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March 2019

Agile Zero Sprint for Data & AI projects

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Agile methodologies have a patchy track record in Data & AI projects. A lot of this is to do with adopting the methodologies themselves – there are a heap of obstacles in the way that are cultural, process and ability based. I was discussing agile adoption with a client who readily admitted that their last attempt had failed completely. The conversation turned to the concept of the Agile Zero Sprint and he admitted part of the reasons for failure was that they had allowed Zero time for their Agile Zero Sprint.

What is an Agile Zero Sprint?

The reality of any technical project is that there are always certain fundamental decisions and planning processes that need to be gone through before any meaningful work can be done. Data Warehouses are particularly vulnerable to this – you need servers, an agreed design approach, a set of ETL standards – before any valuable work can be done – or at least without incurring so much technical debt that your project gets sunk after the first iteration cleaning up after itself.

So the Agile Zero Sprint is all that groundwork that needs to be done before you get started. It feels “un”-agile as you can easily spend a couple of months producing nothing of any apparent direct value to the business/customer. The business will of course wonder where the productivity nirvana is – and particularly galling is you need your brightest and best on it to make sure you get a solid foundation put in place so it’s not a particularly cheap phase either. You can take a purist view on the content from the Scrum Alliance or a more pragmatic one from Larissa Moss.

How to structure and sell the Zero sprint

The structure part is actually pretty easy. There’s a set of things you need to establish which will form a fairly stable product backlog. Working out how long they will take isn’t that hard either as experienced team members will be able to tell you how long it takes to do pieces like the conceptual architecture. It just needs to be run like a long sprint.

An Agile Zero Sprint prevents clogged pipes

An Agile Zero Sprint prevents clogged pipes

Selling it as part of an Agile project is a bit harder. We try and make this part of the project structure part of the roadmap we lay out in our Data & AI strategy. Because you end up not delivering any business consumable value you need to be very clear about what you will deliver, when you will deliver it and what value it adds to the project. It starts smelling a lot like Waterfall at this point, so if the business is skeptical that anything has changed, you have to manage their expectations well. Be clear that once the initial hump is passed, the value will flow – but if you don’t do it the value will flow earlier to their expectations, but then quickly after the pipes will clog with technical debt (though you may want to use a different terminology!)

BI User Personas – are you scaring users with the kitchen sink?

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BI User Personas are a key part of delivering any BI solution. Throughout my career I have encountered clients who have all faced the problem that their BI Solution has not achieved the adoption they had hoped for. This in turn has reduced the impact of the solution and thus the ROI. A common thread in the examples I have seen is the horrifying kitchen sink that is thrown at every user.

 The wrong BI User Persona causes some interesting reactions

The kitchen sink is scary for some!

To explain to those not familiar with the idiom, to include “everything but the kitchen sink” means to “Include just about everything, whether needed or not”. What it means in this context is that the BI solution presents so many dimensions, measures and KPI’s to the user that the experience becomes confusing, overwhelming and as a consequence – useless.

Why building your BI solution is like making a hit movie.

No Hollywood movie is ever made without considering the audience appeal – they even use predictive analytics to drive scripting decisions. So why should your project be any different? You have consumers that need to be satisfied, and their wishes must be taken into account.

A key element of our Data & AI Strategy is to ensure that the end users different needs are planned for. Constructing BI User Personas to define what level of detail gets exposed to each persona helps in this process. To stretch our analogy a little further, your executive team may only care that there *is* a kitchen sink and whether it is working or not. A management team may need to know how hot the water is and how water efficient the tap is. The analysts will need to know detailed water usage statistics over time for analysis. Not everyone needs to know the same thing.

Most BI tools allow you to provide different views of the data model so that you can tailor the output of a very complex model to users with simple needs. An executive may only need a few key metrics and dimensions in a dashboard to examine before they pass further analysis downstream. A manager may need a more complex report with interactivity to drill into an issue. The analyst may simply need access to raw data to answer their questions.

The same applies for less data literate users. If they are not technically minded they may find a much simpler model less intimidating. Data literacy is whole additional topic but with proper preparation, it can be managed and taught.

BI User Personas drive a smash hit!

Understanding your audience is essential. As part of the process of designing your solution, BI User Personas need to be defined so they get appropriately suited content.

Building and understanding the personas of your end user team is of course only part of the equation. There are many human components in a Data & AI Strategy that need to be implemented. Change management, training and ongoing communication help ensure that what you deliver is adopted, and part of the strength of FTS Data & AI is that as part of the FTS Group we can bring our stablemate Cubic in to help with this.