How can you balance data quality and agility in architecture?

Data quality and agility are two essential aspects of data architecture, but they can also pose some challenges. Data quality refers to the accuracy, completeness, consistency, and reliability of the data, while agility refers to the ability to adapt to changing business needs and requirements. How can you balance these two goals and design a data architecture that delivers both high-quality and agile data solutions? Here are some tips and best practices to consider.

1 Understand the data context

Before you design or modify your data architecture, you need to understand the context of the data you are working with. This includes the sources, formats, types, volumes, frequencies, and purposes of the data, as well as the expectations and needs of the data consumers and stakeholders. By understanding the data context, you can identify the data quality dimensions and criteria that are relevant and important for your data solutions, such as validity, timeliness, completeness, consistency, and accuracy. You can also assess the level of agility and flexibility that your data architecture needs to support, such as the ability to handle new data sources, formats, or types, or to scale up or down as needed.

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Greg Anderson

Leader in Data Architecture & Strategy, Analytics, Governance, and AI
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A data architecture needs to understand both the semantic and business context of the data in addition to learning the structure, sources, and formats of the data elements and objects. A successful data architect will be able to communicate with business stakeholders as well as data admins & engineers.

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2 Choose the right data models and tools

Another key factor in balancing data quality and agility is choosing the right data models and tools for your data architecture. Data models are the representations of the data structures, relationships, and rules that define how the data is stored, accessed, and manipulated. Data tools are the software and hardware components that enable the data processes, such as ingestion, integration, transformation, analysis, and visualization. Depending on your data context, you may need to use different data models and tools to achieve optimal data quality and agility. For example, you may use a relational data model and a SQL database for structured and transactional data that requires high consistency and integrity, or you may use a non-relational data model and a NoSQL database for unstructured and dynamic data that requires high scalability and flexibility.

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Greg Anderson

Leader in Data Architecture & Strategy, Analytics, Governance, and AI
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Published modeling methodologies are great support tools when creating a warehouse model. When designing models for ETL, keep it modular. Keep you processes and objects as small and as focussed as possible without negatively impacting performance. A modular design will limit the impact and required rework of future changes upstream.

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3 Implement data quality checks and controls

To ensure that your data architecture delivers high-quality data, you need to implement data quality checks and controls throughout the data lifecycle. Data quality checks are the processes of verifying, validating, and measuring the data quality against the defined criteria and standards. Data quality controls are the actions of correcting, enhancing, or preventing the data quality issues or errors. You can implement data quality checks and controls at different stages of the data lifecycle, such as during data ingestion, integration, transformation, analysis, or delivery. For example, you can use data profiling, data cleansing, data enrichment, or data masking techniques to improve the data quality. You can also use tools and frameworks, such as Apache NiFi, Apache Airflow, or Talend Data Quality, to automate and monitor the data quality processes.

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4 Adopt agile methodologies and practices

To ensure that your data architecture supports agility, you need to adopt agile methodologies and practices for your data projects. Agile methodologies are the approaches of managing and delivering data projects in an iterative, incremental, and collaborative way, such as Scrum or Kanban. Agile practices are the techniques of applying the agile principles and values to the data projects, such as user stories, sprints, retrospectives, or continuous integration and delivery. By adopting agile methodologies and practices, you can improve the alignment and communication between the data team and the business stakeholders, increase the responsiveness and adaptability to the changing data needs and requirements, and deliver faster and more frequent data solutions.

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Greg Anderson

Leader in Data Architecture & Strategy, Analytics, Governance, and AI
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Keep your analysis & development processes agile, modular, and flexible. Apply the same methodology to the work itself. While you can learn quite a bit from established agile & modeling methodologies, remember that these frameworks exist to let you create your own work, your own models, and your own methods. Adapt to change as it happens, and don't get locked into processes that are counterproductive.

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5 Balance trade-offs and priorities

Finally, balancing data quality and agility in data architecture is not a one-size-fits-all solution, but rather a matter of making trade-offs and priorities based on your data context and goals. Depending on the data situation, you may need to compromise or optimize some aspects of data quality or agility over others, such as speed, accuracy, scalability, or consistency. For example, you may need to sacrifice some data quality for agility when you need to deliver a quick and flexible data solution for a new or uncertain data source or type, or you may need to sacrifice some agility for data quality when you need to ensure a high and consistent data quality for a critical or regulatory data solution. Therefore, you need to balance the trade-offs and priorities of data quality and agility in data architecture by evaluating the costs and benefits, risks and opportunities, and expectations and outcomes of each data solution.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Siddharth Rajagopal

Chief Architect at Informatica | Executive MBA at Quantic
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In my experience Data Quality should be implemented from an Enterprise context than an application/data specific context. Else we will end up building the same Data Quality in SQL, Python and multiple other languages. Ideally we need the most common Data Quality checks implemented as libraries which can be instantiated using APIs for instance passing runtime parameters.

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How can you balance data quality and agility in architecture?

1

2

3

4

5

6

1 Understand the data context

2 Choose the right data models and tools

3 Implement data quality checks and controls

4 Adopt agile methodologies and practices

5 Balance trade-offs and priorities

6 Here’s what else to consider

Data Architecture

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How can you balance data quality and agility in architecture?

1

2

3

4

5

6

1 Understand the data context

2 Choose the right data models and tools

3 Implement data quality checks and controls

4 Adopt agile methodologies and practices

5 Balance trade-offs and priorities

6 Here’s what else to consider

Data Architecture

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