Yet again, I reveal the base instincts driving my interest in big data. It’s not the science – it’s the cash. And yes, on some level, I find the idea of all that cash sexy. Yes, I know it’s a failing, but I can’t help it. Maybe in my next life I’ll develop a better appreciation of the finer things, and I will begin to understand the real purpose of the universe…
Until then, however, I’m happy to write about the odd and interesting intersection of big data and big business. As noted in my newest paper, big data is driving a renaissance in IT infrastructure spending. IDC, for example, estimates that worldwide spending for infrastructure hardware alone (servers, storage, PCs, tablets, and peripherals) will rise from $461 billion in 2013 to $468 billion in 2014. Gartner predicts that total IT spending will grow 3.1% in 2014, reaching $3.8 trillion, and forecasts “consistent four to five percent annual growth through 2017.” For a lot of people, including me, the mere thought of all that additional cash makes IT infrastructure seem sexy again.
Of course, there’s more to the story than networks, servers, and storage devices. But when people ask me, “Is this big data thing real? I mean, is it real???” the easy answer is yes, it must be real because lots of companies are spending real money on it. I don’t know if that’s enough to make IT infrastructure sexy, but it sure makes it a lot more fascinating and – dare I say it, intriguing – than it seemed last year.
In life, sex is the key to survival. In business, cash is king. Is there a connection? Read my paper, and please let me know.
Get your free digital copy of Will Big Data Make IT Infrastructure Sexy Again? — compliments of Syncsort.
Insight from a Strata Santa Clara 2014 session
When you think about what goes into winning a Nobel Prize in a field like economics, it’s a lot like machine learning. In order to make a breakthrough, you need to identify an interesting theory for explaining the world, test your theory in practice to see if it holds up, and if it does, you’ve got a potential winner. The bigger and more significant the issue addressed by your theory, the more likely you are to win the prize.
In the world of business, there’s no bigger issue than helping a company be more successful, and that usually hinges on helping it deliver its products to those that need them. This is why I like to describe my company SalesPredict as helping our customers win the Nobel Prize in business, if such a thing existed.
HBase has made inroads in companies across many industries and countries
With HBaseCon right around the corner, I wanted to take stock of one of the more popular1 components in the Hadoop ecosystem. Over the last few years, many more companies have come to rely on HBase to run key products and services. The conference will showcase a wide variety of such examples, and highlight some of the new features that HBase developers have added over the past year. In the meantime here are some things2 you may not have known about HBase:
Many companies have had HBase in production for 3+ years: Large technology companies including Trend Micro, EBay, Yahoo! and Facebook, and analytics companies RocketFuel and Flurry depend on HBase for many mission-critical services.
There are many use cases beyond advertising: Examples include communications (Facebook messages, Xiaomi), security (Trend Micro), measurement (Nielsen), enterprise collaboration (Jive Software), digital media (OCLC), DNA matching (Ancestry.com), and machine data analysis (Box.com). In particular Nielsen uses HBase to track media consumption patterns and trends, mobile handset company Xiaomi uses Hbase for messaging and other consumer mobile services, and OCLC runs the world’s largest online database of library resources on HBase.
Flurry has the largest contiguous HBase cluster: Mobile analytics company Flurry has an HBase cluster with 1,200 nodes (replicating into another 1,200 node cluster). Flurry is planning to significantly expand their large HBase cluster in the near future.
Focusing attention on the present lets organizations pursue existing opportunities as opposed to projected ones
Slow and Unaware
It was 2005. The war in Iraq was raging. Many of us in the national security R&D community were developing responses to the deadliest threat facing U.S. soldiers: the improvised explosive device (IED). From the perspective of the U.S. military, the unthinkable was happening each and every day. The world’s most technologically advanced military was being dealt significant blows by insurgents making crude weapons from limited resources. How was this even possible?
The war exposed the limits of our unwavering faith in technology. We depended heavily on technology to provide us the advantage in an environment we did not understand. When that failed, we were slow to learn. Meanwhile the losses continued. We were being disrupted by a patient, persistent organization that rapidly experimented and adapted to conditions on the ground.
To regain the advantage, we needed to start by asking different questions. We needed to shift our focus from the devices that were destroying U.S. armored vehicles to the people responsible for building and deploying the weapons. This motivated new approaches to collect data that could expose elements of the insurgent network.
New organizations and modes of operation were also required to act swiftly when discoveries were made. By integrating intelligence and special operations capabilities into a single organization with crisp objectives and responsive leadership, the U.S. dramatically accelerated its ability to disrupt insurgent operations. Rapid orientation and action were key in this dynamic environment where opportunities persisted for an often unknown and very limited period of time.
This story holds important and under appreciated lessons that apply to the challenges numerous organizations face today. The ability to collect, store, and process large volumes of data doesn’t confer advantage by default. It’s still common to fixate on the wrong questions and fail to recover quickly when mistakes are made. To accelerate organizational learning with data, we need to think carefully about our objectives and have realistic expectations about what insights we can derive from measurement and analysis.
O'Reilly report covers major trends and tries to connect the neurons
If visualization is key to comprehending data, the field of health IT calls for better visualization. I am not talking here of pretty charts and animations. I am talking, rather, of a holistic, unified understanding of the bustle taking place in different corners of health: the collection and analysis of genetic data, the design of slim medical devices that replace refrigerator-sized pieces of equipment, the data crunching at hospitals delving into demographic data to identify at-risk patients.
There is no dearth of health reformers offering their visions for patient engagement, information exchange, better public health, and disruptive change to health industries. But they often accept too freely the promise of technology, without grasping how difficult the technical implementations of their reforms would be. Furthermore, no document I have found pulls together the various trends in technology and explores their interrelationships.
I have tried to fill this gap with a recently released report: The Information Technology Fix for Health: Barriers and Pathways to the Use of Information Technology for Better Health Care. This posting describes some of the issues it covers.
A Call for Proposals for Strata Conference + Hadoop World 2014
When we launched Strata a few years ago, our original focus was on how big data, ubiquitous computing, and new interfaces change the way we live, love, work and play. In fact, here’s a diagram we mocked up back then to describe the issues we wanted the new conference to tackle:
Insights from a business executive and law professor
If you develop software or manage databases, you’re probably at the point now where the phrase “Big Data” makes you roll your eyes. Yes, it’s hyped quite a lot these days. But, overexposed or not, the Big Data revolution raises a bunch of ethical issues related to privacy, confidentiality, transparency and identity. Who owns all that data that you’re analyzing? Are there limits to what kinds of inferences you can make, or what decisions can be made about people based on those inferences? Perhaps you’ve wondered about this yourself.
We’re obsessed by these questions. We’re a business executive and a law professor who’ve written about this question a lot, but our audience is usually lawyers. But because engineers are the ones who confront these questions on a daily basis, we think it’s essential to talk about these issues in the context of software development.
While there’s nothing particularly new about the analytics conducted in big data, the scale and ease with which it can all be done today changes the ethical framework of data analysis. Developers today can tap into remarkably varied and far-flung data sources. Just a few years ago, this kind of access would have been hard to imagine. The problem is that our ability to reveal patterns and new knowledge from previously unexamined troves of data is moving faster than our current legal and ethical guidelines can manage. We can now do things that were impossible a few years ago, and we’ve driven off the existing ethical and legal maps. If we fail to preserve the values we care about in our new digital society, then our big data capabilities risk abandoning these values for the sake of innovation and expediency.
Collecting actionable data is a challenge for today's data tools
One of the problems dragging down the US health care system is that nobody trusts one another. Most of us, as individuals, place faith in our personal health care providers, which may or may not be warranted. But on a larger scale we’re all suspicious of each other:
- Doctors don’t trust patients, who aren’t forthcoming with all the bad habits they indulge in and often fail to follow the most basic instructions, such as to take their medications.
- The payers–which include insurers, many government agencies, and increasingly the whole patient population as our deductibles and other out-of-pocket expenses ascend–don’t trust the doctors, who waste an estimated 20% or more of all health expenditures, including some thirty or more billion dollars of fraud each year.
- The public distrusts the pharmaceutical companies (although we still follow their advice on advertisements and ask our doctors for the latest pill) and is starting to distrust clinical researchers as we hear about conflicts of interest and difficulties replicating results.
- Nobody trusts the federal government, which pursues two (contradictory) goals of lowering health care costs and stimulating employment.
Yet everyone has beneficent goals and good ideas for improving health care. Doctors want to feel effective, patients want to stay well (even if that desire doesn’t always translate into action), the Department of Health and Human Services champions very lofty goals for data exchange and quality improvement, clinical researchers put their work above family and comfort, and even private insurance companies are trying moving to “fee for value” programs that ensure coordinated patient care.
More than algorithms, companies gain access to models that incorporate ideas generated by teams of data scientists
Data scientists were among the earliest and most enthusiastic users of crowdsourcing services. Lukas Biewald noted in a recent talk that one of the reasons he started CrowdFlower was that as a data scientist he got frustrated with having to create training sets for many of the problems he faced. More recently, companies have been experimenting with active learning (humans1 take care of uncertain cases, models handle the routine ones). Along those lines, Adam Marcus described in detail how Locu uses Crowdsourcing services to perform structured extraction (converting semi/unstructured data into structured data).
Another area where crowdsourcing is popping up is feature engineering and feature discovery. Experienced data scientists will attest that generating features is as (if not more) important than choice of algorithm. Startup CrowdAnalytix uses public/open data sets to help companies enhance their analytic models. The company has access to several thousand data scientists spread across 50 countries and counts a major social network among its customers. Its current focus is on providing “enterprise risk quantification services to Fortune 1000 companies”.
CrowdAnalytix breaks up projects in two phases: feature engineering and modeling. During the feature engineering phase, data scientists are presented with a problem (independent variable(s)) and are asked to propose features (predictors) and brief explanations for why they might prove useful. A panel of judges evaluate2 features based on the accompanying evidence and explanations. Typically 100+ teams enter this phase of the project, and 30+ teams propose reasonable features.
Built-in audit trails can be useful for reproducing and debugging complex data analysis projects
As I noted in a previous post, model building is just one component of the analytic lifecycle. Many analytic projects result in models that get deployed in production environments. Moreover, companies are beginning to treat analytics as mission-critical software and have real-time dashboards to track model performance.
Once a model is deemed to be underperforming or misbehaving, diagnostic tools are needed to help determine appropriate fixes. It could well be models need to be revisited and updated, but there are instances when underlying data sources1 and data pipelines are what need to be fixed. Beyond the formal systems put in place specifically for monitoring analytic products, tools for reproducing data science workflows could come in handy.