Lexmark Acquires Kofax- Becomes A Major Force To Help Companies Digitize Operational Processes

Craig Le Clair

This latest Lexmark move is harder to assess than previous major acquisitions. Give  the Perceptive acquisition an  A, Brainware  a B -,  and Pallas maybe a  C+. The Kofax merger, on the other hand, has two legitimate views and lets start with the positive. Kofax has indeed assembled a range of complimentary components that fit well with Lexmark's market ambition. The key asset of interest is the TotalAgility (KTA) platform and its related components. These enhance Lexmark's process platform that was based on the Pallas, too low a market share and Perceptive’s document-focused workflow. KTA, by contrast,  has a true case platform and is well integrated with the industry-leading capture platform. Kofax has never had been in the ECM space. They are now with one of the strongest. And the list goes on. Brainware will boost forms processing for Kofax' invoice processing customers. The AltoSoft BI tool adds analytics strength that Lexmark did not have. Data integration is improved with Kapow. A top E-Signature product (Softpro) and a growing CCM platform from AiA are all good pickups. These last two fit well with Lexmark’s transitioning MPS business.

The drawback here is that Kofax’s go to market positioning and execution is nowhere near complete, and needs entrepreneurial energy and execution to get there. Perhaps Lexmark can help - but Kofax will now be part of a larger company that has transition issues of its own. Perhaps more importantly, Lexmark may find itself devoting significant investment dollars to purchase a legacy document capture business that has moderate long term value. We estimate around $200m of Kofax’ current business derives from this market with revenue in this area more likely to decline then accelerate. Lexmark would then find itself devoting a lot of management attention to minimizing the impact of that decline.

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EA And Transformation - And The 2015 Enterprise Architecture Awards

Alex Cullen

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
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Big Data, Welcome To Those Awkward Teenage Years

Brian  Hopkins

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.

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Big Changes In Information-related Roles And Processes -- Evolution Or Apocalypse?

Gene Leganza
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. 
 
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This Time, AI Is Truly Here To Help Build Intelligent Applications

Diego Lo Giudice

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.

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3 Ways Data Preparation Tools Help You Get Ahead Of Big Data

Michele Goetz

The business has an insatiable appetite for data and insights.  Even in the age of big data, the number one issue of business stakeholders and analysts is getting access to the data.  If access is achieved, the next step is "wrangling" the data into a usable data set for analysis.  The term "wrangling" itself creates a nervous twitch, unless you enjoy the rodeo.  But, the goal of the business isn't to be an adrenalin junky.  The goal is to get insight that helps them smartly navigate through increasingly complex business landscapes and customer interactions.  Those that get this have introduced a softer term, "blending."  Another term dreamed up by data vendor marketers to avoid the dreaded conversation of data integration and data governance.  

The reality is that you can't market message your way out of the fundamental problem that big data is creating data swamps even in the best intentioned efforts. (This is the reality of big data's first principle of a schema-less data.)  Data governance for big data is primarily relegated to cataloging data and its lineage which serve the data management team but creates a new kind of nightmare for analysts and data scientist - working with a card catalog that will rival the Library of Congress. Dropping a self-service business intelligence tool or advanced analytic solution doesn't solve the problem of familiarizing the analyst with the data.  Analysts will still spend up to 80% of their time just trying to create the data set to draw insights.  

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SAP Is On The Right Track To Address The Pain Points Of Chinese Customers, But It Is Not On The Finish Line Yet

Charlie Dai

On February 9, SAP announced the launch of its next-generation enterprise process application, SAP Business Suite 4 SAP HANA (S/4HANA), in China. This is the third product launch event of SAP globally but it’s the first event during which the product is being launched with customer together.

From my discussions with Chinese customers during the event, I believe that SAP is on the right track to address their major concerns. However, enterprise architecture (EA) professionals in China should take a realistic approach when evaluating the feasibility of the architectural evolution of their enterprise process applications.

  • Chinese clients have suffered from complexity for a long time.As mentioned in my previous report, complexity is one of the key challenges that Chinese companies have faced in their drive to achieve business growth and product innovation, and product innovation must focus on simplicity to enhance customer experiences. This is particularly true when it comes to adopting mission-critical management software. It’s quite normal to hear complaints about the complex user interface, long implementation times, and the significant effort required to maintain and customize software; customization is much more popular and necessary in China than elsewhere due to the need for various types of localization.
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Is Big Data Enough? (Ramping Up For Strata In San Jose)

Brian  Hopkins

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.

meetme.so/BHopkins_1on1_20Mins

[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.

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Welcome back to integration technologies!!!

Henry Peyret

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.

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Beyond Big Data's Vs: Fast Data Is More Than Data Velocity

Michele Goetz

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.  

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