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Unlocking Value in Cloud Analytics: Addressing Common Challenges

Updated: Jun 13


Businesses across the ASEAN region are increasingly turning to cloud-based analytics solutions to optimize their operations and drive growth. As organizations migrate their analytics to the cloud, they often encounter a myriad of challenges related to data management, integration, security, and automation. In this blog post, we'll explore these challenges in detail and discuss strategies for overcoming them, drawing on the feedback and expertise of professional services consultants at btpicon.

 

The Shift to Cloud Analytics

 

The decision to migrate analytics to the cloud is often driven by the need for scalability, agility, and cost-efficiency. Cloud platforms offer unparalleled flexibility, allowing businesses to access powerful analytics tools and resources on-demand, without the need for large upfront investments in infrastructure. However, the transition to cloud analytics brings its own set of challenges, particularly in the realm of data management and governance.

 

Common Challenges in Cloud Analytics Migration


Most of our customers have, or are planning a move of their analytics to a cloud native platform like Snowflake, Databricks, Azure Synapse or Google BigQuery. The benefits of these platforms are indeed incredible, but when customers migrate their analytics to the cloud, we keep hearing the business teams ask similar questions, like:


  1. Are we or did we migrate the right data to our new analytics platform?

  2. Is the data catalog up to date with all the relevant information, including data access rules and security policies?

  3. Do we have a record of all the transformations the data went through to prove its accuracy?

  4. Can we automate the daily management of data pipelines and the health of the datasets?

  5. How can we maximize our analytics usage for business-driven AI initiatives?


As businesses embark on their cloud analytics journey, they are confronted with a series of critical questions and concerns:

 

Data Migration: "Did we migrate the right data to the new data lake?" Ensuring that the right data is migrated to the cloud is paramount for maintaining data integrity and accuracy. Businesses must carefully evaluate their data assets and prioritize migration based on relevance, value, and compliance requirements.

 

Data Catalog Management: "Is the data catalog up to date with all the relevant information, including data security?" Keeping track of data assets in a cloud environment can be challenging, especially as datasets proliferate across multiple platforms and repositories. A comprehensive data catalog that provides visibility into data lineage, metadata, and security attributes is essential for ensuring data governance and compliance.


Data Access Rules and Security Policies: refers to the practice of controlling and securing access to data within an organization. It involves defining policies, permissions, and restrictions to ensure that authorized users can access the right data while preventing unauthorized access.


As an example, customers see the benefit of adding attribute-based access control (ABAC) to augment Snowflake’s role-based access control (RBAC), by allowing fine-grained access permissions based on metadata about users, objects, environments, and intended usage purpose. Similarly, you can orchestrate security for Databricks, adding to the native Unity Catalog controls. These controls include table-level security, row-level security, and column masking, ensuring data protection without disrupting the user experience.


Data Transformation and Accuracy: "Do we have a record of all the transformations the data went through to prove its accuracy?" Data transformation processes play a crucial role in preparing raw data for analytics and decision-making. However, without proper documentation and auditing mechanisms, it can be difficult to trace the lineage of data and validate its accuracy. Businesses need robust data lineage tracking tools and processes to ensure transparency and accountability in data transformation workflows.

 

Automation of Data Pipelines: "Can we automate the daily management of data pipelines and the health of the datasets?" Managing data pipelines and ensuring the reliability and performance of data workflows are ongoing challenges for organizations operating in a cloud environment. Automating routine tasks such as data ingestion, cleansing, and transformation can streamline operations and reduce the risk of errors and downtime.

 

Maximizing Analytics for AI Initiatives: "How can we maximize our analytics usage for business-driven AI initiatives?" Extracting actionable insights from analytics data is the ultimate goal for businesses investing in cloud analytics. By leveraging advanced analytics techniques such as machine learning and artificial intelligence, organizations can uncover hidden patterns, trends, and correlations in their data, enabling data-driven decision-making and driving innovation.

 

Addressing Challenges with btpicon Solutions

 

btpicon, a specialist data consultancy based in Singapore, offers tailored solutions to help businesses overcome the challenges associated with cloud analytics migration. With expertise in integration, automation, analytics, and integrated business planning, btpicon enables organizations to unlock the full potential of their data assets and drive business value.

 

Integration: btpicon helps businesses seamlessly integrate their disparate data sources and systems, ensuring smooth data flows across cloud platforms and applications. By establishing robust data integration pipelines, btpicon enables organizations to aggregate, harmonize, and analyze data from multiple sources, empowering them to make informed decisions and derive actionable insights. 



Automation: btpicon automates the management of data pipelines and workflows, enabling organizations to streamline their data operations and enhance productivity. By leveraging automation tools and techniques, btpicon helps businesses eliminate manual tasks, reduce errors, and improve the data risk posture, efficiency and reliability of their data processes.

 


Analytics: btpicon leverages advanced analytics capabilities to help organizations derive actionable insights from their data and drive business-driven AI initiatives. By applying techniques such as predictive analytics, machine learning, and natural language processing, btpicon helps businesses uncover hidden patterns, trends, and correlations in their data, enabling them to make smarter decisions and gain a competitive edge in the marketplace. We provide consulting services to help organizations with small project work as JumpStart packages to Large Project rolls outs and Centre of Excellence Support and Team Augmentation.



How Can We Help You

 

We offer you 4 ways to start a project with us by purchasing a software solution, making use of technical services and project delivery, outsource the day-to-day operations as a managed service, and/or make us of our technical teams to provide a 24/7 support function to your internal teams. Our teams focus on:



Ready to tackle your cloud analytics migration?


Schedule a call with us today to learn how we can help you unlock the full potential of your data assets and drive business value with our tailored solutions.




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