How AI unites siloed data and reveals the probability of accuracy across insights
Two of the greatest challenges faced by organizations today are the rising volume of data and the lack of confidence to act on the insights this data reveals. Fortunately, there are AI-fueled data management solutions that directly address these two challenges to make data simple and accessible.
Databases should be both powered by AI and built for AI, meaning they use embedded AI capabilities to improve their day-to-day functionality (powered by AI), while also being able to support AI initiatives throughout the entire business (built for AI). For example, marketing analysts could gain access to more extensive, robust data for insights or shop floor managers could use natural language functionality to use a google like request to ask why a machine might be failing regularly.
The eBook, Db2 – The AI Database discusses eight capabilities that make a database powered by AI and built for AI. Two “powered-by-AI” capabilities are discussed here which provide a single view of the overall data and provide trust in insights: data virtualization and confidence-based querying.
43 percent say data availability is a barrier to implementing AI
Data has not only risen in volume, but in variety as well. It is stored on-premises, on private clouds, and across multiple public clouds in both SQL and NoSQL formats. For that same reason, organizations risk their data becoming siloed, or find themselves spending too much time trying to join data together.
Data virtualization, achieved through a combination of data federation and an abstraction layer, helps eliminate these concerns by allowing all users to interact with multiple data sources from a single access point. This remains true even when the data diverges in terms of format, type, size, and location. The single access point provides greater simplicity for data professionals, allowing them to see and use all data across the organization without wasting time moving it around with ETL (extract, transform, load) processes.
One access point also aids governance and security, allowing a single point of entry to be monitored rather than one for each data repository. There are also cost savings on latency and bandwidth issues due to the reduced need for data transfers. So, no matter how divergent or voluminous data becomes, data virtualization helps access all of it in a simple, meaningful way.
Even when data is accessible, some still find the insights produced difficult to trust. Answers to resulting queries may lack the nuance required to find close matches. It’s a very binary process; either the information matches the query and the result is returned or it does not, and it isn’t returned.
Confidence-based querying delivers SQL query results based on probabilities or “best matches” rather than a yes or no answer. This is accomplished by adding machine learning extensions to SQL through the implementation of deep feed-forward neural nets. Simply put, it identifies when “likeness” and likelihood of a match are high.
One of the best examples of this is identification of a potential suspect from a police database using eyewitness testimony. Because the eyewitness won’t be exact on height, weight, and other physical attributes it is often necessary to manually create a SQL statement that looked for a range of values around what they reported. Using confidence-based query, a probabilistic SQL statement can be used instead, which provides the best match compared to the overall witness profile. This is particularly valuable when a close match would have been excluded because it fell out of the manually created range on just one dimension.
In this way, confidence-based querying extends what SQL engineers can accomplish, allowing them to run similarity and dissimilarity queries, inductive reasoning queries, queries related to pattern anomalies, and more.
Where data scientists would have typically been previously necessary, SQL engineers can act on their own – saving time and increasing the value of their work. Data scientists which are already overburdened with tasks will also appreciate the relief.
How to set up your organization for robust confident insights
Implementing data virtualization and confidence-based querying may be easier than you think. Both are core components of IBM’s data management strategy anchored by IBM Db2 and IBM Cloud Pak for Data, which is built on Red Hat OpenShift Container Platform.
To learn more about technologies IBM uses to deliver data management that’s both powered by AI and built for AI read our latest eBook, Db2 – The AI Database. It has more information on data virtualization, confidence-based querying, and six additional features positioned to help you succeed on the Journey to AI.
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