of companies
say that they do not take action on their analytics insights, but they are thinking about it (TDWI)
of companies
utilize interactive analytics to suggest a course of action, but individual managers can then choose whether, or not, to follow that action (TDWI)
of analytic insights generated
through 2022, will not be leveraged or deliver business outcomes (Gartner)

The 4 common pitfalls

The 4 common pitfalls

From our experiences and research, we have identified four typical challenges or pitfalls that organizations encounter when working with the discipline we refer to as: Operationalizing data science. These pitfalls are crucial to address directly to succeed with your journey towards becoming data-driven.


For a deep-dive into the statistics of analytical insights and its non-use, take a look at TDWI.


“Our biggest growth opportunity right now is to use data insights and modelling to create new added value services on our physical products. But with our engineering heritage, there is a real resistance to introducing anything to the market until it is fully matured, so our analytics projects struggle in getting the data and input they need to mature their prototypes.”
Head of Strategy
Engineering Industry

So, what are the key success factors?

So, what are the key success factors?

001 | Prioritizing projects where the stable and reliable stream of source data can both be secured for long-term usage

002 | Building cross-functional project teams combining data science and domain expertise to run agile development projects and actionable insights

003 | Ensuring buy-in, involvement and long-term commitment of business owners before beginning development

004 | Investing time to turn the model’s findings into decision-making tools and to train end users on the tools

005 | Using phased launch plans that allow for an iterative approach to building model and manages business risks during any changes/transitions


How do we help you succeed?

How do we help you succeed?

We have a team of data scientists and data engineers who can work with clients to build, test and deploy new analytical models through three key stages:

001 | Clarifying user requirements and securing clean, reliable data sets to build a clear phased action plan with a stable long-term foundation.

002 | Working with domain experts and end users to engineer features and develop actionable algorithms.

003 | Rigorously testing new models in operational settings and putting them into production.

And as demonstrated through our other services, we can also help clients with building the decision tools, processes and training to put these models in the hands of the right end users.


Would you like to learn more?

If you would like to know more then please reach out to Adam Grønbeck Andersen or Anders Boje Hertz.

We are always happy to talk about the challenges companies face, share our views and experiences, or discuss how Intellishore could help you.
Adam Grønbeck Andersen
Anders Boje Hertz
Head of AI & Data Platforms
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