June 25, 2019
For the past five years, data science has been praised as a technology that can unlock new applications and hidden insights for organisations. However, today it is struggling to live up to expectations.
Few have managed to reap the promised rewards because of the difficulties finding and accessing the data, the lack of data science talent and an inability to ask the right questions. However, even when these obstacles are addressed, few projects have actually been successful.
Why? Although these concerns are important, they remain technicalities. The primary reason that data science initiatives fail is because of a much deeper, more philosophical problem: The data science journey.
Unravelling the journey to data science success
A journey starts with a question to uncover the unknown, which will itself evolve and spawn many more interrogations. The explorer is enriched by the outcome of the journey, which will in itself lead to more questions and more voyages which wouldn’t have been imaginable without the first.
How is this relevant to data science?
Well, data science also starts with questions that aim to help us:
- Where are relationships within our data?
- How can we leverage these relationships for our goals?
- What can we do to enrich our understanding of the domain and enlighten interdisciplinary opportunities?
Any data science journey that is initiated without the explorer’s spirit is artificially created to meet targets and produce imposed answers, as opposed to asking the relevant questions. Fixating on answers creates a disconnect between the organisation’s ambitions and the reality of the business problem, resulting in the disappointing discovery of “insights” which are either already known by the problem owners or completely detached from reality.
Data science should be led by problem owners
Much more can be achieved if problem owners are involved in the questioning process and lead the data science journey, as this enables them to understand the intricacies of their problems from a data science perspective. As a result, they can refine their questions throughout the journey to discover insights that matter.
However, problem owners have not been able to embark on their data science journey until now due to the steep technical and theoretical barriers of data science and machine learning.
Some have been enabled with tools that automate data science, but this automated nature places an emphasis on answers rather than questions, which leads to mediocre results. Moreover, a lack of exploration and learning during the process can stunt the refining and spawning of questions which results in untapped data science potential.
The solution? Choose tools which alleviate technical barriers by augmenting your data science experience and cultivating your inner explorer spirit. These tools should emphasise the exploration of data as opposed to automated processes, and allow users to extract predictive features based on intuition and insider knowledge.
This why we built Mind Foundry, a data science journey companion that works to guide problem owners. It is incredibly easy to use, helping users navigate their data and infuse the modelling with their expertise. By presenting the paths to solutions within a clear dashboard, Mind Foundry models can be seamlessly deployed across an organisation’s workflow, resulting in an immediate impact.