Tag: data architecture

The data architecture outlines how data is managed in an organisation and is crucial for defining the data flow, data management systems required, the data processing operations, and AI applications. Data architects and engineers define data models and structures based on these requirements, supporting initiatives like data science. Before we delve into the right data architecture for your AI journey, let’s talk about the data management options. Technology leaders have the challenge of deciding on a data management system that takes into consideration factors such as current and future data needs, available skills, costs, and scalability. As data strategies become vital to business success, selecting the right data management system is crucial for enabling data-driven decisions and innovation.

Data Warehouse

A Data Warehouse is a centralised repository that stores vast amounts of data from diverse sources within an organisation. Its main function is to support reporting and data analysis, aiding businesses in making informed decisions. This concept encompasses both data storage and the consolidation and management of data from various sources to offer valuable business insights. Data Warehousing evolves alongside technological advancements, with trends like cloud-based solutions, real-time capabilities, and the integration of AI and machine learning for predictive analytics shaping its future.

Core Characteristics

• Integrated. It integrates data from multiple sources, ensuring consistent definitions and formats. This often includes data cleansing and transformation for analysis suitability.
• Subject-Oriented. Unlike operational databases, which prioritise transaction processing, it is structured around key business subjects like customers, products, and sales. This organisation facilitates complex queries and analysis.
• Non-Volatile. Data in a Data Warehouse is stable; once entered, it is not deleted. Historical data is retained for analysis, allowing for trend identification over time.
• Time-Variant. It retains historical data for trend analysis across various time periods. Each entry is time-stamped, enabling change tracking and trend analysis.

Benefits

• Better Decision Making. Data Warehouses consolidate data from multiple sources, offering a comprehensive business view for improved decision-making.
• Enhanced Data Quality. The ETL process ensures clean and consistent data entry, crucial for accurate analysis.
• Historical Analysis. Storing historical data enables trend analysis over time, informing future strategies.
• Improved Efficiency. Data Warehouses enable swift access and analysis of relevant data, enhancing efficiency and productivity.

Challenges

• Complexity. Designing and implementing a Data Warehouse can be complex and time-consuming.
• Cost. The cost of hardware, software, and specialised personnel can be significant.
• Data Security. Storing large amounts of sensitive data in one place poses security risks, requiring robust security measures.

Data Lake

A Data Lake is a centralised repository for storing, processing, and securing large volumes of structured and unstructured data. Unlike traditional Data Warehouses, which are structured and optimised for analytics with predefined schemas, Data Lakes retain raw data in its native format. This flexibility in data usage and analysis makes them crucial in modern data architecture, particularly in the age of big data and cloud.

Core Characteristics

• Schema-on-Read Approach. This means the data structure is not defined until the data is read for analysis. This offers more flexible data storage compared to the schema-on-write approach of Data Warehouses.
• Support for Multiple Data Types. Data Lakes accommodate diverse data types, including structured (like databases), semi-structured (like JSON, XML files), unstructured (like text and multimedia files), and binary data.
• Scalability. Designed to handle vast amounts of data, Data Lakes can easily scale up or down based on storage needs and computational demands, making them ideal for big data applications.
• Versatility. Data Lakes support various data operations, including batch processing, real-time analytics, machine learning, and data visualisation, providing a versatile platform for data science and analytics.

Benefits

• Flexibility. Data Lakes offer diverse storage formats and a schema-on-read approach for flexible analysis.
• Cost-Effectiveness. Cloud-hosted Data Lakes are cost-effective with scalable storage solutions.
• Advanced Analytics Capabilities. The raw, granular data in Data Lakes is ideal for advanced analytics, machine learning, and AI applications, providing deeper insights than traditional data warehouses.

Challenges

• Complexity and Management. Without proper management, a Data Lake can quickly become a “Data Swamp” where data is disorganised and unusable.
• Data Quality and Governance. Ensuring the quality and governance of data within a Data Lake can be challenging, requiring robust processes and tools.
• Security. Protecting sensitive data within a Data Lake is crucial, requiring comprehensive security measures.

Data Lakehouse

A Data Lakehouse is an innovative data management system that merges the strengths of Data Lakes and Data Warehouses. This hybrid approach strives to offer the adaptability and expansiveness of a Data Lake for housing extensive volumes of raw, unstructured data, while also providing the structured, refined data functionalities typical of a Data Warehouse. By bridging the gap between these two traditional data storage paradigms, Lakehouses enable more efficient data analytics, machine learning, and business intelligence operations across diverse data types and use cases.

Core Characteristics

• Unified Data Management. A Lakehouse streamlines data governance and security by managing both structured and unstructured data on one platform, reducing organizational data silos.
• Schema Flexibility. It supports schema-on-read and schema-on-write, allowing data to be stored and analysed flexibly. Data can be ingested in raw form and structured later or structured at ingestion.
• Scalability and Performance. Lakehouses scale storage and compute resources independently, handling large data volumes and complex analytics without performance compromise.
• Advanced Analytics and Machine Learning Integration. By providing direct access to both raw and processed data on a unified platform, Lakehouses facilitate advanced analytics, real-time analytics, and machine learning.

Benefits

• Versatility in Data Analysis. Lakehouses support diverse data analytics, spanning from traditional BI to advanced machine learning, all within one platform.
• Cost-Effective Scalability. The ability to scale storage and compute independently, often in a cloud environment, makes Lakehouses cost-effective for growing data needs.
• Improved Data Governance. Centralising data management enhances governance, security, and quality across all types of data.

Challenges

• Complexity in Implementation. Designing and implementing a Lakehouse architecture can be complex, requiring expertise in both Data Lakes and Data Warehouses.
• Data Consistency and Quality. Though crucial for reliable analytics, ensuring data consistency and quality across diverse data types and sources can be challenging.
• Governance and Security. Comprehensive data governance and security strategies are required to protect sensitive information and comply with regulations.

The choice between Data Warehouse, Data Lake, or Lakehouse systems is pivotal for businesses in harnessing the power of their data. Each option offers distinct advantages and challenges, requiring careful consideration of organisational needs and goals. By embracing the right data management system, organisations can pave the way for informed decision-making, operational efficiency, and innovation in the digital age.

Ecosystm, supported by their partner Zurich Insurance, conducted an invitation-only Executive ThinkTank at the Point Zero Forum in Zurich, earlier this year. A select group of regulators, investors, and senior executives from financial institutions from across the globe came together to share their insights and experiences on the critical role data is playing in a digital economy, and the concrete actions that governments and businesses can take to allow a free flow of data that will help create a global data economy.

Here are the key takeaways from the ThinkTank.

1. Bilateral Agreements for Transparency. Trade agreements play an important role in developing standards that ensure transparency across objective criteria. This builds the foundation for cross-border privacy and data protection measures, in alignment with local legislations.
2. Building Trust is Crucial. Privacy and private data are defined differently across countries. One of the first steps is to establish common standards for opening up the APIs. This starts with building trust in common data platforms and establishing some standards and interoperability arrangements.
3. Consumers Can Influence Cross-Border Data Exchange. Organisations should continue to actively lobby to change regulator perspectives on data exchange. But, the real impact will be created when consumers come into the conversation – as they are the ones who will miss out on access to global and uniform services due to restrictions in cross-country data sharing.

Read below to find out more.

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Organisations have had to transform and innovate to survive over the last two years. However, now when they look at their competitors, they see that everyone has innovated at about the same pace. The 7-year innovation cycle is history in today’s world – organisations need the right strategy and technologies to bring the time to market for innovations down to 1-2 years.

As they continue to innovate to stay ahead of the competition, here are 5 things organisations in India should keep in mind:

• The drivers of innovation will shift rapidly and industry trends need to be monitored continually to adapt to these shifts.
• Their biggest challenge in deploying Data & AI solutions will be identification of the right data for the right purpose – this will require a robust data architecture.
• While customer experience gives them immediate and tangible benefits, employee experience is almost equally – if not more – important.
• Cloud investments have helped build distributed enterprises – but streamlining investments needs a lot of focus now.
• There is a misalignment between organisations’ overall awareness of growing cyber threats and risks and their responses to them. A new cyber approach is urgently needed.

More insights into the India tech market are below.

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Earlier this month, I had the privilege of attending Oracle’s Executive Leadership Forum, to mark the launch of the Oracle Cloud Singapore Region. Oracle now has 34 cloud regions worldwide across 17 countries and intends to expand their footprint further to 44 regions by the end of 2022. They are clearly aiming for rapid expansion across the globe, leveraging their customers’ need to migrate to the cloud. The new Singapore region aims to support the growing demand for enterprise cloud services in Southeast Asia, as organisations continue to focus on business and digital transformation for recovery and future success.  

Here are my key takeaways from the session:

#1 Enabling the Digital Futures

The theme for the session revolved around Digital Futures. Ecosystm research shows that 77% of enterprises in Southeast Asia are looking at technology to pivot, shift, change and adapt for the Digital Futures. Organisations are re-evaluating and accelerating the use of digital technology for back-end and customer workloads, as well as product development and innovation. Real-time data access lies at the backbone of these technologies. This means that Digital & IT Teams must build the right and scalable infrastructure to empower a digital, data-driven organisation. However, being truly data-driven requires seamless data access, irrespective of where they are generated or stored, to unlock the full value of the data and deliver the insights needed. Oracle Cloud is focused on empowering this data-led economy through data sovereignty, lower latency, and resiliency.

The Oracle Cloud Singapore Region brings to Southeast Asia an integrated suite of applications and the Oracle Cloud Infrastructure (OCI) platform that aims to help run native applications, migrate, and modernise them onto cloud. There has been a growing interest in hybrid cloud in the region, especially in large enterprises. Oracle’s offering will give companies the flexibility to run their workloads on their cloud and/or on premises. With the disruption that the pandemic has caused, it is likely that Oracle customers will increasingly use the local region for backup and recovery of their on-premises workloads.

#2 Partnering for Success

Oracle has a strong partner ecosystem of collaboration platforms, consulting and advisory firms and co-location providers, that will help them consolidate their global position. To begin with they rely on third-party co-location providers such as Equinix and Digital Realty for many of their data centres. While Oracle will clearly benefit from these partnerships, the benefit that they can bring to their partners is their ability to build a data fabric – the architecture and services. Organisations are looking to build a digital core and layer data and AI solutions on top of the core; Oracle’s ability to handle complex data structures will be important to their tech partners and their route to market.

#3 Customers Benefiting from Oracle’s Core Strengths

AI has become intrinsic to our personal lives – we are often completely unaware of technology’s influence on our daily lives. For enterprises too, tech solutions often come embedded with AI capabilities. Today, an organisation’s ability to automate processes and decisions is often dependent more on their desire and appetite for tech adoption, than the technology itself. 

In 2022 the key focus for enterprises will be on being able to trust their Data & AI solutions. This will include trust in their IT infrastructure, architecture and AI services; and stretch to being able to participate in trusted data sharing models. Technology vendors will lead this discussion and showcase their solutions in the light of trust.