2017 Trends in Data Modeling

The projected expansion of the data ecosystem in 2017 is causing extremely deliberate, systematic challenges for organizations attempting to exploit the most effective techniques available for maximizing data utility.

The plenitude of cognitive computing options, cloud paradigms, data science, and mobile technologies for big data has demonstrated its business value in a multitude of use cases. Pragmatically, however, its inclusion alongside conventional data management processes poses substantial questions on the back end pertaining to data governance and, more fundamentally, to data modeling.

Left unchecked, these concerns could potentially compromise any front-end merit while cluttering data-driven methods with unnecessary silos and neglected data sets. The key to addressing them lies in the implementation of swiftly adjustable data models which can broaden to include the attributes of the constantly changing business environments in which organizations compete.

According to TopQuadrant Executive VP and Director of TopBraid Technologies Ralph Hodgson, the consistency and adaptability of data modeling may play an even more dire role for the enterprise today:

“You have physical models and logical models, and they make their way into different databases from development to user acceptance into production. On that journey, things change. People might change the names of some of the columns of some of those data bases. The huge need is to be able to trace that through that whole assembly line of data.”

Enterprise Data Models
One of the surest ways to create a flexible enterprise model for a top down approach to the multiple levels of modeling Hodgson denoted is to use the linked data approach reliant upon semantic standards. Although there are other means of implementing enterprise data models, this approach has the advantages of being based on uniform standards applicable to all data which quickly adjust to include new requirements and use cases. Moreover, it has the added benefit of linking all data on an enterprise knowledge graph which, according to Franz CEO Jans Aasman, is one of the dominant trends to impact the coming year. “We don’t have to even talk about it anymore,” Aasman stated. “Everyone is trying to produce a knowledge graph of their data assets.”

The merit of a uniform data model for multiple domains throughout the enterprise is evinced in Master Data Management platforms as well; one can argue the linked data approach of ontological models merely extends that concept throughout the enterprise. In both cases, organizations are able to avoid situations in which “they spend so much time trying to figure out what the data model looks like and how do we integrate these different systems together so they can talk.” Stibo Systems Director of Vertical Solutions Shahrukh Arif claimed. “If you have it all in one platform, now you can actually realize that full value because you don’t have so spend so much time and money on the integrations and data models.”

Data Utility Models
The consistency of comprehensive approaches to data modeling are particularly crucial for cloud-based architecture or for incorporating data external to the enterprise. Frequently, organizations may encounter situations in which they must reconcile differences in modeling and metadata when attaining data from third-party sources. They can address these issues upfront by creating what DISCERN Chairman and CEO Harry Blount termed a “data utility model”, in which “all of the relevant data was available and mapped to all of the relevant macro-metadata, a metamodel I should say, and you could choose which data you want” from the third party in accordance with the utility model. Actually erecting such a model requires going through the conventional modeling process of determining business requirements and facilitating them through IT—which organizations can actually have done for them by competitive service providers. “Step one is asking all the right questions, step two is you need to have a federated, real-time data integration platform so you can take in any data in any format at any time in any place and always keep it up to date,” Blount acknowledged. “The third requirement is you need to have a scalable semantic graph structure.”

Relational Data Modeling (On-Demand Schema)
Data modeling in the relational world is increasingly impacted by the modeling techniques associated with contemporary big data initiatives. Redressing the inherent modeling disparities between the two is largely a means of accounting for semi-structured and unstructured data in relational environments primarily designed for structured data. Organizations are able to hurdle this modeling issue through the means of file formats which derive schema on demand. Options such as JSON and Avro are ideal for those who “want what is modeled in the big data world to align with what they have in their relational databases so they can do analytics held in their main databases,” Hodgson remarked.

One of the boons of utilizing Avro is the complete traceability it provides for data in relational settings—although such data may have originated from more contemporary unstructured sources associated with big data. The Avro format, and other files in this vein, allow modelers to traverse both relational schema requirements with what may be a lack of such schema intrinsic to most big data. According to Hodgson, Avro “still has the ontological connection, but it still talks in terms of property values and columns. It’s basically a table in the same sense you find in a spreadsheet. It’s that kind of table but the columns all align with the columns in a relational database, and those columns can be associated with a logical model which need not be an entity-relationship model. It can be an ontology.”

Predictive Models
Predictive models have been widely impacted by cognitive computing methods and other aspects of data science–although these two realms of data management are not necessarily synonymous with classic statistically-trained predictive models. Still, the influx of algorithms associated with various means of cognitive computing are paramount to the creation of predictive models which illustrate their full utility on unstructured big data sets at high velocities. Organizations can access entire libraries of machine learning and deep learning models from third-party vendors through the cloud, and either readily deploy them with their own data or “As a platform, we allow customers to build their own models or extend our models in service of their own specific needs” indico Chief Customer Officer Vishal Daga said.

The result is not only a dramatic reduction in the overall cost, labor, and salaries of hard to find data scientists to leverage cognitive computing techniques for predictive models, but also a degree of personalization—facilitated by the intelligent algorithms involved—enabling organizations to tailor those models to their own particular use cases. Thus, AI-centered SaaS opportunities actually reflect a predictive models on-demand service based on some of the most relevant data-centric processes to date.

Enterprise Representation
The nucleus of the enduring appositeness of data modeling is the increasingly complicated data landscape—including cognitive computing, a bevy of external data sources heralded by the cloud and mobile technologies in big data quantities—and the need to effectually structure data in a meaningful way. Modeling data is the initial step to gleaning its meaning and provides the basis for all of the different incarnations of data modeling, regardless of the particular technologies involved. However, there appears to be a burgeoning sense of credence associated with doing so on an enterprise-wide scale as “Knowing how data’s flowing and who it’s supporting, and what kind of new sources might make a difference to those usages, it’s all going to be possible when you have a representation of the enterprise,” Hodgson commented.

Adding further conviction to the value of enterprise data modeling is the analytic output facilitated by it. All-inclusive modeling techniques at the core of enterprise-spanning knowledge graphs appear well-suited for the restructuring of the data sphere caused by the big data disruption—particularly when paired with in-memory, parallel processing graph-aware analytics engines. “As modern data diversity and volumes grow, relational database management systems (RDBMS) are proving too inflexible, expensive and time-consuming for enterprises,” Cambridge Semantics VP of Engineering Barry Zane said. “Graph-based online analytical processing (GOLAP) will find a central place in everyday business by taking on data analytics challenges of all shapes and sizes, rapidly accelerating time-to-value in data discovery and analytics.”

 

Originally Posted at: 2017 Trends in Data Modeling

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