Given the above similarities and differences between data science consulting and other classes of consulting, it’s natural to ask how we’d adapt our approach to make sure the long run success and viability of our projects. Aside from the plain elements resembling quality deliverables, timely project delivery and powerful stakeholder management, what are the opposite components that have to be in place to succeed?
Ensuring Robust Data Products
While management consulting typically focuses on immediate organizational changes and one-off deliverables, data science consulting requires a long-term perspective on robustness and sustainability. This has a few consequences, and you possibly can and will probably be judged on the continued performance of your work and will take steps to make sure you deliver good results not only for the time being of handover, but in addition potentially for years to return. (This is comparable to IT consulting, where ongoing performance and maintenance are essential.)
As an example, I’ve built data products which were in production for over 6 years! I even have seen the direct effects of getting data pipelines that should not robust enough, resulting in system crashes and erroneous model results. I even have also seen model variables and labels drift significantly over time, resulting in degradation of system performance and in some cases completely incorrect insights.
I do know that this is clearly not probably the most sexy topic, and in a project with tight budgets and short timelines it may well be hard to make the argument to spend overtime and resources on robust data pipelines and monitoring of variable drift. Nevertheless, I strongly compel you to spend time together with your client on these topics, integrating them directly into your project timeline.
– Give attention to long-term sustainability.
– Implement robust data pipelines.
– Monitor for model and variable drift constantly.
I even have written about one aspect of information pipelines (one-hot encoding of variables) in a previous article that goals for example the subject and supply solutions in Python and R.
Documentation and Knowledge Transfer
Proper documentation and knowledge transfer are critical in data science consulting. Unlike analytics consulting, which could involve less complex models, data science projects require thorough documentation to make sure continuity. Clients often face personnel changes, and well-documented processes help mitigate the loss of knowledge. I even have on multiple occasions been contacted by previous clients and asked to elucidate various facets of the models and systems we built. This just isn’t all the time easy — especially while you haven’t seen the codebase for years— and it may well be very handy to have properly documented Jupyter Notebooks or Markdown documents, describing the choice process and evaluation. This ensures that any decisions or initial results can easily be traced back and resolved.
– Ensure thorough documentation.
– Use Jupyter Notebooks, Markdown documents or similar.
– Facilitate knowledge transfer to mitigate personnel changes.
Constructing End-to-End Solutions
Constructing end-to-end solutions is one other key consideration in data science consulting. Unlike analytics consulting, which could give attention to delivering insights and reports, data science consulting must make sure the deployability and operationalization of models. This is comparable to IT consulting, where integration into existing CI/CD pipelines is crucial.
I’ve seen firms waste years from the event of a model to its production deployment as a consequence of personnel changes and unfinished integration tasks. If we had insisted on seeing the project through to full production ready status, the client would have had the total advantages of the model much sooner than they ended up doing. This may be significant when project costs may be within the tens of millions of euros.
– Construct deployable models.
– Ensure operationalization.
– Integrate into existing CI/CD pipelines.
Visual Artifacts
Including visual artifacts, resembling dashboards or widgets, helps show the worth created by the project. While management consulting deliverables include strategic plans and assessments — normally in the shape of a one-y power point deck — data science consulting advantages from visual tools that provide ongoing insights into the impact and advantages the answer has. These artifacts function reminders of the project’s value and assist in measuring success, much like the role of visualizations in analytics consulting.
One among my most successful projects was once we built a pricing solution for a client and so they began using the dashboard component directly of their monthly pricing committee meetings. Though the dashboard was only a small fraction of the project it was the one thing that management and the executives in the corporate could interact with and thus provided a robust reminder of our work.
– Create visual artifacts like dashboards.
– Show project value visually.
– Use artifacts to measure success and stay relevant to the client.
Evaluating Organizational Maturity
Evaluating organizational maturity before constructing the project is crucial to avoid over-engineering the answer. Tailoring the complexity of the solutions to the client’s maturity level ensures higher adoption and usefulness. At all times keep in mind that when you find yourself finished with the project, ownership normally shifts to internal data scientists and data engineers. If the client has a team of 20 data scientists and a contemporary data infrastructure able to integrate your models directly into their existing DevOps, that’s amazing, but incessantly not the case. Consider as an alternative the scenario where you might be developing a tool for the corporate with 20 employees, a fresh a knowledge scientist and and over worked data engineer. How would you adapt your strategy?
– Assess organizational and analytical maturity.
– Avoid over-engineering solutions.
– Tailor complexity to client readiness.
Following Best Practices in IT Development
Following best practices in IT development is becoming increasingly essential and infrequently required in data science consulting. Unlike analytics consulting, which could not involve extensive coding, data science consulting should stay true to software development practices to make sure scalability and maintainability. This is comparable to modern IT consulting, where writing modular, well-documented code and including sample data for testing are essential practices.
This also ties back to the previous point around documentation and knowledge transfer. Properly documented and structured code, packaged into easy to put in software packages and libraries is far easier to keep up and manage than 1000s of lines of spaghetti code. When personnel changes occur, you will probably be in a significantly better spot if the code has been properly developed.
– Follow IT development best practices.
– Write modular and well-documented code.
– Include sample data for testing.
