Here are the data modeling trends shaping 2021
Despite global collection and creation of data growing at unprecedented rates, extracting insights from that deluge still requires considerable data structuring and architecture efforts. Proper data structure design begins with data modeling – the forming of data requirements and formats that can turn gathered information into structured, applicable insights.
The role of data models is so crucial today that the market for data preparation tools is booming, driven by the incumbent need for data modeling rules that have been developed with human data engineers in mind. Why does this matter? Because the human component of human-computer interaction (HCI) dictates that most of the data is processed from a visual standpoint.
Hence, how data modeling is visually represented is key for establishing a number of things, ranging from the relationship between varying data structures to how graphical representations of information systems can come to identify different data types.
With a variety of electronic devices including computers, smartphones, and over 30.9 million other Internet of Things (IoT) devices siphoning data according to Statista, it is perhaps not so surprising that the data preparation tools market is set to hit US$8.47 billion by 2025, achieving a CAGR of 25.1% more than the forecast period, according to a recent report by Grand View Research.
And data in the known world is expected to only hike upwards beyond 2021. At the present rate of growth, the volume of data out there is already doubling every two years, and how this influx is structured should come to define how businesses interpret and extract value from it, depending on the models used and how they managed.
With the field always in flux as new methods and formats are developed, here are some of the data modeling trends capturing attention now in 2021, but also looking beyond that.
Toolkits for JSON data modeling
JSON simplifies the exchanging and storing of structured data, and the data platforms behind modern application development have come to standardize JSON as their native data storage format. Not only data platforms, but NoSQL databases like CouchDB and MongoDB also utilize the standard, to the extent that traditional data modeling vendors have to include JSON support – while modern formats focus almost exclusively on JSON storage types.
Rise of industry-specific modeling
Digital transformation continues to permeate nearly every sector, causing the rise of bespoke data model applications that are unique to their industry. As more regulation is poured out to monitor and regulate data usage, regulators are also starting to require that data models are designed transparently and with fair trade practices in mind.
Therefore it is not uncommon now for leading vendors to 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 way, businesses can adopt models that were built with the needs of their specific industry in mind – and when successfully implemented, can become the roadmap for future application and adoption for that sector.
Time-series data modeling
Time-series databases are built with a specific purpose in mind, that of housing records that are earmarked with timestamps. Unlike discreet records, time-series data modeling needs to keep track of changes in time, and of shifting conditions that happen over time to the information.
While traditional individual records are hardly changed or updated as continuous new information is always incoming, like how new IoT data is always pouring in, modeling data on time-series changes is an emerging sub-discipline that will be more prominent in years to come.
Data lake models
With data gathering exploding worldwide, data warehouses were becoming increasingly unable to keep up with rising scaling and data processing requirements to structure the data quickly enough. The need began to surface for a centralized repository where both structured and unstructured data could reside.
In data lakes, the raw unstructured data is sent along source systems using a flat, object-oriented storage architecture. After the data is collected, models function as templates to transform raw data into structured data for SQL manipulation, data analytics, machine learning applications, and more.
Models are already vital to bringing order and comprehension to the vast expanse of raw, chaotic data that is currently fighting for our attention. In order to extract findings that would have more meaning and justify business objectives, those data insights need to be founded on organized data structures within the storage systems themselves.