Tag Archives: CRM

Three questions you need to ask

There's three questions you need to ask yourself before you invest a large chunk of cash in some enterprise application:

  • Can I use something, rather than configuring something, rather than customising something?
  • How will the solution support the (social) community who will use it?
  • Is there a reason why I can't buy the solution ‘on-demand’ via SaaS?

Continue reading Three questions you need to ask

Taxonomies 1, Semantic Web (and Linked Data) 0

I’m not a big fan of Semantic Web{{1}}. For something that has been around for just over ten years — and which has been aggressively promoted by the likes of Tim Berners-Lee{{2}} — very little real has come of it.

Taxonomies, on the other hand, are going gangbusters, with solutions like GovDirect{{3}} showing that there is a real need for this sort of data-relationship driven approach{{4}}. Given this need, if the flexibility provided by Semantic Web (and more recently, Linked Data{{5}}) was really needed, then we would have expected someone to have invested in building significant solutions which use the technology.

While the technology behind Semantic Web and Linked Data is interesting, it seems that most people don’t think it’s worth the effort.

All this makes me think: the future of data management and standardisation is ad hoc, with communities or vendors scratching specific itches, rather than formal, top-down, theory driven approaches such as Semantic Web and Linked Data, or even other formal standardisation efforts of old.

[[1]]SemanticWeb.org[[1]]
[[2]]Tim Berners-Lee on Twitter[[2]]
[[3]]GovDirect[[3]]
[[4]]Peter Williams on the The Power of Taxonomies @ the Australian Government’s Standard Business Reporting Initiative[[4]]
[[5]]LinkedData.org[[5]]

The technologies behind the likes of Semantic Web and Linked Data have a long heritage. You can trace them back to at least the seventies when ontology and logic driven approaches to data management faced off against relational methodologies. Relational methods won that round — just ask Oracle or the nearest DBA.

That said, there has been a small number of interesting solutions built in the intervening years. I was involved in a few in one of my past lives{{6}}, and I’ve heard of more than a few built by colleagues and friends. The majority of these solutions used ontology management as a way to streamline service configuration, and therefor ease the pain of business change. Rather than being forced to rebuild a bunch of services, you could change some definitions, and off you go.

[[6]]AAII[[6]]

What we haven’t seen is a well placed Semantic Web SPARQL{{7}} query which makes all the difference. I’m still waiting for that travel website where I can ask for a holiday, somewhere warm, within my budget, and without too many tourists who use beach towels to reserve lounge chairs at six in the morning; and get a sensible result.

[[7]]SPARQL @ w3.org[[7]]

The flexibility which we could justify in the service delivery solutions just doesn’t appear to be justifiable in the data-driven solution. A colleague showed my a Semantic Web solution that consumed a million or so pounds worth of tax payer money to build a semantic-driven database for a small art collection. All this sophisticated technology would allow the user to ask all sorts of sophisticated questions, if they could navigate the (necessarily) complicated user interface, or if they could construct an even more daunting SPARQL query. A more pragmatic approach would have built a conventional web application — one which would easily satisfy 95% of users — for a fraction of the cost.

When you come down to it, the sort of power and flexibility provided by Semantic Web and Linked Data could only be used by a tiny fraction of the user population. For most people, something which gets them most of the way (with a little bit of trial and error) is good enough. Fire and forget. While the snazzy solution with the sophisticated technology might demo well (making it good TED{{8}} fodder), it’s not going to improve the day-to-day travail for most of the population.

[[8]]TED[[8]]

Then we get solutions like GovDirect. As the website puts it:

GovDirect® facilitates reporting to government agencies such as the Australian Tax Office via a single, secure online channel enabling you to reduce the complexity and cost of meeting your reporting obligations to government.

which make it, essentially, a Semantic Web solution. Except its not, as GovDirect is built on XBRL{{9}} with a cobbled together taxonomy.

[[9]]eXtensible Business Reporting Language[[9]]

Taxonomy driven solutions, such as GovDirect might not offer the power and sophistication of a Semantic Web driven solution, but they do get the job done. These taxonomies are also more likely to be ad hoc — codifying a vendor’s solution, or accreted whilst on the job — than the result of some formal, top down ontology{{10}} development methodology (such as those buried in the Semantic Web and Linked Data).

[[10]]Ontology defined in Wikipedia[[10]]

Take Salesforce.com{{11}} as an example. If we were to develop a taxonomy to exchange CRM data, then the most likely source will be other venders reverse engineering{{12}} whatever Salesforce.com is doing. The driver, after all, is to enable clients to get their data out of Salesforce.com. Or the source might be whatever a government working group publishes, given a government’s dominant role in its geography. By extension we can also see the end of the formal standardisation efforts of old, as they devolve into the sort of information frameworks represented by XBRL, which accrete attributes as needed.

[[11]]SalesForce.com[[11]]
[[12]]Reverse engineering defined in Wikipedia[[12]]

The general trend we’re seeing is a move away from top-down, tightly defined and structured definitions of data interchange formats, as they’re replaced by bottom-up, looser definitions.

Decisions are more important than data

Names and categories are important. Just look at the challenges faced by the archeology community as DNA evidence forces history to be rewritten when it breaks old understandings, changing how we think and feel in the process. Just who invaded who? Or was related to who?

We have the same problem with (enterprise) technology; how we think about the building blocks of the IT estate has a strong influence on how approach the problems we need to solve. Unfortunately our current taxonomy has a very functional basis, rooted as it is in the original challenge of creating the major IT assets we have today. This is a problem, as it’s preventing us to taking full advantage of the technologies available to us. If we want to move forward, creating solutions that will thrive in a post GFC world, then we need to think about enterprise IT in a different way.

Enterprise applications – the applications we often know and love (or hate) – fall into a few distinct types. A taxonomy, if you will. This taxonomy has a very functional basis, founded as it is on the challenge of delivering high performance and stable solutions into difficult operational environments. Categories tend to be focused on the technical role a group of assets have in the overall IT estate. We might quibble over the precise number of categories and their makeup, but for the purposes of this argument I’m going to go with three distinct categories (plus another one).

SABER
SABER @ American Airlines

First, there’s the applications responsible for data storage and coherence: the electronic filing cabinets that replaced rooms full of clerks and accountants back in the day. From the first computerised general ledger through to CRM, their business case is a simple one of automating paper shuffling. Put the data in on place and making access quick and easy; like SABER did, which I’ve mentioned before.

Next, are the data transformation tools. Applications which take a bunch of inputs and generate an answer. This might be a plan (production plan, staffing roster, transport planning or supply chain movements …) or a figure (price, tax, overnight interest calculation). State might be stored somewhere else, but these solutions still need some some serious computing power to cope with hugh bursts in demand.

Third is data presentation: taking corporate information and presenting in some form that humans can consume (though looking at my latest phone bill, there’s no attempt to make the data easy to consume). This might be billing or invoicing engines, application specific GUIs, or even portals.

We can also typically add one more category – data integration – though this is mainly the domain of data warehouses. Solutions that pull together data from multiple sources to create a summary view. This category of solutions wouldn’t exist aside from the fact that our operational, data management solutions, can’t cope with an additional reporting load. This is also the category for all those XLS spreadsheets that spread through business like a virus, as high integration costs or more important projects prevent us from supporting user requests.

A long time ago we’d bake all these layers into the one solution. SABER, I’m sure, did a bit of everything, though its main focus was data management. Client-server changed things a bit by breaking user interface from back-end data management, and then portals took this a step further. Planning tools (and other data transformation tools) started as modules in larger applications, eventually popping out as stand alone solutions when they grew large enough (and complex enough) to justify their own delivery effort. Now we have separate solutions in each of these categories, and a major integration problem.

This categorisation creates a number of problems for me. First and foremost is the disconnection between what business has become, and what technology is trying to be. Back in the day when “computer” referred to someone sitting at a desk computing ballistics tables, we organised data processing in much the same way that Henry Ford organised his production line. Our current approach to technology is simply the latest step in the automation of this production line.

Computers in the past
Computers in the past

Quite a bit has changed since then. We’ve reconfigured out businesses, we’re reconfiguring our IT departments, and we need to reconfigure our approach to IT. Business today is really a network of actors who collaborate to make decisions, with most (if not all) of the heavy data lifting done by technology. Retail chains are trying to reduce the transaction load on their team working the tills so that they can focus on customer relationships. The focus in supply chains to on ensuring that your network of exception managers can work together to effectively manage disruptions in the supply chain. Even head office focused on understanding and responding to market changes, rather than trying to optimise the business in an unchanging market.

The moving parts of business have changed. Henry Ford focused on mass: the challenge of scaling manufacturing processes to get cost down. We’re moved well beyond mass, through velocity, to focus on agility. A modern business is a collection of actors collaborating and making decisions, not a set of statically defined processes backed by technology assets. Trying to force modern business practices into yesterdays IT taxonomy is the source of one of the disconnects between business and IT that we complain so much about.

There’s no finer example of this than Sales and Operations Planning (S&OP). What should be a collaborative and fluid process – forward planning among a network of stakeholders – has been shoehorned into a traditional n-tier, database driven, enterprise solution. While an S&OP solution can provided significant cost saving, many companies find it too hard to fit themselves into the solution. It’s not surprising that S&OP has a reputation for being difficult to deploy and use, with many planners preferring to work around the system than with it.

I’ve been toying with a new taxonomy for a little while now, one that tries to reflect the decision, actor and collaboration centric nature of modern business. Rather than fit the people to the factory, which was the approach during the industrial revolution, the idea is to fit the factory to the people, which is the approach we use today post LEAN and flexible manufacturing. While it’s a work in progress, it still provides a good starting point for discussions on how we might use technology to support business in the new normal.

In no particular order…

Fusion solutions blend data and process to create a clear and coherent environment to support specific roles and decisions. The idea is to provide the right data and process, at the right time, in a format that is easy to consume and use, to drive the best possible decisions. This might involve blending internal data with externally sourced data (potentially scraped from a competitor’s web site); whatever data required. Providing a clear and consistent knowledge work environment, rather than the siloed and portaled environment we have today, will improve productivity (more time on work that matters, and less time on busy work) and efficiency (fewer mistakes).

Next, decisioning solutions automate key decisions in the enterprise. These decisions might range from mortgage approvals through office work, such as logistics exception management, to supporting knowledge workers workers in the field. We also need to acknowledge that decisions are often decision making processes which require logic (roles) applied over a number of discrete steps (processes). This should not be seen as replacing knowledge workers, as a more productive approach is to view decision automation as a way of amplifying our users talents.

While we have a lot of information, some information will need to be manufactured ourselves. This might range from simple charts generated from tabular data, through to logistics plans or maintenance scheduling, or even payroll.

Information and process access provide stakeholders (both people and organisations) with access to our corporate services. This is not your traditional portal to web based GUI, as the focus will be on providing stakeholders with access wherever and whenever they need, on whatever device they happen to be using. This would mean embedding your content into a Facebook app, rather than investing in a strategic portal infrastructure project. Or it might involve developing a payment gateway.

Finally we have asset management, responsible for managing your data as a corporate asset. This looks beyond the traditional storage and consistency requires for existing enterprise applications to include the political dimension, accessibility (I can get at my data whenever and wherever I want to) and stability (earthquakes, disaster recovery and the like).

It’s interesting to consider the sort of strategy a company might use around each of these categories. Manufacturing solutions – such as crew scheduling – are very transactional. Old data out, new data in. This makes them easily outsourced, or run as a bureau service. Asset management solutions map very well to SaaS: commoditized, simple and cost effective. Access solutions are similar to asset management.

Fusion and decisioning solutions are interesting. The complete solution is difficult to outsource. For many fusion solutions, the data and process set presented to knowledge workers will be unique and will change frequently, while decisioning solutions contain decisions which can represent our competitive advantage. On the other hand, it’s the intellectual content in these solutions, and not the platform, which makes them special. We could sell our platform to our competitors, or even use a commonly available SaaS platform, and still retain our competitive advantage, as the advantage is in the content, while our barrier to competition is the effort required to recreate the content.

This set of categories seems to map better to where we’re going with enterprise IT at the moment. Consider the S&OP solution I mention before. Rather than construct a large, traditional, data-centric enterprise application and change our work practices to suit, we break the problem into a number of mid-sized components and focus on driving the right decisions: fusion, decisioning, manufacturing, access, and asset management. Our solution strategy becomes more nuanced, as our goal is to blend components from each category to provide planners with the right information at the right time to enable them to make the best possible decision.

After all, when the focus is on business agility, and when we’re drowning in a see of information, decisions are more important than data.

Innovation [2010-02-01]

Another week and another collection of interesting ideas from around the internet.

As always, thoughts and/or comments are greatly appreciated.

We need a better definition for “mash-up”

Mash-up no longer seems to mean was we thought it meant. The term has been claimed by the analysts and platform vendors as short hand for the current collection of hot product features, and no longer represents the goals and benefits of those original mash-ups that drew our interest. If we want to avoid the hype, firmly tying mash-up to the benefits we saw in those first solutions, then we need to reclaim the term, basing its definition on the outcomes those first mash-up solutions delivered, rather than the (fairly) conventional means used to deliver them.

Definitions are a good thing, as they help keep us all on the same page and make conversations easier. However, what often starts our as a powerful concept—with a clear value proposition—is rapidly diluted as the original definition gets pulled in different directions.

Over time, the foundation of a term’s definition moves from the outcome it represents (and the benefits this outcome provides), taking rest on the means which the original outcome was delivered, driven by everyones’ desire to define what they are doing in relation to the current hot topic. Next, the people who consider it to be just a means, often start redefining the meaning to make it more inclusive, while continuing to claim the original benefits. We end up selling the new hype as either means or goals or any half-hearted solution in between – and missing the original outcome nearly completely

The original mash-ups were simple things. Pulling together data from two or more sources to create a new consolidated view. Think push-pins on a map. Previously I would have had to access these data sources separately—find, select, remember, find, select correlation, click. With the mash-up this multi-step, and multi-decision workflow is reduced to a single look, select, click. Many decisions became one, and I was no longer forced to remember intermediate steps or data. 

It was this elimination of unnecessary decisions that first attracted many of us to the idea of a mash-up. As TQMLEAN, et al tell us, unnecessary decisions are a source of errors. If we want to deliver high quality at a low cost (i.e. efficient and effective knowledge workers) then we need to eliminate these decisions. This helps us become more productive by spending a greater proportion of our time on the decisions that really matter, rather than on messy busy work. Fewer decisions also means fewer chances for mistakes.

Since those original mash-up solutions, our definition of mash-up evolved. Todays definitions are founded on the tools and techniques used to deliver a modern web-based GUI. These definitions focus on the technology standards, where the data is processed (client vs. server), standards and APIs, and even mention application architectures used. Rarely do they talk about the outcome delivered, or the benefits this brings.

There’s little difference, for example, between some mashups and a modern portal. We can debate the differences between aggregating data on the client vs. the server, but does it really matter if it doesn’t change the outcome, and the difference is invisible to the user? The same can be said for the use of standards, APIs used, user configuration options, differing solution architectures and so on.

The shift to a feature-function base definition has allowed the product vendors and analysts of seize control of our definition, and apply it to the next generation of products they would like us to buy. This has diluted the term to the point that it seems to cover much of what we’ve been doing for the last decade, and many of the benefits ascribed to the original mash-ups don’t apply to solutions which fit under this new, broader church.

Modern consumer home pages, such as iGoogle and NetVibes for example, do allow us to use desk and screen real estate more effectively–providing a small productivity boost–but they don’t address the root of the problem. Putting two gadgets on a page does little to fuse the data. The user is still required to scan the CRM and order management gadgets separately, fusing the data in their head.  Find, select, remember, find, select correlation, click rather than a single look, select, click.

The gadgets might be visually proximate, but we could do that with two browser windows. Or two green screens side-by-side. The user is still required to look at both, and establish the correlation themselves. The chair might not swivel as much as with old school portlets, but eyeballs still do, and we are still forcing the user to make unnecessary decisions about data correlation. They don’t deliver that eliminate unnecessary decisions outcome that first attracted us to mash-ups.

The gold standard we need to measure potential mash-ups against is the melding of data used to eliminate unnecessary decisions. This might something visual, like push-pins on a map or markup on an x-ray. Or it might cover tabular data, where different cells in the table are sourced from different back-end systems. (Single customer view generated at the user interface.) If we fuse the data, building new gadgets which pull data attributes and function into one consistent view, then we eliminate these decisions. We can even extend this to function, allowing the user to trigger a workflow or process that make sense in the view they are presented, but with no knowledge of what or where implements the workflow.

We need a definition for mash-ups is that captures this outcome. Something like:

A mash-up is a user interface, or user interface element, that melds data and function from multiple sources to create one single, seamless view of a topic, eliminating unnecessary decisions and actions.

This v0.1 definition provides a nice, terse, strong definition for mash-up which we can hang a number of concrete benefits from.

  • More productive knowledge workers. Our knowledge workers only spend time on the decisions that really matter, rather than on messy busy work, making them more productive.
  • More effective knowledge workers. Fewer decisions mean fewer chances for mistakes, reducing the cost of error recovery and rework resulting in more effective knowledge workers.

Posted via email from PEG @ Posterous

We can be our own worst enemy

The only certainties in life are death and taxes, or so we’ve been told on numerous occasions. I’d like to add “change” to the list. Change, be it business change or change in our personal lives, has accelerated to the point that we can expect the environment we inhabit to change significantly in the immediate future, let along over the length of our careers. If we want our business to remain competitive in this ever evolving landscape, then overcoming our (and our team’s) own resistance to change is our biggest challenge.

The rules we have built our careers on, rules forged back in the industrial revolution, are starting to come apart. Most folk—from the Baby Boomers through to Gen Y—expect the skills they acquired in their formative years to support them well through to retirement. How we conduct business might change radically, driven by technological and societal change, but what we did in business could be assumed to change at a slower than generational pace. We might order over the internet rather than via a physical catalogue, or call a person via a mobile rather than call a place via a landline, but skills we learnt in our formative years would still serve us well. For example, project managers manage ever increasingly complex projects over the length of their career, even though how they manage projects has migrated from paper GANTT charts to MS Project, and now onto BaseCamp.

Which is interesting, as it is this what, the doctrine, which most people use to define themselves. A project manager manages projects, and has (most likely) built their career by managing increasingly larger projects and, eventually, programs. Enterprise architects work their way toward managing ever larger transformation programs. Consultants work to become stream leads, team leaders, finally running large teams across entire sectors or geographies. An so on. The length of someones career sees them narrowing their focus to specialise in a particular doctrine, while expanding their management responsibilities. It is this doctrine which most people define themselves by, and their career is an constant investment in doctrine to enhance their skills, increasing their value with respect to the doctrine they chose to focus on.

This is fine in a world when the doctrines a business needs to operate change slower than the duration of a typical career. But what happens if the pace of change accelerates? When the length of the average career becomes significantly longer than the useful life of the doctrines the business requires.

We’ve reached an interesting technological inflection point. Information technology to date could be characterized as the race for automation. The vast bulk of enterprise applications have been focused on automating a previously manual task. This might be data management (general ledger, CRM, et al) or transforming data (SAP APO). The applications we developed were designed as bolt-ons to existing business models. Much like adding an after-market turbo charger to your faithful steed. Most (if not all) of the doctrines in the technology profession have grown to support this model: the development and deployment of large IT assets to support an existing business.

However, the role of technology in business is changing. The market of enterprise applications has matured to the point where a range of vendors can supply you with applications to automate any area of the business you care to name, making these applications ubiquitous and commoditized.The new, emerging, model has us looking beyond business technology alignment, trying and identify new business models which can exploit synergies between the two. A trend Forrester has termed, Business-Technology.

The focus has shifted from asset to outcome, changing the rules we built our careers on. Our tendency to define ourselves by the doctrine we learnt/developed yesterday has become a liability. We focus on how we do something, not why we do it, making it hard to change our habits when the assumptions they are founded on no longer apply. With our old doctrines founded on the development and management of large IT assets, we’re ill-equipped to adapt to the new engagement models Business-Technology requires.

The shift to an outcome focus is part of the acceleration of the pace of business. The winners in this environment are constantly inventing new doctrine as they look for better ways to achieve the same outcome. How we conduct business is changing so rapidly that we can’t expect to be doing the same thing in five years time, let alone for the rest of our career. What we learnt to do in our mid 20’s is no longer (entirely) relevant, and doesn’t deliver the same outcome as it used to. Isn’t the definition of insanity continuing to do something the we know doesn’t work? So why, then, do we continue to launch major transformation programmes when we know they have a low chance of success in the current business and social environment? Doctrine has become dogma.

We need to (re)define ourselves along the lines of “I solve problems”: identifying with the outcomes we deliver, at both personal and departmental levels. This allows us to consider a range of doctrines/options/alternatives and look for the best path forward. If we adopt “I am an TOGAF enterprise architect” (or SixSigma black belt, or Prince2, or …) then they will just crank the handle as the process has become more important than the goal. If we adopt “how can I effectively evolve this IT estate the with tools I have”, then we’ll be more successful.

Rolls-Royce and Craig’s List are good examples of organisations using a focus on outcomes to driver their businesses forward. Bruce Lee might even be the poster child of this problem solving mentality. He studies a wide range of fighting doctrines, and designed some of his own, in an attempt to break his habits and find a better way.

From doctrine to dogma: when did a good idea become the only idea

When does a good method become the only method? The one true approach to solving a problem; the approach which will bind them all. The last few decades has seen radical change in our social and business environments, while the practice of business seems to have changed relatively little since the birth of the corporation. The problem of running a business, the problem we work every day to solve, has changed so much that the best practice of yesterday has become an albatross. The methods and practices that have brought us to the current level of performance are also one of the larger impediments to achieving the next level. When did the yesterday’s doctrine become today’s dogma? And what can we do about it?

Our methodologies and practices have been carefully designed to help steer our leviathan ships of industry, tuning their performance to with five and three year plans. The newspapers of today, for example, hold a marked resemblance to the news papers of 100 years ago, structured as large content factories churning out the stories with some ads slapped in the page next to them.

The best practices evident in companies today represent the culmination of generations of effort in building, running and improving our businesses. The doctrine embodied in each industry in a huge, a immensely valuable body of knowledge, tuned to solving the problem of business as we know it.

doctrine |ˈdäktrin|
noun
a belief or set of beliefs held and taught by a church, political party, or other group : the doctrine of predestination.
• a stated principle of government policy, mainly in foreign or military affairs: the Monroe Doctrine.
ORIGIN late Middle English : from Old French, from Latin doctrina ‘teaching, learning,’ from doctor ‘teacher,’ from docere ‘teach.’

OS X Dictionary, © Apple 2007

However, a number of fundamental changes have taken hold in recent years. The pace of business has increased markedly; what used to take years now takes months, or even weeks. The role of technology in business has changed as applications have become ubiquitous and commoditized. The assumptions which existing doctrine were developed under no longer hold.

Today, most (if not all) newspapers are watching their as revenue is eroded by the likes of Craigslist, who have used modern web technology to come up with a new take on the decades (if not centuries) old classified ad.

Let’s look at Craiglist. I’ve heard people estimate that they are doing close to $100mm in annual revenues at this point. Many say, “they could be doing so much more”. But the Craigslist profit equation is interesting. They apparently have less than 30 employees. That’s about $4mm/year in employee costs. Let’s assume that they spend another $6mm per year on hosting and bandwidth costs and other costs. So it’s very possible that Craigslist’s annual costs are around $10mm/year. Their value equation then is 10 x (100-10) = $900mm. That’s almost a billion dollars in value for a company with only 30 employees.

Fred Wilson, A VC

Craigslist has taken a fresh look at what it means to be in the business of classified ads, and used technology in a new way to help create business value, rather than restrict it to controlling costs and delivering process effencies; an approach Forrester have labeled Business-Technology.

The challenge is to acknowledge that the rules of business have changed, and modify our best practices to suit the new business environment because, as Albert Einstein pointed out “insanity is doing the same thing over and over again and expecting different results.” If we can’t change our best practices to suit, then our valuable doctrine has become worthless dogma.

dogma |ˈdôgmə|
noun
a principle or set of principles laid down by an authority as incontrovertibly true: the Christian dogma of the Trinity | the rejection of political dogma.
ORIGIN mid 16th cent.: via late Latin from Greek dogma ‘opinion,’ from dokein ‘seem good, think.’

OS X Dictionary, © Apple 2007

Enterprise architecture (EA) is prime example. As a doctrine, enterprise architecture has a proud history all the way back to John Zachman’s work in the 70s and the architecture framework which carries his name. EA has leveraged large, multi-year transformation programs to deliver huge operational effencies into the business. These programs have delivered a level of business performance unimaginable just a generation ago.

The pace of business has accelerated so much in recent years that the multiyear engagement model these transformations imply is no longer appropriate. What use is a five or three year plan in a world that changes every quarter? Transformation projects have been struggling recently. Some recent transformations edge across the line, at which point everyone moves onto the next project exhausted, and the promised benefits are neither identified or realized. Some transformations are simply declared a success after an appropriate effort has been applied, allowing the team to move on. A few explode, often quite publicly.

This approach made sense a decade or more ago, where IT was focused on delivering the next big IT asset into the enterprise. It’s application strategy, rather than technology strategy. However, the business and technology environment has changed radically recently since the emergence of the Internet as a public utility. The IT departments we’ve created as application factories have become an albatross for the business; making us incapable of engaging anything but a multiyear project worth tens of millions of dollars. They actively prevent the business from leveraging in innovative solutions or business opportunities. Even when there is a compelling reason to do so.

Simply put, the value created by enterprise architecture has moved, and the doctrine, or at least our approach to applying it, hasn’t kept up. For example, a common practice when establishing a new EA team seems to involve hiring architects to fill each role defined TOGAF’s IT Architecture Role and Skill Definitions to provide us with complete skills coverage. Driving this is a desire to align ourselves with best practice, and ensure we do the job properly.

Some of TOGAFs IT Architecture Role and Skill Definitions
Some of TOGAF's IT Architecture Role and Skill Definitions

Most companies don’t need, nor can they can afford, a complete toolbox of enterprise architecture skills inside the business. A strict approach to the the doctrine will result in a larger EA team than the company can sustain. A smarter approach is to balance the demands and available resources of the company against the skill requirements and possible outcomes. We can tune our approach by aligning it with new techniques, tools and capabilities, or integrating elements from other doctrines—agile or business planning techniques, for example—to create a broader pallet of tools to solve our problem with. This might involve new engagement models. We can buy some skills while renting others. Some skills might be sustainable at a lower levels. It is also possible multi-skill, playing the role of both enterprise and solution architect. Similarly, leveraging software as a service (SaaS) solutions can also force changes in our engagement model, as a methodology suitable for scoping a three year and $50 million investment in on-premises CRM might not be appropriate for a SaaS solution which only requires 10% of the effort and investment as the on-premises solution.

Treating doctrine as prescriptive converts it into dogma. As John Boyd pointed out, we should assume that all doctrine is not right—that it’s incomplete or incorrect to some extent. You need to challenge all assumptions and look outside your own doctrine for new ideas.

Our own, personal resistance to change is the strongest thing holding us back. It seems that we learn something in our early to mid twenties, and then spend the rest of our career happily doing the same thing over and over again. We define ourselves in terms of what we did yesterday. If we create an environment where we define ourselves in terms of how we will help the organization evolve, rather than in terms of the assets we manage or doctrine we apply, then we can convert change from an enemy into an opportunity.

There is light at the end of the tunnel. For all the talk of the end of newspapers, some journalists are banding together to create new business models which can hold their own in a post-Craigslist world. Some old school journalists have taken a fresh look at what it means to be a newspaper. Young but growing strong and profitable, Politico’s news room is 100 strong and they have more people in the white house bureau than any other brand.

As TechCrunch pointed out:

Journalists still matter. A lot. Especially the good ones.

The challenge is to focus on what really matters, get close to your customers and find what really drives your business, question all the common sense (which is neither common or sensible in many cases) in your industry’s doctrine, look into the doctrine of other industries to see what they are doing that you can use, and use technology to create a business which their more traditional competitors will find it impossible to compete against.

Why we can’t keep up

We’re struggling to keep up. The pace of business seems to be constantly accelerating. Requirements don’t just slip anymore: they can change completely during the delivery of a solution. And the application we spent the last year nudging over the line into production became instant legacy before we’d even finished. We know intuitively that only a fraction of the benefits written into the business case will be realized. What do we need to do to get back on top of this situation?

We used to operate in a world where applications were delivered on time and on budget. One where the final solution provided a demonstrable competitive advantage to the business. Like SABER, and airline reservation system developed for American Airlines by IBM which was so successful that the rest of the industry was forced to deploy similar solutions (which IBM kindly offered to develop) in response. Or Walmart, who used a data warehouse to drive category leading supply chain excellence, which they leveraged to become the largest retailer in the world. Both of these solutions were billion dollar investments in todays money.

The applications we’ve delivered have revolutionized information distribution both within and between organizations. The wave of data warehouse deployments triggered by Walmart’s success formed the backbone for category management. By providing suppliers with a direct feed from the data warehouse—a view of supply chain state all the way from the factory through to the tills—retailers were able to hand responsibility for transport, shelf-stacking, pricing and even store layout for a product category to their suppliers, resulting in a double digit rises in sales figures.

This ability to rapidly see and act on information has accelerated the pulse of business. What used to take years now takes months. New tools such as Web 2.0 and pervasive mobile communications are starting to convert these months into week.

Take the movie industry for example. Back before the rise of the Internet even bad films could expect a fair run at the box-office, given a star billing and strong PR campaign too attract the punters. However, post Internet, SMS and Twitter, the bad reviews have started flying into punters hands moments after the first screening of a film has started, transmitted directly from the first audience. Where the studios could rely a month or of strong returns, now that run might only last hours.

To compensate, the studios are changing how they take films to market; running more intensive PR campaigns for their lesser offerings, clamping down on leaks, and hoping to make enough money to turn a small profit before word of mouth kicks in. Films are launched, distributed and released to DVD (or even iTunes) in weeks rather than months or years, and studios’ funding, operations and the distribution models are being reconfigured to support the accelerated pace of business.

While the pulse of business has accelerated, enterprise technology’s pulse rate seems to have barely moved. The significant gains we’ve made in technology and methodologies has been traded for the ability to build increasingly complex solutions, the latest being ERP (enterprise resource planning) whose installation in a business is often compared to open heart surgery.

The Diverging Pulse Rates of Business and Technology

This disconnect between the pulse rates of business and enterprise technology is the source of our struggle. John Boyd found his way to the crux of the problem with his work on fighter tactics.

John Boyd—also know as “40 second Boyd”—was a rather interesting bloke. He had a standing bet for 40 dollars that he beat any opponent within 40 seconds in a dog fight. Boyd never lost his bet.

The key to Boyd’s unblemished record was a single insight: that success in rapidly changing environment depends on your ability to orient yourself, decide on, and execute a course of action, faster than the environment (or your competition) is changing. He used his understanding of the current environment—the relative positions, speed and performance envelopes of both planes—to quickly orient himself then select and act on a tactic. By repeatedly taking decisive action faster than his opponent can react, John Boyd’s actions were confusing and unpredictable to his opponent.

We often find ourselves on the back foot, reacting to seemingly chaotic business environment. To overcome this we need to increase the pulse of IT so that we’re operating at a higher pace than the business we support. Tools like LEAN software development have provided us with a partial solution, accelerating the pulse of writing software, but if we want to overcome this challenge then we need to find a new approach to managing IT.

Business, however, doesn’t have a single pulse. Pulse rate varies by industry. It also varies within a business. Back office compliance runs at a slow rate, changing over years as reporting and regulation requirements slowly evolve. Process improvement and operational excellence programs evolve business processes over months or quarters to drive cost out of the business. While customer or knowledge worker facing functionality changes rapidly, possibly even weekly, in response to consumer, marketing or workforce demands.

Aligning technology with business

We can manage each of these pulses separately. Rather than using a single approach to managing technology and treating all business drivers as equals, we can segment the business and select management strategies to match the pulse rate and amplitude of each.

Sales, for example, is often victim of an over zealous CRM (customer relationship management) deployment. In an effort to improve sales performance we’ll decide to role out the latest-greatest CRM solution. The one with the Web 2.0 features and funky cross-sell, up-sell module.

Only of a fraction of the functionality in the new CRM solution is actually new though—the remainder being no different to the existing solution. The need to support 100% of the investment on the benefits provided by a small fraction of the solution’s features dilutes the business case. Soon we find ourselves on the same old roller-coaster ride, with delivery running late,  scope creeping up, the promised benefits becoming more intangible every minute, and we’re struggling to keep up.

There might be an easier way. Take the drugs industry for example. Sales are based on relationships and made via personal calls on doctors. Sales performance is driven by the number of sales calls a representative can manage in a week, and the ability to answer all of a doctor’s questions during a visit (and avoid the need for a follow-up visit to close the sale). It’s not uncommon for tasks unrelated to CRM—simple tasks such as returning to the office to process expenses or find an answer to a question—to consume a disproportionate amount of time. Time that would be better spent closing sales.

One company came up with an interesting approach. To support the sales reps in the field they provided them with the ability to query the team back in the office, answering a clients question without the need to return to head office and then try to get back in their calendar. The solution was to deploy a corporate version of Twitter, connecting the sales rep into the with the call center and all staff using the company portal via a simple text message.

By separating concerns in this way—by managing each appropriately—we can ensure that we are working at a faster pace than the business driver we supporting. By allocating our resources wisely we can set the amplitude of each pulse. Careful management of the cycles will enable us to bring business and technology into alignment.

What we’re doing today is not what we did yesterday

Telxon
Telxon hand unit

The business of IT has changed radically in the last few years. Take Walmart for example. In the 80s Walmart laid the foundations for its future growth by fielding a supply chain data warehouse. The insight the data warehouse fueled their amazing growth to become the largest retailer in the world. However, our focus has moved on from developing applications. More recently Walmart fielded the Telxon, a barcode scanner with a wireless link to the corporate back-end. This device is the front end of a distributed solution which has let Walmart devolve buying decisions to the team walking the shop floor.

For a long time IT departments have defined themselves by their ability to deliver major applications into the enterprise. CRM, MRP, even ERP; all the three letter acronyms. For a long time this has been the right thing to do. Walmart’s data warehouse, to return to our example, was a large application which was a significant driver in the company’s outlier performance for the next couple of decades.

The world has changed a lot since that data warehouse went operational. First the market for enterprise applications grew into the mature market we see today. If you have a well defined problem—an unsupported business activity—then a range of vendors will line up to provide you with off-the-shelf solutions. Next we saw a range of non-technology options emerge, from business process outsourcing (BPO) and leveraging partnerships, through to emerging software-as-a-service (SaaS) solutions.

What used to be a big problem—fielding a large bespoke (or even off-the-shelf) application—has become a (relatively) small one. Take CRM (customer relationship management) as one example. What was a multi-year project requiring an investment of tens of millions of dollars to deploy a best-of-breed on-premises solution, has become a few million dollar and a matter of months to field SaaS solution. And the SaaS solutions seem to be pulling ahead in the feature-function war; Salesforce.com (one of the early SaaS CRM solutions) is now seen as the market leader (check with your favorite analyst).

Nor has business been standing still while technology has been marching forward. The productivity improvements provided by the last generation of enterprise applications have created the time and space for business stakeholders to solve more difficult problems. That supply chain solution Walmart deployed that was the first of many, automating most (if not all) of the mundane tasks across the supply chain. Business process methodologies such as LEAN (derived from the Toyota Production System) and Six Sigma (from GE) then rolled through the business, ripping all the fat from our supply chains as they went past. The latest focus has been category management: managing groups of product as separate businesses and, in many chases, handing responsibility for managing the category back to the supplier.

Which brings us back to the Telxon. If we’ve all been on the same journey—fielding a complete set of applications, optimizing our business processes, and deploying the latest, best practice, management techniques—then how do we differentiate? Walmart realized that, all things being equal, it was their ability to respond to supply chain exceptions that would provide them with an edge. As a retailer, this means responding to stock-outs on the shop floor. The only way to do this in a timely manner is to empower the people walking the floor to make a procurement decision when they see fit. Walmart’s solution was the Telxon.

The Telxon is an interesting device as it reveals an astonishing amounts of information: the quantity that should be on the shelf, the availability from the nearest warehouse, the retail price, and even the markup. It also empowers the employee to place an order for anything from a pallet to a truck-load.

Writer
Writer Charles Platt during his stint as a Wal-Mart employee in Flagstaff, Ariz.

As one journalist found:

We received an inspirational talk on this subject, from an employee who reacted after the store test-marketed tents that could protect cars for people who didn’t have enough garage space. They sold out quickly, and several customers came in asking for more. Clearly this was a singular, exceptional case of word-of-mouth, so he ordered literally a truckload of tent-garages, “Which I shouldn’t have done really without asking someone,” he said with a shrug, “because I hadn’t been working at the store for long.” But the item was a huge success. His VPI was the biggest in store history—and that kind of thing doesn’t go unnoticed in Arkansas.

Charles Platt, Fly on the Wall (7th Feb 2009), New Your Post

Clearly the IT world has moved on since that first data warehouse went live in Arkansas. Enterprise applications have been transformed from generators of competitive advantage into efficient sources of commodity functionality. Technology’s ability to create value should be focused on how we effectively support knowledge workers and the differentiation they create. These solutions only have a passing resemblance to the application monoliths of the past. They’re distributed, rather than centralized, pulling information from a range of sources, including partner and public sources. They’re increasingly real time, in the Twitter sense of the term, pulling current transactional data in as needed rather than working from historical data and relying on overnight ETLs. They’re heterogeneous, integrating a range of technologies as well as changes in business processes and employee workplace agreements, all brought together for delivery of the final solution. And, most importantly, they’re not standalone n-tier applications like we built in the past.

But while the IT world has moved on, it seems that many of our IT departments haven’t. Our heritage as application factories has us focused on managing applications, rather than technology, actively preventing us from creating this new generation of solutions. This behavior is ingrained in our organizations, with a large number of architects through project managers to senior management measuring their worth by the size of the project (in terms of CAPEX and OPEX required, or head count) that they are involved in, with the counter productive behavior that this creates.

In a world where solutions are shrinking and becoming more heterogeneous (even to the extent of becoming increasingly cross discipline) our inability to change ourselves is the biggest thing holding us back

The Value of Enterprise Architecture

Note: Updated with the slides and script from 2011’s lecture.

Is Enterprise Architecture in danger of becoming irrelevant? And if so, what can we do about it?

Presented as part of RMIT’s Master of Technology (Enterprise Architecture) course.

The Value of Enterprise Architecture