Machine Learning (ML) combined with artificial intelligence (AI) is enriched customer experience with the ability to process huge volumes of data.
Thanks to ML, companies are now in a better position to understand customers, based on the purchase data, process and previous history of transactions, behavioural patterns, and movements.
Customer services via chatbots and virtual assistants have revolutionised the way customer queries are handled. CIMB bank through ML is now able to offer 24 hours of customer service with an integrated chatbot-enabled app. Through this app, customers can perform diverse tasks like money transfers, bill pay, balance check, and much more. Banks can satisfy their customers by serving them round the clock and round the year.
A study conducted by Tetra Data estimated that about 40 per cent of marketers believed that the mountains of data are not put to good use only because of the cumbersome work ahead.
- 2 per cent of your data perishes every month, implying that 25 – 30 per cent data goes waste every year.
- About 63 per cent of marketers these days spend time on data-driven marketing efforts.
- About 85 per cent of organisations has felt the difference after data preparation.
Why Should You Clean Your Data for Machine Learning?
Data cleaning is considered as an essential aspect of all machine learning applications. If your algorithm is strong enough but is not backed by the right data training, then you may not yield desired results.
The primary aim of data cleaning is to identify duplicate and error-prone data. Data cleaning is essential because all marketers need error-free data to process the user information available.
Although a time-consuming activity, data cleaning can be done by two distinct techniques, namely qualitative technique and quantitative techniques. Qualitative methods are about using rules and regulations to point out errors in data, whereas the quantitative techniques focus on statistical techniques to identify issues in data. Data cleaning deals with removing errors and obstacles, and to do that, one must also ask primary questions concerning
- The types of error to be identified
- The method to identify
- The place where the errors could be found
Benefits of Cleaning and Preparing Data for ML
Machine learning has seen immense progress, yet some barriers are preventing it from realising its full potential. These include:
The Power of Reasoning
ML has indeed achieved great heights, but reasoning power restricts its use as well as applications. The reasoning is more or less strictly a human trait, whereas algorithms are designed to be goal-oriented towards specific tasks and targets. Despite the gamut of benefits, ML still lacks in some significant skills; it does not have the introspective mind to understand and ascertain the reasoning behind the occurrence.
ML well understands voice commands, but when it comes to context it falls short. One of the best examples that highlight this is the classic Chinese room argument put forth by John Searle. He suggested that the algorithms and the programs can grab an idea only through symbols, and not by their context.
Natural Language Processing (NLP)
To achieve excellence in NLP, organisations have invested a lot of time and money into making the system better. The challenge to master NLP is still a distant one considering we have crossed less than half the bridge. In many areas, NLP has made its presence felt, especially when we voice Google search or talk to our very own Siri. The Defense Advanced Research Projects Agency (DARPA) is currently testing language translation which is good enough to translate English to Arabic and vice versa. Yet the journey needs to be covered and in abundance now.
Scalability of Data
Data has been growing in terms of scale as well as form. This makes it difficult for ML applications to cope with the relentlessly increasing data and understand it correctly. Algorithms also tend to struggle unless they are updated regularly. Lack of scalability, or in simpler terms, the constant need for regular updates, calls for frequent manual work, ergo, a mundane and tiresome job.
Detection of Objects
The task of object detection is considered difficult even today because algorithms are not advanced enough to understand them. The best way to resolve this issue is by putting in more time, effort, and money into the problem.
Use of Video Training Data
Static images are still considered more reliable, and the use of video training data still leaves a lot to be desired. These video sets are better and superior to static images, and they should be explored further.
Data Powers Everything We Do
Before bringing data into an ML model, it should pass three parameters – quality, dependability, and precision. To create a successful model for ML, the data analysis, training, and test for deployment should be conducted. Data cleaning and preparation are chiefly employed for the same purpose. Some steps need to be followed to enhance ML experience. These include:
- Data collection, an important step that helps in addressing challenges like segregating and identifying data that is relevant from outside depositories. It also helps to automatically determine the attributes in a data string that has been stored in a .csv pattern. When you take into account the DP solution, it has to combine many files into one input, especially when there are umpteen files concerning your daily transactions. The model for ML has to take in the data for the entire year. You also have to be prepared beforehand in case there are anomalies in data. When that happens the ML model also has to be set accordingly.
- After data collection, it is the time to look out for attached conditions like patterns, trends, inconsistency and exceptions. Here you will be able to understand what’s missing from your data. One must also ensure that there is no skewed information because your ML model functions on the data you feed it. Hence, it is important to have a great sample of data while analysing patterns, especially those that are a good representation of the target audience. Once everything is ensured, the second step is complete, and patterns are noted.
- Once the second step is complete, it is now time to format data that best helps the ML Many hands imply different forms of data, and then a plan for data standardisation will help in correctly aggregating your data. If the data formatting is done correctly, then it will go a long way in removing errors.
- Now is the turn to understand your strategy of dealing with flawed data. All the outliers and extremities in the data should be done within this purification stage. The complex problems that are dealt with today are done so with comprehensive data which means there are more quality issues. A Harvard Business Review article says that if the data is bad, then the ML becomes useless and its tools are futile. Another HBR article highlights that if the data quality is poor, then businesses have to bear costs close to USD 3 trillion per year in the USA alone!
- Now it is the time to convert raw data into structured data that can put up a pattern for the learning algorithms. One can break up the data for a detailed analysis. For example, analyse sales performance weekly instead of quarterly with the same parameters.
Data splitting is the final step in the entire scheme of things. The data has to be split into multiple parts based on its use case. One set has to be used at an algorithm level, and the other has to be used for assessment. Non-overlapping subsets here should be used because only that will give you essential numbers.
Data cleaning and preparation presents your business with appropriate data to analyse and make informed decisions. The challenges that ML faces can be solved by data cleaning and preparation. It will allow data and IT teams to maintain scores of data volume that will be valuable for the organisation and various departments in the long and short run. The ML workflow becomes much more refined with preparation and data cleaning which enhances the performance of the application.