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How AI can Clean Your Data and Save You Man Hours and Money


Dirty data is the bane of the analytics industry. Almost every organization that deals with data have had to deal with some degree of unreliability in its numbers.

Studies indicate that enterprises spend as little as 20% of their time analyzing data. The rest of that time is spent cleaning the dataset.

Unfortunately, poor data leads to poor insights. Assessments based on faulty data are inconsistent and often lead to failure to meet goals, or increased operational cost, and customer dissatisfaction.

The term “bad data” is vague, but you can look for a few key red flags:

  • Duplicate data: Bad data tend to have multiple copies of the same event recorded in the dataset
  • Missing data: Bad Data entries might have values missing from important fields
  • Invalid data: the data entered might be old or incorrect
  • Inconsistent formatting: This means all kinds of formatting problems, including spelling errors, old code conventions, etc.

Most bad data comes from human error. Ensuring data quality is time-consuming, and negligence will lead to bad data.

So ironically, you need to analyze your data before you can do data analysis. This is to understand the type of irregularities and errors that have crept in, and which are serious enough to need to be removed. This is why best practices need to be used at every point in the chain.

AI and Its role in data cleaning

The first step in the data analytics process is to identify bad data. The second involves taking corrective action. An example of this corrective action is replacing bad data with good data from another sample of the dataset.

Before the advent of artificial intelligence (AI) and its subset of machine learning (ML), data analytics companies had to use traditional data cleansing solutions to do the job. These methods don’t work at scale or when working with ’empty-calorie data’. The traditional methods simply can’t keep up with large inflows of new data, of varying degrees of usefulness.

The entry of AI now means data cleansing experts can use data cleansing and augmentation solutions based on machine learning.

Machine learning and deep learning allow the analysis of the collected data, make estimates, to learn and change as per the precision of the estimates. As more information is analyzed, so also the estimates progress.

So how does it really work?

Since data flows in from numerous sources, any program using ML needs to get data into a stable arrangement to simplify it and ensure consistent patterns across all points of data collection.

Factors may force you to transform the data for use. At this point itself, the suitability of the transformation activity and the definitions must be analyzed.

Once this is done, the bad data must be substituted with good data in the primary source. This is a very important step as it means all data across the enterprise is refreshed, permeating throughout all the divisions, removing any need for removals in the future.

Human error is found to be the main reason in critical areas of data collection so any AI based model uses ML to replace humans in identifying bad data and refreshing the models as and when needed.

ML algorithms can determine flaws in a data analytics model’s logic.

The more information an ML algorithm can work with, the better its’ predictions. This means contrary to manual cleansing systems, the ML-based algorithm gets better with scale. As the ML-based software improves over time due to deep learning, the cleaning of data gets faster, even as it is flowing in, which speeds up the entire data delivery process.

Automation also guarantees:

  • Clean data
  • Standardized data
  • Reduced time spent coding and correcting faulty data at the source
  • Allows customers to integrate their 3rd party apps easily

ML-based programs generally use the Cloud. When combined with on-premise delivery, models can provide customizable data solutions. In other words, any enterprise across verticals like marketing or healthcare can deploy it. This implementation also offers better metadata management abilities to provide better data governance.

Image by Gerd Altmann from Pixabay


References:

1. How to clean dirty data

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