A few weeks ago I shared something with my friends and family on Facebook that got quite a reaction. It was an article entitled McDouble is ‘cheapest and most nutritious food in human history’ .
The originator of the claim, Stephen Dubner, who co-authored the best-selling book Freakonomics went on to say:
The double cheeseburger provides 390 calories, 23 grams of protein – half a daily serving – seven per cent of daily fibre, 19 grams of fat and 20 per cent of daily calcium, all for between $1 and $2, or 65p and £1.30.
Kyle Smith, a New York Post columnist, agreed:
“For the average poor person, it isn’t a great option to take a trip to the farmers market to puzzle over esoteric lefty-foodie codes”, Mr Smith wrote.
“Facts are facts – where else but McDonald’s can poor people obtain so many calories per dollar?”
It was a ridiculous claim, a controversial one at best and yet one that the data showed to be true. It got me thinking about what Return on Investment (ROI) and how we model it to help better decision making.
ROI measures the amount of return on an investment relative to the investment’s cost.
In the example above the McDouble returns the highest number of calories per dollar. To phrase it in a way that the ‘business’ might describe a potential solution, it “delivers the biggest bang for the buck”. http://idioms.thefreedictionary.com/bang+for+the+buck
As a Business Analyst I strongly believe that it’s my job to table options to my stakeholders and to back up the options with data, modelled in a way that helps them make informed decisions. I know many will find that difficult, stating that it’s not the role of the BA to provide solutions, but I’m not actually suggesting that here (although for the record I disagree – that’s a different blog post). What I’m suggesting here is that however ridiculous or unpalatable the solutions suggested are, it’s the role of the BA to model benefits and potential outputs to aid stakeholders in making a decision.
Lean tells us we should validate a hypothesis with the minimum amount of effort, this sounds like the “maximum amount of calories for the dollar to me”.
In Agile delivery we would say that an MVP can be defined as the least amount of work we can do to in/validate the hypothesis.
Modeling options is the the best way to ensure that you’re helping stakeholders make informed decisions (and gives them the ability to defend themselves when they get caught eating a burger!)
 Daniel Johnson. Jul 30, 2013. “McDouble is ‘cheapest and most nutritious food in human history'” [online]. The Telegraph
 2014. “Return On Investment – ROI” [online]. investopedia.com
One of the great things I get to do as the Principal Business Analyst at Laterooms.com is attend events and engaging with the BA community, promoting the great work that we do here and identifying quality people who share a passion for analysis and want to join our growing BA Practice.
I’m doing a bit of a tour of the country to attend some of the IIBA branch events and meet more of the community, learn some new skills and get inspired by some of the amazingly talented Business Analysts up and down the country.
If you’re about for any of the following events, come and find me and have a chat:
We’re really excited for the opportunity to present and to represent Laterooms.com at such a great event. Previous years conferences have been critical in my development as a BA and I’m thrilled to be speaking there for the first time this year.
If you’re going to be attending, give me a shout!
Delivering value is at the heart of the Business Analyst role, but how easy is it to identify tangible value and prove the success of a project or program?
In agile projects we’ll often define a “definition of done” or ask the question “what does success look like”. At LateRooms.com, we’ve developed a toolkit for our Business Analysts to support the business in using data to define what success looks like, and track it throughout the project lifecycle.
This presentation will look at the ways LateRooms.com collects, analyses and uses data to better define the problem space, setup up KPI driven Critical Success Factors and present Benefits Realisation.
The session will cover:
Leveraging the most out of the data you already have
Setting up baselines and real-time KPI dashboards
Making better decisions from your data
Presenting Benefits Realisation in a way the business will understand
Over the past couple of years I have seen a growing trend in BAs and product teams striving to define what ‘success looks like’. It’s comes from a desire within the business to understand how we’ll know when we’ve achieved what we set out to do and what the world will look like at the finish line.
I think it’s worth recognising that there are probably a collection of terms out there that describe similar things here. You may call the, Critical Success Factors in your organisation and have associated KPIs.
If you’re working in a Scrum team you may refer to theses as the Definition of Done, a series of criteria or success factors that must be met in order for something to be considered as completed, as done.
The closest I could find in the BABOK about measuring success was in chapter 6.3 Specify and Model Requirements :
The activity of analysing expressed stakeholder desires and/or the current state of the organization using a combination of textual statements, matrices, diagrams and formal models
I’m a real advocate of this approach and have found it resonates well with the product team I sit in, as together we defining a testable state in which to measure if we have achieved our intended goal. This is similar to the team’s approach in writing software, where they will write tests that fail, prior to writing a line of code. They then write software that passes the tests that were defined. Once all the tests pass the project is finished.
However, too often I think BA’s try and define success far too late in a projects life cycle and as a result, miss out on being able to show real business value early in the process.
Many of you may have heard of Eric Ries and Lean Startup. You may have discredited it as applying to your organisation, as you’re not a startup, but I believe that everyone can use Lean Startup principles in their work, especially business analysts.
Eric discusses in his book, The Lean Startup, a new approach to testing hypothesis to help innovation. I believe we can use this same approach when identifying the impact of problems we uncover in our projects, whether it is a bug, an inefficient process or a missed deadline. And using the same principles we can continual measure for the improvements made as we get to work on tackling those issues.
Measuring failure, the “as is” state
So before we can measure what success looks like I encourage people to re-frame the problem. I ask them what failure looks like, often to their surprise. What I mean is, if we did nothing to resolve this issue what would the cost or impact top the business be. This may cause them to go and collect some information in order to answer this, but chances are they have a good idea and can articulate it.
This is a great place to start and will have many other benefits that can feed your product vision, and even help with ROI calculations further down the line.
Once you understand the problem or opportunity, you can start to consider solutions for achieving your goals, but how will we know that we’ve actually been successful?
If you work for a company that sells products online it may be easy for you to measure success. If you sell more items or increase your profit on what you sell, you’ve clearly been successful and perhaps these are some of the most obvious ways of measuring success and may not require models or equations to prove it.
However, if your project doesn’t have an obvious revenue stream to measure, you may need to be a little more creative with what you measure – and this I think, is where Business Analyst can really take a lead.
Have you ever considered the impact your project may have on customer satisfaction? Do you know what your current customer satisfaction score is and would you know how to measure it? Do you know where you rank amongst competitors, especially the ones that you are chasing for market share? I’m willing to bet someone in your organisation already knows the answer to all of those questions!
Do you know how long a manual process takes to run in your organisation? If you could identify improvements then you could calculate an efficiency saving to the business in terms of employee cost or an increase in productivity per employee.
Now measurement isn’t just something that we do at the end of projects. I often hear of the Definition of Done or the definition of what success looks like as being the finish line of a race that once crossed, means the project is closed. While this is true, I sort of dislike the analogy as it suggests it’s the last thing that happens in a project. I believe measuring success is a continuous process. if the definition of what success looks like is our finish line we measure at every step along the race course asking the question, have we crossed the line? Have we improved revenue, have we improved customer satisfaction? For teams that work on incremental delivery strategies, this data is crucial in checking that the team are going in the right direction and if they’re not this data will help support the need for changing direction or pivoting. This in itself is ensuring project success by getting early visibility of failure and risks.
Change, measure, change, measure…
So I’ve explained how I think measuring should take place at every step of the journey and how “what success looks like” is the finish line, the goal we’re aiming for. But in reality the improvements and gains that we see from the success of our projects don’t end once we cross that finish line. If you’ve managed to see an increase of 2% in sales as a result of your project, that’s for life. The business will continue to see that additional value month on month and as a BA I think it’s important that we shout about success and recognise the value that we as BA’s and the teams we support add to the organisation we live in. Everyone likes to have evidence they can show their boss that the project they’ve delivered was successful… And if it over achieved, how pleased will you be that you were tracking the data from day 1!
So to conclude I want to just recap on what I’ve said and recommend some further reading:
Measuring success is linked right back to your initial requirements and stakeholder desires. You’re helping your team prove that they’ve been met with real data
Measuring success is a continuous process, measuring it at the end is too late… you could be miles off and have not known
Measuring success continuously reduces risk and allows for innovation and agility
Evidence of success is the best reward of all, and will really help you as a BA
So I’ve not had chance to update my blog recently. A lot has changed in the months that I’ve been away…
Earlier in October I took up a new and exciting role at Laterooms.com, the UK’s leading hotel booking company. It’s an exciting role for a number of reasons:
I’ll be joining a team of 10 other great BAs, so expect more posts and maybe even some collaborations
It allows be to focus on a single product, or at least a single organisation for a while
It’s based right here in Manchester, so I can balance work and home life a little better
The job move coincides with a house move too. Then family and I have moved to a larger house with plenty of room for the kids to grow up in. We’re still surrounded by boxes but as soon as we’re settled I’ll be blogging again.
Some blog posts coming up include:
Paper prototyping with the UX stencil
Writing effective User Stories for backend systems and APIs
Defining success and measuring business benefits
Decoupling UX from Development in Scrum
If anyone has any blog requests please let me know.
Recently I gave a presentation on Agile Estimation, as based on some of the material cover in the course I attended. While I can not take credit for the materials, or the activities, I was keen to write about them as I believe my own perspective could be of benefit to others who find themselves in a similar situation.
Estimation is something that every team does, but approaches vary wildly. Commonly developers will estimate in hours based on their feel for how long a task or activity will take. While this is not terrible the individual doing the estimating makes a number of assumptions about their approach, their experiences of similar tasks, their confidence in finding a solution, the risk involved, etc. However, what if the person that has done the estimate isn’t the person who will complete the task? What if they aren’t aware of the assumptions made and are not able to understand how the estimate was so low or high?
In the session I asked the teams to estimate the distance from our office to Paris. Instantly they started working on understanding the distance, what roads they would need to take, a ferry or a train and if they would need to sleep on the way. Within a very small variance the teams were pretty consistent in the distance it was from our office to Paris. However, when they started to estimate the time required, based on their distance estimate, the hours varied based on speed assumptions, accounting for traffic and other variables.
What I was trying to show the team is that most of us are good at judging the complexity of a task. They were all good at determining the distance, the route and even the car they’d take and those that had driven the route before knew more confidently what the number of miles required to drive.
When teams decouple complexity estimates from duration estimates they are usually more accurate. Even for the guys that had driven to Paris before, the number of miles required to drive was not smaller in any way, the size or complexity of the task didn’t change for them.
Mike Cohn teaches that we should
Estimate size and derive duration
You see duration or velocity is something that a team understands best through practice. Once teams get into a rhythm or cadence they naturally start working more effectively. This will improve the speed that they are working at and the number of tasks that they can complete.
By estimating as a team (or at least in pairs) developers will at least get some peer input into if their estimates to help them check they are in the right sort of range. And through conversation and repeated estimates the teams should see an improved confidence in their estimate ranges.