(Image credit: Shutterstock.)
Ensuring that data translates into meaningful information is what matters at the end of the day, as ultimately information is what is used to make decisions by the businesses. To make things further interesting, businesses prefer flexibility and options in the way information is generated and used. While data forms the heart and soul of all businesses, many IT shops do not quite have a vision of what the data architecture should look like to effectively meet the business demand for futuristic flexible information-generating system.
In this article, I will provide you with key ingredients for a blueprint that will allow IT shops to setup architectures for such systems. This blueprint is flexible and can be adopted at a pace which is palatable to the business.
Think of the blueprint in terms of following 12 Components:
Sources: The architecture should account for being able to consume data from internal systems and be able to integrate external data. An example of internal system would be an ERP system. An example of external data would be a Twitter feed. The consumption can come in many forms including but not limited to enterprise service bus, APIs and manual imports. The key is to make sure you have given thought regarding what sources are important to your business.
Approval: A workflow tool is a great way to integrate sources of data by approving it into a staging area. This approval process can be automatic, based on certain threshold or even straight through. Your architecture should certainly have such a facility. This will allow you to create better quality of data into authorized and approved sources of data.
Structure: A robust futuristic data store should allow for structured, semi-structured and unstructured data. Examples of structured data are typical relational database management systems such as Oracle and SQL Server. Semi-structured data can take many forms such as a messaging bus, XML, JSON and CSV. Unstructured data usually are in the form of MS excel spreadsheets or MS word documents. Most IT organizations struggle from having a good technical solution that allow for all structures of data being stored and integrated in their architecture. This is one of the most important part of the architecture and should be carefully thought.
Stage: If you want to keep your data architecture flexible than you must rendezvous various sources into a staging area before moving the data into a central storage(s). APIs, enterprise service bus or ETL tools are some of the ways to move data around into a staging area.
Access: You must design a variety of ways to access the data wherever possible. The access mechanism will depend on who the users are and what they will be using the data for. For example, developers will likely need ability to trace the data to debug complex issues that may arise in a production environment. End users may be interested in some canned reports. Power users may be interested in accessing the data for advanced analytics purposes. Your architecture should be open enough to allow for various ways of accessing the data such as manual, APIs, and automatic extraction.
Security: Securing your data will always be a requirement, now or in the future. This means that you must consider ways to encrypt your data while it is at rest (in storage or databases) or in transit (within or outside of your network). Further, role-based access of data will always be minimal set of requirements that you must consider from day one.
Standards: Modern data architectures require data to be transformed between relational form and semi-structured JSON (or similar) constructs. Your architecture should allow for such standards to be implemented so you are able to deconstruct and reconstruct from one form to another. This usually means implementing some sort of master ID management system within an event-based messaging services architecture. This means you should hire a data architect who has coding skills or at least strong understanding of modern microservices. In addition, you must also have standard table-stakes processes in place related to data governance, data stewardship data taxonomy and data definition.
Algorithms: Power users love data. In addition to easy access to the data, power users manipulate the data by using advanced analytics platform to predict various business-related key performance indicators such as sales for the next quarter or even stock prices. Your architecture should allow hooks and APIs for easy integration with platforms that allow for advanced analytics facilities such as machine learning and AI.
Skills: Your architecture is only as good as your team. If you want a flexible architecture that can easily accommodate your growing business, then you must do a skill analysis of your resources such as architects, DBAs, integrators and business stewards.
Scale: Non-functional requirements (NFRs) are often overlooked. Good data architecture will consider tools and solutions that will be flexible as time passes, without rewriting or reimplementing new technologies. Scalability is one such NFR that is very important and must be implemented in a way that allows for changes in volume of data over time. This can be accomplished by architects, DBAs, dev-ops engineers and software engineers if given enough thought.
Automation: Do not make automation of dev-ops processes an afterthought in your data architecture implementation. This will allow for consistent and repeatable environments that can be helpful in many ways such as easy access, backup, disaster recovery and scale.
Store: Selection of database technologies is one of the most confusing tasks for IT shops. Where and how you store your data will depend on many factors such as size, growth, structure and business purpose. Your architecture should select these database(s) such that there is minimum need of replacing them in the future.
The flexibility of the blueprint model comes from the right combination of each of these ingredients. The main message here is that your architecture should account for these key ingredients. You can create a roadmap or path for each of these key ingredients and reach your end goal. Figure 1 provides a sample roadmap and architecture.
Manage NFRs as Rigorously as Functional Requirements—6 Steps
Advice to Insurance CIOs: Evolving Legacy Systems to Modern Platforms