Artificial intelligence (AI) and machine learning (ML) are unquestionably becoming more relevant to businesses looking to gain a competitive advantage via digital transformation. More than 75% of enterprises value AI and machine learning over other IT investments, and they're employing data scientists in large numbers to help them succeed. However, most of these projects are compartmentalized within discrete business functions rather than tackling digital transformation across the company.
Traditional analytics cannot handle the magnitude and complexity of data that enterprises now have access to. We need new methods to analyze this deluge of data, and AI and machine learning can help us do just that. To educate ML models, we also require incredibly rich and complete data sets, which we now have thanks to the exponential rise of data. As of 2020, IDC expects 64.2 zettabytes of data were generated or replicated, with 10.6% of labelled data being relevant for analysis or input into AI/ML.
Nonetheless, before AI/ML can be used to transform an organization, organizations must first resolve the data integrity problems that are used to drive AI/ML results.
Let's start with how a company saves its information. Customer and employee data, for example, are typically stored in difficult-to-access data stores. A conventional IT infrastructure for a company includes a range of enterprise applications and databases, several data centres, and fresh data generated in the cloud. These variables contribute to data silos, which are challenges to guaranteeing data consistency and accuracy. Many businesses are hampered by data silos, which restrict them from gaining quick and effective business insights from relevant data.
Even though business leaders use high-quality data to make choices, a survey found that more than 45 percent of freshly created data records contained at least one serious inaccuracy. Data bias and terrible business judgments are caused by incomplete, duplicate, and inaccurate data coming into machine-learning pipelines and analytics. Data can also be prone to inaccuracies, especially when it comes from a variety of sources and providers. Standardizing, verifying, and validating data before AI/ML can use it is typically a time-consuming, human process. Therefore, data scientists still spend most of their time preparing data rather than fine-tuning models or visualizing it.
As we discuss the necessity of data quality for AI, it's also vital to discuss AI for data quality. The use of automation in data pipelines can improve the data's correctness and consistency. Using AI/ML to identify data changes, alert data drift, and establish quality criteria, for example, can assist enhance data quality, automating data pipelines, and minimizing manual workload. Data visibility and observability are made possible by this form of automation.
Data governance is also crucial to address in the context of data quality. Trust in data stems from the capacity to demonstrate with absolute certainty how the data was prepared, track the lineage of data back to its raw source, and give rights management and auditing capabilities.
Data management has always worked on making data precise and consistent, but this isn't enough to make data relevant. I've noticed that firms in various industries fail to take advantage of third-party data to provide essential context to their internal data. Before you can truly trust the market intelligence you gain from that data, you must evaluate it in context – not just who and what, but also where, when, and why.
Location is an essential category of third-party data. Insurance businesses, for example, use geography to underwrite policies, analyze and forecast risk involved with catastrophic events like hurricanes and wildfires, and set pricing strategies. Financial services firms use location data to confirm and accurately attribute transactions to a specific store or merchant and better understand subsidiaries and parent corporations. Telecommunications companies are preparing 5G rollouts based on location data, AI/ML, and analytics to provide a new location and context-aware services to millions of endpoints. Points of interest and mobility data are also included in data enrichment, allowing for the analysis of traffic movements into and around a location.
Demographics is another critical source of third-party data since they aid in client segmentation, tailored outreach, and the development of new services and products. By offering a more comprehensive view of clients, demographic data can also assist firms in reducing data bias. Retailers mix location with demographics and consumer insights to help comprehend their clients, give greater personalized experiences, and determine their purchasing proclivity and generate fresh product recommendations.
While it is true that enriching data using third-party enrichment data set benefits AI/ML models, it is also true that AI and ML are becoming increasingly vital for their development. The use of AI and machine learning speeds up and scales up the process of creating data sets as well as proposing which data sets to utilize for enrichment.
These steps, taken collectively, make up data integrity: data integration, quality of data and governance, data enrichment, and location intelligence. Insufficient data has a massive negative influence on AI and machine learning. For business insights obtained from AI/ML to be trusted, data must be delivered with optimal accuracy, context, and consistency. It is impossible to rely on data and the data-driven business insights based on it without data integrity.
CEOs and business leaders are rightfully concerned about the data integrity to make decisions. Data integrity is essential for trusting the results of sophisticated analytics and the business decisions that follow. Companies that wish to promote digital transformation and outperform competition must manage data integrity successfully.