The new data economy isn’t about data; it is about insights. How can I increase the availability of my locomotive fleet? How can I extend the longevity of my new tires? How can I improve my on-time-in-full rate? Which subscribers are most likely to churn in the near future? Where is the best location to build a new restaurant franchise or open a new retail outlet? Business decision-makers want answers to these kinds of questions, and new insights services providers are eager to help them.
A growing number of companies recognize the opportunity their data provides, and they take that data to market: 1/3 of firms report commercializing data or sharing it for revenue with partners or customers. The recently published Forrester Report Top Performers Commercialize Data Through Insights Services discusses the new trends in data commercialization: who is buying, who is selling, and what offerings are available, from direct data sales to the delivery of data-derived insight services.
While some commercializers avail themselves of data markets such as Dawex or DataStreamX, many are creating more sophisticated data-derived products and services. They are becoming insights services providers, often as an incremental offering to their existing customers. Some offer insights based on smart products and IoT analytics. Siemens Mobility, Boeing, and GM offer predictive maintenance for their planes, trains, and automobiles. In the agricultural products industry, companies such as Monsanto and DuPont offer services that prescribe when and what farmers should plant, when certain interventions, such as water or pesticide applications, are advisable, or when to harvest.
Every year we run a staffing and hiring survey of what used to be “eBusiness” professionals. I say “used to” because increasingly we find that eBusiness teams have morphed into “Digital Business” teams. Why? Well teams are under an increasing set of pressures, including:
A mandate to drive strategic change throughout the organization. Seventy two percent of firms surveyed are executing on digital transformation, and that tables the topic of digital with the C-Suite. Digital business leaders now have more strategic responsibility and must wield stronger influence with their executive peers and leaders.
In 2016, we learned a lot about how complicated it is to gather and interpret data that will help brands uncover people's motivations, interests and behaviors. At Consumer Marketing in NYC next month, we'll be taking these topics head-on, trying to understand how far data can go to helping us understand and target the right people at the right time.
One of our guests who I'm most excited to hear from on this topic is Danielle Lee, Global VP, Partner Solutions at Spotify. I had a chance to chat with her about some of her thoughts around deterministic targeting and music-based insights. Here's what she had to say:
How do you define "people-based marketing" and why do you think it will become the gold-standard?
People-based marketing represents an industry shift from targeting devices to connecting with the right people at the right time, with the right message. Rather than targeting ads to devices based on cookies, which is fraught with inadequacies, marketers can now reach people across the many devices they use, thanks to persistent identity.
The reason we’re so excited about it at Spotify, and the reason we think it will become the gold standard for marketing, is because it works. With people-based marketing, you have greater assurance that your message is reaching your known customer and driving measurable results.
Mobile World Congress (MWC) which took place in Barcelona once again broke new records in terms of attendees, reaching 108,000. Yet, discussions with end-user businesses indicate that mobility is often no longer treated as a standalone focus area by CIOs and CTOs. Mobility has become part of the broader digital transformation initiative. This has implications for mobile strategies. It also affects the decision where a business leader turns to in order to find inspirations for her digital transformation initiative.
Of course, mobility remains a critically important building block for all digital transformation initiatives. But mobility is part of a wider technology-driven business transformation. In my view, the biggest themes at MWC in 2017 that are relevant for digital transformation relate to IoT, AI, platforms, collaboration, and connectivity. I discuss what these themes mean for the CIO in a separate blog.
Importantly, all of these themes are interwoven. Hence, the CIO needs to build her digital transformation strategy on a comprehensive approach - with mobility is right at the heart. Still, there remains a risk that the CIO gets sucked into pursuing a compartmentalized technology strategy that lacks a comprehensive view of the real business objectives. It is essential that the CIO avoids a ‘bolt-on approach’ to these technology investments because of the technology interdependencies.
Yogi Berra once said, "It's tough to make predictions, especially about the future." It is tough indeed, but enterprises that can make probabilistic predictions about customers, business processes, and operations will have an edge over enterprises that can't. These predictions don't have to be macroscopic to be consequential. Predictions about what a customer is likely to buy next. Predictions about marketing content that will resonate with a prospect. Predictions about the next best action to take in a business process. Predictions about when an expensive asset is likely to break down. Virtually any customer journey, business process, and even strategic decision can be made better if permeated with the power to predict.
Predictive Analytics And Machine Learning Solutions Make It Possible
Yes, making accurate predictions is tough, but predictive analytics and machine learning (PAML) solutions provide data scientists and developers alike with the tools to make it happen. Forrester defines PAML solutions as:
Software that provides data scientists with 1) tools to build predictive models using statistical and machine learning algorithms and 2) a platform to deploy and manage predictive production models.
The Forrester Wave™: Predictive Analytics And Machine Learning Solutions, Q1 2017
This is the post in which I make the seemingly crazy claim that the "next big thing" for Apple -- and for consumer tech -- will be smart pets. Don't say I didn't warn you. :)
Trying to predict what Apple will do next or what Apple should do next (these are two different things) has fueled some of my best work and most enjoyable after-work conversations. I'm not alone in this endeavor, of course. For the past few years -- ever since the Apple Watch came out -- clients, the press, and just people in my neighborhood ask me: "What's the next big thing for Apple?" There are several key candidates that often get proposed – many have suggested an Apple car though late developments make that less and less likely, others think a virtual reality headset is around the corner while I myself have suggested a voice-based personal assistant (Siri in your ear, as I have been known to call it). In none of those cases would Apple be introducing a market-changing product that leaps years beyond competitors, like the jump from Blackberry to iPhone was. Even Siri in your ear is already happening, the latest version that has captured my attention is the Vinci, currently crowdfunding on Indiegogo, a headphone and intelligent agent device which exactly fulfills my prediction of what Apple should have done with Beats but for some reason chose not so, at least so far.
The explosive growth of the data economy is being fueled by the rise of insights services. Companies have been selling and sharing data for years. Axciom and Experian made their name by providing access to rich troves of consumer data. Thompson Reuters, Dun and Bradstreet and Bloomberg distributed financial and corporate data. Data brokers of various kinds connected buyers and sellers across a rich data market landscape. Customers, however, needed to be able to manage and manipulate the data to derive value from it. That required a requisite set of tools and technologies and a high degree of data expertise. Without that data savvy, insights could be elusive.
The new data market is different with insights services providers doing the heavy lifting, delivering relevant and actionable insights directly into decision-making processes. These insights services providers come in a number of flavors. Some provide insights relevant to a particular vertical; others focus on a particular domain such as risk mitigation or function within an organizations such as sales, marketing, or operations.
Recently, the largest annual get together of the mobile industry, Mobile World Congress (MWC) took place in Barcelona. In my opinion, the biggest themes at MWC in 2017 that are relevant for enterprise customers were the internet of things (IoT), artificial intelligence (AI), platforms, collaboration, and connectivity. These themes underline how mobility is becoming part of the broader digital transformation initiative. I discuss this shift in this separate blog and report. MWC provided several valuable insights for business and technology leaders to align their mobile to their digital strategies:
-> Not everything that claims to be AI is true AI. Many vendors that claimed during MWC to be AI-proficient are in fact able to deliver true machine-learning solutions to generate transformative customer and operational insights. Most solutions that were branded as AI at MWC rely on preprogrammed responses and statistics rather than machine learning.
■ Text analytics makes sense of unstructured data.Unstructured data, such as tweets, call center logs, and social media comments, provide an increasingly important view into consumer sentiment and trends today. Text analytics software facilitates the analysis of this unstructured data, allowing companies to mine these new data sources for insights. We project that the text analytics software market will grow 16% annually over the next five years.
■ Geospatial analytics harnesses the power of maps.Traditionally, geospatial analytics has focused on mapping data from geographic information systems (GIS). Today, we see an ever-expanding array of sources of geospatial data connecting customers and locations.Forrester believes theinternet of things(IoT) presents a massive opportunity for companies to uncover insights from spatial relationships, as every connected device can be located by some means. We forecast a 10% compound annual growth rate in geospatial analytics over the next five years.
IBM hosted an artificial intelligent (AI) event at its Munich Watson IoT HQ, where it underlined its claim as a leading global AI and internet-of-things (IoT) platform providers in the enterprise context. AI and the IoT are both very important topics for enterprise users. However, there remains some uncertainty among enterprises regarding the exact benefits that both AI and IoT can generate and how businesses should prepare for the deployment of AI and IoT in their organizations.
One year into the launch of its Munich-based Watson IoT headquarters, IBM invited about one thousand customers to share an update of its AI and IoT activities to date. The IBM “Genius of Things” Summit presented interesting insights for both AI and IoT deployments. It underlined that IBM is clearly one of the leading global AI and IoT platform providers in the enterprise context. Some of the most important insights for me were that:
AI solutions require a partner ecosystem. IBM is well aware of the fact that it cannot provide IoT services on its own. For this reason, IBM is tapping into its existing partner ecosystem. Those partners are not only other vendors. IBM’s ecosystem partnership approach embraces also customers such as Schäffler, Airbus, Vaillant, or Tesco. The event demonstrated how far IBM has matured in living and breathing customer partnerships in the IoT solutions space. For instance, IBM’s cooperation with Visa regarding secure payment experiences for any device connected to the IoT is an example of a new quality of ecosystem partnership.