Traditionally, data modeling has been one of the most time-consuming facets of leveraging data-driven processes. This reality has become significantly aggravated by the variety of big data options, their time-sensitive needs, and the ever growing complexity of the data ecosystem which readily meshes disparate data types and IT systems for an assortment of use cases.
Attempting to design schema for such broad varieties of data in accordance with the time constraints required to act on those data and extract value from them is difficult enough in relational environments. Incorporating such pre-conceived schema with semi-structured, machine-generated data (and integrating them with structured data) complicates the process, especially when requirements dynamically change over time.
Subsequently, one of the most significant trends to impact data modeling is the emerging capability to produce schema on-the-fly based on the data themselves, which considerably accelerates the modeling process while simplifying the means of using data-centric options.
According to Loom Systems VP of Product Dror Mann, âWeâve been able to build algorithms that break the data and structure it. We break it for instance to lift the key values. We understand that this is the constant, thatâs the host, thatâs the celerity, thatâs the message, and all the rest are just properties to explain whatâs going on there.â
Algorithmic Data Modeling
The expanding reliance on algorithms to facilitate data modeling is one of the critical deployments of Artificial Intelligence technologies such as machine learning and deep learning. These cognitive computing capabilities are underpinned by semantic technologies which prove influential in on-the-fly data modeling at scale. The foregoing algorithms are effectual in such time-sensitive use cases partly because of classification technologies which âmeasure every type of metric in a single oneâ Mann explained. The automation potential of the use of classifications with AI algorithms is an integral part of hastening the data modeling process in these circumstances. As Mann observed, âFor our usual customers, even if itâs a medium-sized enterprise, their data will probably create more than tens of thousands of metrics that will be measured by our software.â The classification enabled by semantic technologies allows for the underlying IT system to understand how to link the various data elements in a way which is communicable and sustainable according to the ensuing schema.
The result is that organizations are able to model data of various types in a way in which they are not constrained by schema, but rather mutate schema to include new data types and requirements. This ability to create schema as needed is vital to avoiding vendor lock-in and enabling various IT systems to communicate with one another. In such environments, the system âcreates the schema and allows the user to manipulate the change accordingly,â Mann reflected. âIt understands the schema from the data, and does some of the work of an engineer that would look at the data.â In fact, one of the primary use cases for such modeling is the real-time monitoring of IT systems which has become increasingly germane to both operations and analytics. Crucial to this process is the real-time capabilities involved, which are necessary for big data quantities and velocities. âThe system ingests the data in real time and does the computing in real time,â Mann revealed. âThrough the data we build a data set where we learn the pattern. From the first several minutes of viewing samples it will build a pattern of these samples and build the baseline of these metrics.â
From Predictive to Preventive
Another pivotal facet of automated data modeling fueled by AI is the predictive functionality which can prevent undesirable outcomes. These capabilities are of paramount importance in real-time monitoring of information systems for operations, and are applicable to various aspects of the Internet of Things and the Industrial Internet as well. Monitoring solutions employing AI-based data modeling are able to determine such events before they transpire due to the sheer amounts of data they are able to parse through almost instantaneously. When monitoring log data, for instance, these solutions can analyze such data and their connotations in a way which vastly exceeds that of conventional manual monitoring of IT systems. In these situations âthe logs are being scanned in real time, all the time,â Mann noted. âUsually logs tell you a much richer story. If you are able to scan your logs at the information level, not just at the error levelâ¦you would be able to predict issues before they happen because the logs tell you when something is about to be broken.â
Data modeling is arguably the foundation of nearly every subsequent data-focused activity from integration to real-time application monitoring. AI technologies are currently able to accelerate the modeling phase in a way that enables these activities to be determined even more by the actual data themselves, as opposed to relying upon predetermined schema. This flexibility has manifold utility for the enterprise, decreases time to value, and increases employee and IT system efficiency. Its predictive potential only compounds the aforementioned boons, and could very well prove a harbinger of the future for data modeling. According to Mann:
âWhen you look at statistics, sometimes you can detect deviations and abnormalities, but in many cases youâre also able to detect things before they happen because you can see the trend. So when youâre detecting a trend you see a sequence of events and itâs trending up or down. Youâre able to detect what we refer to as predictions which tells you that something is about to take place. Why not fix it now before it breaks?â