Mr. Deepak Jha, Deputy General Manager- AIPF (Artificial Intelligence Platform), NEC Technologies India Pvt. Ltd.
Today, organizations across industries grow by monetizing the huge pool of disparate data. Decisions backed by data helps in anticipating customer needs with accuracy, taking proactive business decisions and building core competencies. While enterprises are tapping into advanced analytics to fine-tune sales, operations and other business strategies, the journey begins with transforming the existing data architecture. As data holds tremendous potential for businesses to grow, it’s difficult for an organization to rely on traditional data architecture which is brittle, involves lots of manual processes and can’t withstand huge volumes, velocities and varieties of data.
So, what exactly is a data architecture?
Data architecture defines a standard set of policies, rules and procedures that governs how data is captured, stored, transformed and delivered within an organization. It provides a roadmap for organizations to follow and determine how the information will flow, consumed and controlled within the company.
Why Modernize Data Architecture?
Traditional data architecture is not capable to meet the current business demands of speed, agility and real-time data processing. This makes it inevitable for organizations to modernize their current data architecture and transform itself into a next-gen cognitive enterprise (which is capable of utilizing cutting-edge technologies such as AI/ML, IoT and Blockchain). Modern data architecture stores the data in its original format, it does not require pre-modelling and can handle enormous volumes of data irrespective of its source thus empowering organizations to quickly discover and unify data across hybrid data storage architecture.
Key Characteristics of Modern Data Architecture
Result-Oriented
Modern Data Architecture focuses on the ease-of-use for the end-user rather than the technology beneath it. It simplifies the use of the technology for end users by handling the complex integration and representing the data in the intended form thus bringing out the hidden patterns and information contained in the dataset. It evolves continuously to meet new and changing demands of the customer, depending on the role and department of the end-users.
Scalable and Adaptive
Predicting the scale and speed of data to be ingested and analysed in future is a difficult task. What seems suitable today may not be sufficient tomorrow to cope up in terms of the required storage and processing capabilities. Modern Data Architecture is capable of handling real-time streams and micro batches of data. It scales up to handle the temporary spikes in the processing requirements and creates a series of interconnected pipelines serving right from ingestion of dataset to bringing the required information in the intended representation to the end-users.
Decoupled Architecture
Modern Data Architecture is a key driver of innovation as it is based on decoupled architecture where one service does not rely on another service, which makes it completely different from the conventional architectures. Due to high interoperability and portability, it can easily replace a portion of the data pipeline with any other tool. Due to Modern Data Architecture’s compatibility with leading open-standard tools & platforms, a quick addition of new capabilities and functionalities get much easier.
Processing Real-Time Data Streams
Modern Data Architecture has the capability to ingest and process real-time data. With machine learning and statistical models applied over the real-time data, businesses can make smarter business decisions. Be it displaying the right advertisement to a prospect based on his past behaviour or sending an instant alert to the user in case of a fraud recognition, Modern Data Architecture possesses the required capabilities. It also allows self-service data access & data discovery and ensures real-time governance to integrate and optimize data, thereby eliminating data swamps.
Flexible and Composite
Flexibility is important to support a myriad of business requirements. Be it load operations, query operations, deployments (on-premise, cloud, hybrid) of pipelines, a data architecture should be flexible enough to fit in the requirements of all data sources, targets and the composite data services through each stage of the data cycle. It should also be adept at handling new sources as and when they arise, while continuing the support for highly rigid Enterprise Data Warehouse targets.
Microservices and Containerization Support
Microservices and containers result in optimum resource utilization and faster deployment & configuration. Modern Data Architecture at the backend offers a decentralized data-store to microservices and containers rather than a monolithic one, to provide individual data store to each service along with independent scalability.
Automates and Orchestrates
Modern Data Architecture transparently automates and streamlines the process of data lifecycle management across infrastructures, formats and locations. To ensure smooth data flow and transformation, data is tagged during ingestion and the metadata is extracted. During the lifecycle of the data, the schema change requirements are detected and executed. These processes are conducted while keeping the appearance of the system unchanged for the end-users.
Advanced and Cognitive Analytics
Modern Data Architecture offers a necessary framework, library and tools required for the confluence of cognitive science, data science, and an array of computing technologies, representing the information similar to processing and representation by the human brain. It helps organizations in detecting patterns from the available data, generating actionable insights and in-turn boost their productivity.
Security and Governance
Modern Data Architecture is synonymous to a fortress when it comes to protecting the data from the users with malicious intent and at the same time, it ensures seamless access to the authenticated users towards the datasets which they are authorized to use. Be it encrypting the data at the time of ingestion or tracking the data lineage, Modern Data Architecture ensures effective data lifecycle management keeping the data available for business users while keeping the intruders and hackers at bay.
Let’s begin the transformation……….
Given that “Data” is the key asset to drive business transformation in today’s customer- centric digital environment, it’s imperative to invest in a modern data architecture in order to make smart business decisions. While the implementation of a Modern Data Architecture may differ in size and scale as per different business requirements, these characteristics remain consistent.
