What is Digital Transformation?

It sounds a bit misleading perhaps - maybe one gets the impression that this is something akin to rendering special effects for movies. In mainstream IT however, Digital Transformation has come to mean something quite different.

The shorthand definition for it is this; "the coordinated integration of all customer-facing digital capabilities." This includes mobile, web and social media. There is perhaps an implied level of underlying integration supporting of all it, but that isn't always required - at least not at first anyway.

We use the word "customer" here a bit loosely, as Customer can refer to any users or members of a certain organization. So, a government entity might consider citizens as customers and the military could consider its rank and file 'customers' in this sense also. Digital Transformation can occur in just about any type of organization specifically because the set of technologies involved is common across industries.

Ten years ago a 'Digital Transformation' would have likely been referred to as a "Portal Strategy." At the time, it was thought that the portal metaphor (and associated software products) would suffice to meet all customer-facing needs. A typical portal might include the following elements in 2005:

• Extranet
• Intranet
• Collaboration tools
• Content Management tools

What happened since then? Well, at least two and possibly three revolutions in technologies with more on the cusp; the key revolutions include:

• Mobile - (not laptops of course, but the smartphone and mobile)
• Social Media - some re-branding of collaborative capabilities merged onto smartphones as well
• Cloud Computing - re-branding of visualization - and importantly opening that up to customer facing apps (web and mobile).
• with Big Data coming on strong (although to be honest, most places still don't know how to use it so it's an outlyer with respect to customer facing technology).

On the backend of course are all the systems that actually make the organization go - but for the customer this is like the engine within the chassis. Most customers don't care what's under the hood until or unless it fails to perform.

Digital Transformation is a lot like enterprise integration but on the customer facing side of things. It is the alignment, coordination and update in tandem of the core mission apps and interfaces. And it can potentially include the Web of Things as well. So for example, if you have a grocery store then perhaps there are kiosks inside and gas pumps outside and nifty new cooler video displays etc. All of that technology is customer-facing so for a true transformation it all must be managed within a shared context.  The transformation has one goal - to go from a heterogeneous mix of random technologies to a holistic solution designed to facilitate strategic goals.

So, how does an organization undergo a Digital Transformation? We will look at this in more detail in one of our upcoming posts.

Some organizations (like larger retailers) face dual Digital Transformations; in so much as they must serve both traditional customers and the needs of many thousands of employees (with separate capabilities) such as in the example above targeted to employees...


Copyright 2014, Stephen Lahanas


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3 Common Cloud Challenges

Like all new technology trends, Cloud Computing brings with it both opportunities and challenges. Unfortunately, the current hype cycle across the IT industry hasn't done the best job of defining either very well (at least not yet). Most of the information out there tends to portray the Cloud as a the ultimate (latest) silver bullet technology.

So, is it?  From my perspective - it could only be viewed as a a conditional, evolutionary improvement - and only if - the organization adopting it fully understands the implications of the technology. Which brings us to a discussion of common Cloud challenges...

Before we jump into that though, let's quantify what we're talking about a little better. There are several types of Cloud-related capability that an organization can pursue; these include:

• Construction of one's Clouds
• Exploitation of 3rd party Clouds (Amazon, Rackspace, Google etc.)
• Adoption of (limited) 3rd party Cloud services or software (SAAS)
• or some type of Hybrid solution

We shouldn't get too bogged down in the differences between Infrastructure, Platform and Software as a Service or Public versus Private Clouds at this point because the challenges we're examining today cut across most of these distinctions. 

Common Cloud Challenges:

1. Proliferation and Governance
2. Automation
3. Integration

Now if any of these seem familiar, they should. These are all challenges that first became apparent during the explosion of  "legacy" data centers for distributed computing back in the 90's and early 2000's. Let's look closer...

Proliferation: Just because you can provision a completely new environment rapidly doesn't mean that you really need to or some cases even should. The model that Amazon uses to serve millions of different customers shouldn't be the same as the model you use for a single enterprise. The more environments (virtual or otherwise) that you have to create, the more you have to manage. Growing these exponentially is a particularly bad idea (although knowing that you can is somewhat cool). This is where Governance should come into play. However Cloud Governance is a practice that's running about 2 to 3 years behind deployment and provisioning - not good...

Automation: It took about 15 years to begin to get the traditional data centers running smoothly - much of that was due to the introduction of network and system administration automation tools. The explosion of Cloud solutions over the last 3 or 4 years has led to the creation of mountains of custom code and glueware to help run IAAS, PAAS and SAAS solutions. This is a serious problem and one that can be remedied soon given that nearly every major automation vendor has now re-architected their solutions for Cloud environments.

Integration: The last issue bleeds into this one. What happens when you introduce a Cloud, or multiple Clouds into your organization? Does all of the legacy capability go away? How do you control data, security, performance and interfaces across hosting platforms? Integration is the number one challenge facing Cloud adopters today and will remain so for quite some time. There is only way to solve the Cloud Integration dilemma - that's through the introduction of comprehensive Cloud Architecture. We will define that in our next post...



Copyright 2013, Stephen Lahanas

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IT Predictions for 2013

Before we continue with our discussion regarding how to develop Conceptual Models, we're going to pause for a moment and look at the year ahead. In many ways, Enterprise IT seems to move a bit slower than the overall tech sector - that's often because the nature or mission of Enterprise Information Technology is the application of new technology rather than its invention (in most cases). Even still, the last two years have seen some fairly remarkably developments, including:

• Industry-wide adoption of Cloud Computing models (both internally and externally)
• Rapid movement towards the concept of Big Data (before that concept has been fully fleshed out).
• Very rapid movement towards adoption of both social media and mobile applications (and integration of the traditional Enterprise IT with both).

Sometimes, the future is hard to read...

Two years is a quick turnaround and what it really represents is the turning of the ship in a new direction - as opposed to having reached the distant shores. While progress is being made in these new directions - they are in most places 'works in progress.' And of course they must be added to the backlog of works in progress that are already present - yesterday's hype-driven initiatives such as:

• Cyber security
• ITIL
• SOA
• EA
• ERP (yes it's still out there and very much alive).
• Business Intelligence
• Semantic Technologies (the Semantic Web)   and so on...

Today's IT department is a busy place indeed - it is coping with more new technology expectations than ever before. Yet seldom do IT budgets reflect the widening scope of those expectations. Given this background we're going to make a handful of predictions for 2013...

• Prediction 1 - Big Data will be redefined into a more actionable approach - this approach will become more architecture driven and less focused on roll-out of databases alone.

• Prediction 2 - Big Data and Semantic Technology will begin to merge - this will occur through management of meta-data as well as through management of architecture-driven Big Data solutions.  

• Prediction 3 - More project teams will be consolidated - much of this will happen onshore but some it will be near-shore and offshore. Why? Because more firms are beginning to understand that globally dispersed teams aren't that efficient - initial costs saved must be measured against overall lack of effectiveness in understanding client needs.

• Prediction 4 - There will be some backlash against the frenzy of IT consolidation that's been occurring for the past several years (driven mainly by Google, Oracle and IBM). This may lead to more anti-trust actions.

• Prediction 5 - There will be increasing pressure on Congress to examine H1b policy decisions. Most of the influence on these policies have been driven by a handful of the largest IT related companies - their arguments and contention that there is an IT skills shortage in the US will be called into question.

• Prediction 6 - Mobile App development will become the hottest / most desired job title - at least near-term.

• Prediction 7 - Massive Online Multi-player gaming environments will unveil new monetization strategies. Massively available online courses will do the same and move towards interchangeable content.

Of course, the most enjoyable part of our industry is how often we end up getting surprised - we're looking forward to that as well...

Copyright 2012  - Technovation Talks, Semantech Inc

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The Semantic COP Motivation

The number one challenge associated with the entire domain of Information Technology is data overload. This problem is not merely a matter of increasing complexity, because even as other areas within IT are becoming more integrated or manageable, the ability to effectively exploit the increasing quantity of available data is decreasing. There are a number of reasons why this is the case; those reasons include:

·       Traditional data technologies, standards and related solution architectures were simply not designed to handle this much data. Data sources were built to exist as silos and most of them have not changed in principle for about 30 years.

·       The requirements for sharing data across organizations or across the globe is a recent expectation and changes all previous expectations regarding data management, integration and discovery.

·       Unstructured data is now becoming as important as structured data. This adds a nearly infinite curve to potential data growth.  

Several key capabilities are needed to help get control of data overload

For those organizations or individuals charged with performing sophisticated analytics-based tasks, the future will be challenging. While data continues to grow at nearly an exponential rate, the tools and technologies dedicated to making sense of ever-larger data sets is not keeping pace. This is not merely a military or intelligence-focused problem; it affects many commercial domains as well. The fact that this is such a common problem makes the prospect of a solution architecture designed to address this challenge more appealing and commercially viable. Such a solution could be presented both as specific commercial products and a best practice architectural approaches (not unlike SOA or Cloud Computing). While this solution may take advantage of Big Data technologies such as Hadoop it is not primarily a "Big Data" approach.

This challenge requires both a new architectural approach but also requires a new way of managing IT lifecycles to better exploit the emerging technologies which comprise that architecture. Semantic Technology is the key to facilitating a new type of analytics, one that has the power to harness any type of data source or architecture.  The overall solution approach that we will be proposing is what we're referring to as a Semantic Operating Picture. The primary solution elements which build upon the S-COP principles we presented yesterday are as follows:

1. Development of a clear definition the Problem Space and related technical challenges.
2. Conducting applied or directed research into specific areas:
• Dynamic Tagging , Master Data & Metadata Management.
• Current Semantic Standards; focus on RDF, OWL, RIF.
• Semantic-based Visualization technologies; especially those using OWL, RDF.
• Semantic Search and Inference Engine technologies.
• Semantic Lifecycle Management capabilities
• Natural Language Processing (algorithms, strategies etc.)
• Translation of RDBMS or DW structures to Semantic Standards & DBs.
• Semantic-driven reporting capabilities; integration w/ visualization.
3. Identification of multi-modal datasets for use in the prototype solution architecture.

We will explore these in more detail in upcoming posts.

Copyright 2012  - Technovation Talks, Semantech Inc

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Standardization versus Innovation

In yesterday's post, we discussed how the most common components of any Enterprise Transformation is usually a set of specific or tactical Standardization exercises. These exercises are meant to help:

1. Improve inefficiencies
2. Drive down costs
3. Enhance current capabilities

The third point here is key - most standardization efforts are directed at moving an organization towards commoditization of already well-understood, previously deployed capabilities. Now it so just happens that standardization efforts are also needed in order to facilitate migration to new capabilities as well (within the context of an Enterprise Transformation), so standardization does play a dual role in some cases. Often times standardization occurs separate from any larger Transformation initiative - a good example of this might development of standard desktop images or movement from traditional desktop management to virtual desktops. Now at first glance, it may seem as though the Desktop Standardization efforts are in fact facilitating enterprise innovation, but this is not always the case.

Virtual Desktops represent progress, right?

Today's topic deals with the more or less constant tension between Standardization and Innovation that exists within most enterprises. We'll use the Desktop Image Management example as our case study. So, let's step back for a minute and ask the obvious - why should Innovation and Standardization necessarily be at odds? The quick answer is this - IT moves between cycles of expansion and distribution to periods of consolidation and centralization. This has been occurring in one form or the other since the beginning of Information Technology as an industry. A good example of a current manifestation of this cycle is the movement towards Cloud Computing as a way to facilitate centralized management (or the consolidation of the data centers needed to provision those Cloud solutions).  

At the same time as Cloud solutions are making inroads, other more disruptive Mobile solutions are forcing a more distributive paradigm - although the hope is to link all of the new disruptive technologies through Cloud. The battle between "taking control" of the enterprise and "taking advantage" of emerging technologies never ends and it never will. The reason that this is the case is that each activity (standardization and innovation) has a somewhat different Use Case:

1. Standardization is primarily focused on optimization of existing capabilities
2. Innovation is primarily focused on exploitation of newer capabilities

As we pointed out earlier, there are ways to combine the two Use Cases together in the context of a larger Use Case (e.g. Transformation). There are other ways to do it as well, but we'll return to that in a minute.

So, let's examine the desktop case study again. How might we interpret the ability to centrally provision standard desktop images or do so using virtual desktop technology? Is the goal of standardization to:

1. Reduce licensing costs (for desktop software)
2. Simplify OS management, thus reducing Desktop management staff 
3. Make it is easier for the organization to build new applications and services and apply them to standard environments

or are the goals to...

1. Extend use of nonstandard devices by being able to centrally manage all sorts of OSs and OS combinations
2. Facilitate the development of systems that use more than the standard desktop or laptop hardware
3. Help to open up new business models or mission opportunities for the organization by supporting a flexible set of platforms?

The first set of goals are connected to the Standardization Use Case, the second to the Innovation Use Case. So, what happens if an organization chooses the first set and refuses to acknowledge the latter set? This often results in an innovation bottleneck; wherein only small amounts of innovative new capability can be absorbed into the enterprise - whether the larger organization wants it that way or not. And this is where it gets tricky, because often times decisions made to optimize or commoditize certain areas of IT that are seen as needing to be super-efficient can thus lead to severe restrictions in the ability of any part of the enterprise to be innovative. Worse yet, these conflicts are generally not understood or often not even recognized. The constraints and dependencies of a complex organization aren't generally what's communicated to senior leadership - CXO level folks are generally more interested in hearing where savings have been achieved. 

The problem is though, that more often than not the inability to reconcile the Use Cases leads to more losses than the savings gained in achieving super-efficiency if the enterprise fails to recognize what's really going on. 

Copyright 2012  - Technovation Talks, Semantech Inc.

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Whatever happened to SOA?

Not too long ago Service Oriented Architecture (SOA) was the hottest IT trend around, at least if measured by the sheer amount of attention or hype that was associated with it. The fate of SOA is interesting for a number of reasons:

• It helps to illustrate the nature of Hype Cycles in IT and how they influence both industry and individual enterprises.

• It helps to provide context for why Cloud solutions have now become the "killer solution" (it can't be described as an app) that most folks are examining today.

• It is an interesting case study in regards to the difference between product-based Hype-Cycles and architecture or standards based hype cycles.

So what did happen to SOA? Strictly speaking, it's still around and going strong, however it is no longer at the top of the 'must have' or 'must do' lists in most IT departments these days. Many SOA initiatives took the bridge to nowhere or have been merged into newer or perhaps more specific enterprise goals and objectives. Before we go any further though, let's recap what SOA represents:

1. SOA is not limited to one product or product family.
2. SOA encompasses both capabilities and process.
3. SOA is very focused on a key set of (mostly W3C) interoperability standards, notably SOAP, XML, WSDL and UDDI.
4. SOA is not merely a solution but also a solution strategy and architectural approach. This last point is both critical and very subtle. 
5. SOA has an systems integration focus, not merely a software development one.

This diagram illustrates a number of the Use Cases associated with SOA Adoption

 Each of the last five points is important and several of them are relatively complex issues. Essentially, taken together they tend to imply that anyone wishing to deploy SOA in a significant way would in effect be attempting some form of enterprise-wide transformation. That's a big deal regardless of the technology or technological paradigm targeted in the transformation. Change is never easy - it's less so when dealing with an entirely new way of managing your IT portfolio. These are some of the reasons that SOA is not the driving force in industry it was just four years. So, let's look at what did happen with SOA:

• SOA was and SOA is hard. There's no silver bullet solution - all options require a lot of work and some risk.

• SOA initiatives usually become Systems Integration exercises. Many providers or organizations without that systems integrations background attempted SOA solutions and quickly realized it's a skill that takes years to develop (both personally and organizationally).

• There's more than one way to do SOA - in fact there's quite a few ways. This implies a wide array of possible tools or software options. Navigating those decisions is confusing to say the least and since this happens up front it often short circuits projects before they ever get off of the ground. There's not one piece of software associated with a proto-typical SOA solution.  

• Often times key elements of SOA infrastructure are not implemented as part of an initiative. Common examples of this include failure to deploy a SOA Registry or Governance paradigm. This tends to limit the effectiveness of the solution and eventually restricts the scope of the entire solution.

One of the reasons people were initially enthused about SOA was that it seemed to represent an easy path to bypass many of the ills associated with the ad hoc heterogeneous sprawl that had become the typical IT enterprise by the mid-2000's. That initial assessment was a misconception, but so too are similar assessments being made now about "Cloud Computing" and "Big Data." Behind the Hype and the marketing buzz, there are certain underlying problems that all IT trends are focused on resolving. More often than not however, no single trend is positioned to solve the challenges alone. We will talk more about Cloud Computing, Big Data and SOA in future posts and presentations. Also be sure to check out our Technovation Glossary page.

Copyright 2012, Semantech Inc. All rights Reserved 

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