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How do you leverage data mart features to enhance your BI reporting and analysis?

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Data marts are subsets of data warehouses that focus on specific business functions or departments. They can help you improve your business intelligence (BI) reporting and analysis by providing faster, more relevant, and more secure access to data. In this article, we will explain what data marts are, how they differ from data warehouses, and how you can leverage their features to enhance your BI capabilities.

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1 Data marts vs data warehouses

A data warehouse is a centralized repository of data from various sources, such as operational systems, external databases, or web services. It is designed to support enterprise-wide reporting and analysis across multiple domains and dimensions. A data mart, on the other hand, is a smaller and more focused version of a data warehouse that serves a specific business unit or function, such as sales, marketing, or finance. It contains only the data that is relevant and useful for that particular domain or purpose.

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Hina S.

Customer Success Leader | Amazon Web Services
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In the context of a healthcare organization, an example of datasets available in a data warehouse are vendor, pharmacy, provider, claims, and patient enrollment. A data warehouse should have mechanisms in place to assess and improve the quality of the data. A data mart is a business layer derived from a data warehouse containing data specific to the business unit, so for example, a clinical data mart will include clean and reliable data about patients, labs, providers and health outcomes that can be used for self-service and business analytics

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2 Benefits of data marts

Data marts offer several advantages over data warehouses for BI reporting and analysis, such as faster performance, higher quality, and better security. Data marts require less processing power and storage space and have fewer data transformations and integrations, which reduces the complexity and latency of data loading and querying. Furthermore, they are tailored to the specific needs and preferences of the users, have more consistent and accurate data definitions, formats, and standards, as well as more granular and detailed data. Additionally, they have more restricted and controlled access than data warehouses, allowing users to access only the relevant and authorized data for their role. This enhances the data security and privacy by minimizing the risk of data breaches or misuse. Moreover, they have more audit trails and logs which facilitates the data governance and compliance.

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Elena Goryainova

Sr. Solutions Architect at Snowflake - The AI Data Cloud | SnowPro SME | Women in Data Ambassador
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From a data cataloging perspective, describing the attributes and measures of data marts is easier than of data warehouses. This makes the work of data analysts and stewards more effective.

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3 Types of data marts

There are three main types of data marts, each of which has its own distinct characteristics. Dependent data marts are derived from and dependent on a data warehouse, using the same data model and schema, but only storing a subset of the data. This makes them easy to implement and maintain, but they may not be as flexible or scalable. Independent data marts create and manage their own data sources, models, and schemas which may differ from those of the data warehouse. This allows for greater flexibility and scalability, but can lead to more data inconsistency and redundancy. Hybrid data marts combine aspects of both dependent and independent data marts, using some data from the warehouse as well as other sources with different models and schemas. This makes them more adaptable and comprehensive, but also more complex with potential integration challenges.

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4 How to leverage data marts

To leverage the benefits of data marts for BI reporting and analysis, you need to define your business objectives and requirements, choose the right type and design of data mart, implement and test it, and monitor and update it regularly. Before creating or using a data mart, you must identify the goals, scope, and specifications of your BI project. Based on your business objectives, you need to decide which type of data mart is best suited for your project. After selecting and designing the data mart, you must load the data from the sources, validate its quality, verify its accuracy, check its security, and evaluate its performance. Additionally, you should provide documentation and training for users and stakeholders. Finally, you must track the data usage, feedback, and issues to make adjustments or improvements as needed.

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Elena Goryainova

Sr. Solutions Architect at Snowflake - The AI Data Cloud | SnowPro SME | Women in Data Ambassador
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Data lineage is also an important aspect of data mart usability. If a report or a dashboard returns incorrect results, it should be possible to trace the data mart data issue back to the original source of data to compare and identify the root cause of the issue.

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Srikanth Muthyala

Data Management Thought Leader
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In addition to creating a data mart that caters to business needs, it is equally important to democratize it. Cataloging the data definitions, profiling results, lineage and data quality of the data mart tables and publishing them to a Data Marketplace is fundamental to ensure its adoption.

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Manoj k Acharya

Enterprise Data Architect||Data Sarategy|| Data product by principle Green Enregy & low carbon foot print in daily life
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A challenge in the business scenario would need related information to analyze the situation and related aspects. A data mart built, targeting the relevant domain is the id the first place to look at. The user or SME is usually aware of the problem in hand and in a position to do various query and analysis related to the challenge. Assuming the data mart has all the relevant data points, related metadata, required historical data set and the audit fields, it should be possible to apply the analysis techniques and slicing and dicing over various dimensions to derive insight over the data set.

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5 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 do you leverage data mart features to enhance your BI reporting and analysis?

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5

1 Data marts vs data warehouses

2 Benefits of data marts

3 Types of data marts

4 How to leverage data marts

5 Here’s what else to consider

Data Architecture

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How do you leverage data mart features to enhance your BI reporting and analysis?

1

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3

4

5

1 Data marts vs data warehouses

2 Benefits of data marts

3 Types of data marts

4 How to leverage data marts

5 Here’s what else to consider

Data Architecture

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