The Role of AI and Machine Learning in Data Quality

In this digital era of advanced information technology and communication, the ever-expanding channels bring both opportunities and challenges. It is very easy today to acquire customers’ data which can help you design effective marketing campaigns, personalized efforts, and fundraising efforts. This increases the chances of the business succeeding if the data acquired is accurate.

However, there are so many data entry points both on the company and customer-end which increases the chances of inaccurate organizational databases. If there are no strategies to prevent inaccurate data entry or cleanse the inaccurate data from databases, marketing campaigns, efforts awareness and other outreaches to users may not be effective.

What is data quality?

Data quality is an assessment or a perception of data’s fitness to fulfill its purpose. Simply put, data is said to be high quality if it satisfies the requirements of its intended purpose. The quality of data can be measured by six dimensions:

Six dimensions of data quality:

Completeness : Data completeness is the expected comprehensiveness. Data is considered complete if it meets the expected expectations.

: Data completeness is the expected comprehensiveness. Data is considered complete if it meets the expected expectations. Consistency : Data is said to be consistent if all the systems across the entreprise reflect the same information.

: Data is said to be consistent if all the systems across the entreprise reflect the same information. Accuracy : Data accuracy is defined as the degree with which data correctly reflects the event in question or the ‘real world’ object.

: Data accuracy is defined as the degree with which data correctly reflects the event in question or the ‘real world’ object. Timelessness : It references whether data is available when required

: It references whether data is available when required Validity: Data is valid if it conforms to type, format and range of its definition

Data is valid if it conforms to type, format and range of its definition Uniqueness: Every data entry is one of its kind

Why is Data Quality Important?

Decision making

When the quality of data is high, its users have high confidence in the outputs. The old saying ‘garbage in, garbage out’ is true as is its inverse. When quality is recorded and used, the outputs are reliable which mitigates risks and guesswork in decision making.

Productivity

Good quality enhances productivity. Workers spend more time working towards their primary mission instead of spending time validating and fixing data errors.

Effective marketing

High quality data increases marketing effectiveness. Accurate data allows accurate targeting and communications and companies are more likely to achieve desired results.

Compliance

Maintaining good quality data makes it easy for companies to ensure compliance and save probably huge fines for non-compliance. This is particularly so in industries where regulations govern trade with customers such as finance industry.

How data quality has been maintained traditionally?

Traditionally, data management experts have been involved in refining data analysis and reporting platforms while overlooking data quality. Traditional data quality control mechanisms are based on users experience or predefined business rules. Apart from being a time-consuming exercise, it also limits the performance and has low accuracy.

New and smarter way – use of AI and AI powered MDM platforms data quality

Every organization values the importance of data and its contribution to its success. The case is even worse in this era of big data, cloud computing and AI. The relevance of data goes beyond its volume or how it is used. If a company has terrible data quality, actionable analytics in the world will make no difference. How Artificial Intelligence, Machine Learning and Master Data Management can work together is a hot topic right now in the MDM realm. MDM platforms are incorporating AI and Machine Learning capabilities to improve accuracy, consistency, manageability among others. AI has managed to improve the quality of data through the following ways.

Automatic data capture

According to research done by Gartner, $14.2 million are lost annually as a result of poor data capture. Besides data predictions, AI helps in improving data quality by automating the process of data entry through implementing intelligent capture. This ensures all the necessary information is captured, and there are no gaps in the system.

AI can grab data without the intervention of manual activities. If the most critical details are automatically captured, workers can forget about admin work and put more emphasis on the customer.

Identify duplicate records

Duplicate entries of data can lead to outdated records that result in bad data quality. AI can be used to eliminate duplicate records in an organisation’s database and keep precise golden keys in the database. It is difficult to identify and remove recurring entries in a big company’s repository without the implementation of sophisticated mechanisms. An organisation can combat this by having intelligent systems that can detect and remove duplicate keys.

An excellent example of AI implementation is in SalesForce CRM. It has an intelligent functionality that is powered on by default to ensure contacts, leads and business accounts are clean and free from duplicate entries.

Detect anomalies

A small human error can drastically affect the utility and the quality of data in a CRM. An AI-enabled system can remove defects in a system. Data quality can also be improved through the implementation of machine learning-based anomaly.

Third-party data inclusion

Apart from correcting and maintaining the integrity of data, AI can improve data quality by adding to it. Third-party organisations and governmental units can significantly add value to the quality of a management system and MDM platforms by presenting better and more complete data, which contributes to precise decision making. AI makes the suggestions on what to fetch from a particular set of data and the building connections in the data.

When a company has detailed and clean data in one place, it has higher chances of making informed decisions.

Algorithms that can be used for data quality

It is imperative for companies to have the right algorithms and queries to operate on their big data.

Random forest

Random forest is a flexible machine learning algorithm which produces reliable results. It is the most used algorithm due to its simplicity, and it can be used for regression and classification purposes.

How it works

Like its name, a random forest algorithm creates a forest and makes it random. It establishes several decision trees and combines them to achieve a more stable and accurate prediction.

Advantages of random forest

The main advantage of random forest is it can be used in both classification and regression tasks

It is easy to see the relative importance it gives the input features

It’s easy and reliable since it produces good prediction results

Disadvantages

A large number of trees makes the algorithm slow and ineffective for real-time predictions.

Applications of random forest algorithm

The Random Forest algorithm for data quality is used in various institutions such as banks, e-commerce, medicine, and the stock market. In a banking institution, Random Forest is used to determining account holders who use the bank services more frequently than others and pay back their debt in time. In the same field, it is used to identify fraud customers having the intention to Scam the bank.

In finance, the algorithm is used to determine stock behavior and influence decision making in the future.

Random forest is used in the medical field to detect the most appropriate combination of medicine components and analyze a patient’s medical history. The results from such predictions help in determining the frequency of a disease occurring in a particular area and the best treatment.

In E-commerce, the algorithm is used to predict customer behavior in buying products. It helps in presenting a customer with their most preferred products having analyzed their purchase behavior from past experiences. It can also predict the probability of a customer buying a particular product based on the behavior of other customers.

For the application in the stock market, the algorithm can be used to determine stocks behavior and identify the expected loss or profit.

Support vector machine (SVM) algorithm

It is a supervised machine learning algorithm that can be used for both classification and regression. The primary goal of SVM is to classify unseen data.

Applications of SVM

Text and hypertext detection: the algorithm allows for categorisation of text and hypertext for transductive and inductive models. It uses training data to classify documents into different categories. The categories are put based on scores generated and then comparing with the highest value.

Handwriting recognition: SVMs are used to identify widely used handwritten characters. These characters are majorly utilised in validating signatures of vital documents.

Bioinformatics: This includes protein and cancer classification. SVM algorithm is used to identify the classification of genes and other biological problems in patients. In recent years, the SVM algorithm has been used to detect protein remote homology.

Image classification: as opposed to traditional query-based searching techniques, SVMs provide enhanced search accuracy for the classification of images. The algorithm classifies images with higher search accuracy compared to the traditional query-based scheme.

Advantages of SVMs

Calculation simplification

It has comprehensive algorithms that simplify predictions and calculations since the algorithm is presented in a graphic image.

Efficient data generation.

Use cases

AI in business is progressively advancing. What was once science fiction is being implemented by many organisations around the globe. In today’s business era, companies are using machine algorithms to determine trends and insights. Upon cleansing and increasing the quality of the data, the information obtained helps in decisions making to increase the company’s competitiveness.

SAP used to turn databases into useful intel

HANA is SAP’s cloud platform that replicates and ingests structured data like customer information and sales transactions from apps, relational databases, and other sources. This platform can be configured to operate on-premise through an organization’s servers or run via the cloud.

HANA takes the collected information from various access points across the business such as desktop computers, mobile phones and sensors. If an organisation’s sales staff uses company devices to record purchase orders, HANA can analyse the transactions to identify trends and irregularities.

The intent of HANA, as is with other machine learning solutions, is to come up with data-driven decisions which are potentially better informed. Walmart, a multinational retail corporation, uses HANA to process its high volume of transaction records within seconds.

AI is used in GE to predict repairs and machinery upkeep. The ever-increasing prevalence of sensors in vehicles, production plants and other machinery means physical equipment can be monitored through AI.

Many firms in different industrial sectors such as oil, aviation and gas have been using GE’s Predix operating system which powers industrial apps to process the historical performance data of the equipment. The acquired information can be used to identify different operation information, including when the machine will fail. Besides the small scale logistics, Predix can process large amounts of information taken over long periods to develop its focus.

In the aviation sector, aircrafts use applications like Prognostics from GE built on Predix. The app helps airline engineering crews to determine how long the landing gear can serve before it is put in service. This time prediction can be used to create a maintenance schedule and eliminate unexpected issues and flight delays.

Avanade is a joint venture between Accenture and Microsoft that uses Cortana Intelligent Digital Assistant and other solutions to form predictive data-based insights and analytics. Pacific Specialty, an insurance company, used Avanade to establish an analytics platform to give its staff more insight and perspective to the insurance business. The goal of the exercise was to use policy and customer data to influence more growth. The firm sought to provide better products by understanding policyholder, trends and behaviour through analytics.

Plagiarism checkers

Renowned plagiarism checker platforms such as Turnitin use ML at their core functioning to detect plagiarised content. Traditional plagiarism detection applications rely on massive databases to compare the text in question.

Machine learning helps in detecting plagiarised content that is not in the database. It has the ability to recognise text from foreign languages. The algorithmic key to plagiarism is the resemblance function which gives a numeric value representing the similarity of one document to the other. An effective similarity function improves the accuracy of the results and also increased the efficiency of computing them.

Endnote

Artificial intelligence and Machine Learning are expected to change the present and future business world. Businesses using AI are getting better at their predictive tasks like determining the preferences of different customers. The prediction results are based on the information fed to the system. It is clear that this new development will affect many industrial sectors such as banking, stock market, E-commerce, learning, health care, manufacturing and many others. The overall effect of implementing AI in businesses would be increased productivity, better customer experience, improved decision making and timely planning.