In the era of AI, automated decision making, and continuous process optimization data quality is extremely significant. Companies have to be data-driven and good quality data is a prerequisite for achieving it. Issues pertaining to data quality usually highlight lack of trust in data and poor decisions. So the next time you think you made a breakthrough data discovery, cross-verify its quality.
What are the data quality issues faced by most organizations?
Most organizations cited the following data quality issues they usually face in their day-to-day operations:
- Poorly labeled data
- Unlabeled data
- Incomplete cases
- Corrupted binary data
- Unstructured data that is tough to organize
- Broken URLs
- Dearth of available resources to address data quality issues
What are the root causes behind these issues?
Data quality issues mainly happen due to the following causes:
Typographical errors and non-conforming data - Despite automation in data architecture, there are instances of manual entry. Data inaccuracy usually occurs when people enter a wrong value or tick mark an incorrect box.
Unclear Information - Several times people provide incomplete information due to privacy concerns. At other times, there is no option for the user to enter the correct data as the field is not available.
Inconsistent data formatting - Data formatting problem is a common factor due to which data can suffer. If there are severe data formatting problems, it might be too difficult to mine and process any queries.
Hence contemporary systems must be designed in such a manner that they are able to handle sudden data quality issues and problematic data feeds.
Below, I have discussed steps to create a data processing pipeline.
Identify data sources
Here, I have briefly described the different sources of data
Type of data contained - Good examples can be user documents, customer records, web traffic
Storage Category - Is it a relational database, flat file, document store or an events stream ?
Time frame - How long is the data available?
Frequency of updates - What is the frequency of different updates you receive - delta, events, or aggregated data?
Source of data - Is the data a continuous feed of events from another system? Or is it a batch process taken from another integrated system?
How do you prepare training data for machine learning?
Post identification of data sources, data collection and pre-processing are the next steps involved before training a Machine Learning model.
The data set collection can be done from different sources such as database, file, sensor. But this data will contain unorganized or noisy text, large values, or missing data. Therefore it cannot be used directly for analysis, you need to do data preparation. Alternatively, free data sets can be used from reliable repositories on the internet.
This is an important step in creating Machine Learning models. Most data scientists adhere to the 80/20 rule, whereby 80% of their time is spent in data pre-processing (finding, cleaning, organizing) and 20% time is spent in actual analysis. Data pre-processing cleans the raw data available in the real-world to a clean data set, which can be used to train a model. Most of the available data falls under these categories -
Missing data - It occurs as a result of technical issues or when no value is stored for the data.
Noisy data: This kind of data is also termed as ‘outliers’, this mainly occurs due to human errors or due to some technical glitches at the time of data collection.
Inconsistent data: This sort of data collection includes duplicate data or when the same data exists in several formats. Suppose a student’s record in the college database has 2 home telephone numbers, it will be difficult to determine which number is to be used.
How data pre-processing can be performed?
These 5 pre-processing techniques can be implemented for the 3 types of data - numeric, categorical, and ordinal:
Data conversion - As ML models can only handle numeric data, ordinal and categorical data must be converted to numeric data.
Removing any missing values - If any missing data is encountered in a specific data set, then that row/column can be deleted. But this method may not be appropriate if there are numerous missing values.
Fill any missing data - If any data is missing, it can be entered manually using mean, median, or highest frequency value.
Machine Learning Technique - We can predict what data should be present in the empty position by using the existing data set.
Detecting outliers - There are some error data that might be present in our data set that deviates drastically from other observations in a data set. An example can be a woman’s weight = 800kg, due to a typo of extra ‘0’.
4 strategies to improve data quality before training an ML model
1. Automating data entry process
According to Gartner research, approximately $14.2 million is lost annually due to poor data capture. AI not only helps with data predictions, but it also improves data quality by automating the data entry process through intelligent capture. This ensures all the necessary information is captured, and there are no gaps in the system. AI can be trained to capture data without any human intervention. If critical data is automatically captured using AI, the company’s employees can focus their time on other value added activities like customer needs.
2. Identify duplicate records
Duplicate data entries can lead to obsolete records, which results in poor data quality. AI can play a significant role in identifying and deleting recurring entries from huge company databases. By implementing intelligent systems, companies can spot and eliminate any duplicate keys. A perfect example of AI implementation is SalesForce CRM, which removes any duplicate entries of leads, contacts, or business accounts.
3. Anomaly detection
Even a minor human error can have a far-reaching impact on data quality and utility in a CRM. An AI-enabled system can remove defects in a system and improve data quality by implementing Machine Learning-based anomaly detection.
4. Third-party data inclusion
Besides rectifying and maintaining data integrity, AI can significantly improve data quality by adding value to it. Government institutions and third-party organizations add value to MDM (mobile device management) platforms and data management systems by enhanced data presentation. AI gives suggestions on what can be fetched from a data set and when a company has access to clean data, it has increased chances of making informed decisions. This better and complete data presentation contributes to accurate decision making.
Over the last few years, data is mostly generated, as businesses have adopted IoT (Internet of Things). However, improving data quality and sustaining good quality data output remain the key challenges. Problems related to data quality can lead to wrong decisions and redundant data management practices. This may result in huge revenue losses and customer attrition.
How Skyl can help you analyse and improve data quality for Machine Learning
Skyl.ai is a scalable, easy-to-use SaaS platform that can automate your end-to-end ML workflow. Skyl can help in identifying feature sets quickly, spot errors, outliers, and visualize data adequacy. Using the data visualization feature of Skyl.ai, you can see and understand data patterns, trends, and outliers.
Skyl.ai provides numerous ways for data visualizations like confusion matrix, word cloud, column-wise data analysis. This can help data scientists and data analysts to figure out the quality of data much earlier in the process. So they can improve upon it, and eliminate any back and forth in the iterative process of building machine learning models.
1. Data Collection
When it comes to data collection Skyl.ai provides the trends of data collection in real-time and also the statistics about the data collected from various methods. This kind of analysis helps project managers and data scientists to understand how much data is being collected from each kind of source or if there are any exceptions or bad records being uploaded.
2. Data Labeling
For Data labeling, Skyl.ai provides trends in real-time and also the statistics around the data labelled from various methods. This kind of analysis helps project managers and data scientists to understand how much data is being labeled from each kind of source.
In addition to this, the labeling confusion matrix provisions you to understand if there are any outliers or biases in your data.
3. Data Visualization
Skyl provides various kinds of visualization techniques for your data set depending upon what kind of problem you are trying to solve. It also gives you column-wise statistics of each column in your data set depending upon the datatype.
For CV or image-based data sets, Skyl provides beautiful overlay visualizations for multi-class classifications and confusion matrix visualization for multi-label classification. These can really help data analysts, data scientists or product managers to analyse the quality of the data set.
Additionally, column-wise statistical visualizations based on data type can convey ample information, which would otherwise take a lot of time and resources to figure out.
Improving data quality requires a time-consuming and thoughtful approach. Skyl.ai will guide you towards efficient data management and help you solve all data-related issues. Partner with us in your journey towards ML adoption with less specialized skills, improved collaboration, and enhanced visibility.