Data Science requires
craftsmanship

Itility focuses on using data science to make your business more effective. It is tricky to expect a lot from just combining the data scientist with the domain expertise and wait for results. No magic is going to happen using that approach.

Although data science techniques are perceived as promising, applying these actually often shows to be difficult. If you zoom in, it is less a magic tool, and more an engineering practice which has been known for a long time.  We do not believe in ‘just start and see what happens’. We believe in a systematic approach, where you shape your thoughts into models, rethink them, test them out in a lab, refine them and then work your way through industrializing to actually get the results.

A consistent and aligned set of best practices are to be applied when turning data into value. For this, Itility designed a comprehensive framework to assist you in your digital journey: Itility Applied Analytics.

Shape. Deliver. Run.

Your benefits

Itility Applied Analytics takes the magic away from data science and treats it in a systematic engineering approach. 

The benefits:

Our framework offers the structure to first draw out the problem to solve. An early glimpse of the algorithmic models that could be useful and the data requirements such as volume, variance and quality. This enables the business owner to assess feasibility in a matter of days.  

Facilitate the domain expert looking for tangible results. Express low hanging fruit in data and data models. We ease collaboration between domain expert and data scientist by reducing line-based labeling and steer toward classification in groups for which a domain expert can validate in a more general way if the group is ‘good’ or ‘bad’.

Plan for the whole and see it through. Magic can happen overnight, a craft needs time. Plan the project from the start on,  to have a realistic expectation of what is required – accurately estimate the required effort for data gathering, algorithmic modelling and implementation. 

The framework addresses the required skill set for manning the team.  Experience shows this to be critical for fast success and ongoing results. Methods to score the use case to fit  the first 4 critical sprints.  

Itility Applied Analytics is implemented at:

How does it work?

3 steps to craft toward true data science value

The shaping starts with 3

In our Itility Applied Analytics framework we start with 3 roles: a digital consultant, the data scientist, and the domain expert.

The digital consultant is the translator between the domain expert and the data scientist, he acts as product owner and thinks about how things could be approached differently. The digital consultant understands the process and the added value that should be delivered in the end.
In parallel he will define the minimal viable product (MVP) and knows when to involve others: the software engineer, the process expert, or the data engineer.

In the lab – data deep dive and modelling

Data science starts with diving into the data. But not just any data. A skillful data craftsman thinks about requirements to the data to fit the prediction to be made.

To be predictive, the data set should contain the features and correlations required to predict the event one would consider as an event worth predicting.

To be able to recognize patterns, the data volume and variety should be sufficient to allow for learning and thus proper recognition output.

In parallel several data models are suggested to work with the data. The model parameters are tweaked and tuned. Preprocessing steps are becoming clearer. Train- and test-sets are defined.

Eventually a model is selected based on the available data. Data quality, volume and variety are perceived as sufficient. The model is evaluated again.
No magic, it shows to be hard work.

From lab to production – rethinking the system

The data scientist leaves his lab armed with a model performing on the data set he got earlier. Knowing his proof of value is adequate to allow for some tests in the real world.

The domain expert shows up, to evaluate the model and its results. But can this be done at sufficient precision in real life? A decision is needed to put this in practice, to run this in pilot and validate the results, tough for the domain expert as evidence is needed in practice, a catch 22.

How can the domain expert prove that the system is good enough? What should be done if the model loses context and safety is at stake? Thoughts about proper fail-safes pop-up. Fail-safe scenarios are defined, evaluated and implemented. Change of processes and systems, the impact is substantial.

In parallel, the data scientist is hungry for new data to further train the created model. Where lies the trade-off? Data versus certainty versus effectiveness, no easy decision making.

Only a DevOps model can support this. Multiple people will be involved; software developer, process expert, data scientist and other roles are to assist in driving the system or process to a new data driven based setup. True redesign.

Our framework for
crafting data science

Itility Applied Analytics is a framework for the craft of data science.
The framework starts with shaping the use case. Then the framework offers the steps to engineer toward a minimum viable product.
And continue where others stop; we drive the solution through – all the way until it is actually embedded in your business process or system.

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"Remove the magic, start today with crafting your data into business value"

References

Blogs

Reference | Mobility | Digital
Reference | Digital | Data
Reference | Digital | Data

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