of data, in fact just about every company we talk to is drowning in data. As the volume of data continues to rise exponentially, companies need a better way to understand, use and monetize the data they already have. Narrative Science helps companies leverage their data by automatically creating easy-to-use and consistent narrative reporting”

Their algorithms create original content for publishers like Forbes and the Big Ten Network.

////Google car using big data – http://www.cs.cmu.edu/~zkolter/pubs/levinson-iv2011.pdf

“Improved perception and recognition algorithms now enable Junior to track and classify obstacles as cyclists, pedestrians, and vehicles; traffic lights are detected as well. A new planning system uses this incoming data to generate thousands of candidate trajectories per second, choosing the optimal path dynamically.”

Big Data proponents claim huge ROI and promise significant results from investments in Big Data, stemming from the collection of new types of data and/or the analysis of data. Sadly, they offer no empirical proof.

Determining which data elements are more important than others, figuring out how to use the analysis of data to develop offers and messages and sequence messages, and how integrating that data with existing data stores may be a big DUH to you, but since none of that is ever mentioned by Big Data proponents, I am compelled to point it out.

Self-driving cars and robots are great, but have little to do with marketing-related claims regarding Big Data. Are self-driving cars and robots examples of Big Data? I can claim that the examples you list are NOT examples of Big Data. Since there is no generally accepted definition, you have no grounds to dispute my claim.

Fidelity Investments tweeted the following:

@Fidelity: Big data can unlock new insights for companies, governments and organizations.

It’s this kind of BS that I’m railing against. Insights are not “locked up” waiting to be unlocked. Having more data or new types of data is no guarantee that new insights will be generated.

BD consultants have an 18 month — and counting — window to sell their snake oil.

Ron Shevlin

Just last week, I was with a mid-tier bank that had combined all of their internal data silos and have built an offer portal using a Teradata Relationship Manager. This offer portal uses account, balance, transaction as well as external insights. Finally, Bank of America is using purchase insight combined with geolocational information to provide offers with their rewards program.

While I agree that there are very limited examples of ‘big wins’ with ‘big data’ there are some. The key remains to use what is available first and then expand as the potential value dictates.

==1. The Real Drivers of Marketing Effectiveness==

Translation: Information will not help you if you don’t act on it.

My take: Duh.

My response: Of course you need the infrastructure, plan, and talent to be ready to act on the data. So, like any new project, have a plan and be prepared. Jeremy Howard’s piece on the Drivetrain Approach to building data products will help you get your mind right: http://strata.oreilly.com/2012/03/drivetrain-approach-data-products.html

==2. The Real Challenges of Customer Data Integration==

Translation: Financial institutions are too much of a bureaucratic mess to make use of big data.

My take: This has nothing to do with whether big data is a fad or not and everything to do with financial institutions standing in the way of their own progress.

My response: This argument is like saying driving isn’t useful because you can’t be bothered to learn to drive. LEARN TO DRIVE.

==3. The Real Cause of the Data Scientist Shortage==

Translation: Statistics aren’t perceived as sexy.

My take: People fear or ignore what they don’t understand.

My response: Robot magazine writers, the Google self-driving car, and Moneyball…data science is coming into its own sexiness. As there’s more opportunity for curious minds to dip their toe in the water of data science through competition sites like Kaggle and open education like Coursera’s Machine Learning course, i believe this will be an increasingly accessible field. It only takes a dip to begin to see the level of creativity and intuition alongside hard data anlysis wrapped up in data science.

. . .

Like any hyped technology (see: the last decade of bullshit social media conversations), big data won’t be useful to you until you come with a strategic objective in hand and the have talent and infrastructure to act on it.

Don’t confuse industry/organizational ineptitude with a fad.

The critical aspect of “Big Data” that no one talks about is the “OK, now you know this–so what are you going to DO about it?” If you can’t take action in an appropriate time frame, what good is knowing anything in real time? Shevlin’s anecdote about snail mail response to real-time Big Data is spot-on.

What’s most frustrating to me, though is how poorly companies contend with data integration. Having worked for marketing information companies (including Nielsen and Harte-Hanks), I’ve witnessed clients of all industries struggle to bring together data from different, incompatible systems. Central data repositories were the mid-2000s fad–and they still haven’t solved the problem of needing data that live in different places. It doesn’t have to be this way–but I’ve never seen a client make a concerted effort to solve this problem.

Full disclosure: I’m the product marketing director for JackBe, a real-time business intelligence company. JackBe builds software that allows data from any system to be pulled into interactive dashboards–in real time. And it’s designed for business users to use and control, once the basic data connections are made and the security governance set up.

I really believe that, for companies that surmount the “Where’s the data?” issue, Big Data is going to pay big dividends by freeing up resources to tackle the “What are we going to do about what we know?” dilemma.

So, while ‘big data’ is a big aspirational term, banks can do a much better job of focusing on collecting the better insights to deliver real time messages. And, in the meantime, they can use data (big, small or otherwise) much more effectively than is currently done. For instance, the data is available to determine which households in a customer base are shopping for a lending product daily. Yet very few organizations use this insight to acquire new loans or retain current loan relationships from their customers. This can provide a steady annuity of opportunities that most banks miss. The same is true for ‘quick hit’ programs like new movers acquisition programs and balance trigger programs that can lead to cross-sells.

So, as opposed to thinking that ‘big data’ is a fad, I prefer to think that the term may help banks focus on common sense ideas they continually miss as they chase the next ‘shiny object’. If this buzz term gets banks to collect more email addresses, capture more cell phone numbers, utilize social media more effectively and implement programs based on logical triggers, so be it.

Maybe with this foundation, these institutions will be in a better position to move to the next level, better integrating (and using) the current silos of data they have at their disposal and building the first wave of proactive marketing initiatives that can sense and respond, moving closer to SoLoMo initiatives.