Tag Archives: Information technology governance

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.

Having too much SOA is a bad thing (and what we might do about it)

SOA enablement projects (like a lot of IT projects) have a bad name. An initiative that starts as a good idea to create a bit more flexibility in the IT estate often seems to end up mired in its own complexity. The problem is usually too much flexibility, as flexibility creates complexity, and complexity exponentially increases the effort required to manage and deliver the software. Without any solid guidance on how much flexibility to create (and where to create it) most SOA initiatives simply keep creating flexibility until either the project collapses under its own weight, or the projected development work to create all the services exceeds the available CAPEX budget. A little flexility is good, but too much is bad. How can we scope the flexibility, pointing it where it’s most needed while preventing it from becoming a burden?

The challenge with SOA enablement is in determining how much flexibility to build into the IT estate. Some flexibility is good – especially if it’s focused on where the business needs it the most – but too much flexibility is simply another unnecessary cost. The last decade or so is littered with stories of companies who’s SOA initiatives were either brought to an early close or canned as they had consumed all the cash the business was prepared to invest into a major infrastructure project. Finance and telecoms seem particularly prone of creating these gold-plated SOA initiatives. (How many shelf-ware SDFs – service delivery frameworks – do you know of?)

The problem seems to be a lack of guidance on how much flexibility to build, or where to put it. We sold the business on the idea that a flexible, service-oriented IT estate would be better then the evil monolithic applications of old, but the details of just how flexible the new estate would be were a little fuzzy. Surely these details can be sorted out in service discovery? And governance should keep service discovery on track! We set ourselves up by over-promising and under-delivering.

Mario Batali: Too much is never enough!
Mario Batali

This much was clear: the business wanted agility, and agility requires flexibility. As flexibility comes from having more moving parts (services), we figured that creating more moving parts will create more agility. Service discovery rapidly became a process of identifying every bit of (reusable) functionality that we can pack into a service. More is better, or, as the man with the loud shoes says:

Too much is never enough!
Mario Batali

The problem with this approach is that it confuses flexibility and agility. It’s possible to be very flexible without being agile, and vica versa. Think of a formula one car: they’re fast and they’re agile (which is why driving them tends to be a young mans game), and they’re very stiff. Agility comes from keeping the weight down and being prepared to act quickly. This means keeping things simple, ensuring that we have minimum set of moving parts required. They might have an eye for detail, such as nitrogen in the tyres, but unnecessary moving parts that might reduce reliability or performance are eliminated.

This gold plated approach to SOA creates a lot of unrequired flexibility, this additional flexibility increases complexity, and the complexity becomes the boat anchor that slows you down and stops you from being agile. Turning the car is no longer a simple of tugging on the steering wheel, as we need governance to stop us from pulling the wrong lever in the bank of 500 identical levers in front of us.

It's really that simple!
It's really that simple!

We’ve made everything too complicated. Mario was wrong: too much is too much.

What we need is some guidance – a way of scoping and directing the flexibility we’re going to create. Governance isn’t enough, as governance is focused on stopping bad things from happening. We have a scoping problem. Our challenge is to understand what flexibility will be required in the future, and agreeing on the best way to support it.

To date I’ve been using a very fuzzy “business interest” metric for this, where services are decomposed until the business is no longer interested. The rational is that we put the flexibility only were the business thinks it needs to focus. This approach works fairly well, but it relies too much on the tacit judgement of a few skilled business analysts and architects, making it too opaque and hard to understand for the people not involved in the decision making process. It’s also hard to scale. We need something more deterministic and repeatable.

Which brings me to a friend’s MBA thesis, which he passed to me the other week. It’s an interesting approach to building business cases for IT solutions, one based on real options.

The problem with the usual approaches to building a business case, using tools like net present value (NPV) and discounted cash flow, is that we assume that the world doesn’t change post the decision to build the solution (or not). They don’t factor in the need to change a solution once it’s in the field, or even during development.

The world doesn’t work this way: the solution you approved in yesterday’s business environment will be deployed into a radically different business environment tomorrow. This makes it hard to justify the additional investment required for a more flexible SOA based solution, when compared to a conventional monolithic solution. The business case doesn’t include flexibility as a factor, so more flexible (and therefore complex and expensive) solutions lose to the cheaper, monolithic approach.

Real options address this by pushing you down a scenario planning based approach. You estimate the future events that you want to guard against, and their probabilities, creating a set of possible futures. Each event presents you with options to take action. The action, for example, might be to change, update or replace components in the solution to bring them in line with evolving business realities. The options are – in effect – flex-points that we might design into our solutions SOA. The real options methodology enables us to ascribe costs to these future events and the create a decision tree that captures the benefits of investing in specific flex points, all in a clear and easily understandable chain of reasoning.

The decision tree and options provide us with a way to map out where to place flex points in the SOA solution. They also provide us with strong guidance on how much flexibility to introduce. And this is the part I found really interesting about the approach. It also provides us with a nice framework to govern the evolution of the SOA solution, as changes are (generally) only made when an option is taken: when it’s business case is triggered.

It’s a bit like those formula one cars. A friend of mine used to work for one F1 manufacturer designing and testing camshafts. These camshafts had to fall within a 100,000 lifetime revolution window. An over-designed camshaft was unnecessary weight, while an under-designed one means that you wouldn’t win (or possibly even finish) the race. Work it out: a 100,000 revolutions is a tiny window for an F1 car, given the length of a race.

An approach like real options helps us ensure that we only have the flexibility required in the solution, and that it is exactly where it is required. Not too much, and not too little. Just enough to help us win the race.