How to Build an Innovative Data Architecture

In order to drive market-driven innovations like personalised offers, and real-time alerts, businesses have had to deploy new data technologies. However, these technical additions—from data lakes to customer analytics platforms have increased the complexity of data architecture.

Such intricacies limit an organisation's capacity to provide new capabilities like the accuracy of AI models. Companies that attribute 20 per cent or more of their organisations' earnings to AI may face data quality issues. Consequently, it can delay the introduction of new capabilities like the democratisation of data access, data catalogue, etc. apart from AI.

Further, it will increase the complexity and expense of AI development processes. Hence, cloud service providers have introduced cutting-edge offerings like serverless data platforms for immediate deployment. It allows adopters to benefit from increased agility and a quicker time to market.

Analytics users are looking for more seamless tools like application programming interfaces (APIs). APIs integrate insights directly into front-end applications and expose data from various systems to the company’s data lakes.

Hence, the need for adaptability and agility has increased and stands at the locus of innovative data architecture.

What is Data Architecture and Why Do Companies Need it?

An organisation's data architecture is the plan and design for the entire data lifecycle. It starts from data collection to when analytics generate value from it. In simple terms, data architecture is data management and determines how data is stored and moved through storage systems.

95% of businesses cite managing unstructured data as a problem for their business growth. In such cases, a good data architecture enables organisations to maximise the value they create from data with analytics. How does it do it?

It implements the vision of a comprehensive data strategy that revolves around business goals, people, processes, technology, and data.

Data Architecture vs Data Strategy

Data architecture is data acquisition, storage, processing, dissemination, and consumption. On the other hand, data strategy is the overall vision and underlying framework of an organisation's data-centric capabilities and activities.

Hence, it is an umbrella term that includes categories like data governance, data stewardship, master data management (MDM), Big Data management, etc.

How to Build Innovative Data Architecture?

The most effective and innovative data architecture strategy is selecting a cutting-edge data platform based on any cloud. It may be a public or private platform that stores centralised information and governs the organisation's data.

The platform enables the creation of an intelligent ecosystem that allows data-driven decision-making by streamlining data and applications from various sources. Hence, companies can feed in other applications or technologies that render the solution adaptable and scalable.

With Growth Jockey, you can design and implement a data strategy to create an optimised data architecture. We always advise businesses to focus on this six-step framework to build data architecture that is powerful and agile -

Switch to Cloud Platforms: a Disruptive Driver of a New Data Architecture

The cloud is the disruptive driver of a new data architecture because it can scale AI tools and build capabilities for a competitive advantage. Further, cloud platforms are extensible and flexible so organisations of all sizes can run data infrastructure, applications, and platforms.

Some global players like Amazon (AWS), Google (Google Cloud), and Microsoft (Azure) have shifted to cloud platforms. Google BigQuery and Amazon S3 allow businesses to develop and run data-centric applications at an infinite scale. They do not have to install and configure solutions or manage workloads.

Such services can reduce the amount of expertise, speed up deployment from weeks to minutes, and require virtually no monitoring. Further, containerized data solutions help complex data setups to retain data from one application to another seamlessly.

Adapt Real-Time Data Dynamics Instead of Relying on Stagnant Data

Businesses can upgrade their applications by using real-time data processing, which provides insight into real-time behaviours. For instance, transportation companies can provide customers with precise, up-to-the-second arrival predictions as their taxi approaches.

Another common example is companies can predict sales and access goals based on real-time e-commerce metrics. Real-time processing also allows streaming across core functional areas at a significant cost reduction. Further, it aids in the rapid identification and resolution of problems in various sectors.

Additionally, the majority of solutions for streaming processing and analytics use advanced analytics with integration capabilities. At Growth Jockey, we combine previous data to compare patterns and analyse real-time data to offer behavioural data insights.

Upgrade to Highly Modular Architectures from Outdated Storage Solutions

There must be a shift from legacy data systems to a highly modular data architecture for an architectural innovation strategy. Legacy data is information stored in an old or outdated format that is difficult to access or process.

However, in modular data, one can replace open-source parts with new technologies without affecting other parts. By 2025, the global data sphere is to reach 175 zettabytes. Consequently, businesses are facing a phenomenal increase in the volume of data that needs storage and processing.

Renowned names in data storage solutions, Seagate, and Kronicles are launching a revolutionary modular approach to building and deploying data. It caters to the requirements of traditional data centres, public and private cloud providers, and modern service providers.

At Growth Jockey, you can enjoy a significantly simplified user experience while successfully addressing diverse business needs with a modular infrastructure for versatility and usability.

Further, companies can use data pipelines and API-based interfaces to connect platforms and tools across different layers. These will reduce the emergence of new issues in existing applications and speed up the time to market.

It can also foster the development of end-to-end solutions in a modular architecture.

Focus on Dynamic and Agile Data - Decoupling

According to the Ponemon Institute's State of Cybersecurity Report, small and medium-sized businesses worldwide have reported experiences with cyberattacks in 2023. Utilising APIs to expose data ensures security while providing access to the most recent common data sets.

This is the decoupling of data where companies extract information from a variety of sources (such as CRMs). However, the various sources are not linked to one another which enables safety while maintaining agility and dynamicity.

To construct effective AI use cases, decoupled data can enable data reusability across teams and make seamless collaboration possible. This is common in pharmaceutical companies, where APIs share internal data marketplace with all employees.

Hence, the concept of real-time business is now a reality; referred to as "business at the speed of thought" by Bill Gates.

Shift to Flexible and Extensible Data

Generally, organisations use extensible data schemas with a rigid database. Highly normalised schemas build these data models with data tables and elements that reduce redundancy. However, data integrity may shake when organisations try to incorporate new data into existing data.

Hence, companies are moving toward "schema-light" approaches, which organise data for maximum performance using denormalised data models with fewer physical tables.

With Growth Jockey, you can gain greater flexibility and a powerful competitive edge. Our focus lies on agile data exploration and increased storage flexibility for both structured and unstructured data.

Companies can build data architecture with a focus on data-point modelling and data Vault 2.0 techniques for extensible data with minimal disruption.

Approach to Building an Upgraded Data Architecture

Since data technologies are changing quickly, businesses can focus on these essential practices to build an innovative architecture -

1. Beta testing - As part of agile practices, build architecture with a test-and-learn mindset and incorporate it into the application development process.

2. Establish data tribes – Developers and data engineers can collaborate to construct a data architecture with a sense of accountability.

3. Invest in DataOps – DataOps is a collaborative data management that focuses on making data automation, integration, and communication better. This expedites the design, development, and deployment of new data architecture components.

4. Establish a data culture – Businesses should encourage teams to achieve objectives in more adaptable ways by utilising new data. At Growth Jockey, we focus on aligning the goals of the company with a data-centric approach.

Pick up Domain-Based Architecture as Opposed to Regular Warehousing

Many adopted data architectures have been divided among organisations according to their domains, making it an efficient choice. Domain-based architecture has replaced a central data lake (enterprise warehousing) in the leading industries. Why?

It allows for customisation and perfectly syncs to speed up the time it takes to market new services and products. The domain data architectures will contain data sets on a single physical platform and separate them among product owners so that they can easily extract the data they require.

Users can navigate too without much hassle. For example - Domain architecture can transfer customer data to data scientists in sales and marketing. There are three ways to achieve this:

• Using data infrastructure as a platform to create one's data assets

• Combining data virtualisation techniques with distributed data sets

• Providing end-to-end access through data cataloguing tools

Wrapping Up

Data structure's crucial abilities of speed, flexibility, and power make it difficult to face next-level technological challenges. However, maintaining an organisation's ability to provide its services in a structured manner seamlessly is possible by focusing on data innovations.

In today's AI-driven world, building a competitive data architecture to drive innovation in any business is crucial. It includes the utilisation and expansion of AI for day-to-day operations. At Growth Jockey, we assist you to build your data architecture to become adaptable, resilient, and competitive in the future.

At Growth Jockey, we are committed to building customised solutions that effectively address the critical challenges faced by our clients across diverse industries. Regardless of the size of your company, whether it's a small-scale enterprise or a large corporation, you can now optimise your IT architecture. Take the decisive step towards unlocking the next level of growth for your brand by contacting us today!