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Artificial intelligence (AI) is rising in prominence with the proliferation of chatbots, virtual assistants and other conversational tools that companies are using to improve customer service, productivity and operational efficiency. But AI is also helping to automate and streamline tasks in data-intensive industries traditionally ruled by rigorous science and good old-fashioned human analysis.
Seed retailers, for example, are using AI products to churn through terabytes of precision agricultural data to create the best corn crops, while pest control companies are using AI-based image-recognition technology to identify and treat various types of bugs and vermin. Such markedly different scenarios underscore how AI has evolved from science fiction to practical solutions that can potentially help companies get a leg up on their competition.
Are you app-solutely missing the boat?
These days, we carry our smartphones everywhere. People are using them to play, order food, make purchases, do research, communicate, check reviews, read books, find love and generally get by day to day. Some people are even running their business from their phone! In fact, many of you probably prefer using apps over your desktop when it comes to things like checking emails, updating your social media accounts or checking your online banking.
Smartphones have already revolutionized the way brands can market to consumers through branded apps that connect businesses to their customers 24/7. Brands are already using apps successfully to connect with their customers, think of Dominoes and Starbucks, Ebay and Amazon they are using apps really successfully. Apps offer a far more direct marketing channel now than ever in the past, from an push notification that offers you a personalized offer when smart geolocation technology detects a customer is near their store, to in-app offers that offer a 10 percent discount if you purchase those 3 items that have been sitting in your basket for the past 3 days.
Zuora aims to win the next IT stack war – but it’s probably not the stack war that’s comes most readily to your mind. Tien Tzuo, CEO and co-founder of Zuora, wants to own the application stack that drives your subscription business and he believes that virtually every company will be a subscription business before long.
Key to success in an A.I. or IoT or data strategy is data scientist productivity. Data scientist productivity is defined as the volume of business-critical results driven through data science. The difference between successful data-driven companies and not so successful ones is the productivity and throughput of the data science team. Increasing data scientist productivity and throughput leads to positive side effects, including standardization of data science processes, tooling and data science methodology, as well as an increase in the availability of case studies and foundational data science that can trigger and speed up other data science efforts.
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.”