Lots of reasons go into why data is found to be of poor quality. One of the more obvious is the need for companies to play the volume game, but the quality of your data should be of more importance than its quantity. ELEKS completed over 20 machine learning projects last year and around half of the cases demanded a data-cleansing effort before the modelling could start.

Use bad data and your machine-learning model will yield bad results, so any successful implementation of a machine-learning algorithm should require some form of data cleansing.

How can you tell good data from bad data?

Data quality is imperative, but how are you to know if your information really isn’t up to the required standard? Here are some of the ‘red flags’ for you to watch:

It has missing variables and cannot be normalised to a unique basis.

The data has been collected from lots of very different sources. Information from third parties may come under this banner.

The data is not relevant to the subject of the algorithm. It might be useful, but not in this instance.

The data contains contradicting values. This could see the same values for opposing classes or a very broad variation inside one class.

Upon your meeting of any one of these points, there’s a chance that your data will need to be cleaned prior to your implementation of a machine-learning algorithm.

Cleansing, rather than replacing, is likely the action you’re looking for here. Like with point three, it might be that your data is fit for use, just not for the purpose outlined. From our experience, you may need to allocate around 70-80% of your overall modelling time on things like data cleansing or the replacement of missing and contradicting data samples. Discovering poor data triggers actions like the merging of information into one database, the adding of new data or the refining of existing sources.

Conclusion

It’s possible to turn a poor database into one that’s ready for the transformation of a business. Actions like focusing on quality over volume and the uniformity of your information can go a long way to ensuring a seamless implementation of machine-learning algorithms.

The big point is to conduct this before commissioning any serious work on big data projects. In our own experience, the allocation of resource towards these actions often means that only 20-30% of our time is dedicated to actually modelling an algorithm.

Are you getting the most out of your data? Contact us to get expert assistance with your data-driven digital transformation.