5 steps do Data Science

These are a few steps necessary so that the data science  journey to successfully complete a data science program:

Step 1: Diagnostic

This is a crucial step where we help to identify the problem and how Machine Learning may help to leverage your business.

  • What is the problem your are trying to solve?
  • What resources and data are / can be available? Is it clean, reliable, easy to access/migrate?
  • Is your data available?
  • Are your goals feasible?
  • What legal and regulatory concerns are important?

Step 2: Strategy

A good Data Science strategy This is cornerstone for the success of any DS project.

  • What is a DS strategy and how to be implemented?
  • How do you maximise the value from the data you have?
  • How your data science strategy aligns with company strategy

Step 3: Resources

  1. Do you have resources? How realistic are they to achieve the goals?
  2. HR: What autonomy do they have?
  3. HR: How are they managed?
  4. What physical resources do you have and can use?

Step 4: Execution

How is the roadmap to execution? I will explain why it is important to balance quick wins with long term goals. How to set the right KPIs, manage the team and setup the conditions for perfect execution.

Step 5: Culture

Artificial Intelligence and Data Science is becoming prevalent in every business. However, not all organizations are prepared for Data Science. If the company doesn’t have the right culture ready to accommodate the transformations imposed by the Data Science team, then it may be a waste of resources. Successful data science projects are painful and much more than hiring clever data scientists.

What are the key phases and outputs of an AI project?

Project Planning and Preparation

Data AccessThe department must ensure that the data required for the project is accessible.SMEsAccess to the data
Knowledge TransferSMEs should provide data documentation and other business process documentation required to ensure that the project can be delivered SMEs Scientists have an overview of the data content and business requirements

Data Science Project Execution

Scientific DesignData scientists will decide on methods and techniques required to address the problemLidinwise SMEsImplementation Plan
Exploratory AnalysisExploration of data and summary (this includes cleaning, data preparation and feature engineering)LidinwiseExploratory Analysis Briefing
Train/Test (offline)train -> validation -> test Lidinwise Model results (offline)
Implementation and deployment testThis will be an iterative process and will likely involve online test(s)Lidinwise SMEsPipeline test and Model results (online)

Project Close out

Full productionFull implementation of model into current processes; Training and knowledge transfer to DepartmentPartnershipsFinal ML production pipeline
DocumentationWrite up descriptionLidinwise with participationDocument with a description of the project delivered