How AI Can Help with Data Governance and Management

In a recent Deloitte survey, 45% of tech industry leaders said that collecting and protecting ever-larger volumes of data was their biggest obstacle to achieving their data management goals. And yet, with the amount of data that companies have to manage increasing every day, it’s hard to overstate the importance of data management and analytics. It’s true that this sounds a bit paradoxical. Businesses use data to make better decisions and identify opportunities — but the data itself is increasingly problematic to process and make truly useful.  

Master data management can help businesses generate insights from large volumes of data — but volume isn’t the only challenge. To stay ahead of the competition, modern businesses need to integrate a dynamic flow of disparate information drawn from numerous sources. To act on it, they also need to make sure that the data-driven insights are accessible and usable by their stakeholders. 

A variety of data management tools exist and are used by businesses to handle huge quantities of fast-moving, irregular data. But these tools consume resources and add complexity to the data management function. Rather than improve efficiency, they can slow down the process, increase time to value, and undermine data democratization initiatives. Luckily, artificial intelligence (AI) can help by powering modernized data management systems and techniques. 

How AI Can Help with Data Governance and Integration 

Dynamic Data Processing and Trend Analysis 

AI can help process huge amounts of data from multiple streams quickly and efficiently. Even with decades of experience and the best tools on the market, human analysts can only keep a few variables in mind at once. AI, by contrast, can factor in hundreds of different elements into each analysis, which lets it help identify trends that are too broad or subtle for humans to detect. For example, it may be able to aid in real-time fraud detection by flagging potentially malicious behavior patterns. 

Data Governance 

Data governance is the practice of setting consistent standards for an organization’s data management. Traditional data governance employs internal rules to control how data is handled. Every organization is different, but typical data governance rules may cover:  

• How data is gathered, stored, processed, and removed 

• Who can access data and how much control they have 

• Who is responsible for which processes  

While these rules may be necessary, they can compromise efficiency; they require ongoing attention and maintenance, and enforcement may be challenging. AI, however, can effectively replace the need for data governance rules, becoming the mechanism that handles data according to the policies set by the organization. It can also assist with:  

• Analyzing trends to help identify areas for data governance policy improvement 

• Catching violations of data governance policies 

• Detecting and helping to prevent fraud 

Handling Challenging Data 

Data governance initiatives — whether AI-based or not — are most effective when the organization’s data is uniform, consistent, and straightforward. Data governance can be hampered by: 

• Irregular data. Data rules are typically only compatible with one type of information. When the data contains entries of multiple types, the rules tend to break down. 

• Fast-moving data. Rapid changes to data can overwhelm human analysts, especially when they continue over an extended period of time. That’s because constantly shifting expectations can make it hard for analysts to create useful rules or meaningful insights. 

• Complex data. Broad-based rules are often too simplistic to capture the nuances in complicated datasets. 

Unfortunately, modern businesses often have to deal with irregular, fast-moving, and complex data. For example, many financial companies collect ​alternative data, which is miscellaneous data such as customer behavior records, online search histories, or satellite imagery — as well as the interactions and relationships among those things. Alternative data can be highly irregular by nature, and much of it shifts based on new developments.  

AI can help because it’s able to learn rapidly without getting overwhelmed, which helps it handle irregular, shifting data. Unlike traditional rules-based systems, a well-designed AI model can develop specialized capabilities on the fly to manage and process unusual information. This makes it possible to integrate data from many sources, produce rapid insights, and make better decisions based on the newest information. 

How AI Helps with Master Data Management 

Businesses typically implement master data management with four principles in mind. Data should be ​clean, consistent, rich, and unified.  

AI-powered master data management tools can help align your data with each of these principles. Here’s how. 

1. Clean Data 

Any errors and inconsistencies in data — also known as “dirty” data — can create serious problems downstream. In 2021, Zillow lost $500 million when its attempt to automate real estate investing backfired. Why? In part, because it relied on month-old data to make in-the-moment buying decisions — and its algorithm repeated those poor decisions over and over.  

Catastrophic losses like this one are just the tip of the iceberg. Most problems arising from dirty data are far less dramatic — small errors here and there that quietly drag down profits. Even if they never snowball into anything bigger, they can still result in a great deal of lost revenue over time.   

Let’s look at some of the everyday problems dirty data might cause for various corporate functions:  

• Duplicate records may result in marketing teams sending multiple emails to the same individual — a guaranteed annoyance that can drive up unsubscribe requests and damage your brand.  

• Outdated or misspelled addresses can delay delivery of bills and invoices to customers, which means that finance teams will see reduced cash flow and missed revenue targets. 

• Incorrect or missing data undermines the accuracy of know-your-customer (KYC) and know-your-third-party (KYTP) screenings by compliance departments, increasing the likelihood of false positives and the possibility that actual risks will be overlooked.  

That’s why it’s important for data to be clean, which means it’s complete, accurate, actionable, up-to-date, and free of conflicts or duplicate entries. AI can help with data cleaning by:  

• Adjusting dynamically to irregular, fast-moving, or complex data 

• Checking data for conflicts, duplicates, and missing fields 

• Using linguistic analysis to identify and flag overly complex or confusing information for manual review and clarification  

2. Consistent Data

To be processed uniformly, data should be consistent: It should all have the same fields and parameters. Traditionally, processing irregular data requires either: 

• Building specialized subsystems to handle data of different types (which is expensive and can create data silos) 

• Manually adding missing fields through data integration (which is time-consuming and doesn’t scale well) 

AI can make many data integration procedures unnecessary by automatically finding ways to process irregular data. It can also help with data integration by automatically creating and populating missing fields based on the information already present. By quickly identifying trends across multiple irregular datasets, AI may be able to create new insights that drive better decision-making. 

3. Rich Data 

Traditionally, businesses use data enrichment to improve the value of their data for end users. Rich data has been made more useful by appending information from third-party sources — usually through manual research.   

For example, analysts at an ad agency might enrich a database of potential clients by reviewing business media sites to learn what executives have been promoted or hired within key target accounts. They might gather information on those individuals’ likes, dislikes, interests, hobbies, friends, and career ambitions. The account team could then use that information to craft a hyper-personalized pitch. 

While data enrichment can pay off, it’s very time-consuming and somewhat unreliable. AI can help by quickly surveying and appending third-party information, making the process much quicker, cheaper, and more scalable. That makes data enrichment far quicker and cheaper, enabling more frequent and effective use of rich data than ever before. 

4. Unified Data 

When data is scattered across multiple silos, it can become difficult to keep consistent records, which can lead to confusion and chaos — and that can be costly. There have been cases involving global companies whose finance departments paid consulting fees to shady individuals, unaware that those “consultants” had been red-flagged by the compliance department – resulting in Foreign Corrupt Practices Act (FCPA) investigations and steep penalties. This is but one example of why data should be unified into a single source of truth that’s centralized and maintained via a master data management platform.  

When data is unified, stakeholders can more easily access the information they need. They can also safely input new information, knowing that when anyone updates a record, the change populates so everyone can see it. This keeps records unified and consistent, preventing the sort of miscommunications that could lead to costly mistakes. 

Improve Your Data Management with AI 

Too many businesses today are drowning in an ever-increasing torrent of data, or struggling to break data out of impenetrable silos so they can empower their workers to make better decisions. AI-powered master data management can be the key to overcoming these challenges. It’s helpful to work with a partner that understands all three pillars — AI, data, and MDM.