For businesses intent on success in today’s business world of technology, data is a core asset which needs to be properly collected and utilized to increase overall business value and performance. Not so long ago, having a data warehouse was sufficient; it acted very much like a warehouse, storing the mounds of data dumped into it, for possible access at future dates. It was like everyone knew that collecting data was important, but had no real idea how to leverage that data to boost business performance and market shares.
We live in a smarter, more productive world of technology today and are better informed as to the many uses and values which collected data can yield to enterprises which know what to do with it. Which brings us to the need for modern data architecture, where data flow and usage supersede the value of collecting masses of data.
If before you had a large open warehouse where data was accumulated, and perhaps stored in large batch of similar data records (i.e., customer data versus sales data), think of a modern data architecture as a structural design that converts that large open space into a smooth flowing unit where data is managed at the point of entry instead of searched out when new marketing ideas arise or accounting information needs to be collated. It can still be a warehouse, but it is now a warehouse vibrant with life, demonstrating flexibility, adaptability, and agility instead of a static storage unit.
Common Characteristics of a Modern Data Architecture
With a modern data architecture, you add greater dimension to the data you are managing, making it a central aspect of business operations rather than simply an additional information stockpile which accumulates from the daily process of running your business. Some of the characteristics of a modern data architecture which makes it such an integral aspect of business operations are described below.
This is as simple as turning the tables on what we value as important. Rather than expecting clients and customers to conform to existing data standards and expectations, a modern data architecture evaluates the needs of the users, gathering and dispersing data to streamline performance and present valid options for proceeding. In terms of modern data architecture, “clients” refer to both internal and external users of the data system, and each client role is structured to receive customized data appropriate to their needs and uses. A well-designed modern data architecture will meet existing and changing customer information needs.
If you envision data to be like water, you want to manage that data through well-structured pipelines which can deliver it to the right locations efficiently. Pipelines are built with base data objects which function as building blocks regularly repurposed, reused, and replenished to maintain a smooth and continuous flow of data relevant to business operations. Base data objects can include:
- Data Snapshots
- Data Increments
- Data Views
- Reference Data
- Master data
- Flat, Subject-Oriented Tables
The bottom line is to ensure a steady data flow from the many systems to its business users.
Being able to tag and profile all incoming data is essential to maintaining an adaptable, flexible, and agile modern data architecture. This imparts immediate life to all new data, making it instantly useful and valuable. Other advantages automation delivers are the ability to detect operation anomalies and alert the appropriate departments or operators and to detect changes in source schema and identify its impact on downstream applications and objects.
A good modern data architecture understands the varying demands and needs of the business and how best to deliver the information. This means delivering support for:
- Multiple Types of Users – internal and external users, many demanding varying levels of access and authorization
- Load Operations and Refresh Rates – able to manage batch, mini-batch, and streaming operations
- Query Operations – create, read, update, and delete data (depending upon authorization levels)
- Deployments – working with data on premises and in public, private and/or hybrid clouds
- Data Processing Engines – relational, OLAP, MapReduce, SQL, graphing, mapping, programmatic, and custom engines
- Pipelines – data warehouse, data mart, OLAP cubes, visual discovery, and real-time operational applications
Think of your modern data architecture as able to serve all people all the time.
Instead of exacerbating tensions between IT and business operations, a modern data architecture can be designed to appropriately share the tasks of data acquisition and data translations so each department can work on their strong suits without interfering or delaying the other half of operations. As it is now a trope that IT is not expected to grasp and integrate business context with their data, it is more effective to leave the IT department with the tasks of which they are familiar, such as managing the traffic of data to core operational systems and developing and manipulating base data objects.
While flexibility refers to multidimensional support systems operating in tandem, elasticity can be considered the fine tuning elements. For instance, with the growing popularity and use of cloud platforms, on-demand scalability frees administrators from constant monitoring and tweaking of capacity and usage. Elasticity can also generate its own applications and use cases, like analytical sandboxes, test environments, and prototype playgrounds.
Despite the complexity of a modern data architecture, a well-designed system will present a simple interface for its various users and departments. A fair analogy is to consider it like a modern automobile with a multiplicity of features accessible by the turning of a key or pressing of buttons. Likewise, developing a modern data architecture containing a vast array of features and services accessible to different user groups with an intuitive and responsive interface should be a top feature of your data architecture.
Your modern data architecture must allow unfettered access to authorized users while deterring hackers and other malicious intruders. An integral aspect of a secure data architecture is the inclusion of compliance applications and software to satisfy the legal demands and security requirements of the Health Insurance Portability and Accountability Act (HIPPA) and the General Data Protection Regulation (GDPR) issued by the European Union. This way, as data is accepted, reporting is automatically generated for necessary agencies.
Extending beyond basic automation of services and tasks, modern data architectures also take advantage of machine learning and artificial intelligence to maximize the use of their system, creating tables, models, data objects, and other views to perpetuate the data flow. It can also serve as analyst, uncovering and correcting data quality errors, identifying data types and relationships, mapping tables, and performing analytics. All of these tasks are then automated and reported to the proper departments and executives for appropriate action.
As you can see, modern data architecture goes well beyond the mere collecting and tabulating of data. When properly designed and deployed, your modern data architecture will more than pay for itself in improved operations and increased revenues. As our field of expertise, at Caserta we are ready to help. Contact us today to bring your data to life and put it to work to earn you even more.