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Data mart

Data mart isn’t a new concept; it has been around for decades. But as data is growing continually, businesses are challenged by issues like data silos, inadequate data governance and other security concerns. Data silos inhibit seamless access and collaboration, inadequate data governance and lack of data security, leading to inconsistencies in data quality. These challenges hamper efficient decision-making and innovation. To harness the insights from massive data, businesses must store, process and analyze it efficiently. This is where a data mart comes in. 

Data mart helps beat the odds by bringing domain-specific datasets to the table for defined users to quickly access and meet their analytical and decision-making requirements without wasting time on exploring the entire data warehouse.

What is a Data Mart?

A data mart is a subset of a data warehouse that focuses on a single functional line within an organization and is designed for use by a specific set of users, for example, marketing, finance, sales or HR. The mart usually pulls data from a few sources and is more flexible than a data warehouse. 

Now, why is a data mart important for businesses? Let’s take the example of a retail business operating in multiple regions and selling numerous products. The daily influx of sales, inventory and customer data is enormous and stored in a central repository. The sales team must access this data to gather insights into sales trends and make informed decisions. 

Now, imagine how difficult it would be for the sales team to analyze one year of sales data from a repository that contains years of data. They would have to spend a lot of time and effort filtering through large amounts of data–like finding a needle in a haystack. This would lead to inaccurate and delayed decision-making, resulting in incorrect decisions and missed prospects. Furthermore, if the sales teams have access to the entire data warehouse, there may be security and data breach issues.  

In situations like these, employing a data mart can act as a redeemer. The sales team can design a data mart that comprises one year of sales data by region, customer demographics and other relevant metrics. This mart enables them to focus only on sales information and make decisions based on pricing, offers and inventory management insights.  

The Benefits of a Data Mart

Today, organizations need to store their data in a way that empowers them to analyze it effectively and make informed decisions. A data mart provides a simplified and quick approach to accessing specific data by storing structured data obtained from multiple sources in a separate repository. Here are some benefits of using it: 

  • Ensure quick data retrieval: A data mart focuses on a specific business unit to provide easier and quicker access without requiring them to search an entire data warehouse. Hence, it reduces the time and effort business users would otherwise spend uncovering the necessary information. In addition, a data mart helps pre-aggregate and summarize information based on query patterns, improving overall query performance, irrespective of its underlying complexity. 
  • Enable effective decision-making : If each business unit has a dedicated data mart, they can retrieve relevant information instantly and perform real-time or near-real-time data analysis. They can find trends and patterns, understand customer needs and gain insights to make intelligent decisions. For instance, a marketing team might launch a targeted campaign at the right time to increase sales based on customer buying patterns, seasonal trends or best-selling products.
  • Guarantee data integrity: Data mart helps specific business departments to ensure that only relevant data is stored in it and made available for analysis. It also condenses detailed information to reduce the amount of irrelevant data and ensures consistency and accuracy in query results.  
  • Implement robust security: By restraining who can access specific information, data marts essentially lock the door to sensitive data. This reduces the chances of someone getting unauthorized access or causing data leaks, ensuring that valuable information stays protected. 

Types of Data Marts

  • Dependent data mart : A dependent data mart is derived from a centralized data warehouse, and it uses the latter as its primary data source. However, it is flexible enough to include additional data processing or aggregation specific to the department’s needs. It is a subset of an existing data warehouse designed to fulfill the analytical needs of a particular business unit within an organization. 
  • Independent data mart: An independent data mart is designed for smaller groups within the organization and doesn’t require a centralized data warehouse. As the name suggests, it depends neither on the organization’s data mart nor any other data mart. It is created from operational systems or external data sources. It is known to quickly meet the ad hoc analytical needs of any specific department within the enterprise.  
  • Hybrid data mart : A hybrid data mart is an amalgamation of both dependent and independent data marts. It combines the benefits of both—the centralized data management of a dependent data mart and the flexibility of an independent data mart to create a scalable solution. 

How to Use a Data Mart? 

Using a data mart involves several stages to ensure effective execution and utilization -  

Identify business pursuits: Determine the specific business objectives or logical requirements the data mart will address. Understand the conditions of the user groups or departments that will deploy it. 

Plan and design: Plan the structure and design of the data mart. Define what data will be included, how it’ll be organized, and the relationships between different data sets. Choose a suitable schema (e.g., star schema, snowflake schema) for effective querying. 

Data collection and integration: Extract, transform and load (ETL) relevant data from various sources into the data mart. Ensure data quality by cleansing and validating the data during the integration process. 

Implement security measures: Set up robust security measures to control access to the data mart. Apply authentication, authorization, encryption and access controls to safeguard sensitive data. 

Testing and quality assurance: Run tests to ensure the data mart’s accuracy, consistency and reliability. Validate that the data meets the intended requisites and that queries return anticipated results. 

Training and user adoption: Train users who’ll interact with the data mart. Assure they understand how to access, query and harness the data effectively for their requirements. 

Monitoring and maintenance: Monitor the performance of the data mart and conduct regular maintenance. This includes streamlining data, optimizing queries, resolving issues and assuring data thickness. 

Iterative improvement: Gather feedback from users and stakeholders to identify areas for advancement. Continuously reiterate the data mart by refining data models, adding new data sources, or adapting the structure based on evolving business requirements. 

Attestation and governance: Document the data mart’s structure, data sources, definitions and business rules. Establish governance policies to maintain data quality, norms and compliance. 

Measure impact and effectiveness: Assess the impact of the data mart on business operations, decision-making and results, ensuring its effectiveness in meeting the foremost business aspirations and making adjustments as necessary. 

By utilizing data mart in this way, organizations can effectively support decision-making, perform analytics and meet the specific requirements of different business units or user groups.

Challenges of Creating & Maintaining Data Marts

There are numerous challenges in creating and maintaining a data mart, such as: 

  1. Data integration complexity: Data mart collates data from disparate sources, which becomes challenging for organizations because data stored in multiple sources are different in format, structure and quality. It isn’t easy to seamlessly integrate data while maintaining data consistency.
  2. Data quality and cleansing: If the data stored in the data warehouse is inaccurate, incomplete or inconsistent, it can affect the reliability and efficacy of the data mart. The data cleansing process can be time-consuming in these situations.
  3. Scalability and performance: With growing data volumes, it becomes challenging to ensure the scalability and performance of the data mart.  Business users will need to optimize queries, indexes and storage structures while maintaining high query performance.
  4. Data security and privacy concerns: To protect data mart from unauthorized access and security breaches, implement security measures that ensure compliance with regulations. However, enforcing these regulations may hinder the data mart’s accessibility for users.
  5. Resource and skill constraints: Creating and maintaining a data mart requires resources with expertise in data management, ETL processes, database administration and analytics.

Addressing these challenges involves specialized skills, effective planning, uninterrupted monitoring, flexibility and ongoing efforts to ensure data quality, security and alignment with evolving business demands. 

Difference Between Data Marts, Data Lakes, and Data Warehouses

All three are different data management methods. Data warehouses act as centralized repositories, gathering structured data from multiple sources across the enterprise to support comprehensive analytics and decision-making requirements. They use predefined schemas optimized for complex querying and reporting. 

In contrast, data marts are specialized subsets of data warehouses centering on specific departments or business units. They offer domain-specific datasets structured to stimulate targeted analysis and reporting for these particular areas. Data marts are more agile and user-centric, delivering quick access to specific domain users. 

Data lakes store vast volumes of raw, unstructured or semi-structured data in its aboriginal format. Unlike warehouses and marts, they don’t evaluate a predefined schema upon ingestion, allowing storage of different data types. Data lakes support data exploration, enabling organizations to perform advanced analytics and gain insights from various data sources. 

Data Mart Architecture

There are three data mart architectural approaches: 

Bottom-up approach 
The bottom-up architectural approach uses operational data sources to create data marts. Respective business units identify the data they need to support their specific business process and then create a mart using that data. The data loaded into the mart from diverse sources is structured into dimension tables for simplified access by business users. The approach fosters a more agile data repository that can provide faster querying and aid in data mining for specific data trends. 

Top-down approach 
The top-down approach focuses on creating a centralized data warehouse that can be the primary data source for all the data marts. These data marts are designed for specific business units or processes and developed based on business users’ requirements. Being warehouse-focused, they are often preferred by larger organizations to align business strategies with their goals and address complex data requirements. 

Federated approach 
A federated data mart architecture offers data management techniques that autonomously integrate multiple data marts while maintaining their independence. Unlike the other architectural approaches, it doesn’t require moving the data physically from a centralized repository to a mart, facilitating organizations to access and analyze data from its original location. This architecture provides simplified data access, flexibility and agility, making it easier for business users to query data for faster insights.  

Best Practices for Implementing Data Marts

Following are the best practices to follow while implementing data mart: 

  • Start with departmentally structuring the source of the data mart.
  • Measure the data mart implementation cycle in short periods of weeks.
  • Involve all the stakeholders or business users of a domain as they might help with the planning and designing phase of implementation.
  • Budget all the expenses carefully in the plan, such as data mart hardware/software costs, networking and implementation costs and much more.
  • Evaluate additional processing power or disk storage requirements.
  • Ensure enough networking capacity, as it might be possible that the data mart and the respective data warehouse are located in different locations, so it’s easy to handle massive data volume transfer. 
  • If the transformation complexity increases, the loading time of the data mart also increases. Budget the implementation cost of the data mart loading process accordingly.

Drawbacks of Using Traditional Data Marts 

Over the past few years, data marts have been very efficacious in making data accessible to decision-makers, but they also come with some limitations that can’t be overlooked. 

  • Data Duplication -  Since a data mart is created by copying data from a central data warehouse or repository, it leads to numerous versions of the same data. This duplication of data takes up additional storage space and also presents some synchronization issues. 
  • Data inconsistency – Maintaining consistency between the central repository and various data marts is time-consuming and prone to errors. Any inconsistencies can lead to discrepancies in analytical results, resulting in flawed decision-making.
  • Increased storage costs – Creating various data marts for different departments in the organization can substantially increase storage costs, forcing organizations to increase their storage budgets.

As companies continuously look to optimize their data infrastructure and management processes, the data mart, with its substantial financial burdens, data inconsistencies and limited agility, is not what businesses need today. 

Introducing Kyvos’ Semantic Model 

Kyvos is a GenAI-enabled, cloud-native, high-speed data analytics platform that enables sub-second querying on massive datasets. It addresses the challenges of traditional data marts by offering a universal semantic model that enables AI-driven analytics by organizing, simplifying and accelerating data consumption while hiding its complexities. Kyvos standardizes business logic across different BI tools to ensure a consistent and trusted view. Some of the benefits it offers are: 

Unified view: Kyvos’ semantic layer creates a unified view of data across disparate big data sources such as Hadoop, cloud data lakes or other platforms. Unlike data marts limited to specific databases, the platform offers a comprehensive view across diverse sources. 

Elastic scalability: Users will get dynamic scalability, to handle the growing volume needs and perform high-speed, multidimensional analytics without compromising performance or response times. 

Self-serve analytics: Kyvos’ semantic model streamlines self-service analytics, encouraging business users to explore and analyze data using any BI tool of choice and gain insights at interactive speeds. 

Real-time insights: With Kyvos, users can gain real-time insights by analyzing both historical and streaming data, offering a comprehensive view for informed decision-making. 

Accelerated performance: Kyvos leverages its AI-powered smart aggregation technology to deliver sub-second query responses on billions of rows of data—interactively without any latency. 

Eliminates data movement: Unlike data marts that involve data movement and duplication, Kyvos operates on a price-performant querying paradigm. It eliminates the need to duplicate data to create a semantic layer, saving storage and management overhead. 

Robust security: Kyvos’ semantic model provides a native three-tiered security architecture that works seamlessly with cloud platforms and ensures data protection at multiple levels. The platform ensures granular-level access control through row and column-level security at the group and user levels.

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