Checklist for Data Maintenance
Is your organization interested in implementing preventative maintenance or predictive insights into its operations?
If you’re like many organizations, the benefits such a project could bring to your company are alluring, but you might feel overwhelmed by the unknowns of such a project. If you’re not a data scientist by trade, you might be wondering:
• What data you need to get started
• How to best ensure your investment will be successful, and
• What personnel you need to help this endeavor thrive
We’ve collaborated with our data science resources to create this helpful checklist for starting and sustaining a healthy, successful data preventative maintenance or predictive insights project.
• My organization has identified a business problem we’d like to solve.
• My organization has a basic understanding of how the predictors lead to the outcome to be predicted.
• I know the type of predictions I need: Near real-time, or Monthly
• My organization has 6 months to 1 year of data to begin our project with.
• The data that we have has strong predictive power for the business problem we are trying to solve.
• I am familiar with the Cross-Industry Standard for Data Mining or CRISP-DM methodology that shows how the business and data understanding work in concert to support each other.
• The data all resides in one place so it can be easily trained and scored.
• My data is not aggregated into 1 place, but it has a common set of data elements available within the disparate data sets to join the data on.
• I have Data Engineering resources that can assist Data Scientists in aggregating data together from Big Data systems, SQL databases, APIs, and locally stored files, and cleaning the data so it’s ready to be entered into a tabular format.
• My organization has established a data-engineering pipeline can continually coerce data from disparate sources into a tabular form from training and scoring.
• My organization has an established cloud environment to which we can migrate our model code and data once it’s ready for further deployment and development.
• I have Data Analysts resources that can build visualizations and dig deep into the data to find relationships between the predictors and the outcome.
• I have Project Manager resources to keep the project on track and on budget.
• I have partnered with DevIQ to create a functional product out of the model built and Machine Learning Operations (MLOps) support to productize the model and create triggers for retraining the model when the model gets out of date.
For future reference, download the full checklist here!
Dev IQ can help your organization navigate the complexities of data science and machine-learning (ML) projects – including walking your team through this checklist of items, helping you problem-solve around organizational gaps you might have in this arena and set the stage for successful, productive data science models. If you’re looking for data science and ML development expertise, let’s connect, and in the meantime, check out our other data science, machine learning and artificial intelligence tips here:
To find out more about how DevIQ leverages data science to build more effective data solutions, let’s connect.
Let’s build something beautiful together.
CEO of DevIQ, triathlete, and technology philosopher.