Metalogix’s Acquisition Of Axceler Increases SharePoint Footprint

Alan Weintraub

Metalogix increases its extension of SharePoint capabilities with the acquisition of Axceler’s SharePoint governance products. As I pointed out in my research document, Putting Together The SharePoint ECM Puzzle, SharePoint’s ECM holes have created opportunity for partners to fill in the missing functionality required by organizations looking to implement an ECM solution. Metalogix focuses its efforts on archiving and storage, and with the Axceler acquisition, it ventures into the administration and governance areas that provide key capabilities to streamline the processes for migration, user administration, and policy compliance.

Our recent ECM survey showed that 46% of respondents indicated that the lack of governance was the single biggest challenge to their ECM implementation. My interactions with Forrester clients indicate that SharePoint implementations may actually suffer a higher percentage of failures due to the lack of governance. Organizations struggle to gain control over their SharePoint implementations, caused by the “SharePoint sprawl,” resulting in the explosion of sites that don’t follow any standards. The combination of Metalogix’s archiving products with Axceler’s governance and policy management products has the potential of providing organizations with a foundation that will help facilitate the implementation of a sustainable governance program. The merging of these two organizations and products will help address three key aspects of governance: archiving of sites, document libraries, and documents; the implementation governance policies; and the enforcement of site level quotas and security access.

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“All Projects Are Business Projects”

Derek Miers

I read this somewhere recently – I think it was the CIO of Intel, Kim Stevenson (quoting IT folklore). But it stuck in my mind, long after the link that I harvested it from had evaporated. I like it since it gets to the heart of the discussion . . . what’s the business problem you are trying to solve. So often I find myself fielding queries where the people on the other end of the phone have decided on a technological solution (a hammer), and are now looking for a problem (the right nail).

The business doesn’t want a hammer or a nail; they want something of value – the house. It’s not important that your solution has this product or that techno buzzword. They don’t care for how cute your big data credentials are, or whether your mobile mojo has trumped your social ace in the hole. These sorts of trends – big data, mobile, social – are just like, well, like the context within which the house sits.

Of course, we need that application delivered to our customers on a digital device nearby to them. Of course, we want that engagement to leverage the history of what we’ve done with that customer in the past – their wants and preferences taken into account. Of course, we want to leverage what we know others in the same context considered the right choice. But we also expect the customer to channel-hop to the Web, and then perhaps wander into a branch or store, and ring up about it to see where things are at (WISMO – what is the status of my order).

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Data Quality And Data Science Are Not Polar Opposites

Michele Goetz

Big data gurus have said that data quality isn’t important for big data. Good enough is good enough. However, business stakeholders still complain about poor data quality. In fact, when Forrester surveyed customer intelligence professionals, the ability to integrate data and manage data quality are the top two factors holding customer intelligence back.

So, do big data gurus have it wrong? Sort of . . .

I had the chance to attend and present at a marketing event put on by MITX last week in Boston that focused on data science for marketing and customer experience. I recommend all data and big data professionals do this. Here is why. How marketers and agencies talk about big data and data science is different than how IT talks about it. This isn’t just a language barrier, it’s a philosophy barrier. Let’s look at this closer:

  • Data is totals. When IT talks about data, it’s talking of the physical elements stored in systems. When marketing talks about data, it’s referring to the totals and calculation outputs from analysis.
  • Quality is completeness. At the MITX event, Panera Bread was asked, how do they understand customers that pay cash? This lack of data didn’t hinder analysis. Panera looked at customers in their loyalty program and promotions that paid cash to make assumptions about this segment and their behavior. Analytics was the data quality tool that completed the customer picture.
  • Data rules are algorithms. When rules are applied to data, these are more aligned to segmentation and status that would be input into personalized customer interaction. Data rules are not about transformation to marketers.
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Kofax Acquires Kapow — Targets Content Transformation Market

Craig Le Clair

Kofax continues its acquisition rampage with a cash purchase of Kapow. I came across Kofax a few years ago while doing the research for "Take A Process View Of Content Integration." Apparently Kofax has taken the "process view." The idea behind that piece was that enterprises had so many diverse content stores that they needed to view conversion and migration of unstructured content as an internal competency.

But while content integration can reduce infrastructure costs and license fees, the real value is from improving business processes by linking content to business process management (BPM) and dynamic case management systems to reduce cycle time and improve compliance, customer support, and decision-making. These projects can be complex, difficult, and challenging, but Kofax correctly sees this as a large opportunity. I do as well.

Another Kapow capability is to scrape websites and create consolidated views. For example, customer service reps often switch between apps in a clumsy and inefficient manner while the customer is on hold. In some cases, ECI software should grab the needed content behind the scenes and present it in a unified way. Kapow Technologies' content integration solution works like a robot to extract, transform, and load content from Web-based apps to consolidated views. I interviewed one large telecommunications company that used Kapow's robot for customer service business processes to eliminate task switching and repetitive tasks. According to the company:

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Data Quality Blooms With Crowdflower

Michele Goetz

Sometimes getting the data quality right is just hard, if not impossible. Even after implementing data quality tools, acquiring third-party data feeds, and implementing data steward remediation processes, often the business is still not satisfied with the quality of the data. Data is still missing and considered old or irrelevant. For example: Insurance companies want access to construction data to improve catastrophe modeling. Food chains need to incorporate drop-off bays and instructions for outlets in shopping malls and plazas to get food supplies to the prep tables. Global companies need to validate address information in developing countries that have incomplete or fast-changing postal directories for logistics. What it takes to complete the data and improve it has now entered the realm of hands-on processes.

Crowdflower says they have the answer to the data challenges listed above. It has a model of combining a crowdsourcing model and data stewardship platform to manage the last mile in data quality. The crowd is a vast network of people around the globe that are notified of data quality tasks through a data stewardship platform. If they can help with the data quality need within the time period requester, the contributor accepts the task and get to work. The crowd can use all resources and channels available to them to complete tasks such as web searches, visits, and phone inquiries. Quality control is performed to validate crowdsourced data and improvements. If an organization has more data quality tasks, machine learning is applied to analyze and optimize crowd sourcing based on the scores and results of contributors.

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To get national healthcare right requires Adaptive Intelligence

James Staten

With the employer mandate delays being the latest setback to U.S. president Obama's push for national healthcare, it's worth looking at how other countries are successfully tackling the same problem. The United Kingdom has had nationalized healthcare for years, and one of the things that makes this effort so successful is its approach to data collaboration — something Forrester calls Adaptive Intelligence.

While the UK hasn't successfully moved into fully electronic health records, it has in place today a health records sharing system that lets its over 27,000 member organizations string together patient care information across providers, hospitals, and ministries, creating a more full and accurate picture of each patient, which results in better care. At the heart of this exchange is a central data sharing system called Spine. It's through Spine that all the National Health Service (NHS) member organizations connect their data sets for integration and analysis. The data-sharing model Spine creates has been integral in the creation of summary care records across providers, an electronic prescription service, and highly detailed patient care quality analysis. As we discussed in the Forrester report "Introducing Adaptive Intelligence," no one company can alone create an accurate picture of its customers or its business without collaborating on the data and analysis with other organizations who have complementary views that flesh out the picture.

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Business Design Patterns: Myth Or Reality?

Gordon Barnett

The other day, I had one of those eureka-like moments. As I lay in the bath, my thoughts shifted back and forth between the past and the present, recognizing how advances (or the lack of advances) in technology have affected our lives. When thinking about the past, I remember the days of my communication engineering apprenticeship; this was in the days of electro-mechanical exchanges. Some of you may remember or may have seen, in an old film, a telephone operator connecting two phone lines by placing a connecting cord between two phone line jacks. This was the world of telecommunication exchanges in the 1970s — no fancy computing technology existed in telecommunications at the time. In was certainly not a trivial exercise in upgrading capacity, maintaining the exchange, or connecting to another exchange. When thinking about the present, I marvel at the continuing improvements in plug-and-play hardware and software technology. As an example, I buy a new camera and, hey presto! I now have the ability to edit and post pictures on forums or cloud applications, to send them by email, or to store them on third-party storage from my camera.

So back to my eureka-like moment. I’m thinking that, surely, all these present-day technology advances have been enabled because of standards, design patterns, and common interfaces. My mind keeps focusing on design patterns, and the question arises: "Is there such a thing as business design patterns?" I have done some initial research, and I am yet to find evidence of the term or concept of business design patterns. However, I do have my suspicions they exist because:

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Data Science And "Closed-Loop" Analytics Changes Master Data Strategy

Michele Goetz
I had a conversation recently with Brian Lent, founder, chairman, and CTO of Medio. If you don’t know Brian, he has worked with companies such as Google and Amazon to build and hone their algorithms and is currently taking predictive analytics to mobile engagement. The perspective he brings as a data scientist not only has ramifications for big data analytics, but drastically shifts the paradigm for how we architect our master data and ensure quality.
 
We discussed big data analytics in the context of behavior and engagement. Think shopping carts and search. At the core, analytics is about the “closed loop.” It is, as Brian says, a rinse and repeat cycle. You gain insight for relevant engagement with a customer, you engage, then you take the results of that engagement and put them back into the analysis.
 
Sounds simple, but think about what that means for data management. Brian provided two principles:
  • Context is more important than source.
  • You need to know the customer.
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Visionary companies are driving next generation enterprise architecture in China – are you ready?

Charlie Dai

For the past ten years, the major IT initiative within Chinese organizations has been service oriented and/or process driven architecture.  The pace of change has been slow for two reasons: 1) From an end user perspective, related business requirements are not clear or of high priority; 2) more importantly,  solutions providers have not been ready to embrace  technology innovation and  meet emerging technology requirements through new business models.

Times are changing. IBM and other major ISV/SI in China (as well as end users) are driving momentum around emerging technology, such as cloud and enterprise mobility.  I recently attended the IBM Technical Summit 2013 in Beijing from July 11 to 12.  Here’s what I learned:

  • Telecom carriers supported by technology vendors will accelerate cloud adoption by SME.  Contributing to more than 60% of total GDP in China, small and medium enterprises (SMEs) have always sought to simplify their IT operation as much as possible, and at the same time scale it up when business expands as quickly as possible. IaaS solutions appear to be a perfect match for SMEs; however IT professionals have concerns about the security and data privacy over the operations by other companies.
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Enable business strategy through technology innovation

Charlie Dai

How is it possible for a local company to defeat global giants like Pepsi, Coca-Cola, and Watsons in your market segment and establish market leadership for more than a decade? The answer is given by Nongfu Spring, a Chinese company in manufacturing and retail industries. In my recent report “Case Study: Technology Innovation Enables Nongfu Spring To Strengthen Market Leadership”, I analyzed the key factors behind their success, and provide related best practice from enterprise architecture perspective. These factors include

  • Business strategy is enterprise architecture's top priority.  EA pros often need to be involved in project-level IT activities to resolve issues and help IT teams put out fires. But it's much more important that architects have a vision, clearly understand the business strategy, and thoroughly consider the appropriate road map that will support it in order to be able to address the root causes of challenges.
  • Agile infrastructure sets up the foundation for scalable business growth. Infrastructure scalability is the basis of business scalability. Infrastructure experts should consider not only the agility that virtualization and IaaS solutions will provide next-generation infrastructure, but also network-level load balancing among multiple telecom carriers. They should also refine the network topology for enterprise security.
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