What do you do if your data analytics struggle to keep up with the impact of cloud computing?

Assess the current state and document it along with what your needs are from an analytics perspective so you can decide where you need to be and how to get there with new or existing tools.

Start by conducting a comprehensive assessment of your current data analytics infrastructure to identify any gaps or inefficiencies resulting from the integration of cloud computing. Determine the root cause for those shortcomings and then proceed to explore tailored solutions.

When assessing your needs, consider exploring cloud-native data analytics solutions. These tools are designed to leverage the scalability and elasticity of cloud computing environments. They can offer significant advantages like faster processing speeds, seamless data integration with cloud storage, and built-in functionalities for real-time analysis.

In my experience, always assess your data analytics needs and make a long term data analytics strategy. Hybrid environment (on-prem and cloud) is one of the good approaches to keep up with the ever-evolving data analytics space.

When cloud computing outpaces our data analytics capabilities, the first step is to assess our needs. It's like taking stock before a major expedition—you need to know what you have and what you're missing. I evaluate our current analytics framework against the demands of cloud computing, identifying gaps in technology, processes, and skills. This assessment lays the groundwork for a targeted improvement plan.

To optimize your current tools you can look for solutions that offer features such as distributed computing, auto-scaling, and native integration with cloud storage services. Research cloud-native platforms and evaluate their features and capabilities such as data processing capabilities, support for real-time analytics, ease of use, and compatibility with your existing systems.

Upgrading our tools is the next crucial step. Cloud computing offers a vast array of services and capabilities that traditional analytics tools may not fully leverage. I explore cutting-edge analytics platforms and tools that are designed for the cloud, ensuring they align with our specific needs. This might involve adopting scalable data storage options, powerful computing resources, and advanced analytics software that thrive in a cloud environment.

I would rather skill up my teammates with online learning for use of cloud technology because I think cloud is the best solution for future of data analytics and science

Upgrading your tools isn’t enough if your team isn’t equipped to use them effectively. Invest in training and development for your team to master new tools and technologies. Consider bringing in cloud and data analytics experts who can fast-track your team's capabilities through workshops or ongoing education.

Keeping pace with cloud computing requires a skilled team. I prioritize upskilling and reskilling our data analysts to ensure they are proficient in cloud technologies and methodologies. This includes training in cloud-specific analytics tools, data management practices, and security protocols. By investing in our team's expertise, we turn the challenge of adaptation into an opportunity for growth.

Integrating data sources that sit in different platforms with diff business owners is one of the most complex tasks. Getting approval from CPO/CIOs can take ages esp in large enterprises. That's why investing in platforms that have well defined APIs will help plug into the right public cloud. Execs need to mandate that any customisation or new feature built on the platform needs to be exposed via APIs, else project doesn't get funded.

To optimize cloud environments, prioritize seamless data integration across sources. Consider adopting cloud-specific tools or platforms to ensure smooth data flow and accessibility, facilitating timely and accurate analytics.

Finally, integrating data across cloud and on-premises environments is key to harnessing the full potential of cloud computing. I implement strategies for seamless data integration, ensuring that data flows freely and securely between systems. This involves adopting interoperable platforms, employing APIs, and ensuring compliance with data governance standards. By effectively integrating data, we unlock new insights and enhance our analytical capabilities.

Prioritize workloads if the need is to run them across multiple cloud platforms.Create data products in the respective cloud environments and look for publisher and subscriber model to harness benefits of data marketplace. Avoid data duplication by utilizing metadata driven workload management across cloud environments.utilize enterprise governance layer to manage metadata and lineage across data workloads across multi/ hybrid cloud platforms.

Data silos are a major challenge in maximizing the potential of analytics. Ensure that your data is integrated across all systems, making it accessible and actionable. Effective integration involves not only technical solutions but also ensuring that different parts of your organization can collaborate and share data effortlessly.

When faced with challenges in keeping up with the impact of cloud computing on data analytics, it's essential to prioritize finding key use cases and targeting small wins. By identifying specific areas where cloud technology can add value, data analytics teams can focus their efforts on implementing practical solutions that demonstrate tangible benefits. This approach allows for incremental progress and enables organizations to gradually adapt to the evolving landscape of cloud-based analytics. Additionally, leveraging cloud computing for targeted initiatives helps build momentum and confidence, paving the way for broader adoption and integration of cloud capabilities across the analytics workflow.

With your tools upgraded and your data integrated, the next step is to streamline your analytical processes. This could mean automating routine data processing tasks, optimizing data storage and retrieval, or refining data quality management. Streamlining these processes will help your analytics function keep pace with the demands of cloud-based operations.

With your tools upgraded and your data integrated, the next step is to streamline your analytical processes. This could mean automating routine data processing tasks, optimizing data storage and retrieval, or refining data quality management. Streamlining these processes will help your analytics function keep pace with the demands of cloud-based operations.

The tech landscape is always changing, and staying competitive means continuously reviewing and refining your analytics strategies. Establish regular check-ins to assess the performance of your analytics system, and be ready to adapt as new technologies and methodologies emerge.

Single DW platform approaches are often to slow to apply to cloud. As data teams move towards to cloud you need to rethink your approaches. Forget about getting data into one platform instead think of data as a series of individual Data Products build for different Domains.

When faced with challenges in keeping up with the impact of cloud computing on data analytics, it's crucial to embrace the new technology and explore its applications. Cloud computing offers numerous opportunities to enhance data analytics processes, such as scalability, flexibility, and cost-effectiveness. By leveraging cloud-based tools and platforms, data analysts can streamline workflows, access vast computing resources, and implement advanced analytics techniques. Additionally, staying updated on cloud computing trends and investing in relevant training can empower data analytics professionals to harness the full potential of cloud technology for driving innovation and achieving business objectives.

This is very true. A big challenge to the traditional data warehouse/ data mart architecture is they are often assembled, cleaned, enriched, and modeled by a centralized data engineering team that is too far removed from the original domain that created the data. If instead you have the same domain teams curating and reshaping the same domain data elements as a domain data product then expose that to consumers for consumption through data service interfaces the result is data that has higher value and fidelity. From there it’s about composing or aggregating those data products across domains for any number of valuable use cases through a well understood and as automated as possible discovery, governance and access model.