Expect an increase in business-driven and elegant Data Modeling – the plans, and activities around diagramming requirements for data architecture. These Data Modeling trends will gain traction as budgets for newer projects decrease and mandates to improve Data Quality increase, as resolution to each data incident has risen by 15 hours between 2022 and 2023.
Stakeholders clamor for returns from AI after investing heavily, making business-focused Data Modeling critical. Having trustworthy and governed data for AI to learn and make recommendations is becoming the topmost priority in many firms.
As executives and companies revisit Data Modeling over 2024, they will demand changes from the older approach, which captures all physical systems across organizations to a business-component perspective. A data model stuck at the physical level, with lots of details, will increasingly fail to find value in 2024.
Instead, businesses will want and own customized models of a specific product or service and create elegant data models to provide answers to business questions. These requirements will become apparent with the six Data Modeling trends described below.
A Proliferation of Industry-Specific Models
As Peter Aiken, an acknowledged Data Management (DM) authority and associate professor at Virginia Commonwealth University, emphasizes, data models address what needs to be built and how. To get to the relevant solution, companies hone in on modeling their domain data, an ontological context containing information about the behavior of entities and the relationships between them. Industry-specific models address this focus.
Knowledge fields, such as finance, medicine, or law, express various nuances needed to comply with oversight bodies and regulators in that area. Moreover, each industry has specific, consistent terminology and concepts necessary to conduct business. Companies need to capture these subtleties through out-of-the-box data models and templates, readily available to apply to data architecture components.
That way, organizations will save time from re-creating standard data model entities and relationships that are part and parcel of their business sectors. Instead, they can spend more time understanding and agreeing on modeling their particular services and defining their business rules.
This trend toward industry-specific models will increase rapidly through 2024 and beyond as companies want a more efficient way to access real-time and batch-processed data without unnecessary extra work. Moreover, as organizations adopt data mesh architecture and domain teams focus on the data architecture components they own, using pre-defined industry-specific models will become more attractive and make taking charge of data more accessible.
An Increased Use of Conceptual Modeling
With a focus on domain-based Data Modeling and a resurging interest in improving Data Quality, organizations will increasingly turn to conceptual models. Conceptual models describe what entities exist and their relationships, making up the focus and scope of a data architecture component.
Through fleshing out a conceptual data model, business and technology teams will engage each other to develop a shared vocabulary and alignment over what data infrastructure to update or build. Ideally, companies will follow up with a logical data model to formalize the implementation of the agreed-upon conceptualization; however, with pressures to deliver, many firms will try to skip or spend a little less time working with logical models in 2024.
Greater Popularity of Knowledge Graphs
While Data Modeling takes on many formats, with entity-relationship (ER), relational, and data-flow diagrams (DFD) remaining popular, expect to see knowledge graphs, visualizations of entities, and their relationships rise to the top of the list. Companies face a shortened time frame to get usable data models, want immediate insights, and deal with increasingly unstructured data.
Knowledge graphs provide the tools to handle all three requirements. Especially great for conceptual data modeling, knowledge graphs give an understanding of the solution to build, related and relevant factors to consider, and include metadata and the context around the data. Moreover, a knowledge graph keeps track of changes as the data and metadata evolve.
According to Juan Sequeda, principal scientist and head of the AI Lab at data.world, knowledge graphs simplify complex concepts at one glance by giving “rich, meaningful context and connections between datasets.” These advantages lead to faster generated and more relevant data models.
Additionally, workers find knowledge graphs simple to build. These tools can be found in many database management systems (DBMSs) and other apps, generating conceptual models sooner, thus encouraging even greater use of knowledge graphs beyond 2024.
Better Self-Service Capabilities
With the proliferation of industry-specific models and increased use of conceptual data models, businesspeople will use and demand better self-service capabilities to experiment with data models through interactive visualizations and take a proactive role in conversations with technology teams. Moreover, easy accessibility to data sets through cloud computing and pressure to make more timely and informed decisions based on real-time data will drive businesses to update and create data models on the fly without consulting technology teams first.
With better self-service capabilities, businesspeople will increase their initiative in iterating on existing data models and discussing and prioritizing requirements. Continuing improvements in AI and machine learning (ML) will simplify the data modeling process for businesses by readily giving recommendations about hidden relationships within the data and new understandings about them.
Consequently, analysts and other data consumers with a solid understanding of their data will participate fully in data modeling, even if they do not have formal training. To that end, businesses will increase their interest in having trustworthy and governed data assets to model data well.
As companies learn from businesspeople about their self-service data modeling activities and processes, these organizations will want faster, better, and more relevant tools to assist their workers with model customization. So, expect self-service Data Modeling tools to improve in 2024 and beyond.
More Frequent Real-Time Data Modeling to Mine Processes
Organizations will use real-time Data Modeling to process and analyze data as it is consumed to understand and streamline their operations better in the future. To do so, companies will create data models to design their business’s digital twins, representing their production line or services’s exact states, information, and organization, so AI and ML can recommend processes for improvement.
Expect this process mining, using apps and talent to analyze enterprise transactional system events, to occur more regularly as part of day-to-day maintenance and to reduce costs. This growing trend will impact Data Modeling for process mining in five ways:
- Higher demand by companies that are industry leaders for data modelers who code, build data models, consult, understand requirements, deploy, and support the improvement of processes
- More time series analysis to uncover patterns from past data flows and make predictions on how to improve business outcomes
- Increased requirements for time-series Data Modeling and visualizations for AI and ML to sense data flow fluctuations, learn from them, and offer recommendations
- Shorter time frames to create and deliver data models and quicker Data Modeling feedback loops
- Smoother Data Modeling, by focusing on JSON storage formats, to structure data in a self-describing way for business understanding
More Joint Data Modeling Sessions Through Data Governance
With increased AI and ML use, trustworthy and governed data will become a new imperative in 2024. Joint Data Modeling sessions will increase to guide Data Governance to achieve these objectives, especially with AI and ML projects.
Expect to see data architects, business analysts, engineers, and data scientists use these Data Governance sessions to align and understand business requirements and definitions through the Data Modeling process. This joint effort in creating visualizations of data entities, relationships, and flow will attempt to address complexities and priorities around insights, access, and legal compliance.
Moreover, metadata management, the engine providing effective action on information and context, will continue to grow as an important objective in Data Governance, increasing from the 66% identified in a 2022 Trends in Data Management study. So, Data Modeling will integrate metadata management processes to improve data findability and gain agreement about which shared data activity to address next for access or security.
By building on business conversations with technical folks, advances in automation and ML promise to provide more significant insights in these joint Data Governance and Data Modeling sessions. Also, companies will integrate Data Modeling and Data Governance objectives in their technology platforms. These capabilities will generate realistic data models faster during a Data Governance session, potentially improving the time to recognize and solve data accessibility or security issues.
Organizations will renew their interest and practices in Data Modeling because of budgetary and Data Quality concerns. Additionally, throughout 2024 and beyond, Data Modeling will become more business-focused and elegant.
Companies will take a more modular approach when modeling data, hashing out the big-picture conception of each component through a greater focus on conceptual modeling. Pressures for a quick turnaround time will increase demand for industry-specific models, knowledge graphs, and self-service models.
In addition to ML and AI, Data Modeling will gain traction in newer settings. It will inform business activities and the operational changes needed to run them better in the future. Additionally, Data Modeling will find an expanding role in handling Data Governance issues around data analysis and security.
Moreover, Data Modeling processes, tools, automation, and ML over 2024 will advance, and the capabilities to customize and generate data models on the fly will also increase. As data models better respond to business demands and foster better ML outcomes, expect to see more businesspeople involved in Data Modeling activities and conversations.
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