On February 25, 2015, Google publicly announced its latest functionality and updates to the Android OS, titled "Android for Work" (AFW). Some of the new functionalities include secure work profiles, secure personal information management, and an enterprise app store through "Google Play for Work." These new changes in AFW will impact the businesses, the Android ecosystem, and the overall market in a far-reaching way. EMM vendors and enterprise EMM buyers must review these technology changes and understand how they will influence future product direction before making any purchases. It took just a few years for core MDM functionality to commoditize to a $0 price tag. I wonder how long until the advanced security components being folded into Android via AFW are also essentially free?
Cloud Data Protection (protecting data in SaaS, IaaS and PaaS workloads with a centralized and industrial strenght solution) remains a key priority of CIOs, CISOs and architects.
In this market overview report, we identified 17 key vendors in the CDP space (see the figure below) that provide data protection in SaaS, IaaS and PaaS environments. This report details trends and predictions in CDP and also our findings about how each vendor is approaching CDP and to help security and risk (S&R) professionals select the right partner for CDP.
All broadband is local. If the Internet pipe that reaches your home or small business is too slow (or too expensive), then all the net neutrality regulations in the land won't help citizens avoid the Netflix spinning wheel (or the logy load times of valuable Internet services for education, employment, communications, and banking).
Companies -- both technology leaders and marketing leaders -- should care about the quality of broadband to homes and small businesses. Why? Because your ability to deliver great digital customer experiences is hampered when broadband speeds are low.
I'm all in favor of a robust national discussion about net neutrality, particularly if the discussion balances market conditions for Internet services against market conditions for broadband providers, a challenge that begins with transparency and competition rather than controls. (See this for some ideas on the importance of transparency, market forces, and local competition.)
And I'm certainly massively in favor of Internet-driven "human rights, innovation, and progress" as Tim Berners-Lee espouses. But I am not convinced that over-regulating our country's Internet pipes will solve our spinning wheel problems. Ask yourself these questions:
Why did the Internet at home slow to a crawl during the Boston blizzards?
Why does Google invest some of its massive profits to provide 100 gigabit bandwidth to homes in Kansas City, Austin, and Provo, with 34 more cities coming?
I’m getting a lot calls from clients who are using “white box” and “bare metal” interchangeably when discussing network switches. It might not seem like a big deal, but it is when customers are trying to accomplish a certain task and are examining the wrong products or don’t see the full picture. This is especially true when it comes to assuming that the hardware cost of an Accton 5712 switch is significantly lower than the hardware cost of Broadcom-based switches from Arista, Cisco, or HP. The reality is that pundits are mixing up terms and products.
Fundamentally, they are not making an apples-to-apples comparison and therefore are setting up the wrong expectations for customers. Forrester’s research document The Myth Of White-Box Network Switches dissects the cost of the Accton Edge-Core 5712 (Broadcom Trident II ASIC) switch layered with Cumulus Linux OS and compares this combination against a generic vendor switch built on Broadcom II ASIC, such as Cisco Nexus 3172PQ. We built a model showing the cost of goods sold from the components up and found less than a 5% difference between the switch from the original design manufacturer and traditional network vendor. This holds true when comparing other Accton Edge-Core switches against other traditional vendor Broadcom Trident II switches. The real cost is in the software, global distribution channel, compatibility testing between hardware and software, and global support. “The Myth Of White-Box Network Switches” gets into a lot more detail regarding actual costs.
Enterprise architecture programs deal in change – that’s where EA provides value. And the businesses and government organizations they are part of are in the midst of a lot of change. Witness the accelerating turnover in the Fortune 1000, or how Apple is poised to be a powerhouse in electronic payments, or how healthcare is being transformed by new technologies and new entrants. Market dynamics and digitally-powered competitors are forcing organizations to find new ways to acquire and retain their customers. That means change, and change brings opportunity and risk. Successful firms navigate these changes better when they have the insights that a high-performance, business-focused enterprise architecture program provides.
For this year’s Enterprise Architecture Awards, sponsored by InfoWorld and Forrester Research with the Pennsylvania State University’s Center for Enterprise Architecture, we are seeking entries from EA leaders who have helped their business change. For example:
Helping their organization engage more agilely with their business and customer ecosystem
Translating high level business strategies into plans of change
Guiding a business’s digital transformation
Engaging with product, marketing, sales and customer experience initiatives to accelerate results
We’re also looking for EA programs who have transformed themselves to make their value easier to consume by the organization they are part of – for example, by:
Restructuring their operating model away from the traditional data, application and technology domains to the new competencies of digital customer experience architecture or digital operational excellence
Enabling more flexible architecture practices through architecture zoning or greater federation with other resources
Open data is critical for delivering contextual value to customers in digital ecosystems. For instance, The Weather Channel and OpenWeatherMap collect weather-related data points from millions of data sources, including the wingtips of aircraft. They could share these data points with car insurance companies. This would allow the insurers to expand their customer journey activities, such as alerting their customers in real time to warn them of an approaching hailstorm so that the car owners have a chance to move their cars to safety. Success requires making logical connections between isolated data fields to generate meaningful business intelligence.
But also trust is critical to deliver value in digital ecosystems. One of the key questions for big data is who owns the data. Is it the division that collects the data, the business as a whole, or the customer whose data is collected? Forrester believes that for data analytics to unfold its true potential and gain end user acceptance, the users themselves must remain the ultimate owner of their own data.
The development of control mechanisms that allow end users to control their data is a major task for CIOs. One possible approach could be dashboard portals that allow end users to specify which businesses can use which data sets and for what purpose. Private.me is trying to develop such a mechanism. It provides servers to which individual's information is distributed to be run by non-profit organizations. Data anonymization is another approach that many businesses are working on, despite the fact that there are limits to data anonymization as a means to ensure true privacy.
Just a few years ago, when big data was associated primarily with Hadoop, it was like a precocious child…fun for adults, but nobody took it seriously. I’m attending Strata in San Jose this February, and I can see things have changed. Attendance doubled from last year and many of the attendees are the business casual managers – not the blue jeaned developers and admins of days gone by. Big data is maturing and nobody takes it lightly anymore.
Industry analysts travel—a lot. It is, therefore, no surprise that I care deeply about airlines’ frequent flyer programs and track the changes to those programs as closely as baseball obsessives track star players’ slugging percentages. When I want information on what these changes mean practically in my situation (Will the new loyalty program make it harder for a 75k+ elite member looking to book a companion ticket’s upgrade on an alliance partner airline, for example), I typically do not turn directly to the airline. Instead, I log on to Flyertalk, a forum that bills itself as “the largest expert travel community.” The forum—populated by thousands of frequent fliers far more obsessive than I will ever be—consistently houses discussions of exactly the thing I want to know.
The lion’s share of people answering questions on Flyertalk and other forums like it—Cruisecritic for the cruising fans, TripAdvisor for travel and hospitality broadly, AutomotiveForums for car enthusiasts, etc.—are other consumers, albeit well-informed ones. But these non-brand controlled communities provide opportunities to brands to differentiate themselves through service. Because affinity communities have barriers to entry, including registrations and jargon, community members are usually deeply interested in the topic at hand. In communities that regularly discuss brands, these customers are also more likely to be exactly the type of high-value customers that companies want to provide with great customer experiences. But brands need to decide when and how to engage customers in these forums they do not control.
It’s not news that business user self-service for access to information and analytics is hot. What might not be as obvious is the overhaul of information-related roles that is happening now as a result. What’s driving this? The hunger for data (big, fast, and otherwise) to feed insights, very popular data visualization tools, and new but rapidly spreading technology that puts sophisticated data exploration and manipulation tools in the hands of business users.
One impact is that classic tech management functions such as data modeling and data integration are moving into business-side roles. I can’t help but be reminded of Bill Murray’s apocalyptic vision from “Ghostbusters:” “Dogs and cats, living together… mass hysteria!” Is this the end of rational, orderly data management as we know it? Haven’t central tech management organizations always seen business-side tech decision-making (and purchasing, and implementation) as “rogue” behavior that needed to be governed out of existence? If organizations have trouble now keeping data for analytics at the right level of quality in data warehouses, won’t all this introduction of new data sources and data lakes and whatnot just make things worse?
Well, my answers are “no,” “yes,” and “no” in that order. The big changes that are afoot are not the end of order and even though “business empowerment” translates to “rogue IT” in some circles, data lakes/hubs and the infusion of 3rd party data have actually been delivering on their promise of faster, better business insights for the organizations doing it right.
What’s taken artificial intelligence (AI) so long? We invented AI capabilities like first-order logical reasoning, natural-language processing, speech/voice/vision recognition, neural networks, machine-learning algorithms, and expert systems more than 30 years ago, but aside from a few marginal applications in business systems, AI hasn’t made much of a difference. The business doesn’t understand how or why it could make a difference; it thinks we can program anything, which is almost true. But there’s one thing we fail at programming: our own brain — we simply don’t know how it works.
What’s changed now? While some AI research still tries to simulate our brain or certain regions of it — and is frankly unlikely to deliver concrete results anytime soon — most of it now leverages a less human, but more effective, approach revolving around machine learning and smart integration with other AI capabilities.
What is machine learning? Simply put, sophisticated software algorithms that learn to do something on their own by repeated training using big data. In fact, big data is what’s making the difference in machine learning, along with great improvements in many of the above AI disciplines (see the AI market overview that I coauthored with Mike Gualtieri and Michele Goetz on why AI is better and consumable today). As a result, AI is undergoing a renaissance, developing new “cognitive” capabilities to help in our daily lives.