Data grid use cases and applications in IoT, artificial intelligence and machine learning

A grid distributes data across physical and virtual networks in a decentralized manner. Unlike traditional data integration tools that require highly centralized infrastructure, data grids can work across on-premises, multi-cloud and single-cloud edge environments.

In this article, we discuss the practical applications of grids in different settings.

Data Grid: Solving Several Common Problems

According to MIT survey results, only 13% of organizations surveyed were able to successfully deliver on their data strategy. Data grids are solving many of the root causes responsible.

Using a data grid can solve several problems that arise in smaller-scale data pipelines. If left unaddressed, these issues can quickly become problematic and fragile over time, as a disjointed peer-to-peer system creates its own network over time.

At the same time, the data grid also solves larger problems in the organization, such as core business facts that may differ across different parts of the company.

By implementing a data grid, the system is less likely to have copies of the facts.

Using a data grid not only brings order to the system, but also provides you with better manageability, mature and evolved data architecture.

As we see the rise of cloud-based applications, application architecture is shifting and transitioning from traditional centralized IT to distributed service meshes or microservices. A real-time data platform called K2view is a step ahead and has successfully implemented the use of micro-DB in its fabric and grid architecture. Each micro-database only stores data for a specific business partner (customer), while its grid platform stores millions of such micro-databases.

Data Grid: Use Cases

Data grids can support multiple analytical and operational use cases across multiple domains. Some examples include:-

1. Customer Lifecycle

It provides 360-degree support for customer care and significantly reduces average customer handling time. It also improves customer satisfaction and improves first-contact resolution rates.

Marketing departments can also deploy a single view of the customer for next best offer decisions or predictive churn modeling.

2. Utilities in the Internet of Things (IoT)

Through IoT device monitoring, product teams can gain insights into usage patterns of edge devices. They can use this pattern information to iterate and improve their profitability and product adoption.

By adopting mesh networking for IoT devices, companies can reap several benefits that make it a popular technology when choosing a network.

Companies can store all their IoT, enterprise, streaming and 3rd party data together into an S3 data lake at a very low cost.

3. Healing algorithm

As mentioned before shortest path bridging, the self-healing algorithm will automatically choose the best path to send data even if some nodes lose connection. .

This algorithm allows the system to use only available and working connections. Therefore, even if some devices stop functioning, the network is still able to send and receive the information needed to maintain or complete a given task.

4. Distributed and more effective security

Now, when it comes to security, enterprises are well prepared and constantly updating their protocols. However, SMEs lack the necessary guidance. According to Accenture’s cybercrime research, 43% of attacks target smaller organizations, and only 14% of attacks are self-preventable.

With modern data management solutions like Mesh, SMBs have the opportunity to keep up.

Security is crucial when data is highly fragmented and distributed.

Such systems should delegate authorization and authentication activities to different users, providing them with different levels of access as needed.

The following key security capabilities for data grids were identified in the Market Premier 2022 report:

• Various forms of data privacy management
• Data encryption, whether At rest or in motion
• Data masking, effective management of PII obfuscation
• CCPA and GDPR compliance and other regulations
• Identity management covering all IAM/LDAP type services

5. Self-configuration

Thanks to the automatic discovery of mesh networks, IoT devices can now configure themselves. It automatically calibrates new nodes and connects them to the required network without any prior setup.

With this feature, the network can be easily expanded and managed.

6. Marketing and Sales

Marketing and sales teams can easily curate a 360-degree view of consumer profiles and behaviors from disparate platforms and systems by using distributed data.

This allows them to create more targeted campaigns, CLV (Customer Lifetime Value), better lead scoring accuracy, and perform several other important performance metrics.

Marketing teams use hyper-segmentation to deliver campaigns to the right customers through the right channel at the right time.

7. Artificial Intelligence and Machine Learning

Intelligence and development teams can easily create data catalogs and virtual warehouses from multiple sources to deliver AI and machine learning models.

This gives them more insights without having to collect all the data in a given central location.

Teams can also use federated data preparation to enable domains to deliver trusted data and quality for data analytics workloads.

8. Loss Prevention

By implementing a data grid in the financial sector, companies can gain insights faster while reducing operational risks and costs.

This feature enables international financial institutions and organizations to analyze their data locally. This can be done in any region or country, and it helps identify any fraud threats without creating any copies of the data set that can be transferred to a central database.

Data Privacy Management allows companies to protect their customer data as they must comply with evolving regional data and privacy laws such as the VCDPA.

Several Practical Implementations of Data Grids

Financial Services Institutions

In one of their blogs, Thoughtworks discusses the impact of data grids on financial institutions’ data processes .

Because such applications process large amounts of transactional data in real time, it is important to have accurate and timely data streaming to the analytics system.

In this case, executives have the flexibility to quickly manipulate data and have access to domain-oriented data products.

This enables them to ask more relevant questions and ultimately gain more reliable answers and valuable insights to take action in less time.

Not only that, but domain teams are also able to use analytics data and build it directly into the user’s digital experience.

AWS S3

A big change happened about 15 years ago when AWS commoditized its storage layer and replaced it with AWS S3 object storage.

Due to the affordability and ubiquity of S3 and other cloud storage, companies are now moving their data to cloud object storage. This allows them to build data lakes that can ultimately analyze data in different ways.

Fashion Retailer Brand

Zalando, Europe’s largest online fashion retailer, learned there was a simple way to guarantee access and availability at scale. This can be done by shifting more responsibility to the team that originally collected this data and has the required domain knowledge. And also by keeping all metadata information and data governance in a central location.

Trust me, there is not enough space to cover all use cases. This is a push market and businesses want to get the most out of it.

What’s next? Embracing data product thinking

There are several innovative practices for data products that bring together different concepts such as design thinking, theories of jobs to be done, and breaking Organizational silos that hinder cross-functional innovation. By 2022, businesses should seize the opportunity and evolve their data management strategies with Web 3.0 in mind.

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