The top 7 data problems that AI engineers face?

Data is the heart and centre of Artificial Intelligence technology and algorithms. Data is what drives the machine learning models and algorithms. Past data forms the basis of the future prediction of the model. So if you give garbage in, you will get garbage out. Imagine running a vehicle with no diesel or the wrong fuel, that’s what happens when data goes out of hand in an AI-driven process. What could go wrong with data that a full-blown technology such as the AI comes to a stand still?

1. Data Collection/Procurement

As mentioned before, data is the primary factor driving the models and training. The first step to implementing machine learning is to procure or acquire data. There could be a possibility that we do not have data pertaining to the labels or values that we are trying to predict. We could also have less data than necessary for training the model. Companies will have to invest in techniques for data collection and the data engineering team must work out a strategy to procure data enough to build the model.

2. Data Privacy

The next thing while procuring the data, is its privacy. When we collect data, it is possible that some of the data labels or aspects are very private and the companies will be violating privacy rules when using the data. Effective strategies must be discussed and implemented to make sure private data is not available to the public. Masking or removing parts of the data which should not be revealed should be done before using the data.

3. Stale Data

Most of the times, we find that even after procuring and processing the data, we are not arriving at conclusions and predictions that are accurate. The reason behind such faults is usually very old or stale data. The data may not be fresh pertaining to the present conditions under which we are doing our predictions. Measures must be taken to make sure that the data we obtain is fresh and relevant to the current situation.

4. Irrelevant and Wrong Data

Not all data is useful to AI algorithms. Some values may be redundant and some values may even be irrelevant. Caution must be taken to make sure we use the data that is most relevant to the problem at hand. Irrelevant data can lead to wrong predictions and assumptions. Redundant data will result in resource wastage as time and effort are spent on data processing and training.

Wrong data is another reason why your machine learning models and predictions are going haywire. As we mentioned earlier, garbage in is garbage out in the AI algorithms. So if we feed wrong data to the algorithms, then the predictions and assumptions it makes will be wrong and inaccurate.

5. Missing Data

When we collect data, while processing we often find missing data. Not all values are available and this leads to a wrong or a biased prediction. If we are working with data over a couple of months and if it is found that for the last few months, that value is missing or not listed, then we may be working with a wrong set of data or values.

6. Data Bias

When we have all the data, some of the values or parameters could be more inclined towards a direction creating a bias. For example, if we are assessing data for obtaining loans, we could have collected more data from the younger population than the older population or even gender bias could have happened in the data collection. When there is a bias on the data, then the predictions will also be biased and an accurate model cannot be built. We should take care of obtaining a non-biased balanced data for the algorithms to work accurately.

7. Data Preparation

With all the possible things going wrong with the procured data, data preparation becomes a crucial step before feeding it into the training models or algorithm. Data Preparation involves identifying the correct relevant data, looking for missing data and patterns, recognize and eliminate stale data, identify missing data and replace it with relevant values. It is also important to see whether the right data is used for the problem statement. It is often said that 80% of the time goes in the preparation of the data and only 20% of the time is used in testing and training.

These are some of the pressure points of data while developing algorithms using AI technology. Data collection, preparation and processing form a huge part of making sure that the employed AI techniques are fruitful or not. We can bring out the potential and efficiency of AI-driven processes only when we feed the right coordinated and balance data.

References

Implementing a Successful AI Strategy: Best Practices and Pitfalls to avoid