How can you design effective information architecture for data-intensive applications?

1. Understand user needs and data usage. 2. Create a data model and define data relationships. 3. Organize data logically into categories. 4. Prioritize important data for easy access. 5. Design user-friendly navigation and robust search. 6. Plan for scalability and data security. 7. Utilize metadata for context. 8. Ensure data validation and quality. 9. Gather user feedback and iterate. 10. Optimize performance and document architecture. 11. Test thoroughly and comply with regulations. 12. Define data retention and backup policies. 13. Train users and monitor data interactions. 14. Implement collaboration features for teams.

In my experience, the best way to approach understanding data types, isn’t just with the actual sources of the data but the meanings behind them. Understanding how the business views a data point and its definition for it defines how accurately it measures compared to the definition. Mapping a businesses goals and then their understanding of data and what the business sees as a definition is the first step to rationalising data in my opinion.

Effective information architecture for data-intensive applications is an ongoing process that requires careful planning, monitoring, and adaptation as your application and data requirements evolve. Collaboration among developers, data engineers, and data scientists is essential to design and maintain a robust and efficient data architecture. . Understand Data Requirements . Data Modeling: . Scalability: . Data Storage: . Data Partitioning: . Data Security: . Data Quality: . Data Lifecycle Management: . Monitoring and Analytics: . Data Access Patterns: . Load Balancing: . Documentation and Metadata: . Testing and Optimization: . Data Governance: . Feedback Loop:

Build flexible data catalog is very important to make our data architecture extensible. Determining the relationship between the data of our catalog using one graph database must be another approach in our first definition. Also in the design for geospatial or time series analysis can be considered too.

While sometimes it makes sense to start with an inventory of data assets, often we get better results by focusing first on the business outcome we're trying to achieve. If, for example, it's building a customer-facing data application, we should find out the data we need to gather, its quality, temperature, relationships, etc. Then find out if we have it or need to collect it. Expending manual effort building a comprehensive data catalog can be an exercise in futility with little ROI.

This step truly sets the outcome of the project you are architecting. By talking with end-users, team members and stakeholders you should be able to generate a clear picture of both initial MVP and eventual feature additions. Not taking into account future development could create problems and cost time and money.

End-user empathy is critical to creating valuable data products, both internal- and external-facing. Talk to your target audience about their most frustrating problems, and dream solutions, e.g. "If you could wave a magic wand and fix this, what would it look like?"

I would start this step using Design Thinking methodology, getting together important stakeholders in the same room, sharing their desired outcomes and business pains, and creating a PR / FAQ (Press Review / Frequently Asked Questions) document, that should specify what the project is about, and address most of the stakeholders contributions if possible.

What about users that start using data analytics for decision make? I have some experience with this type of users, for them it is more productive to make an exploratory data analysis, later work with the data science team and show them some prototypes

Data is used in interactions with end users in many different ways. You need to well identify why, what, when, how, and who needs to use some data managed by a system. You also need to identify if data is to be provide in raw format as stored, needs to be transformed for extract/transform/load (ETL) systems, or needs to be manipulated by some algorithm before presentation such as visualizations, computations, and aggregations. Users may require that data needs to be presented for further supply of related data in interactive modes, in grids for review and fast actions, in dashboards for better visual decision making assisting reports, or via printout or exportable report formats. Or even to be exported to other systems.

Implementing a universal semantic layer for the data model can allow you to share the data model between many front-end applications and users with different requirements. Standardizing your business logic for metrics and KPIs and maintaining them under source control gives you a single-source-of-truth.

Your data architecture should be specific to the desired outcome of the project. Generic architectures become very complex eventually, and add little value to stakeholders. I would go with a multiple layer architecture, structured with a top-down approach, starting with stakeholders KPIs on top, going down with deep dives as necessary to clarify any design point.

In this pivotal phase, your decisions should be informed by various factors. Consider the nature of your data sources, their types, and your specific needs and expectations. Additionally, it's crucial to account for the unique technical and business requirements of your application. By carefully choosing the right data architecture and crafting well-defined data models, you ensure that your data is not just stored, but utilized to its fullest potential, setting the stage for a successful and efficient data-driven journey.

Data structures lead to selection of proper data architecture. You first need to understand you data structures and interactions among different data types and structures before attempting to build the right data architecture. It is not necessary to have only one data structure within an architecture; as you may find yourself in need to mix multiple data structures coming from different sources to be incorporated within the same system for better serving needs of end users and consumers of data. How data is being stored id dependent on the nature of data and those requirements. Some may better be in an RDBMS or NoSQL, some may better be residents of a filesystem or an object store. Be careful about data with volatile nature!

I believe we also need to mention storing business contexts ( business metadata) and searchability of it. Eg: what use a data point could serve the business and How users can search in this context to identify the data point of interest. This is played today by some team member to assist the business and is very fragmented causing chaos.

This is the future IMHO. Creating a data connectivity framework will empower future state data flow. The best example is a spider web. Each strand connected to others allowing the entire system to absorb new data quiclkly.

In practice, users tend to ask for "all-the-things" in a front-end experience but satisfying that request results in an over-complicated, and under-utilized interface. Be judicious in implementing features and protect screen real estate to deliver the core value of the product effectively.

This the fun part! The hard work starts here, choosing carefully the right tools for the job. Most of the time of a data project will be spend in this step. The first critical part is making sure you have all the required data and sources to extract, load, transform, ingest, analyze and present. You should define standards for data representation, i.e. date formats, number formats, csv files, and so on. The second critical part is creating a simple, effective UI. You should be quite flexible to have a successful deployment, as stakeholders are different and have different visualization needs.

System performance and throughput is highly affected by interactions and workflows where data needs to be captured, transformed, stored, retrieved/extracted, further transformed, and either presented, visualized, or exported. The frequency at which data is being injected and inquired is directly related to the performance of the solution as well as technologies used to acquire, transmit, store, and retrieve information. One important are that you need to take care of is the retention lifetime of data and how data gets purged or destroyed once it expires its lifetime. Different types and levels of data retention should be considered for different purposes.

Data flows are established 1stly in the relationships developed in the 'Knowledge' model and 2ndly in the relationships developed in the LDMs.

Data should be secure while being generated at edge sources, in transit, and at rest. The three levels of data security needs to be well addressed and solutions provided without jeopardizing solution performance and throughputs. Masking some critical information at capture time such as OTPs and and account numbers, using TLS while transmitting data online, and encrypting data at rest within RDBMS, filesystem, or object stores might be needed at some stage or another. Communicating data securely between sources is subject to compliance requirements, privacy, and may be customer protection. Be aware of standards that enforce those compliance requirements such as PSD2/3, PCI-DSS, and ISO/ANSI standards for security.

Protecting your data and the data entrusted to you by your customers cannot be an afterthought. Each time data is to be written or read throughout its lifecycle, make sure it's done with respect to a least-privileges policy for users, service accounts, and compute roles.

Data security is established by allocating a unique identifier to each 'Knowledge' class. The navigation table (describing the hiernet - a combination of a hierarchy of Knowledge classes and relations between classes) can then be used as the table name. This means that no programmer will ever know the name of the table as it will only ever be known by the unique key in the Navigation table which will require stringent security protocols to access. Hence no outsider hacker will ever be able to use the SQL statement 'Select * from customer' because the customer table will only be known as a number (or a series of numbers). Information Governance will replace the need for data governance.

I have experienced that defining data security and governance is imperative to establishing a comprehensive data framework to ensure that data across the organization is managed and protected effectively. Regular security audits of master data are crucial for the early detection of any potential security threats. A critical aspect of data governance is metadata management, which involves keeping a catalog of data that is available and understanding its lineage and quality. Together, data security and governance provide a structured approach to managing an organization's data assets, ensuring they are used effectively and ethically and are well protected against threats.

I think testing should be an integral part of every step, not a separate one. I see this step as a "lessons learned, what went well and not, what are the gaps that the project should address on a future iteration". Usually, the gaps at the end of the project don't exist at the beginning. They become evident as we have detailed information, a better understanding of the organization's data, stakeholders feedback and how to leverage this knowledge.

Data stands as the cornerstone of any organization, possessing the potential to significantly propel its success or contribute to its downfall. Proper handling, regular updates, and thorough analysis of data are paramount. To harness its full potential, it's imperative to precisely define user needs and establish clear expectations. Often, stakeholders may lack clarity regarding the practical application and implications of data. Employing techniques like user research, interviews, surveys, and personas is essential to extract and analyze. This approach not only aligns data strategy with real-world needs but also ensures that the data serves as an insightful tool driving informed decision-making within the organization.

I believe we need to add a section for "data lifecycle". We need to know how long the data could be put for actual use and come up with retirement policies. This would help us to set the boundaries.

If your project has CXO stakeholders, you should have an open mind and be prepared for change along the way. CXOs are busy people with changing demands, and have the power to raise or shutdown the project. Be flexible, understand their needs and concerns, and you will get a successful outcome.

If you cannot define the word 'information' & provide a mathematical formula that describes the definition than you have virtually no hope of implementing an architecture/governance labeled 'Information Architecture'. In 1990 I managed to achieve all 3