As data creation around the world grows at an unprecedented pace, collecting it is often a trivial task – computers, smartphones, and any number of Internet of Things (IoT) devices (last count 30.9 billion) make these efforts like drinking out of the proverbial fire hose. Despite the flood, information extraction is a delicate matter that requires considerable effort in the design and architecture of the data structure. This critical step begins with data modeling – defining the requirements and formats to turn the collected data into useful, structured information.
Currently, data modeling activities and related tools focus 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 attributes of the data model. In addition, these models address specific business requirements / use cases and enable greater contextual relevance and deeper, domain-specific insights.
Data Modeling Growth
The critical role of data models is reflected in the size of the data preparation tool market. These platforms and solutions provide data modeling capabilities and capabilities to streamline data profiling, manage interchangeability and enable collaborative efforts in data model design, and much more. According to a recent report by Grand View Research, the tool market is projected to reach $ 8.47 billion by 2025, a CAGR of 25.1% over the forecast period.
With data expected to grow exponentially beyond 2021, the ability to structure this influx becomes increasingly important 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.
5 Trends In Data Modeling
From creating model management solutions to designing time series data models, here are some of the key data modeling trends to keep in mind for the months and years to come.
1. Emergence Of Tooling For JSON Data ModelingÂ
JavaScript Object Notation (JSON) simplifies the exchange and storage of structured data and is now the de facto standard for Internet communication, whether between IoT devices, computers, web servers or a combination thereof. The data platforms that drive modern application development standardize on JSON as the native data storage format, as do NoSQL databases such as CouchDB and MongoDB. In response, traditional data modeling tool vendors like erwin and ER / Studio are offering JSON support, while newer offerings like Hackolade focus specifically on modeling for JSON storage formats.
2. Continued Focus On Model Management
Applications today may include mostly human-designed schemas and data models, but future software offerings will rely on machine learning (ML) to automatically develop data models. The latter involves identifying and providing the correct models. To this end, model management systems are currently being developed and refined in order to manage production data models that require regular updates or complete changes.
3. Emergence Of Industry-Specific Models
As digital transformation continues to spread across different industries, different uses and nuances of data models have emerged that are unique to their domains. For example, industry-specific regulatory and supervisory authorities demand a fair and transparent design of data models. Leading vendors now offer industry-specific data frameworks and models with the necessary terminology, data structure designs, and reports to facilitate governance and compliance efforts. This allows companies to adopt pre-built models to meet the needs of their industry – a blueprint for the data and analytics needs of a particular industry organization.
4. Time-Series Data Modeling
Time Series Databases (TSDB) are specifically designed to host records that are associated with timestamps, which makes them ideal for use cases where the occurrence of an event is the most important dimension. Individual records are usually immutable or never updated. they are treated as continuous data streams, such as continuous data collection from IoT sensors or fluctuations in stock exchange prices. 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 recording of records. This is the emerging sub-discipline of data modeling for time series data.
5. Developing/Handling Data Lake Models
Data lakes were created in response to the limitations of schema-sensitive data warehouses in the face of the big data explosion. Since data warehouses were in many cases unable to meet the increasing performance and scaling requirements, there was a need for central repositories for structured and unstructured data that can store unrestricted and untransformed data (read: data is stored unchanged). In a data lake, raw data flows in its native format from the source system to the destination, using a flat, object-based architecture for storage. The models are applied to the data lake as contiguous resources and act as templates to transform raw data into structured data for SQL manipulation, data analysis, machine learning applications, and more.
Conclusions
In short, models are critical in bringing order and meaning to the vast amounts of chaotic data we are currently inundated with. Obtaining deeper and more meaningful knowledge from this surplus is based on the corresponding data structures for data storage. Whether in the backend, in the cloud or on the desktop – tooling innovations and trends make it easier than ever to design custom, industry-specific data models that scale with current and future data volumes.
