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Can AI solve IT’s eternal data problem?

Artificial intelligence and ML already provide a lot of real benefits to businesses, from detecting fraud to chatbots to predictive analytics. ChatGPT’s ambitious creative writing abilities, on the other hand, have pushed AI and ML expectations to new heights. IT leaders must ask: Is AI/ML finally able to move beyond solutions and solve core corporate issues?

Consider the biggest, oldest, and most difficult IT challenge of all: organizing and integrating data all across the company. Now, that approach demands aid from AI and ML technologies as the volume, type, unpredictability, and spread of data across on-premises and cloud platforms reach an endless exponential curve.

Will AI/ML help to manage data chaos? Sure, but they are still in the infancy stage. AI and ML are used to automate work by established companies such as IBM, while emerging companies such as Monte Carlo include AI and ML in their products. Nobody competes in providing AI/ML solutions that simplify data management and integration.

The complexity of the business data issue

The majority of companies currently maintain a variety of data stores, each with its own set of apps and use cases. Since business units quickly create cloud apps with their data stores, cloud computing has hindered this expansion. Some of these data stores may be used for transactions or other operational activities, whereas others (mostly data warehouses) assist those involved in analytics or business intelligence.

At its most basic, data integration’s primary goal is to map the schema of diverse data sources so that different systems may exchange, sync, and/or augment data. The latter, for example, is essential for establishing a 360-degree image of clients. Nevertheless, seemingly basic activities like evaluating whether consumers or corporations with the same name are the same entity—and which facts from which records are correct—require human interaction. Domain specialists are frequently called upon to assist in the development of rules to manage various exceptions.

The chief product officer of Tamr claims that the vulnerability of rules-based systems is resolved by his MDM solution. He says the machine learning-based approach offers an advantage in adapting to changes in data sources or the data itself. Yet, much like previous ML systems, this one requires ongoing training with huge data sets, and dispute resolution still requires decision-making.

AI/ML are not solutions. Yet, it may provide highly crucial automation not only for Master Device Management (MDM) but also for many other aspects of information integration. To fully benefit, businesses must first get their affairs in order.

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Integrating AI/ML into the same data fabric

The operative word used to define the enterprise’s crazy quilt of important data is “data fabric.” Knowing what the data is and categorizing it is the first step towards scoping out that fabric. This work may be automated in part by using the AI/ML capabilities of platforms such as IBM’s Watson Knowledge Catalog.

Enhancing data quality

High-quality data is required for commercial and other operating systems that manage critical customer, employee, vendor, and product information. Even so, it may make life a lot simpler for data scientists who are engaged in analytics. Data scientists are believed to spend 80% of their time cleaning and processing data.

Data quality cannot be a one-time activity. Given the ever-changing way data behaves and the various systems it flows through, a new category of solutions has emerged: data observability software.

The issue is that detecting an issue with data comes after the fact. One simply cannot stop harmful data from reaching users without bringing processes to a standstill.

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Data management and integration software companies will continue to introduce relevant AI/ML features at a quick pace for automating data discovery, mapping, transformation, pipelining, governance, and more. 

On the other hand, if the data architecture is fundamentally strong as well as the skills to assure the company can deploy AI/ML solutions appropriately, then the company can reduce a great deal of stress for data administrators, analysts, and scientists. These benefits will only grow as these solutions get smarter.