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Modernizing your analytics architecture part 1: Key objectives

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by Terje Vatle

12. Oct 2021, 8 minutes reading time

Modernizing your analytics architecture part 1: Key objectives

Imagine you’re in charge of analytics in your company. You are tasked with breaking down information silos and letting the right people in your organization getting access to data and insights. The data and insights must be managed, trusted, and actionable, available when and where it is needed and in accordance with regulations.  

However, your current analytics architecture, which should enable efficient delivery of data and insights, seems outdated. It doesn’t scale well with rapidly growing data volumes and growing number of users, new analytics ambitions, new expectations for self-service, and new use cases from digitalized business processes. In addition, time-to-insights is surging in part due inefficient development and maintenance processes requiring extensive manual work for your data engineers. You conclude that your analytics architecture is ready for modernization. 

An analytics architecture typically includes one or more concepts such as a data warehouse, a data lake, a data science environment, and operational intelligence. The architecture would need to define functional capabilities such as being able to ingest, store, transform, model, serve, and analyze data. Each capability may be further broken down into the ability to store structured and unstructured data. In addition, there are over-arching capabilities related to data security, data protection, and data governance that are necessary to succeed. Eventually, the architecture will have to be translated into a set of technology components that serve as the “physical” implementation.   

Let us say that your current data architecture is on-premises. You consider modernization as an opportunity to reduce the burden of managing infrastructure and provide more flexibility in the choice of technologies. Also, as most of your other IT systems are slowly being lifted and cloud-enabled, your new analytics platform should be in the cloud.  

 

5 key objectives when modernizing your analytics architecture

Before venturing into different design patterns and designing your new analytics architecture you sit back and reflect. Looking at limitations from the other architectures both on-premises and in the cloud, you can define a set of key objectives for your modernized analytics architecture:  

  1. Business-driven, not IT-driven 
    Understand the business goals and business strategy that the architecture should support. Talk to the business units, understand what questions they are not able to answer, and potential challenges they are facing with IT today. Ideally, you would like to decide on an architecture that could support your business for the next 5 to 10 years. 
  2. Right-size (scale up/down) at any time  
    Just as quickly as your business environment may change and new opportunities may arise, your architecture must also adopt. Since you are not likely to predict business needs next 5 to 10 years you need to design for flexibility. Flexibility means the ability to scale your architecture for optimal price-performance, not just at the beginning, but during the lifetime of the architecture.
  3. Minimize time to insights  
    You would need to deliver data to your different stakeholders faster, smarter, and easier to keep up with growing business requirements. It calls for increased levels of standardization, minimizing as much manual work involved in the data production lines as possible, and enforcing best practices to ensure the development of robust solutions.
  4. Govern and quality assure your data  
    Many organizations experience that data quality and data trust issues grow exponentially when moving from on-premises to the cloud. Reasons can be manyfold including adding more and less curated or less structured data, lack of ability to find and use data correctly, and in part due to less mature data management solutions and processes in the cloud compared to on-premises.
  5. Avoid or minimize lock-in 
    Avoid or minimize being locked into any single technology, whether it is a database, an MPP-solution, distributed file system, data integration tool, or a data processing environment. 

 

The following blog will go into detail on objective nr 2, right-sizing, or scaling up or down, your architecture, at any time.  It addresses one of the biggest challenges when moving your analytics architecture to the cloud, balancing price performance.  

The assumption is that processing power and storage capacity in the cloud is only limited by your budget. Ideally, you should at any point in time during the lifespan of your analytics architecture be able to spend just the right amount of money to solve your current business needs and keep the total cost of ownership (TCO) down. As business needs change over time, so must your analytics architecture.  

Next: Modernizing your analytics architecture part 2: Different approaches to sizing

Read more about data warehouse automation in our online guide.

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Terje Vatle

Terje Vatle

Terje Vatle is Chief Technology Officer at BI Builders following global market trends within data & analytics. Terje has a technology and advisory background, and focuses on how to make organizations achieve their goals by becoming more data driven.

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