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How can you visualize data gaps and redundancies in your architecture?

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Data gaps and redundancies are common challenges in data architecture, especially when dealing with complex and heterogeneous data sources, systems, and processes. They can affect the quality, efficiency, and usability of your data assets and impact your data strategy and goals. How can you visualize data gaps and redundancies in your architecture and identify opportunities for improvement? In this article, we will explore some methods and tools that can help you achieve this.

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1 Data gap analysis

A data gap analysis is a process of comparing your current data state with your desired data state and identifying the missing or insufficient data elements, attributes, or metrics that prevent you from achieving your data objectives. A data gap analysis can help you prioritize your data needs, plan your data acquisition, integration, or transformation projects, and align your data architecture with your business requirements. To visualize data gaps, you can use tools such as data catalogs, data dictionaries, data models, data lineage diagrams, or data quality dashboards that can show you the metadata, structure, relationships, origin, and quality of your data sources and elements.

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Nadeem Malik

Enterprise Architect - providing enterprise-wide insights, promoting standardization, reducing costs, mitigating risk, increasing agility, realizing strategies, and driving innovation.
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To enable data-driven decision-making, architects must continuously analyze gaps between current and target data states, and then prioritize closing high-impact gaps through iterative data acquisition and integration projects aligned with business needs.

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Prabhaker Narsina

Vice President, Data and AI @ First American | Masters in Data Science | Modernizing and transforming using AI & Technologies
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Data gap analysis is a continuous process as data changes continuously in most organizations. Data gaps can happen because of missing records or missing values in certain fields. One of the most typical things to do would be to do a missing/null analysis on all the important fields. Missing records could be tricky unless you have a clear idea of what is complete dataset is.

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2 Data redundancy analysis

A data redundancy analysis is a process of detecting and eliminating unnecessary or duplicated data elements, attributes, or metrics that consume storage space, increase processing time, or create inconsistencies or conflicts in your data architecture. A data redundancy analysis can help you optimize your data performance, reduce your data maintenance costs, and improve your data consistency and accuracy. To visualize data redundancies, you can use tools such as data profiling, data mapping, data deduplication, or data normalization that can show you the frequency, distribution, similarity, or dependency of your data sources and elements.

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Soon Heng TAN ???? ???? ??????????

Head of Finance Data & Analytics at SG Public Service | Jack of all Trades & Master of Some | MTech AI | CDMP | TOGAF | AWS-SAA | GCP-DE | GCP-MLE | DCS-DS | L3-CSDA | CSM | CSSLP | Enterprise Data Harmonisation Leader??
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Data Lineage, which tracks the origin and movement of data from its source through various processes and transformations to its consumption, can play a significant role in analyzing and identifying data redundancies within an organization's data ecosystem. Data Lineage can assist in identifying these redundancies: 1. Redundant Data Sources; 2. Duplicate Data Transformations; 3. Storing Multiple Copies of Same Data; and 4. Low Usage of Irrelevant Data Products. Data Lineage aids in comprehensively understanding the flow, transformations, storage and usage of data within an organization, enabling organizations to streamline their data architecture, reduce redundancy, improve efficiency, and ensure better data governance and quality.

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Prabhaker Narsina

Vice President, Data and AI @ First American | Masters in Data Science | Modernizing and transforming using AI & Technologies
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Data redundancy is a common problem in most organizations; there are many reasons why we end up here. There are some intentional and mostly unintentional reasons e.g. due to multiple groups working in silos, new acquisitions, performance efficiency, and more. The big impact of redundancy is data inconsistency which is really important to provide trust on your datasets to your customers. Normalizing data to standard form and finding duplicates after normalization is one of the best way to identify redundancy and implement processes to keep them in sync or merge two sources.

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3 Data visualization techniques

Data visualization techniques are methods of presenting your data in graphical or interactive forms that can help you communicate, explore, or analyze your data effectively. Data visualization techniques can help you visualize data gaps and redundancies in your architecture by highlighting the patterns, trends, outliers, or anomalies in your data and enabling you to compare, contrast, or correlate your data across different dimensions or perspectives. Some examples of data visualization techniques are charts, graphs, tables, maps, dashboards, or reports that can show you the quantitative, qualitative, spatial, temporal, or relational aspects of your data.

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4 Data visualization tools

Data visualization tools are software applications or platforms that can help you create, customize, or share your data visualizations easily and efficiently. Data visualization tools can help you visualize data gaps and redundancies in your architecture by providing you with various features and functions that can enhance your data visualization capabilities and experience. Some examples of data visualization tools are Power BI, Tableau, QlikView, or D3.js that can offer you different options for data connectivity, data manipulation, data analysis, data presentation, or data collaboration.

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5 Data visualization best practices

Data visualization best practices are guidelines or principles that can help you design and deliver your data visualizations effectively and appropriately. Data visualization best practices can help you visualize data gaps and redundancies in your architecture by ensuring that your data visualizations are clear, accurate, relevant, and engaging for your intended audience and purpose. Some examples of data visualization best practices are choosing the right type of visualization, using appropriate colors, scales, and labels, avoiding clutter and distortion, and telling a meaningful story with your data.

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6 Data visualization challenges

Data visualization challenges are difficulties or limitations that can affect your data visualization process or outcome. Data visualization challenges can hinder your ability to visualize data gaps and redundancies in your architecture by causing errors, confusion, or dissatisfaction with your data visualizations. Some examples of data visualization challenges are dealing with large or complex data sets, ensuring data security and privacy, adapting to different devices or platforms, and evaluating the impact or value of your data visualizations.

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7 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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How can you visualize data gaps and redundancies in your architecture?

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3

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1 Data gap analysis

2 Data redundancy analysis

3 Data visualization techniques

4 Data visualization tools

5 Data visualization best practices

6 Data visualization challenges

7 Here’s what else to consider

Data Architecture

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How can you visualize data gaps and redundancies in your architecture?

1

2

3

4

5

6

7

1 Data gap analysis

2 Data redundancy analysis

3 Data visualization techniques

4 Data visualization tools

5 Data visualization best practices

6 Data visualization challenges

7 Here’s what else to consider

Data Architecture

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