My Three Assumptions For Why The Next Generation Of SW Innovation Will Be Cognitive!

Diego Lo Giudice

I am just back from the first ever Cognitive Computing Forum organized by DATAVERSITY in San Jose, California. I am not new to artificial intelligence (AI), and was a software developer in the early days of AI when I was just out of university. Back then, if you worked in AI, you would be called a SW Knowledge Engineer, and you would use symbolic programming (LISP) and first order logic programming (Prolog) or predicate calculus (MRS) to develop “intelligent” programs. Lot’s of research was done on knowledge representation and tools to support knowledge based engineers in developing applications that by nature required heuristic problem solving. Heuristics are necessary when problems are undefined, non-linear and complex. Deciding which financial product you should buy based on your risk tolerance, amount you are willing to invest, and personal objectives is a typical problem we used to solve with AI.

Fast forward 25 years, and AI is back, has a new name, it is now called cognitive computing. An old friend of mine, who’s never left the field, says, “AI has never really gone away, but has undergone some major fundamental changes.” Perhaps it never really went away from labs, research and very nich business areas. The change, however, is heavily about the context: hardware and software scale related constraints are gone, and there’s tons of data/knowledge digitally available (ironically AI missed big data 25 years ago!). But this is not what I want to focus on.

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The Good The Bad And The Ugly Of Enterprise BI

Boris Evelson
Unified information architecture, data governance, and standard enterprise BI platforms are all but a journey via a long and winding road. Even if one deploys the "latest and greatest" BI tools and best practices, the organization may not be getting any closer to the light at the end of the tunnel because:
  • Technology-driven enterprise BI is scalable but not agile. For the last decade, top down data governance, centralization of BI support on standardized infrastructure, scalability, robustness, support for mission critical applications, minimizing operational risk, and drive toward absolute single version of the truth — the good of enterprise BI — were the strategies that allowed organizations to reap multiple business benefits. However, today's business outlook is much different and one cannot pretend to put new wine into old wine skins. If these were the only best practices, why is it that Forrester research constantly finds that homegrown or shadow BI applications by far outstrip applications created on enterprise BI platforms? Our research often uncovers that — here's where the bad part comes in — enterprise BI environments are complex, inflexible, and slow to react and, therefore, are largely ineffective in the age of the customer. More specifically, our clients cite that the their enterprise BI applications do not have all of the data they need, do not have the right data models to support all of the latest use cases, take too long, and are too complex to use. These are just some of the reasons Forrester's latest survey indicated that approximately 63% of business decision-makers are using an equal amount or more of homegrown versus enterprise BI applications. And an astonishingly miniscule 2% of business decision-makers reported using solely enterprise BI applications.
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Introducing a new Senior Analyst

Hello, world. Welcome to my first blog as a new Senior Analyst serving Application Development & Delivery professionals.

I come to Forrester after working in the Solution Marketing and Corporate Marketing groups at a large customer service software provider. That role put me in touch with contact center technology buyers and the overburdened folks responsible for actually making great customer service happen every day. I saw close up the impact of the age of the customer on the thinking, processes, behavior, and technology choices of contact center professionals around the world. They are facing a world in which consumers are much less willing to settle for mediocre and impersonal experiences when dealing with customer service organizations. As consumers we all want effortless service delivered via whatever channel is most convenient at the moment, and we want companies to know just the right amount of information about us, but not too much, at the moment of the interaction.

That is a very tough nut to crack for contact center managers, supervisors, and agents. My research coverage will primarily focus on two areas that can help contact center pros begin to address these issues:

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Planning Your Big Data Strategy: Five Keys To Success

Martha Bennett

To compete in the age of the customer, it’s essential to make the most of the data you have access to, whether it’s from internal or external sources. For most organizations, this implies a need to review and challenge existing approaches to how they capture, process, and use data to support decision-making. But it’s important first of all to move beyond a technology-centric view of big data. This is why at Forrester, we define big data as:

The practices and technologies that close the gap between the data available and the ability to turn that data into business insight.

Moving beyond a technology-centric view doesn’t mean, however, that a bottom-up, technology-led approach to big data strategy won’t work. After all, it’s often the case that business executives can’t see the potential of a technology until they’ve seen it in action. A bottom-up approach also provides the opportunity to acquire technical skills, and gain an understanding of what needs to be done to integrate new technologies with existing systems (even if it’s just at the level of getting the data out – often easier said than done). But a pilot project or proof-of-concept demonstrating the “art of the possible” in a business context is different from implementing a Hadoop cluster and expecting the business side to start asking for projects.

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Closing The Experience Gaps Requires A New Technology Architecture And Philosophy

Ted Schadler

Forrester’s Customer Experience Index (CXi) research reveals a shocking business result: Over five years, CXi leaders outperformed the S&P with 43% stock growth, while CXi laggards had negative returns of -34%. (See this Forrester report to learn about our new customer experience index.)

As a result, firms are in an arms race to mobilize their services, deliver new digital capabilities, and delight customers on every step of their journey. eBusiness, marketing, and customer experience teams are eagerly adopting new software to deliver these digital experiences. At times, they chose a conscious uncoupling from the CIO’s team in order to move quickly and stay ahead of customers’ expectations.

Unfortunately, the mismatch of customer-facing teams scrambling to build new digital services while CIOs and their teams hunker down to cut cost and risk has caused a disconnect on the role of technology management in delivering great experiences. In a new Forrester report, Closing The Experience Gaps, my colleague John C. McCarthy and I interviewed more than 35 companies and analyzed survey results from 3,502 US consumers, we uncovered this misalignment and identified the four experience gaps that result (see Figure 1).

Figure 1 Experience Delivery Requires A New Architecture And Philosophy

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Lost In Data Translation? Forrester's Data Taxonomy To The Rescue

Boris Evelson
  • When it comes to data technology, are you lost in translation? What's the difference between data federation, virtualization, and data or information-as-a-service? Are columnar databases also relational? Does one use the same or different tools for BAM (Business Activity Monitoring) and for CEP (Complex Event Processing)? These questions are just the tip of the iceberg of a plethora of terms and definitions in the rich and complex world of enterprise data and information. Enterprise application developers, data, and information architects manage multiple challenges on a daily basis already, and the last thing they need to deal with are misunderstandings of the various data technology component definitions.
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Is Your CRM Not Working For You?

Kate Leggett

I get a lot of inquiries which go something like this: “we implemented a CRM solution from Vendor X, and it doesn’t work. Nobody is using it, and when they are forced to use it, it is slowing them down instead of making their life easier.  Are there solutions from Vendor Y or Z that would do a better job for us?”

My answer goes something like this: "CRM solutions are mature. Most vendor solutions are chock full of features and functions – probably more than you would ever need. Your CRM is not supporting your needs, perhaps, because:

  1. You don't have crisp definitions of your processes, the stages within processes, and the exit criteria to move to the next stage (ex. what are your criteria to promote a lead to an opportunity? Are they the same for all business units?)
  2. You have implemented your CRM without doing any customization or configuration. As a result, your organizational processes are not well supported in your CRM
  3. You have not paid attention to your data quality. Users don't trust the data that they use.
  4. You haven't spent the time to integrate other systems to your CRM, so you cannot empower your customer facing personnel with all the information they need from your CRM. It's not helping them get their job done easier or faster.
  5. You don't have the right reports available to your end users to allow them to measure their performance.
  6. You haven't focused on usability or the user experience. The UI is probably not role based, or tailored to what your users need, and you haven’t thought though the actual data elements that are important to your users at the various stages of your processes.
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The Modern CRM

Kate Leggett

Its Not Your Mother's CRM Anymore

CRM technologies are over two decades old. Companies first used them to provide “inside-out” efficiencies;operational efficiencies for sales, marketing and customer service organizations when interacting with customers. They aggregated customer data, analyzed that data, and automated workflows to optimize customer engagement processes. Companies could easily argue  business benefits by measuring operational metrics like reducing marketing costs, increasing revenues from sales people, decreasing sale cycle times, better pipeline visibility, decreasing service resolution times and more.     

Because of this quantifiable ROI, CRM became a must-have in large organizations. This strong demand prompted CRM vendors to tackle huge swaths of business problems, and fueled ongoing innovation and consolidation in the marketplace. Today, much of CRM technology is commoditized, and leading vendors offer competitive solutions, choke-full of features and functions, including deeply verticalized solutions.

Being successful at CRM today builds upon yesterday’s internal operational efficiencies and extends the power of these solutions to better support customers through their end-to-end engagement journey to garner their satisfaction and long term loyalty – an “outside-in” perspective. Modern CRM strategies enable good customer experiences. They support customer interactions with one another over a range of social, digital and mobile channels. How? By leveraging the vast amounts of interaction and transaction data to deliver contextual experiences that add value to the customer, and preserve the value of the company brand. 

How do you modernize your CRM?

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Is Your Agency Or SI Trying To Sell You A Digital Experience Delivery Platform?

Anjali Yakkundi

We recently published our digital experience delivery platform wave (you can find the blog post and accompanying report here).  These platforms have emerged to help solve customer needs around integration between digital experience technologies and data management.  

Over the past year, many agencies and systems integrators (SI) have also gotten on the digital experience platform bandwagon. These partners have been white labeling and directly licensing/selling digital experience platforms-as-a-service (PaaS). These solutions are typically built on the backbone of proprietary web content management (WCM) and eCommerce solutions (usually Adobe’s toolsets, though we found some notable exceptions built on Oracle and SDL), and are meant to provide an “as a service” model to delivering multichannel content- and commerce- driven experiences. Many, many services firms from both agency and systems integrators backgrounds have started to promote these solutions including well-known names like: SapientNitro, Publicis Groupe, Wipro, Infosys, Cognizant, Deloitte, and Capgemini.

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Digital Experience Delivery Platforms: Oasis Or Mirage?

Mark Grannan

To answer this question, we compared 13 digital experience delivery vendors across 29 criteria in our recent Wave report, "Digital Experience Delivery Platforms, Q3 2014." Overall, we found many areas of differentiation, but client adoption and usage is a mixed. While some organizations have made strides in contextual, omnichannel delivery, many fail at customer data management. Almost all of the vendors focused on customer acquisition but many haven't begun to support the entire customer life cycle. In the end, no vendor achieved Leader status.

Despite no Leaders, these 13 vendors are definitively tracking toward the goal of an integrated platform for enterprise digital customer experiences. Specifically, Adobe and hybris outpaced the competition as an aggregator and all-in-one, respectively, but IBM and Sitecore also placed as Strong Performers. Each of the Contenders in our evaluation  -- Acquia, Demandware, Digital River, HP Autonomy, Intershop, OpenText, Oracle, salesforce.com, and SDL -- have strengths and bring an enterprise track record around their core differentiation, but most vendors' platform efforts are still building credibility among enterprise clients.

Earlier this year, Forrester asked 148 digital customer experience decision-makers from across enterprise technology, marketing, and commerce roles, "What are the biggest technical barriers to creative and effective customer-facing systems?" Systems integration and data management solidly led as today's top challenges. Our Forrester Wave analysis aims to uncover which platforms address these top technical barriers and additional priorities such as contextual delivery and bridging content and commerce-driven experiences.

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