How ETL Tools fit into a forward-thinking data-platform-approach to BI

Introduction

Imagine a modern, centralised platform that integrates commercially supported ETL (extract, transform, load) connectors / plugins and reputable open-source ETL tools. This platform would streamline data management by unifying data from various sources, ensuring consistency and reliability. Not to mention, the fixing of any fundamental data accuracy issues in the process. 

By enhancing data quality through advanced ETL processes, the platform ensures that data is clean and ready for analysis. Its dynamic nature allows it to scale with the growing data needs of the organisation, ultimately empowering your business to make confident, data-driven decisions. 

Now let’s dissect why this should be your reality, and offer more insight into what a  forward-thinking data-platform-approach to Business Intelligence (BI) includes…

Relevant company pain-points and data opportunities 

• “My data supply is inadequate” 
  • Perhaps you’ve identified that you need to increase the amount of data feedback you get from your processes. A forward-thinking data-platform approach aims to track more useful data, visualise it better and automate where possible.
• “My data quality is an issue”
  • Your data may just be inaccurate – full stop. Perhaps the mechanisms to collect data effectively just don’t exist, and this would be a more fundamental issue pushing you towards a platform-approach.
• “I lack a single source of absolute truth”
  • You might feel like most of the data you need to make informed decisions exists, but is difficult to interpret because of how disconnected it is from other relevant business data, or how badly it is visualised for human beings to analyse.
• “My current data ‘plan’ is costing too much”
  • Perhaps a lot of manual effort is required when it comes time to report every week/month/quarter? Not to mention the human error that creeps into data more and more when processes aren’t automated.
  • Perhaps your company is using legacy platforms and ETL tools – meaning you’re paying more and more to maintain and update these sub-par tools, while still not getting the performance advantages of the industry leading tech. Often biting the bullet on a new platform, and then saving tons on a monthly basis is the clear formula for success.
• “My current level of data integration with my product is limiting its evolution and revenue opportunities” 
  • One of the deadly sins committed by IT & data teams is to allow the data strategy to be a limiting factor when it comes to product evolution. When business intelligence insights aren’t powerful enough to inform the decision making process effectively – that’s a sign you need a new data strategy.
• “I’m not currently taking advantage of AI’s full potential, and don’t necessarily know what that is” 
  • Far beyond the value of all the ChatGPT fluff pieces we’re subjected to these days, the underlying generative AI technology is incredibly powerful when applied within a data strategy. Especially when given the right guardrails and data to work with. More on this in the last section.

The remedy for these pain points tends to come in the form of adopting a unifying platform approach to data management, which most often includes seamless connections to existing software using ETL tools.

For an expansion on these pain points and the solutions for them, download expert content on “6 Proven Strategies Leaders Use to Boost Company Data ROI”

Commercial Vs Open Source ETL Tools for data warehousing

ETL tools are essential for managing and integrating data from various sources into a centralised data repository, such as a data warehouse. 

Here’s a brief overview of the ETL tools related processes that happens before the data enters the data warehouse or data platform for visualisation and analysis:

Extract: ETL tools pull data from multiple sources, which can include databases, cloud services, and flat files.

Transform: The extracted data is then cleaned, formatted, and transformed into a consistent structure. This step often involves filtering, aggregating, and enriching the data to meet specific business requirements.

Load: Finally, the transformed data is loaded into a data warehouse or another target system where it can be accessed for analysis and reporting.

By automating these processes, ETL tools ensure that data is accurate, consistent, and readily available for decision-making and analytics.

Commercial Vs Open Source ETL Tools in the context of a modern data platform:

• Commercial ETL tools are typically developed by vendors and come with dedicated support, regular updates, and extensive documentation. They often offer advanced features, scalability, and integration capabilities, but they can be expensive due to inflexible licensing fees depending on the product. Examples include Matillion and Microsoft SQL Server Integration Services (SSIS), and more flexible vendors – Matatika.
• Open source ETL tools, on the other hand, are freely available and can be customised to fit specific needs. They are maintained by a community of developers, which can lead to rapid innovation and flexibility. However, they may require more technical expertise to implement and maintain, especially in the case of lesser known or less reputable ETL tools. Reputable open-source ETL tools Matatika has integrated into one platform include Spring, dbt and Meltano.

Modern data platforms like Matatika are designed to be compatible with a wide range of ETL tools, both commercial and open source. This compatibility ensures that organisations can choose the best tools for their specific needs and integrate them seamlessly into their data ecosystems. This flexibility is crucial for handling diverse data sources, formats, and volumes, enabling efficient data management and analytics.

For a more detailed look at ETL Tools, read the “Ultimate Guide to ETL Tools for Modern Business Intelligence”

A key ingredient to forward thinking Data platforms: AI

With all the advancements in AI data analysis, allowing you to transform data within your company database (or online) into actionable business intelligence, it’s a must-have.

AI makes the often untapped knowledge within masses of unstructured data instantly accessible. Now, one person can perform the research of 10x the number of people with the help of smart implementations of generative AI.

Here at Matatika, we believe that it’s a foregone conclusion that the future of work is Human + AI. For more information about how AI can be built into your data strategy, you’ll enjoy our video here.

Conclusion

Adopting a modern, centralised platform that integrates both bespoke and reputable open-source ETL tools can revolutionise your data management strategy. This approach ensures data consistency, reliability, and quality, while offering flexibility and cost-effectiveness. 

By addressing common pain points, reputable ETL tools (baked into one powerful, user-friendly platform) empower businesses to make confident, data-driven decisions. 

Embracing this forward-thinking data platform approach not only enhances business intelligence but also unlocks the full potential of AI, driving product evolution and revenue opportunities.

If you’d like to get more customised, expert advice for your company, why not book your free data strategy mapping call?

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