Organizations slowed by legacy information architectures are modernizing their data and BI estates to achieve significant incremental value with relatively small capital investments. This evolution is also being driven by many industry factors. Organizations are being challenged to harness the explosion of data to create the next competitive advantage.

Technology is not new as a disruptive force, but it is accelerating change like never before. Increased exploitation of AI and cognitive computing is giving early adopters a competitive advantage. Increased global data regulation combined with customer expectations for data and AI-powered services and products are changing the way organizations interact with and value data.

What is data estate modernization?

At a fundamental level, it is a transformation of people, process, technology, and data to allow an organization to become data powered. But, more practically, data and BI modernization are the creation of a data foundation of secure, trusted, and democratized data to support AI and analytics at scale.

This is a critical consideration as many organizations face data-estate hurdles. It means they must manage and innovate on a data and reporting ecosystem of technology which has been developed over the last couple of decades.

The speed of business transformation is hindered or blocked by the existing IT landscapes, processes, governance, and data:

• Lack of business autonomy
• Excessive time to market
• Poor data quality, privacy, and security
• Data governance inhibiting innovation
• Increasing TCO and IT debt
• Data silos and lack of federated master data
• Disparate worlds of big data and operational analytics

The existing technology landscape is often unable to meet the agility and innovation required by the business and customer demands of today and tomorrow.

Democratization of secure, trusted, and ethically managed data will:

• Act as an accelerator to innovation and industrialization, enabling more extensive use of agile methods
• Become the single version of the truth to support innovation and industrialization
• Ensure all data is governed appropriately, even though it is not governed equally.

This will empower businesses and accelerate the time to market by creating:

• A data asset which supports business self-service, data science, and shadow IT
• Technology enabled scalability, cross self-service, shadow IT, data science, and IT industrialized solutions.

It also leverages the TCO and reduces IT debt:

• Shrinking IT debt generated by silo solutions which do not scale
• Centralizing do-once, use-multiple-times tasks, which increases the quality while lowering the total cost of solutions.

How does Snowflake support data and BI modernization, ensuring the challenges of the past are addressed and new cloud technology is leveraged fully?

1.  Security and compliance (secure)

The Snowflake Cloud Data Platform is built on a multilayered security foundation that includes encryption, access control, network monitoring, and physical security measures, in conjunction with comprehensive monitoring, alerts, and cybersecurity practices. Every aspect of the platform is geared toward protecting your data, both in transit and at rest.

2.  Governance (trusted)

Not all data is equal, but all data needs to be governed appropriately. Snowflake Cloud Data Platform provides:

• Robust transaction management (ACID) and automatic data partitioning for query performance
• Metadata management, forcing accuracy and consistency to track where data is coming from, who touched that data, and how various data sets relate to one another.

3.  Simplicity instead of silos (democratized)

Snowflake’s new data platform combines data lakes, EDWs, and data marts in a single SQL-based platform. Snowflake’s multi-cluster, shared data architecture provides virtually unlimited concurrency and performance on a single copy of the data. This simplifies the architecture, creates a single version of the truth, and reduces the cost of ownership.

4.  Scalability and performance (democratized)

Snowflake combines petabyte scale with decoupled limitless compute. To improve query run time, Snowflake Virtual Warehouse (compute resource) can be scaled up and down on the fly while queries are running independently of other warehouses. The compute resource can be scaled out automatically as a multi-cluster to support concurrency and queuing.

5.  Cross cloud (secure, trusted, and democratized data)

Snowflake cross-cloud capability delivers the unified data-management platform needed to enable secure data sharing, fully execute multi-cloud strategies, and provide organizations with a single source of truth. By enabling data to move freely, cross-cloud capability delivers on the promise of multi-cloud strategies.

Companies embarking on data estate and BI modernization can expect to achieve significant value. Capgemini recommends a minimum target value return of 10 times cost in the first tranche.

For more info about how Capgemini can help your business in data and BI modernization, please reach out to the author, Fiona Critchley, Global I&D Portfolio Lead – Data & BI Estate Modernization. To  read the full whitepaper, click here.

Fiona Critchley

Expert in AI, Data Engineering, Solutions Architecture

I help organizations transform the way they manage and utilize data, to infuse AI & Analytics at scale. I believe in turning business events into actionable insights to allow organizations to achieve business transformation and innovation. Enabling clients to rapidly transform current data landscape into digital platforms and build future state platform capabilities in an agile, innovative, and cost-efficient way.