Why do Machine Learning strategies fail and how to deal with them? – Analytics Insight

Why do Machine Learning strategies fail and how to deal with them? – Analytics Insight

Why do Machine Learning strategies fail and how to deal with them?

Why do Machine Learning strategies fail and how to deal with them?

Yes, the world is seeing an advancement by virtue of technology. Implementing machine learning has aided in a lot of fields and made remarkable inventions as well. However, this isn’t the entire reality. Seeing the progress companies have made because of machine learning (ML) has made almost every company to try its hands on the same and have miserably failed in it. What might have gone wrong here?

Experts believe that the strategies implemented by the companies failed to work the way they were pictured to be. Here are some reasons that could have led to such a situation–

• Needless to say, machine learning relies a lot on data. So much so that the quality of data collected and analysed is directly reflected on the results achieved. Since this forms the base, bad quality data results in undesirable results. This is the most common problem to deal with especially when the task revolves around health industry, government, industrial sector and similar such areas. This is because, the data involved here is usually either rare or is guided by some strict regulations. Start-ups too tend to face this issue as they lack the requirement of the right resources to be able to come up with high quality data that is good enough to draw necessary conclusions.

In order to cope with this issue of bad quality data, it is important for the companies to do complete evaluation about their data infrastructure. Having the right procedures to clean the data so that the data is of good quality, is accurate and in a position to help the company to make better decisions is the need of the hour.

• The role of data scientists in implementing machine learning models cannot be merely put into words. This is where most of the companies struggle. Recruiting the right talent that’ll be able to deal with machine learning initiatives and help deploy models is where companies fail. This is generally seen in smaller companies that do not have the right talent in the form of data scientists, data analysts, data engineers, etc. who can develop the AI models.

• The average salary of data scientists, data analysts or data engineers is not a small amount. They are paid handsomely and in most cases is somewhere close to experienced software engineers. Small companies find it difficult in managing the same and hence do not get the required expertise.

• Another area where companies seem to struggle is the ability to forecast the value of their strategies developed.

• Lack of commitment from the top management is yet another reason that’s seen to not result in fruitful conclusions.

All in all, there’s no doubt to the fact that machine learning models can ease out things beyond imagination. Countless organizations have benefitted from the same and the years ahead will see more companies getting inclined towards this field of technology. However, for companies to not fail and make the most of the ML models, it is important to lay a strong base in the first place. Checking on the data quality, recruiting the right talent, paying them well in combination with the management’s support can help achieve the desired results.

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