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Companies are investing more money in emerging technologies that can help anticipate and detect a variety of threats, including phishing scams and advanced persistent threats, both of which are weighing heavily on the minds’ of corporate board members. For 2017 CIOs are eyeing tools that use anomaly-detecting analytics and machine learning algorithms to protect their companies’ data.
“Our level of investments is increasing because of the increasing capabilities of the threat actors,” says Bob Worrall, CIO of Juniper Networks, who spent 12 percent more on cybersecurity tools in 2016 that he spent in 2015. His budget will increase more in 2017 as he purchases tools to shield Juniper’s corporate data and intellectual property. “As the bad guys get smarter we have to as well.”
We write a lot about collaboration and partnerships at CIO.com. After all, it has never been more important for IT leaders to partner — whether that means working with the growing number of vendors that provide critical competencies once firmly rooted on-premises or, perhaps more importantly, partnering with CMOs and other C-level executives in departments that command their own technology budgets.
While our primary role as technology journalists is to provide information to help you do your jobs better, leverage your expertise and advance your careers, we sometimes have the opportunity to practice what we preach.[ Analytics 50 winners for 2016 ]
We’re proud of our partnership with Drexel University and the LeBow College of Business, and the fruits of that collaboration: the Analytics 50 awards program. This initiative, which we expect to expand in the coming years, is a blend of academia and media. A lot of partnerships look good on paper. This one certainly did, because Drexel’s Decision Sciences department and CIO.com are both committed to reporting on analytics. It’s a natural fit. But partnerships don’t thrive because they look good on paper.
Data and analytics are reshaping organizations and business processes, giving organizations the capability to interrogate internal and external data to better understand their customers and drive transformative efficiencies.Children’s Hospital of Philadelphia
John Martin, senior director of enterprise
Mani Janakiram, Intel’s director of supply chain strategy and analytics.
Like mobile and cloud, big data and advanced analytics have been reshaping organizations and business processes. In 2016, organizations increasingly moved data analytics projects into production as they sought the capability to better interrogate internal and external data to better understand their customers and drive efficiencies.[ Analytics 50 winners for 2016 ]
Here are our picks for the most significant big data and advanced analytics trends in 2016, as illustrated in 15 stories from the past year.
In previous articles, we talked about three of the five capabilities needed to turn data into insight. The fourth key capability is to have “data-centric processes.” What we mean by this is twofold:
Many articles have been written about data-management-specific processes, including the two previous installments in our CIO.com series — "Ensuring the Quality of 'Fit for Purpose' Data" and "Mastering and Managing Data Understanding." In this installment, we cover processes that already exist within an organization. We look at the role of data and discuss how to make the related processes more data-centric. We also break down the ways data-centric processes can have the most impact on an organization in:
Your business spends a lot of time analyzing the market you sell to, but can you take a step back and instead create the market that you want? Experts in the little-known field of behavioral economics and market design say “yes.”
SAN DIEGO -- CIOs have long struggled to connect the dots between the cost and value of IT services they delivered to their business partners. This disconnect is bound to widen as IT implements new digital capabilities for business initiatives. To help CIOs cross the chasm and communicate with their business peers, a startup has pioneered what it calls technology business management (TBM).
Apptio sells a SaaS analytics platform and applications to help CIOs calculate and benchmark IT costs and value. Since launching in 2007, Apptio has racked up 40 percent of the Fortune 100, including FedEx, Cisco and Microsoft. Apptio, which went public this summer, just announced revenues of $40.6 million, up 26 percent from the third quarter of 2015.
Data science is having its 15 minutes of fame.
Data and analytics are increasingly becoming central to business decision-making, especially in areas such as driving customer growth, improving productivity and managing risk. But even as organizations push to make their decision-making more data-driven, business leaders accustomed to making decisions based on gut-instincts and experience are having trouble trusting insights from data and analytics (D&A).
Forrester Consulting, commissioned by the Data and Analytics Global team at professional services firm KPMG, recently surveyed 2,165 data and analytics decision-makers from a range of industries in Australia, Brazil, Canada, China, France, Germany, India, the U.K. and the U.S.
Sam Schmidt didn’t need a steering wheel to manage a 152 mph pace at the Indianapolis Motor Speedway in May. He just tilted his head, and technology did the rest.
The winning Indy Car driver and team owner is a quadriplegic, the result of a 2000 Orlando Speedway crash. Working with automotive engineers and medical professionals, Schmidt transformed a 2016 Chevrolet Corvette into a semi-autonomous motorcar (called SAM for short). SAM responds to Schmidt’s head and eye movements through lightning-fast advanced computing technologies so he can pursue his passion: racing.Arrow Electronics, Inc. What took us so long?
The SAM car is possible thanks to massive amounts of data, advanced technologies like analytics, and affordable, big computing power. You can toss around buzzwords like “big data” and “IoT” all day, but affordable data storage and big computing are what make the real difference.
In recent months, we’ve explored how digital transformation within businesses has sparked parallel transformations in C-suites across the globe. The rise of CDOs (chief digital officers) has created big change at the top of many organizations as they work to address the digital demands hitting all levels of business today. Pick a department — finance, customer service, IT, marketing, HR, R&D — and you are very likely to see one or more mobile, social, cloud and data initiatives at work. While business leaders at the top are looking ahead and asking “What’s next for digital?” and “How can we prepare?” many teams on the ground are working through “What’s now?” They are still adapting to the many ways digital is reshaping how they collaborate, engage and deliver.
Big data is a term that is used in the information technology industry to mean building multiple sources of information together into a data lake, a data repository built on relatively inexpensive high performing computer hardware. The value of your data can be extracted from a data lake through existing reporting and business analytic systems. Furthermore, the advent of machine learning capabilities for big data solutions provides additional analytical capabilities. Machine learning has derived meaningful insights which can be used to support business development and organizational growth.Machine learning and big data
Big data in its current form will reduce your operational and infrastructure costs, but will not provide you with any additional value for your business over what enterprise data warehouses provides. Why is that? The machine learning of today, that is employed within big data solutions, has no more capability than the statistical packages that are already in use within enterprise data warehouses solutions.