agiledatascience

- There can be such a broad range of answers to that, I'm finding that MVP to the business, is an outcome that meets the initial desired outcome even if it's not entirely correct, or pretty, i.e. "show me the data" ;-)

Let's start on Question Three: Is it meaningful to speak of a data science "minimal viable product" in an Agile context?

- I've seen orgs happy with an MVP being a simple model that's done in Excel to prove the business case that then went on to be built into a tool that was used via webpage from the network

MVP can simply be a predictive model that’s more accurate than random guessing

@dez_blanchfield That's interesting. Like app-dev in general, the desired outcome might be as minimal as "deliver a very finely focused insight derived from the data." Depending on what that insight is, it might be the springboard to more.

- absolutely, the whole premise of Agile in my mind ( and the book's definition ) is about reaching MPV in any way shape and or form by leveraging Agile methodologies and practices ;-)

If it provides an insight that is useful.

Or a ew predictive model that’s more accurate than the current model/method

IMO the MVP had better provide minimum viable insight or else it's useless

@dez_blanchfield Right. The MVP for "data science" might be something you can gain from other analytics tools, perhaps a spreadsheet. Adding ML or predictive analytics (and associated tools/data lakes/libraries) might extend that core insight

@joe_caserta - absolutely, the second you can "show" something that talks to the original question the business was asking, you have an MVP as they will instantly start "using" or "consuming" it ;-)

Moving on to Question Four. Look above.

@sardire - that's awfully Escher of you Steve ;-)

A company that collects proprietary data could build any number of MVPs ranging from new tech to display the data to a new model that predicts outcomes

@8pathsolutions - Jen how long do you think it usually takes to go from Question or Idea to an MVP for most projects, in your experience, days, weeks, months?

- indeed, and the newer tools which come with "out of the box" models and algo's where coding is optional and data ingest is key, you could get to MVP in hours rather than days or weeks or months even !!

@dez_blanchfield Right. Data science is traditionally an ongong exploration workstream without being tied to discrete app-dev workstream. The "product" of data science is insight. The "product" of app-dev may be code wrapped around that insight

@dez_blanchfield the amount of time you need to go from question/idea to MVP depends on whether you have the data, whether you need to preprocess/process the data, whether it requires a model, and how the output needs to displayed - could be days or years

@joe_caserta - ah yes, and accurate is key, I've seen far too many supposed MVP style "I'm done now" outcomes where someone likes what they saw but forgot to check "the math" and it caused real pain ;-(

@bobehayes - or simply speaks to whatever the user or customer or business was seeking in the first place right ;-)

@bobehayes - or simply speaks to whatever the user or customer or business was seeking in the first place right ;-)

@dez_blanchfield I've also seen companies deliver an MVP that is just a shell - essentially no model is built on the back end and the front end just pulls data...

- the last couple of "generations" of staff in Dev / Data / Ops / Business don't know any other way ;-) it's in their DNA almost ( thanks Facebook & Twitter ) .. the 140 character generation ;-)

- is that by design or just a natural thing that occurs becasue of the people / or corporate culture?

@dez_blanchfield you mention Dev and Ops! DevOps is also the new paradigm hand-in-hand with #agiledatascince

@joe_caserta

- Yes but not everybody in the business made the transition smoothly

- Business folk found it tough shifting to Agile as an approach after a lifetime of Prince2

- I've successfully used an Agile approach on ALL size and scale of project when it's done right with a good Agile lead on the project.

Agile is becoming more and more common even in larger enterprises

@sardire - for many it's a slow learning process though don't you think? culture and behaviour changes they aren't always comfortalble making..

- Some of the Data Science team worked as long wolves and found hunting in packs a challenge

- Others jumped at it and loved it and make the shift quickly and painlessly

@dez_blanchfield Is Agile your predominant methodology in your data science/app-dev team. How complete is the transition in your org to Agile?

- Results have been mixed but the overall outcome seems be positive if the shift to Agile is kept simple

Our team is using an agile methodology to build in new features to the data platform... and always informed by what our users want and use.

- In an existing environment Agile is often a tough idea and value proposition to convey and inject

- In new projects, Agile is usually in my experience far easier and more comfortable to inject or apply - esp. with younger team members without pre-set ideas about "how we do it here" ;-)

- Results have been mixed but the overall outcome seems be positive if the shift to Agile is kept simple

- Some find it time-consuming & distracting attending all the meetings and stand-ups

- Others also found short-duration iterations stressful and useless or couldn’t change pace

@dez_blanchfield I agree, Dez. Keep it simple.

@sardire Steve: what are the factors that might keep larger orgs from bringing Agile into their data science initiatives? Less tolerance for un/under-documented development? Need for structured processes and audit trails for compliance?

- in every single project or programme of work in Data Science et al, It’s a balancing act between tech / dev / biz culture, people, personalities & experience

@sardire - for a long time it was tough, at least in APAC and Australia, to find good Agile coaches and Agile savvy Data folk, as it's been a DevOps / Dev "thing" for the most part..

@bobehayes Bob: To what extent are users/customers directly engaged in your Agile data science collabs/workstreams?

if larger orgs have a more 'open world' culture and behavior agile will be adopted much easier

we create data environments tooled to foster agile collaboration

Question 3 coming in just a sec. Look at the tops of your screens.

@bobehayes - that's the true value of Agile right, the short sharp sprints and then instant input and feedback from the user / consumer / customer ;-)

Yes, for short term investigations #agiledatascience is useful and a good fit given the short timeline

@joe_caserta - where do you start to inject that sort of thinking Joe, how early do you get it into the conversation, Agile that is, as an idea, a value propositon?

We look at how they use the project (has usage gone up/down since we made the changes?). We also listen to them when we ask for feedback about how we can improve the product. So, both implicit and explicit feedback.

@sardire - amen to that, but at the same time IMHO we as "practitioners" and "thought leaders" have to be savvy to the fact that some times it's a huge leap of faith for orgs to "get Agile" ;-)

@dez_blanchfield What's the primary data scientist habitual behavior they need to "unlearn" to do Agile effectively?

@dez_blanchfield agile coaching has become a common role to transition lone wolf mindset

@8pathsolutions - Jen how do you think Agile works in Medium to Long term projects, I'm finding that short term is ok, medium perhaps, but long term Agile is a tough challenge to "maintain" at times.

@dez_blanchfield How do you measure the effectiveness/results from Agile? Do you report it up to a Chief Data Science Officer in your org? Or, are you that Officer?

@dez_blanchfield @sardire Yes... change is difficult, especially when you're old like me... but all I need to be convinced is some data that tells me what we are building is working (improving user sat and usage).

@joe_caserta - yes you've hit the proverbial nail on the head there mate, I'm finding it as tough to find good Agile Coaches as I am good Data Scientists & Analysts !!

@dez_blanchfield biggest challenge can be keeping 10-min stand-up to 10 min.

@joe_caserta - it's almost like Agile Coaches, good ones, are the new Unicorn ;-)

@8pathsolutions Jennifer: Is Agile less well-suited to longer-running projects than quick-turnaround data-sci modeling/exploration?

@dez_blanchfield Long term projects tend to have more extensive requirements, deadlines, and milestones - In my experience, agile isn't well suited to handle meeting all of these expectations