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5 Steps for Higher-Quality Data

Higher-Quality Data, data, data science, data, data scientist, data management, data modeling, data model, smart data

Introduction

Source: Harvard Business Review

There’s one thing that you as a business owner really has to look out for, and that’s poor data quality.  Machine learning (ML) especially can get harmed by using data that’s bad, as higher-quality data is a high demand part of ML.  Bad data can appear in historical data that’s used to train the predictive model, causing new data contamination, therefore giving bad solutions to future decisions that might be made for the business.

In order to train the predictive model correctly, the data’s correct, properly marked, formatted and be the right data.  You can’t make a predictive model if by mistake the data scientist is given the wrong information to sort out.  Presently most data fails to meet standards.  Causes include that the data creators don’t understand what’s expected of the data, measurements are poorly calibrated, processes are to complex and just plain human error.

It can in turn take up to 80% of data scientists time just to clean up the data given to them, even though it isn’t guaranteed that everything’s repaired before putting it into the predictive model.  This can cause further problems as more and more ML technology becomes popular.  The output from one model feeds another and another, all the way down the line, crossing department lines.  So if there’s even a small error it will cascade, causing more and more errors.

5 Steps of Higher Quality Data

  1. Clarify Objectives and Assess if you Have the Right Data to Support the Objectives – If it doesn’t meet goals, find new data or scale back goals, or both.
  2. Build Plenty of Time to Execute Data Quality Fundamentals into the Overall Project Plan – Start 6 months’ out
  3. Maintain Audit Trail Preparing Training Data – Helps to understand biases and limitations in the model. The audit trail helps to sort it out.
  4. Charge Specific Person or Team with Responsibility for Data Quality When Releasing the Data Model – They need to have some strong knowledge of the data, set and enforce standards, and are in charge of finding and getting rid of the root causes of any errors found.
  5. Have Independent, Exact Quality Assurance – The key word here is independent.

Summary

These steps won’t fully guarantee that your data is completely error free.  But it’ll be better than using data that hasn’t gone through these 5 steps.  This in turn makes for use of an extremely powerful tool in ML.  Think of everything that done if the data is of a higher quality, and how much more you can learn about your business, the competition, and your customers.

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