The Promises and Limitations of Big Data
Although many people claim we have entered the era of big data, research firms tell us that most collected information is never used. It sits uncleaned, unanalyzed, unused in databases. But when data analytics is used successfully, organizations reap the benefits. Financial services firms are using digital information about their customers to offer them a whole new range of customized products under the category of fintech. Cities are using data from Google Street View to guide economic development. And companies are finding that in some cases machines can make better hiring decisions than humans. In these stories from our recent archives, Harvard Business School researchers outline the promises of big data—and the limitations of trying to harness data from a firehose.
Financial Institutions in the U.S. Are Falling Behind In The Innovation Arms Race
In contrast to the other regions, the main barrier to innovation for the Americas seems to be culture rather than IT systems or lack of funding. While regulation and compliance issues are more of a challenge in the U.S. than most other regions of the world, this challenge is less of an issue today than it was just a couple years ago. “Innovation is simply not in the DNA of most bankers,” explains Nicols. “They’ve been trained throughout their whole career to identify and avoid risks, and innovation is about taking small risks and failing fast and cheaply and learning from those mistakes to get to the right answer quickly.” Nicols continues, “Another challenge is analysis paralysis. Most banks have too many silos with conflicting agendas, and that makes it hard to actually put new ideas into action. ...”
8 “Simple” Guidelines For Data Projects
Countless data posts out there will tell you to do things like “harness the cloud” or “run experiments.” The vagueness of these posts is not helpful. You can’t “tip and trick” your way to a successful data product. You have to have the right mindset. I got frustrated reading these posts and decided to write my own, but one that’s not presented as collection of tips, tricks, or rules, but as guidelines. Following all of these doesn’t guarantee success, but they might be useful for you… What follows is a collection of things I have recently observed at client meetings and also during project work. This post is inspired by an excellent article by Martin Goodson, “Ten Ways Your Data Project is Going to Fail” and includes my personal views on many things I currently see in data projects
73% of enterprises will run almost entirely on SaaS by 2020
Enterprises are rapidly shifting to Software as a Service (SaaS), with the industry poised to generate more than $112.8 billion in revenue by 2019, according to IDC. Enterprises now use 16 SaaS apps on average—up 33% from last year, according to a new report from BetterCloud. And 73% of organizations said nearly all of their apps (more than 80%) will be SaaS by 2020. BetterCloud—which, it should be noted, provides SaaS management software—surveyed more than 1,800 IT professionals for their report. Some 38% of tech workers said their company is already running almost entirely on SaaS, and that they run 2.1x more SaaS apps than the average organization, the survey found. Tech professionals from these SaaS-focused companies are 52% more likely to say that the delivery model helps them attract better talent than the average workplace, BetterCloud found.
Promise and pitfalls in the application of big data to occupational and environmental health
Big data and data sharing have the potential to inform occupational and environmental health by exploiting innovations related to non-traditional data sources or providers and novel partnerships. Promising applications include real time analysis and forecasting, and innovative analyses of clinical trial or observational data originally collected for other purposes. However, in order to support these innovations, advances are also required in data curation, protection of privacy and security, as well as data analysis methods. Challenges related to messy and unrepresentative data and spurious findings, as well as epistemological issues and equity considerations must also be addressed.
Financial services firms advised to ditch private datacentres and invest in cloud
“Cloud computing has reached the tipping point as the capabilities, resiliency and security of services provided by cloud suppliers now exceed those of many on-premise datacentres,” the whitepaper stated. “The combination of technology commoditisation with the scale and competition from public cloud suppliers is driving the unit prices of computing, storage and network services towards zero. “This gap will continue to grow at an accelerated rate, leaving laggards in cloud adoption at increased risk from a resiliency and cost perspective,” it added. This shift has altered the way organisations talk about cloud over the course of the past decade, with conversations about the safety and security of using it giving way to discussions about how shunning the technology could negatively affect an organisation.
The Promise of Blockchain Is a World Without Middlemen
In a world without middle men, things get more efficient in unexpected ways. A 1% transaction fee may not seem like much, but down a 15-step supply chain, it adds up. These kinds of little frictions add just enough drag on the global economy that we’re forced to stick with short supply chains and deals done by the container load, because it’s simply too inefficient to have more links in the supply chain and to work with smaller transactions. The decentralization that blockchain provides would change that, which could have huge possible impacts for economies in the developing world. Any transformation which helps small businesses compete with giants will have major global effects. Blockchains support the formation of more complex value networks than can otherwise be supported.
Brainstation's Fintech Panel Says Retraining Key To Preparing For AI Disruption
What big FIs should really be thinking of is, “what core competencies can we not afford to outsource?” says Gene. Once you’ve answered this, the options actually extend far beyond ‘buy versus build’ into buying, building, buying-then-customizing, investing, partnering, and incubating. EQ Bank, for instance, is a small bank with limited budget, meaning that both “buy” and purely “build” are not really on the table, says Dickinson. However, what EQ does instead is build infrastructure that enables integrations with FinTech companies. Then they invest in companies building the technologies their customers need. “We focus on an ‘execute what the customer wants’ mentality,” says Dickinson. “which means that investments and partnerships are good strategies for us.” BMO feels the same way, says Gene, explaining that BMO has three FinTech incubators, including one in Toronto.
Interview: Mark Potter, CTO, HPE
“The Machine’s architecture lends itself to the intelligent edge,” he says. One of the trends in computing is that high-end technology eventually ends up in commodity products. A smartphone probably has more computational power than a vintage supercomputer. So Potter believes it is entirely feasible for HPC-level computing, as is the case in a modern supercomputer, to be used in IoT to process data generated by sensors locally. Consider machine learning and real-time processing in safety-critical applications. “As we get into machine learning, we will need to build core datacentre systems that can be pushed out to the edge [of the IoT network].” It would be dangerous and unacceptable to experience any kind of delay when computing safety-critical decisions in real time, such as for processing sensor data from an autonomous vehicle. “Today’s supercomputer-level systems will run autonomous vehicles,” says Potter.
If Software Eats Everything, Are Network Engineers On The Menu?
In-house networking teams will need to match the speed of public cloud providers in tasks like spinning up new virtual machines, Stanford professor David Cheriton said, echoing a concern users expressed in private interviews at the conference. "At some point, the CIO is going to ask, 'Why is it that it costs so much more and it takes so much longer to do this (ourselves)?,'" Cheriton said. SDN takes over configuration tasks that some network engineers have spent their careers doing manually, which has raised concerns about job security and what these technicians should do next. There are big changes afoot, panelists and participants said. Freed from configuring ports and routes, some network engineers are taking on higher tasks like designing better systems.
Quote for the day:
"Never let the fear of what other people think stop you from being yourself." -- Joubert Botha
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