"The race to make autonomous cars is over and the traditional car companies still get to control who makes cars. But the race to control the data from those cars is just getting going. It's a brand new marketplace that will play to the strengths of Silicon Valley as the car companies lay their plans to grab what they can get."
NewVantage Partners LLC, which specializes in helping enterprises implement big data initiatives, has seen this scenario repeatedly, said Chief Executive Randy Bean, who joined Cottone for a recent interview on theCUBE, SiliconAngle Media’s mobile live-streaming studio.
The European Union’s (EU’s) General Data Protection Regulation (GDPR), as amended in March 2014 by the European Parliament, could restrict the use of personal data in medical research without specific consent.
Given the size of genomic research projects such as the UK’s 100,000 Genomes project, asking every participant for permission for every data access would have been a weighty task.
The Wellcome Trust Sanger Institute in Cambridgeshire, which led the world in first mapping the human genome, said in October 2015 that the proposals would “pose a serious barrier to future genomic research and will hinder its use as valuable tool for healthcare”.
A group of organisations led by the Wellcome Trust, the multi-billion pound charity which funds much of the UK’s genomic research, pushed for a compromise – and this was adopted by the EU in December 2015.
But some politicians are talking about how Britain could go further after Brexit. “It means we will be able to write a UK regulatory playbook and environment,” George Freeman, member of Parliament and chair of the prime minister’s policy board, told The Economist’s War on Cancer conference in December 2016.
He told the event that he had campaigned for Britain to stay in the EU and agreed that Brexit will pose challenges for medical research, but added: “We’re liberated from some of the very restrictive – increasingly restrictive – regulations in Europe around data and trials and genetics.”
The UK will have to comply with GDPR before it leaves the EU, as the May 2018 deadline falls before the earliest possible date for Brexit.
On 1 February 2017, digital minister Matt Hancock told a House of Lords committee that the government will comply and that it wants to pursue unhindered data flows between the UK and the EU, including in medical research.
The government has not yet decided whether to make changes after leaving the EU, with a spokesperson only saying “post-Brexit, the government is committed to ensuring that the UK’s data protection regime will continue to provide necessary opportunities and safeguards”. But how could it do so and what would be the implications if it did?
The chief executive of the Civil Service, John Manzoni, says the UK needs to begin to consider the "collection and storage of data as part of [our] core national infrastructure".
In a speech delivered this morning, Manzoni articulated the Civil Service's dreams about "public service modernisation", which focused on how critical data can be to the nation.
"Services driven by open data are already giving people more choice in where they get their healthcare, where they live and where their children go to school," the chief executive said, before adding: "There's even a Great British Public Toilet app – a sort of relief map of the country."
Manzoni stated that "the impact of data analytics and big data in our lives — for example the way online retailers tailor their recommendations for the food, books and music we buy — is quite familiar" but "less has been said about the transformative power of this technology for the delivery of high-quality public services, and it's time that changed."
Data is a public asset, Manzoni argued, and with the publication of the Government Transformation Strategy earlier this month as well as the Digital Economy Bill continuing its passage into law, that asset can be used to make government more efficient and appropriate.
There are examples at home and abroad where data is being used to address people's real concerns about their daily lives; providing solutions that were not available before. In June last year, for example, Land Registry and partners published the first UK House Price Index, and provided a single source of information as opposed to the multiple competing versions which existed before. Land Registry data has also been used to create a range of information services. From whether rude-sounding street names have an impact on house values – (they do!), to more serious matters, such as whether your home is on a floodplain.
Ford will pour $1 billion over the next five years into an artificial intelligence company tasked with developing the technology that will one day drive its autonomous vehicles.
The technology could also be licensed to other automakers in the future, executives said.
Pittsburgh-based Argo AI was founded late last year by Bryan Salesky and Peter Rander, who previously worked on self-driving car initiatives at Google and Uber, respectively. The company will include the staff inside Ford that has been developing its virtual driver system for the past several years.
In a phone call last week, CEO Mark Fields said the investment will help Ford bring its self-driving cars to market by its previously stated goal of 2021. It will also open a new revenue stream if Ford licenses the technology to carmakers who have not developed their own autonomous driving systems.
That licensing model will put Ford in direct competition with Waymo, Google's self-driving car company, which announced plans this year to develop both hardware and software for self-driving cars. Previously, Waymo focused solely on software, but executives decided that it was necessary to also build the sensors and cameras on the vehicle if its system was to be sophisticated enough to handle fully autonomous driving.
Over the past several years, it has been well documented that big data -- and more specifically, big data analytics -- has been a struggle for IT operations. The analytics results for many have simply fallen short of expectations. Because of this, many IT departments set out to uncover the root cause of analytics failures. But what they didn't realize is that the problem may have been the IT department itself.
Early adopters of big data endeavors initially placed the blame for poor analytics results on the big data tools. The first explanation was that the databases used to store and sort collected inputs were more complex to setup and maintain than initially expected. Next, the blame shifted to the analytics tools and platforms, with many -- including Gartner Research -- claiming they weren’t quite ready for primetime. Even more commonly, the blame shifted to business leaders with the claim that they simply weren’t asking the right questions that big data could answer.
In many ways, the issues investigated indeed were problematic. Yet they ultimately may not have been the true root cause. In the past few years, most the technical issues involved with data collection and analytics intelligence have been addressed. Additionally, business leaders are asking better questions, questions that big data analytics should be able to provide answers to. Yet, even after all these advancements, many organizations are still struggling to find the analytics pot of gold at the end of the rainbow.
Ultimately, this has led some IT departments to do some serious soul-searching. By taking an external view and looking inwardly on the big-data attempts thus far, many are concluding that the field of data analytics is a unique computer science skillset that they simply do not possess in-house. Even if they do have the right people in the data analytics roles, they risk losing key internal staff that can cripple any big data efforts.
Businesses must boost operational efficiencies enterprisewide if they want to keep up with rapid market and technology shifts. And many are investing in machine data analytics to do so, according to a recent survey from Logtrust and 451 Research. The resulting report, "The Need for Speed: Machine Data Analytics in 2016-17," indicates that organizations are deploying the latest in machine data analytics to benefit big data and internet of things (IoT) efforts, as well as overall IT operations and cyber-security.
Subsequently, they're increasing revenue and productivity, with quicker incident resolutions and time to market for products and services. To continue to make progress, IT will need to overcome obstacles in infrastructure and staffing demand, as well as difficulties in scaling, to manage data ahead of the pace of business and technology changes, instead of falling behind. "It's been shown time and again that in all sorts of areas, the value of data erodes dramatically as it ages," according to the report. "In other words, the faster you can run some analytics on data and subsequently respond to the findings, the greater the chance of having achieved something that adds business value.
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Last month, I wrote about why simply making predictions isn’t enough to drive value with analytics. I made the case that behind stories of failed analytic initiatives, there is often a lack of action to take the predictions and turn them into something valuable. It ends up that identifying and then taking the right action often leads to additional requirements for even more complex analyses beyond the initial effort to get to the predictions! Let’s explore what that means.
Identifying The Action Is The Next Step
Once I have a prediction, simulation, or forecast, the next step is to identify what action is required to realize the potential value uncovered. Let’s consider the example of using sensor data for predictive or condition based maintenance. In this type of analysis, sensor data is captured and analyzed to identify when a problem for a piece of equipment is likely. For example, an increase in friction and temperature within a gear might point to the need to replace certain components before the entire assembly fails.
Identifying the problem ahead of time sounds great. All we have to do is to identify when something is going to break and then fix it before it breaks. Doing so saves money and allows us to avoid unplanned maintenance downtime. We’re ready to move forward, right? Wrong! It can be a complicated process to identify how to best execute the repair.
How To Take The Action Can Require More Analysis
I had a discussion on this very topic with a senior executive at a large aircraft manufacturer. He was discussing the complexities of managing maintenance...
To attract visitors and to encourage guests to return, hotels have come up with many unique amenities over the years. But until recently, many of these hotel amenities were determined by "gut feel" and manual surveys of customers in which guests were asked which free amenities they enjoyed most. Now, thanks to analytics, the task of determining the right amenities for a hotel to offer has become more scientific.
"We want to help hotels determine which free amenities give them the best chance to boost their hotel's appeal, increase sales, and improve customer satisfaction," said Anil Kaul, CEO of Absolutdata, which provides marketing and customer analytics to hotels.
Kaul characterizes the hotel industry as a "constant war of amenities." Hotels know that free amenities bring in customers, but they also know they're competing against other hotels that offer free amenities, so it's important to offer a set of amenities that customers are most attracted to.
Hotels deal with two scenarios when it comes to attracting customers. "The first scenario is when the customer first begins to seek a hotel. You want to offer a free amenity package that will convince him or her to choose your hotel over many other possibilities," said Kaul. "The second scenario is providing a great customer experience to your guest during his or her stay. Part of this customer satisfaction is achieved by offering the right free amenities. If you do this well, the guest is likely to return."
So, which free amenities are most likely to get customers in the door the first time?
Big data, Internet of things (IoT), Artificial Intelligence (AI) and Machine learning are terms that are used fairly routinely today. Organisations across the value chain are using these to enhance their market competitiveness, improve time to market, develop new products and services, reduce costs and garner multiple other benefits. Of course, most of these new technologies require data as the raw material of feed to create the relevant outcomes. According to IDC, the top five data generating industries are – Financial Services, Communications & Media, Manufacturing, Healthcare and Education.
The challenges in today’s healthcare environment are unprecedented, and so are the opportunities to provide better care for individuals, better health for populations, and reduced per-capita costs. Healthcare organizations across the spectrum – from hospitals to large pharmaceuticals – are now looking at staying ahead of major market disruptions and seeking next-generation innovations in healthcare delivery. From innovations in cancer research, to ensuring more effective clinical trials and enabling better innovation, organizations that can generate the most insights from their data assets will be most likely to deliver better and quicker outcomes for patients and customers at lower costs.
Just consider the megatrends reshaping the life sciences competitive landscape:
* • Research and development organizations are challenged to keep the pipeline full as profitable drugs face competition from generic versions and pricing pressures.
* • With new drug development costs reaching $1 billion or more, the stakes are incredibly high. The data-related challenges are just as great – with tens of billions of records and data volumes measuring petabytes- as dynamic biotechs and global pharmaceuticals seek new compounds and correlations between genes and diseases.
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