How can AI improve data quality and consistency across multiple sources?

AI streamlines data cleansing by applying machine learning algorithms to recognize patterns, thereby enabling the detection of anomalies and errors. Utilizing natural language processing, AI can comprehend text data, flag inconsistencies and inaccuracies. Supervised learning models enhance the detection of outliers and duplicate entries. Unsupervised models, on the other hand, can be leveraged to reveal hidden structures within unclean data. Furthermore, reinforcement learning allows AI to continuously improve its performance based on feedback. Consequently, AI ensures data consistency, decreases manual intervention, and boosts the overall efficiency of data cleansing.

Artificial intelligence (AI) can significantly enhance data quality and consistency across multiple sources through various means. For instance, AI employs machine learning and natural language processing to automatically identify and rectify data errors like typos, duplicates, missing values, outliers, and inconsistencies. It can also standardize data formats, such as dates, names, addresses, and currencies, across diverse sources. Additionally, AI can continually improve its data cleansing abilities by learning from user feedback and historical data, thus enhancing accuracy and efficiency in maintaining data quality and consistency.

AI can be very effective to create your data model or ontology. In fact, Ontology + Data = Knowledge Graph! Ontology & knowledge graph are implemented to better manage data and scale across sites easily. For example in manufacturing, AI can be leveraged to create an ontology on top of Bill of Materials (BOM) and therefore enable many use cases such as parts standardisation cross programs / products.

In the context of marketing, these capabilities help startups with customer segmentation strategy by making it easier to analyze trends, identify common user paths, fall-out points, and channel affinity. This saves ad $ by connecting customer interactions across websites, digital ads, social media, offline purchasing, support calls … Larger teams deliver long-term value through accurate campaign measurement by linking ad views, site visits, registrations, conversions, and future repeat business to marketing campaigns for multi-touch attribution.

AI can be very effective in categorising data and impacts downstream applications. For example Commerceflow AI models can automatically categorise product data into 6000 categories and this directly improves search and browse experience on a eCommerce store or marketplace. Another application is creating higher level attributes based on underlying data. For example creating dietary labels for food products based on their ingredients or nutrition labels. This allows users to find products that match their needs.

AI can be used to predict the threshold limits based on historical values and seasonality instead of using pre-defined thresholds.

We should also consider the role AI can play to create and apply rule-based validation techniques to define conditions check net new data.

AI governance model should also consider the following: 1. Ethics and Bias: Establish policies for fairness and transparency to avoid biases in AI. 2. Data Privacy: Respect privacy laws like GDPR; ensure policies handle data anonymization, retention, and user access rights. 3. Data Security: Maintain robust security measures, including encryption and secure access controls. 4. AI Explainability: Ensure AI models are interpretable and transparent. 5. Monitoring and Auditing: Regularly monitor and audit AI systems to ensure adherence to policies and standards.

Don't buy shark attacks to predict ice cream sales! When evaluating data, the question we should be asking is: "is this data source causally related to my KPI?" I.e, if we have an ice-cream stand on the beach, it would be silly to make a model that relies on shark attacks to predict sales. In this case we are seeing a "spurious correlation", which happens when there's a confounding variable (temperature) that drives both my KPI (ice cream sales), and my spurious variable (shark attacks). Differentiating between causality and correlation is crucial in settings where stakeholders need to understand which levers need to be pulled to improve KPIs: throwing baby sharks into the water is not an intervention that will boost your business.

With the help of AI & Statistics the data imputation quality can be improved for the missing data. For Ex: for some categorical data we can get Association score to impute the possible data

Know the person before you meet! AI, equipped with powerful algorithms and machine learning capabilities, can revolutionise the way we comprehend and categorize individual personalities.