What Working at ThoughtWorks Has Taught Me About Consulting So Far

In my book The Nomadic Developer, I spent an entire chapter covering techniques that allow you to thrive as a technology consultant.  Of course, I wrote that before I joined ThoughtWorks.  Since joining, I can certainly say that ThoughtWorks has given me quite an education about technology consulting.  This post explores some of the things I have learned over the past 18 months.

Technology Consulting is 10% Technology, 90% Consulting

Being a great technologist is around 10% of the skillset required for being a good technology consultant.  I used to think it was 50%, but my understanding has now vastly changed towards the direction of the so called “soft skills” being more important.  In most companies, there are thousands of opportunities to make things better using technology in some way, shape, or form.  The trick to opening those opportunities is overcoming the massive “wall of cynicism” towards these kinds of investments.  Discovering the opportunities, overcoming the wall of cynicism, getting the human stakeholders on board (not just upper management!), and then actually putting this all together to get a project funded and delivered seems to be 90% of the challenge.

Technology Consulting is a Subset Of, not Different Than, Management Consulting

You can do a project without having it have deep implications for the overall business.  But I doubt that is the kind of project I would ever want to work on.  Most technology investments are, in fact, capital investments in the business.  It is very common for technology to end up both constraining and enabling corporate strategy. Even minor implementation details can have significant effects on strategic choices that a company will be able to make down the road. Everything from ability to do effective mergers, price changes, new product launches, and other strategic initiatives deeply depend on business technology.  For good or ill, technology decisions drive business strategy – and until that condition changes, it is my conjecture that effective technology consulting is really a part of the overall management consulting picture.

ThoughtWorks, having recognized this, is formally rolling out our Consulting Practice which, among other things, seeks to formally do what we have been doing informally since our inception, which is to advise companies not just on how to implement technology, but what technology to implement.

Clients are Never Perfect

Expecting to come to TW where you will work on perfectly “aligned” clients that always espouse our values is, to be blunt, a poor expectation. Most people call the doctor when they are sick, not when they are well. Sometimes – no – all the time – organizational transformation is HARD, and you will have to, excuse my French, slog through some shit in order to get to the holy grail of your client that needs help becoming a perfectly functioning agile client that perfectly practices continuous delivery.

Never Underestimate The Importance of Soft Skills

Good consulting involves:

  • Controlling your temper and not being “shocked” when you see things like bad code and retrograde practices. When you go on site, expect anything and everything.
  • Understanding and addressing the skepticism of organizations for which you are the 4th, 5th, or 10th person who has been put there to try to fix things.
  • Learning how to build credibility so you can spend it later when you need to.
  • Understanding the limits of your own capabilities, so you can know when to call for help (aka you do not have all the answers).
  • Learning how to understand a domain quickly and credibly, so you can talk in the language of the client
  • … and a million other little things that have more to do with relations between humans than they have to do with technology …

It has been quite an experience, personally, just finding out how much I didn’t know, learning how to apply these principles in a large programme of work.  That these things are important is rather intuitivley obvious, but at scale, it becomes a list of things you have to remind yourself about every day.  When the stakes go up, and the number of people increase, the importance of these basic fundamentals really starts to outweigh almost everything else!

The Bottom Line

If you want to actually deliver a great technology solution, getting the technology right is just the table stakes.  Getting the people thing right – the consulting – is 90% of the actual work.  It is thrilling, engrossing work, but it certainly isn’t just about software!

What Working at ThoughtWorks Has Taught Me About Consulting So Far

The Unheralded Benefits of the F# Programming Language

As many long time readers know, I am an enthusiast of the F# programming language.  I make no apologies for the fact that, if you are developing software on the .NET platform, F# is one of the better choices you can make for numerous reasons.  It is one of the reasons I proudly contributed as a co-author to the book, Professional F# 2.0, which is being published by Wrox in October.

Some of the oft cited benefits of F# are that, to distill them quickly, it is good at doing intensely mathematical operations, it is built for parallelism, and it is good at helping define domain specific languages.  Those benefits are so often cited by speakers on the F# speaker circuit that they pretty much seem cliche to me at this point (note, yours truly is proud to call himself a member of said circuit, and often gives this talk!)  As great as these features are, there are a couple features, that in my more mundane F# experiences, seem to stand out as things that “save my ass”, for lack of a better phrase, more often than not.

Advantage 1: Near Eradication of the Evil NullReferenceException

The first feature I am most dutifully grateful for is that when working with F#, you almost never deal with the concept of null.  A side effect of being “immutable by default” is that the pattern of “lets leave this thing uninitialized until I use it later, then forget I didn’t initialize it when I try to use it” mostly goes away.  In other words, immutable code almost never has a reason to be null, therefore, no null pointer exceptions if you stick to immutable structures.

How often do you see this pattern in a legacy code base:

if (thisThing != null && thisThing.AndThatThing != null)

… or worse, deeply similar nested examples?  When I am doing code archaeology, sometimes even on more recent code bases, I usually spot this kind of code as places where:

a.) Some very careful programmer is doing null checks to make sure she does not get a null pointer exception

… or, more commonly…

b.) Someone fixed one or more NullReferenceExceptions they were getting.

The only time you routinely deal with the concept of null in F#, typically, is when doing interop work, likely someone else’s ill-initialized C# code.  Of course, one may wonder how one represents the idea of something actually being “missing”, that is, something roughly analagous to null, in F# code.  Well, that is where the option comes to the rescue.  If, for example, you have a concept of weather, you might have this:

type Weather =
Skies : Sky
Precip : Some(Precipitation) // Precip : Precipitation option will also work

In this example, weather will always have a sky, but only might have some precipitation.  If something is optional, you say so, which means that you can have the value of Precip either be Some(somePrecipValue) or None.  For C# programmers, this is roughly analogous to Nullable<T>, only it applies to objects, not just value types.  What this does is force the programmer to state which objects can be in a state of “absence” by exception.  In the same way that a database design becomes more robust when you make more of your fields non-nullable, software becomes more robust and less prone to bugs when fewer things are “optional” as well.

Advantage 2: Your Entire Domain in One Page of Code

The second advantage – at least in my mind, is that unlike C# and Java – the lack of syntax noise in F# means that nobody uses the “one class per file” rule that is conventional in the world of most mainstream programming languages.  The nice thing about this is that, frequently, you can put a reasonably complex entire domain model on one printed page of code.

One pattern I use quite a bit is that I put the set of relationships between types in one file, along with common functions between elements.  If I need to extend the domain in any way – such as adding later a conversion function from a domain to a viewmodel or something like that, I put that in a separate file, where I can write extension methods that adapt the domain to whatever I need it to be adapted to.

Extension methods?  What if I need to use a private member to extend the domain?  Well – that does beg a question – when do functional programmers use private members?  I can only vouch for myself, but seldom do I feel the need in F# programs to hide anything.  Think about why we ever need encapsulation – it is usually to stop outsiders from changing the member variables inside our class.  If there is nothing that varies – as is the case with a system built using immutable constructs, then there is less need for such encapsulation.  I may have a private member somewhere to hide an implementation detail, but even that tends not to be a mainstream case that I would ever use in an extension scenario (i.e. projecting a domain object to a viewmodel).

The overall advantage, in the way F# is written, is that you can have all of your related concerns on a single page of code in most systems.  This ability to “print out and study the domain in the loo” leads to a subtle, but important reason, why F# is good for expressing domains.

Is it for Everything?

No – not by a long-shot.  But more and more, I am seeing F# as useful for many things beyond the traditional math, science, and obviously parallel type applications that functional languages are traditionally considered useful for.  Specifically, the more I use it in MVC and REST style applications, the more it grows on me.  Especially when I am working with Java or C# code, and fixing someone else’s NullReferenceExceptions!

The Unheralded Benefits of the F# Programming Language

The “Dark Matter” of Technical Debt: Enterprise Software

Bespoke software is expensive. As we all well know, it is risky to build, technical debt can easily creep in, and you can easily end up with a maintenance nightmare. And software developers, well – we all know they are hard to work with, they tend to have opinions about things, and did I mention, they are expensive?

The argument has always been that with purchased software, you get an economy of scale because you share the software with others. Of course, this works out well most of the time – nobody should ever be developing their own internal commodity software (think operating systems, databases, and other “utilities”).

However, not all software is “utility”. There is a continuum of types of software, going from something like Microsoft Windows or Linux on one end, which nobody in their right mind would write, and company specific applications of all kinds that have zero applicability outside of a given, well, “Enterprise”. The software I am talking about in this post lies somewhere in the middle of these extremes.

Almost anyone who does work in corporate IT has probably encountered one of these systems. The following traits commonly pop up:

  • It is oriented at a vertical market.  The number of customers is often measured in 10s or 100s.
  • The cost for purchase is usually measured with at least 6 figures in USD.
  • It usually requires significant customization – either by code, or by a byzantine set of configuration options.
  • It was almost certainly sold on a golf course, or in a steak house.
  • You usually need their own consultants to do a decent installation.  The company that sells the software has professional services revenues at or higher than the software license revenues.

It is my observation that software in this category almost always is loaded with technical debt.  Technical debt that you can’t refactor.  Technical debt that becomes a permanent fixture of the organization for many years to come.  Enterprise Software – especially software sold as “Enterprise Software” to non-technical decision makers, is more often than not, a boat-anchor that holds organizations back, adding negative value.

Why is this?  Enterprise software is often sold on the basis of flexibility.  A common process, sadly, in the world of package selection, is to simply draw up a list of features, evaluate a set of vendors on the basis of desired features, and balance that against some license cost + implementation cost threshold.  Lip service is given to “cost-of-ownership”, but the incentives in place reward minimizing the perceived future costs.  What this process selects for is a combination of maximum flexibility, moderate license cost relative to a build (but often high), and minimized estimates of implementation cost.  Even if one company bucks the trend, the competitive landscape always selects for things in this direction.

Why is that true?  We don’t assess the technical debt of enterprise software.  I have seen a lot of buy versus build analysis in my years as a technology consultant, and not once did I see something that assessed the internal quality of the solution.  Enterprise software is bought based on external features, not internal quality.  Nobody asks about cyclomatic complexity or afferent coupling on the golf course.

Does internal quality of purchased software matter?  Absolutely.  In spades.  It is hardly uncommon for companies to start down a path of packaged software implementation, find some limitation, and then need to come to an agreement to customize the source code.  Rarely does anyone have the intent to take on source when the software is purchased, but frequently, it happens anyway when the big hairy implementation runs into difficulty.  But even if you never take possession of the source code, the ability for you to get any upgrades to the solution will be affected by the packaged software vendor’s ability to add features.  If the internal quality is bad, it will affect the cost structure of the software going forward.  APIs around software that has bad internal quality tend to leak out that bad quality, making integration difficult and spreading around the code smells that are presumably supposed to be kept “inside the black box”.

What is the end result?  Package implementations that end up costing far in excess of what it would have been to build a piece of custom software in the first place.  Lots of good money thrown after bad.  Even when the implementation works, massive maintenance costs going forward.  It gets worse though.  The cost of the last implementation often colors the expectations for what the replacement should cost, which tends to bias organizations towards replacing one behemoth nasty enterprise software package with something equally as bad.  It is, what the French like to call, a fine mess.

So what is the solution?  We need to change how we buy enterprise software.  The tools we have for buy versus build analysis are deficient – as few models include a real, robust cost-of-ownership analysis that properly includes the effects of insufficient internal quality.  It is amazing that in this day and age, when lack of proper due diligence in package selection can cost an organization literally billions of dollars, that so little attention is paid to internal quality.

What would happen?  There would be a renewed incentive to internal quality.  Much of today’s mediocre software would suddenly look expensive – providing room for new solutions that are easier to work with, maintain, and provide more lasting business value.  More money could be allocated to strategic software that uniquely helps the company, providing more space for innovation.  In short, we would realize vastly more value out of our software investments than we do today.

The “Dark Matter” of Technical Debt: Enterprise Software

Using Dynamic with C# to read XML

On April 10th (less than 1 week away), I am doing an updated version of my talk at Twin Cities Code Camp about using dynamic with C#.

One core technique I am seeking to demonstrate is to use the concept of a dynamic XML reader as a more human readable way to use XML content in from C# or any other dynamic language.

Consider the following usage scenarios:


What we would like is an object that will use dynamic in C# to make it so that we can read XML without having to think about all the nasty mechanics of search, XPath, and other stuff that isn’t “I am looking for the foobar configuration setting” or whatever it is we are looking for in the XML we want to look at.  The following is the basic spiked implementation:


The real hard part was figuring out the mechanics of how DynamicMetaObject actually does it’s work.  Doing a dynamic object, if you are not going to do it the easy way and simply inherit from dynamicobject, means you are going to write two classes:

  • Something that implements IDynamicMetaObjectProvider
  • Something that inherits from DynamicMetaObject

The job of IDynamicMetaObjectProvider – at least as far as I can tell – is simply to point to the right DynamicMetaObject implementation, and somehow associate something with it that will potentially drive how the dynamic object will respond to various calls.  Most of the interesting stuff happens in DynamicMetaObject, where we get to specify how various kinds of bindings will work.

In a simple case like this where we are doing everything with properties, we merely need to override BindGetMember.  The return value of BindGetMember will generally be another DynamicMetaObject derived class.

Generally, DynamicMetaObjects take three parameters on construction:

  • A target expression
  • A binding restriction (something that tells the object a little about what it has – not entirely sure why…)
  • The actual thing to pass back

In the way I am using this, there are three main ways we return.  If we find we can resolve the property call to be a string, we are just going to wrap the string in a constant expression, specify a type restriction on string, and send that back.  If we are resolving to another XElement that has sub-items – we are going to wrap the XElement into a new DynamicXmlMetaObject – as a means to allow for further dot notation to get at sub-elements.  Lastly, if we have a group of the same item, we are going to return an array of either wrapped strings, or wrapped DynamicXmlMetaObjects.  Managing through these three cases is where most of the complexity is.

This is a work in progress – and I have already been told by some that this is a bad idea (i.e. “why not XPath?”, “that looks dangerous”, and “what if the Republicans use this?”).  But certainly, for certain kinds of problems, I can definitely see myself using this kind of thing to remove lots of Linq to XML plumbing code! (note, some work to integrate this and perhaps somehow combine this with XPath queries will probably happen).

Using Dynamic with C# to read XML

i4o v2

An update to a project I have been working on for some time, for which the time definitley ripe for an update.

It was an afternoon in 2007 when I was pondering… “Why I am writing the same Dictionary<K,V> collections just for indexing and putting them internal to my collection classes so I could do query optimization.”  At the same time thinking about how Linq could work with such a thing, I decided to go forth and spike out a specialized collection type that would internally implement indexes and just use them when you issue a query to the collection.  The result was the i4o project (codeplex.com/i4o).

If you are not familiar with i4o, the problem it solves is that it takes any IEnumerable<T> in .NET, and allows you to easily put indexes on it such that LINQ queries over the Index rather than doing a “tablescan” style search through the collection.  You would create an IndexableCollection<T> as follows:

var someIndexable = new IndexableCollection<T>(someEnumerable, someIndexSpecification);

At this point, someIndexable wraps the someEnumerable, applying an index to one or more properties, as specified in someIndexSpecification.  You could also inherit from IndexableCollection<T> and get indexing in your own custom collections, such that you could do a query like this:

var someItems = from s in someIndexable where s.SomeProperty == 42 select s;

… and the query would use the index for searching, rather than scan each item in the collection.

Over the years since (approaching 3!) – the project has evolved, little by little.  In 2008, Jason Jarrett started contributing work, bringing in important performance enhancements, better code organization, continuous integration, and various other things, that he elaborates on here.

For 2010, we wanted to do a significant refresh, with some new capabilities for core functionality.  The second major revision of i4o will feature the following:

* Support for INotifyPropertyChanged

Previously, if an indexed property changed, you had to manually notify the index of the change and reindex the item.  For i4o2, we have formally added support for property change notification, such that if T supports INotifyPropertyChanged, changes to any child will result in automatic reindexing of that item.

* Support for Observable<T>

If a collection inherits from Observable<T>, and T also supports INotifyPropertyChanged, you will be able to wrap an index around that which will notice changes in the collection.

* Support for a far wider range of query operations

Previously, if a query was anything other than object.Property == <some constant | some expression>, i4o would simply use Enumerable.Where, rather than try to optimize the query using the index.  The index was based on strict hash tables, which limited the kinds of operations that could be optimized.  In v2, there is a much more sophisticated query processor that:

1.) Optimizes the structure based on what the child type supports.  If the child type is IComparable, it uses a Red-Black tree to hold the index so that queries that use comparison operations rather than equality can be optimized.

2.) Uses a more sophisticated algorithm for visiting the expression tree of the predicate, allowing for indexes to work on much more complex predicates.  So if you are doing a query where your predicate is more complex (A and B and (C or D), for example) the processor will handle those as well.

* Creation via a builder pattern:

Rather than make the user try to figure out what index is best, creating an index is a matter of calling IndexBuilder.BuildIndicesFor with a source collection and a spec:

var someIndex = IndexBuilder.BuildIndicesFor(someCollection, indexSpec);

The builder detects the types passed to it, and simply builds the most optimal index type (ObservableIndexSet or just an IndexSet) based on the type of enumerable passed to it and the child type.

* More DSL-like index specification builder

While this has been supported for some time, we added some language to the builder, so the code would read better:

            var indexSpec = IndexSpecification<SimpleClass>.Build()
                .With(person => person.FavoriteColor)
                .And(person => person.Age)
                .And(person => person.Name);

These changes are very much in early alpha testing right now, with a goal of having them fully baked prior to the Visual Studio 2010 release.  At that time, we intend to move the changes into the project trunk and do a full release.

If you want early access to the changes before then, I encourage anyone who is interested to visit codeplex.com/i4o and see the i4o2 branch of the code repository.  Once we are sufficiently stable and have integrated some of the older perf improvements into the new code base, we will likely move the i4o2 branch to the trunk.

i4o v2

Three Key Technologies Your Clients (Should) Care About

My second InformIT article is now up, this one, focusing on some conversation starters you should be having with clients.  There are a lot of things out there that are going to capture imagination, but I think these three that I allude to after the jump are particularly meaningful in this year of shrinking budgets and needs to “do more with less”.

Three Key Technologies You Should be Pointing Out to Your Clients

Three Key Technologies Your Clients (Should) Care About