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How does data reporting automation impact data storage?

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Data reporting automation is the process of using software tools and scripts to generate and distribute reports based on data from various sources. It can save time, improve accuracy, and enhance insights for data analysts, managers, and stakeholders. But how does it affect the way data is stored and managed? In this article, you will learn about some of the benefits and challenges of data reporting automation for data storage.

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Mohammed Irfan M.Tech, CDMP, PSMII

Data architect with expertise in data management, data modeling, data quality, data governance, integration, ETL and…

1 Data sources and formats

Data reporting automation requires the ability to access and integrate data from various sources and formats, such as relational databases, spreadsheets, web APIs, and text files. To accomplish this, you need a data storage system that can support multiple data types, formats, and protocols. Common options include data warehouses, data lakes, and data marts. Data warehouses are centralized repositories for structured and semi-structured data from various sources; they are optimized for fast and efficient querying and analysis. Data lakes are large-scale storage systems that store raw and unprocessed data from multiple sources; they are designed for flexibility and scalability. Data marts are subsets of data warehouses that store data for specific purposes or domains; they provide easy and quick access by users or groups.

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2 Data transformation and preparation

Data reporting automation necessitates the ability to transform and prepare data for reporting purposes. This could involve cleaning, filtering, aggregating, joining, or calculating data from different sources and formats. To do this, you need a data storage system that can support various data transformation and preparation tools and techniques. Common options include ETL (Extract, Transform, Load), ELT (Extract, Load, Transform), and data pipelines. ETL is a process that extracts data from various sources, transforms it according to predefined rules and logic, and loads it into a data warehouse or a data mart. ELT is a process that extracts data from various sources, loads it into a data lake or a data warehouse, and transforms it on demand using query languages or tools. Data pipelines are workflows that automate the data movement and transformation from various sources to various destinations; they can use ETL, ELT, or a combination of both methods.

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3 Data quality and governance

A third essential component of data reporting automation is the capacity to guarantee and sustain data quality and governance. To do this, you must have a data storage system that can accommodate a range of data quality and governance tools and standards. Two of the most popular solutions are data catalogs, which are metadata repositories that store information about data sources, formats, schemas, definitions, and usage; and data lineage, which is a record of the data origin, movement, transformation, and destination. Additionally, you should consider setting up data policies and rules to define how data should be accessed, used, shared, and protected for reporting purposes. This will help ensure that users comply with data quality and governance requirements.

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4 Data access and delivery

A final essential aspect of data reporting automation is the ability to access and deliver data for reporting purposes. To do this, you need to have a data storage system that can support various data access and delivery tools and methods, such as data dashboards, data reports, and data alerts. Data dashboards are graphical interfaces that display key metrics, trends, and insights for reporting purposes, while data reports present findings, conclusions, and recommendations. Data alerts are notifications that inform users about changes, anomalies, or events for reporting purposes. All of these tools help users to monitor, explore, communicate, inform, persuade, influence decisions, stay updated, aware, and responsive to data situations.

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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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Mohammed Irfan M.Tech, CDMP, PSMII

Data architect with expertise in data management, data modeling, data quality, data governance, integration, ETL and database management. Bigdata | Teradata | Ab initio.
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In an enterprise embracing data reporting automation, the impact on data storage was transformative. By automating report generation and archiving, they optimized storage usage. Redundant data was systematically cleared, reducing storage costs. This efficiency not only enhanced data retrieval speed but also resulted in significant savings. The story illustrates how embracing data reporting automation can streamline storage management, fostering a more cost-effective and agile data infrastructure.

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How does data reporting automation impact data storage?

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1 Data sources and formats

2 Data transformation and preparation

3 Data quality and governance

4 Data access and delivery

5 Here’s what else to consider

Data Management

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How does data reporting automation impact data storage?

1

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1 Data sources and formats

2 Data transformation and preparation

3 Data quality and governance

4 Data access and delivery

5 Here’s what else to consider

Data Management

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