With global data creation increasing at unprecedented rates, collection is more often a trivial task: computers, smartphones, and any number of Internet of Things (IoT) devices — 30.9 billion at last count — make these efforts akin to drinking from the proverbial fire hose.
Despite the deluge, extracting insights is a delicate affair that requires considerable data structure design and architecture efforts. This crucial step begins with data modeling: establishing the requirements and formats to transform collected data into useful, structured information.
For now, data modeling activities and related tools are focused on the efforts of human-based data engineers. And because human-computer interaction (HCI) is so heavily reliant on sight, data modeling hinges on visual representations of information systems for establishing different data types, relationships between data structures, and other data model attributes. Additionally, these models address particular business needs/use cases, allowing for greater contextual relevance and deeper domain-specific insights.
Data Modeling Growth
The critical role of data models is reflected in the market size for data preparation tooling. These platforms and solutions offer data modeling capabilities and features for streamlining data profile creation, managing interchangeability, and enabling data model design collaboration efforts, among others.
The data preparation tools market is slated to reach $8.47 billion by 2025, a CAGR of 25.1% over the forecast period, according to a recent report by Grand View Research.
With exponential data growth expected beyond 2021, the ability to structure this influx becomes increasingly critical as the size of the digital universe doubles every two years. How businesses define, interpret, and extract value from data will depend on the efficacy of the models used and how they are created and managed.
See more: Database Trends 2021
5 Trends in Data Modeling
From the emergence of solutions for model management to time-series data models design, the following are some of the top trends in data modeling to be aware of in the coming months and years.
1. Emergence of Tooling for JSON Data Modeling
2. Continued Focus on Model Management
Today’s applications may mostly involve data models and schemas designed by humans, but future software offerings will rely on machine learning (ML)-assisted processes for developing data models automatically. This means end-to-end data automation, from collection and preparation to exploration and modeling, with the latter involving identification and deployment of the correct models. To this end, model management systems are currently being developed and refined to manage production data models that need periodic updating or changing out entirely.
3. Emergence of Industry-Specific Models
As digital transformation continues to sweep across varying industries, different data model applications and nuances have emerged that are unique to their domains. For example, industry-specific oversight bodies and regulators are starting to require that data models be designed fairly and transparently. For this reason, leading vendors now offer industry-specific data models and frameworks with the requisite terminology, data structure designs, and reporting to help ease governance and compliance efforts. This enables firms to adopt models pre-designed to conform to requirements of the specific industry in question — a blueprint for the data and analytic needs of an industry-specific organization.
4. Time-series Data Modeling
Time-series databases (TSDB) are designed specifically for housing data records associated with timestamps. That is, they are ideal for use cases where an event’s occurrence(s) are the primary dimension of concern. Individual records are typically immutable or never updated. Instead, they’re treated as continuous data flows, like the continuous collection of IoT sensor data or stock market price fluctuations.
In contrast to traditional data modeling, time-series data modeling must account for changes in time intervals and how conditions/parameters evolve over time, versus the tracking of discreet records. This involves the emerging sub-discipline of data modeling for time-series data.
5. Developing/Handling Data Lake Models
Data lakes were created as a response to the limitations of schema-dependent data warehouses vis-à-vis the big data explosion. Because data warehouses were in many cases unable to support increasingly demanding performance and scaling requirements, the need arose for centralized repositories of both structured and unstructured data capable of unrestricted, untransformed data storage (read: the data is saved as is).
In a data lake, raw data flows from the source systems to the destination in its native format, utilizing a flat, object-based architecture for storage. After data collection, models are applied as adjacent resources to the data lake, functioning as templates for transforming raw data into structured data for SQL manipulation, data analytics, ML applications, and more.
In short, models are critical for bringing order and meaning to the vast volumes of chaotic data we currently find ourselves awash in.
The extraction of deeper, more meaningful insights from this glut is predicated on the proper data structures in place for data storage.
Whether it’s in the back end, the cloud, or on the desktop, innovations and trends in tooling are making it easier than before to design custom, industry-specific data models that will scale with today and tomorrow’s data volumes.
See more: Top Cloud Data Warehouse Companies