Conceptual
More than 70% of data science projects fail and never
deliver an ROI for the business. What’s behind the high failure rate and how
can we change this? Many companies are unable to consistently gain business
value from their investments in big data, Artificial Intelligence, and Machine
Learning. A study of the data science functions and initiatives in three of
India's largest private-sector banks identified five obstacles to successful
data science projects and suggests remedies that can help companies obtain more
benefit from their data science investments.
Data Science
The high failure rate
According to a recent Gartner report, only between 15% and
20% of data science projects get completed. Of those projects that did
complete, CEOs say that only about 8% of them generate value. If these figures
are accurate, then this would amount to an astonishing 2% success rate.80-85%
of projects fail before completion. Then there is a further drop-off when organizations
fail to implement the data scientists’ findings.
Possible causes of failure
On the business side,
1. There is a top of data science on the business side, but that person
struggled to get traction with the executives to bring in the changes
recommended by the data scientists.
2. The person who commissioned the project has moved on in the organization
and their successor won’t champion the project because they won’t get credit
for it.
3. Communication has poor & down as the business stakeholders were too
busy with day to day operations. Once stakeholders don’t have time to engage,
it is very hard to rescue the project.
4. All data science projects are long term& In that time the business may
have changed direction or the executives may have lost patience waiting for an
ROI.
5. Although some stakeholders were busy, the executive whose sign off was
needed was never interested in the project. This is often the case in large
companies in conservative industries.
On the data science side,
6: The data scientist lost focus and spent too long
experimenting with models as if they were in academia.
7: The data scientist wasn’t able to communicate their
findings effectively to the right people.
8: The data scientist was chasing the wrong metric.
9: The data scientist didn’t have the right skills or tools
for the problem.
On both sides,
10: the main objective of the project was knowledge transfer
but it never transpire because the business was too busy or the data scientist
had inadequate communication skills.
We want to
structure the data science project effectively into a series of stages, so that
engagement between the analytics team and the business does not break down.
· Tool
and technology mismatch.
IT departments have consumed the last decade-plus building a big data
infrastructure to support data storage and processing – but that infrastructure
doesn’t necessarily lend itself to data science. Data scientists can use as
many as 3-5 different tools or packages monthly, leveraging the latest packages
consistently. There were over 365,000 updates to the popular open-source
programming language Python in 2017 alone! Furthermore, data science work
demands access to elastic compute to perform specific experiments, like deep
learning that requires powerful machines with GPUs. Lacking access to elastic
compute and the latest tools limits your team’s agility, constrains the pace of research - and results
in delayed development.
·
Solving the Wrong
Problem
Ever started a project with a fuzzy idea of the goal? Or alternatively, a
project with a clearly defined goal that is not realistic or that does not add
any meaningful value?
Indeed, Domino Data Labs reports that “We’ve seen large organizations hire
30+ PhDs without clear business alignment upfront.
Oops!
To mitigate this risk, start your project on the right foot and ask the
right questions before starting a project. Don’t just take any request at face
value.
·
Silos of knowledge
Hiring data scientists does not guarantee that your organization will
profit from data science. A recurring theme is that data scientists working on
individual laptops or other solo environments often duplicate efforts. They
don’t have visibility into what work has been done by others that they could
benefit from. One major insurance company, for instance, had dozens of
scientists working in uncoordinated ways on the same business problems —
leading to lost investment and missed opportunities. There’s a difference, in
other words, between having a collection of individuals who create models, and
having a dynamic team capable of leveraging its collective knowledge, skills
and past work to collaboratively build better and better models with faster
time to value.
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