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Business intelligence and data to insights at scale

Unlock your potential with an innovative data and analytics platform

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To accelerate the digital transformation journey, turn data to insights, and realize tangible business outcomes, many enterprises find that they need effective, flexible, and amplified data and analytics platforms that operate seamlessly across the organization's ecosystem of services and technologies.

A well-architected data and analytics platform has the capacity to unlock real-time insights and business intelligence, enabling companies to make informed decisions and drive better results. Along the way, it is important to keep four key considerations in mind: SaaS/PaaS capabilities, data engineering, advanced analytics, and security and governance.

SaaS/PaaS capabilities support better business intelligence

Enterprises are transitioning to platforms that reduce or eliminate the need for procuring, installing, upgrading, patching, safeguarding, backing up, restoring, and managing their environment and require minimal upfront costs. These platforms are designed to provide companies with the latest features and functionalities with no hardware constraints, as well as the ability to run multiple versions of the same software. Benefits include:

Breaking free from dependence on compute and storage.

Maximizing elasticity and scalability.

Planning ahead for disaster recovery.

Embracing the open-source revolution.

Data engineering to enable real-time insights

By designing and building systems for collecting, transforming, and storing data for analysis, companies are able to:

• Unify streaming and batch processing. Edge data analytics are gaining momentum due to high-powered, modern edge hardware capable of collecting, analyzing, and creating actionable insights in real time.

• Embrace automation with CI/CD and DevOps. Automating testing, production isolation, and monitoring allows for a streamlined, consistent process that requires fewer human resources and reduces the potential for errors and delays.

• Unlock cloud freedom with cloud-agnostic software. Moving to multi-cloud platforms enables enterprises to leverage business intelligence and analytics tools and technologies that are compatible with any cloud infrastructure, allowing data to be migrated between cloud environments more seamlessly.

• Copy data without copying any data. Duplicating data into lower units and sharing it with consumers becomes increasingly time-consuming and costly. Cloning can help by securely sharing point-in-time data with other users and having them use their own compute power to analyze it.

Advanced analytics

By leveraging data and applying advanced techniques, such as predictive, descriptive, and prescriptive analytics, businesses can gain valuable insights to create more efficient processes, improve customer service, and develop new products and services. Among the benefits:

•  Unleash data to insights. By unifying data lakes and data warehouses into one platform, numerous advantages can be gained, such as a centralized managed service; increased data security access; and faster, cheaper, and better insights.

•  Explore the possibilities: AI/ML. Enterprises should evaluate data management software’s inherent AI/machine learning (ML) capabilities, native MLOps, integration with market-leading AI/ML tools, and incorporation of multi-compute instances for model development and execution.

•  Explore above and beyond: Generative AI. AI-driven text generation models can simulate reality and generate previously unseen data to enhance AI-driven decisions, generate business intelligence, forecast outcomes, and improve the performance of applications.

•  Empower with self-service analytics. Traditional self-service analytics don’t meet the demands of the business community. Many businesses need to access both raw and transformed data as soon as it is available, connect with other enterprise data sources, and develop their own dashboards and reports.

Security and governance

The cloud is getting more secure every day. End-to-end data security and compliance for the entire enterprise are increasingly important in selecting a data and analytics platform. Meanwhile, governance, observability, data quality, logging, and monitoring are crucial for sensitive data.

As organizations struggle with consolidating, locating, and analyzing vastly distributed and diverse data sets, modern enterprise data catalog and data quality tools solve the problem through their augmented ML capabilities, enabling faster information access and helping to ensure collaboration and trust among data scientists, data engineers, and businesses.

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Vendor-backed open-source software can enable a more long-term approach to product development, making it easier to integrate with other services and solve bugs and performance issues faster.

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Ashwin Patil

Ashwin Patil

Principal | Data Modernization & Analytics Practice Leader
+1 214 505 9948
Rick Whitford

Rick Whitford

Managing Director, AI & Data Engineering | Deloitte Consulting LLP
+1 703 251 1658