Once again, the mobile world is getting ready for the most important mobile event of the year, the Mobile World Congress (MWC), which will take place in Barcelona from March 2 to 5. In my role as analyst with a focus on CIO requirements, I expect the following themes to dominate this year's show:
Everybody will talk about data — and many about data privacy. The long-anticipated marriage between big data and mobility is finally happening. I expect just about every vendor at MWC will claim a stake in these mobile data wedding arrangements. However, many big data business models remain building sites, and it remains far from clear which players will benefit via which types of business models. The growing awareness of regulatory constraints on the use of customer data as well as what the Financial Times recently called the "creepiness quotient", i.e., hyper-personalized advertising, further complicate a convincing business model for mobile analytics on a mass scale. Despite all the hype, mobile data is one of the must-focus areas for CIOs who attend MWC.
I’m ramping up to attend Strata in San Jose, February 18, 19 and 20th. Here is some info to help everyone who wants to connect and share thoughts. Looking forward to great sessions and a lot of thought leadership.
I’ll be setting aside some time for 1:1 meetings (Booked Full)
[Updated on 2/17] - I have set up some blocks of time to meet with people at Strata. Please follow the link below to schedule with me on a first come basis.
[Update] - I booked out inside 2 hours...didn't expect that! I may open up my calendar for more meetings but need to get a better bead on the sessions I want to attend first. Shoot to catch me at breakfast, will tweet out when I'm there.
I’ll be posting my thoughts and locations on Twitter
The best way to connect with me at Strata is to follow me on Twitter @practicingea.
You can post @ me or DM me. I’ll be posting my location and you can drop by for ad hoc conversations as well.
I’m very interested in your point of view - data driven to insights driven
I am concluding very quickly that “big data” as we have viewed it for the last five years is not enough. I see firms using words like “real-time” or “right-time” or “fast data” to suggest the need is much bigger than big data – its about connecting data to action in a continuous learning loop.
There’s a renewed interest in integration technologies due to new needs for integration to mobile, the Internet of Things (IoT), and cloud — but also because integration requirements betwen systems of engagement and systems of record are requiring realtime for seamless boundaries omnichannel, higher volume, with end-to-end security highlight the changes in integration practices. Forrester will soon publish a report about the integration trends around these subjects.
I am happy to pick up this subject again from Stefan Ried after being away from the space for the past six years. Stefan left Forrester in December and I regret his departure, because he was a very passionate analyst and a smart guy to work with.
“With the gift of listening comes the gift of healing.”
-Catherine de Hueck Doherty
We’ve all heard the canned notifications when we call companies for customer service: “this call may be recorded for security or quality purposes.” Most customer service organizations today record their phone interactions with their customers. Often those recordings just sit untouched on the digital equivalent of a dusty shelf in a storage closet. The recordings are there to ensure regulatory compliance or, in rare cases, to be pulled off the shelf in case of a major dispute with a customer. In essence, the part of the notification about security rings true; the quality part, not so much.
But, as part of continuous improvement programs, companies have begun to change that by actually analyzing the recordings for quality purposes. That process of quality monitoring allows firms to select recordings for review and assessment. In forward-thinking organizations, the tools enable managers to replay agent screen actions, allowing evaluations to include screen activity in addition to voice content. Managers use these reviews to pinpoint which agents perform well, which need further training, and to identify processes that need to be refined.
Companies doing this basic form of quality monitoring, however, find they cannot change the outcome of those calls — the interactions are long since over. This is where the emerging field of real-time speech analytics comes into play. Vendors of real-time speech analytics tools promise to allow companies to intervene at the moment of truth, while the customer and the contact center agent are still talking.
The battle of trying to apply traditional waterfall software development life-cycle (SDLC) methodology and project management to Business Intelligence (BI) has already been fought — and largely lost. These approaches and best practices, which apply to most other enterprise applications, work well in some cases, as with very well-defined and stable BI capabilities like tax or regulatory reporting. Mission-critical, enterprise-grade BI apps can also have a reasonably long shelf life of a year or more. But these best practices do not work for the majority of BI strategies, where requirements change much faster than these traditional approaches can support; by the time a traditional BI application development team rolls out what it thought was a well-designed BI application, it's too late. As a result, BI pros need to move beyond earlier-generation BI support organizations to:
Focus on business outcomes, not just technologies. Earlier-generation BI programs lacked an "outputs first" mentality. Those projects employed bottom-up approaches that focused on the program and technology first, leaving clients without the proper outputs that they needed to manage the business. Organizations should use a top-down approach that defines key performance indicators, metrics, and measures that align with the business strategy. They must first stop and determine the population of information required to manage the business and then address technology and data needs.
With 25 years of history and 178 million active public websites around the world, you would think that the backbone technology for websites would be mature, sophisticated, basically done as a market. But it's simply not true. Web content management (WCM) systems are still in their infancy. Here's the one-minute history:
1995. These ever-changing systems first had to learn to deliver content interactively, tailoring the experience to the needs of the day. Think Yahoo.com.
2000. Then they had to deliver business services directly into customers' hands. Think eBusiness.
2010. Then they had to deliver experiences on smartphones and tablets. Think Google Maps app.
2015. And now they have to deliver highly personal digital experiences on any device directly into a customer's immediate context and moments of need along every step of her journey (see Figure 1).
2020. What's coming next? Well, let's get the platforms up to 2015 requirements first. But those of you with a future slant need to be thinking about modern app architectures, where the building blocks -- content systems, digital insights, customer databases, integration, delivery tier, and so on are decoupled to handle IoT, glanceable moments on wearables, and a gazillion other digital scenarios.
Software is getting smarter, thanks to predictive analytics, machine learning, and artificial intelligence (AI). Whereas the current generation of software is about enabling smarter decision-making for humans, we’re starting to see “invisible software" capable of performing tasks without human intervention.
One such example is x.ai, a software-based personal assistant that schedules meetings for you. With no user interface, you simply cc “Amy” on an email thread and she goes to work engaging with the recipient to find a date and optimal place to meet.
It’s not a perfectly automated system. AI trainers oversee Amy’s interactions and make adjustments on the fly. But over time, she becomes a great personal assistant who is sensitive to your meeting and communication preferences.
One can imagine Amy extending into new domains — taking on parts of sales/customer service operations or business processes like expense management and DevOps. Indeed, we’ll see a new generation of AI-powered apps, as predicted here.
To capture, manage, and deliver live and on-demand video you need a video platform. Selecting the right platform helps companies maximize the impact of video on customer and employee experiences.
Enterprises looking at applications for video across marketing, corporate communications, and training need to consider products in multiple categories. Our just-published Forrester Wave on Enterprise Video Platforms and Webcasting evaluates the 16 leading providers focused on live presentations with slides and publishing video on demand. We included BrightTALK, Cisco, InXpo, Kaltura, Kontiki, Kulu Valley, MediaPlatform, Nasdaq, On24, Panopto, Polycom, Qumu, Ramp, Sonic Foundry, TalkPoint (PGi), and VBrick in the evaluation.
When you hear the term fast data the first thought is probably the velocity of the data. Not unusual in the realm of big data where velocity is one of the V's everyone talked about. However, fast data encompasses more than a data characteristic, it is about how quickly you can get and use insight.
Working with Noel Yuhanna on an upcoming report on how to develop your data management roadmap, we found speed was a continuous theme to achieve. Clients consistently call out speed as what holds them back. How they interpret what speed means is the crux of the issue.
Technology management thinks about how quickly data is provisioned. The solution is a faster engine - in-memory grids like SAP HANA become the tool of choice. This is the wrong way to think about it. Simply serving up data with faster integration and a high performance platform is what we have always done - better box, better integration software, better data warehouse. Why use the same solution that in a year or two runs against the same wall?
The other side of the equation is that sending data out faster ignores what business stakeholders and analytics teams want. Speed to the business encompasses self-service data acquisition, faster deployment of data services, and faster changes. The reason, they need to act on the data and insights.
The right strategy is to create a vision that orients toward business outcomes. Today's reality is that we live in a world where it is no longer about first to market, we have to be about first to value. First to value with our customers, and first to value with our business capabilities. The speed at which insights are gained and ultimately how they are put to use is your data management strategy.
The perennial call for public sector reform has not slackened. The pain of austerity measures and the pressures for increased efficiency heighten that call. And, the hype around “smart cities” amps up the pressure for municipal leaders faced with decisions about which problems to attack first, and which tools are most appropriate. But most organizations are not starting from a clean slate. That’s exactly the issue. In most cases we’re talking about reform, about doing things differently, not starting from scratch.
When we asked government leaders what their top priorities are, improving the customer experience comes in on top: 68% report the customer experience is either a high or critical priority. But reducing costs is right up there with it. That’s the age-old do-more-with-less mantra. And, from a technology perspective their top priority is to upgrade or replace legacy systems, which might not sound like the wiz bang “smart” technology we’ve been hearing so much about. But it’s likely the smartest thing these governments can do; and when they do, they should do it together.