Principles of Modern Data ArchitectureVinayak Patil
An article published by The Economist in 2017 declared, “The world’s most valuable resource is no longer oil, but data.”
Data is the new oil of the digital economy.
Data in the 21st Century is like Oil in the 18th Century – a whopping, untapped, valuable asset. Like Oil, Data too can be powerful if utilised aptly.
Data is helping organizations improve their processes, reduce costs, save time, closely understand buyer personas, optimise sales funnels and effectively measure marketing performance. It’s the key to the smooth functioning of everything from the government to start-ups. Without it, development would move along extremely slowly and also, possibly be largely irrelevant.
The potential of data is limitless. However, leveraging data to its fullest is largely dependent on implementing the right strategy, at the right time and in the right manner. Accomplishing this goal can appear arduous because as data usage increases, it puts your data operations at risk.
A modern data architecture serves as a framework to help mitigate these issues and devises enhanced and potent data capabilities.
Modern Data Architecture
Think of an architect who designs homes or buildings adhering to a blueprint. Similarly, a data architect creates a blueprint of a data environment that aligns with the business goals of an organization. These goals could be short-term or long-term.
Simply put, data architecture is about organizing data in the right way. In general, an organization’s data architecture defines its approach to data in 3 ways – how it is stored, how it is processed and how it is used. It also encompasses a standard set of products and tools an organization uses to manage data.
Organizations that don’t revamp or reinvent their data architectures lose customers, money and market share.
It benefits everyone from the government to local companies, from MNCs to small scale businesses.
Data Architecture Components
Data architecture can be categorised into 3 overall components:
- Data architecture outcomes: Often attributed to as data architecture artifacts, this includes models, definitions and data flows.
- Data architecture activities: This is comprised of forms, deployments and satisfy data architecture intentions.
- Data architecture behaviours: This refers to the collaborations, aptitude and skill sets of those involved who can influence an enterprise’s decision-making.
5 Principles Of Modern Data Architecture
As data sources and data usage has expanded rapidly, the best practices of modern data architecture have evolved. The five principles of Modern Data Architecture for today’s data-driven economy are listed below.
Viewing Data As A Shared Asset
Viewing data as a shared asset is paramount for corporate efficiency. This implies eliminating regional and departmental data silos and allowing your stakeholders to have access to a 360 degree view of data. Access to data analytics and insights at the right time can give your business an edge over your competitors.
Providing Accurate Interfaces For Users To Consume The Data
Allowing the teams to use data as needed is not enough. For people (and systems) to profit from a shared data asset, you need to provide interfaces that make it easier for users to employ that data. This could be in the form of an OLAP interface for business intelligence, an SQL interface for data analysts, or a real-time API for targeting systems. In the end, it’s about giving your team the freedom to work with the tools they know are right for the project to perform better.
Ensuring Security And Access Controls
With the rising demand for the availability of real-time data, deeply reliable self-service access has increasingly become a necessity. The advent of unified data platforms like Google BigQuery, Amazon Redshift and Hadoop has commanded the execution of data policies and access controls directly on the raw data.
Establishing A Definite Lexicon
It’s imperative to ensure that data users analyse and understand it using a common and defined vocabulary. Product catalogues, fiscal calendar dimensions and KPI definitions all need to be common, irrespective of how users consume or analyse the data. With shared common vocabulary, businesses will spend more time in maximising performance and less time quarrelling or adjusting results.
Eliminating Data Copies And Movement
Any time data is transferred or copied, a great deal of time and resources are invested and data fidelity is compromised. Modern data platforms need MPP (massively parallel processing) to support a multi-workload, multi-structure environment. The only means to attain this is through a cloud data platform that can leverage economies of scale (up, down, or out) to meet fluctuating business workload requirements.
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