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  • Writer's pictureAnantaya Pornwichianwong

How to build robust data infrastructure to optimize your data



What is data infrastructure? How is it important? And how to build a strong data infrastructure to start analyzing your data and creating value? Let's find out!


Data can be value-added only when it is used properly with the proper approaches and tools, so that we can analyze and understand what the data is telling us. Then, we can plan the business strategies and directions, and make informed decisions based on that insight, in order to generate profits and maximize business growth.


To analyze data with maximum efficiency and leverage data-driven practices across the organization, just proper data analytics tools are not enough. One of the most necessary foundations that the organization needs is to build a robust data infrastructure first.


Building a strong data infrastructure consists of complicated steps, from planning and preparing data, to setting up a pipeline to connect and analyze data. Nonetheless, having a strong infrastructure will help make data easily accessible and provide accurate and practical analytics results, which are a strong foundation for taking your business to the next level.


Data infrastructure is a topic that we will discuss today. Sertis invites everyone to look at how we can start building a robust data structure and what are the most significant things to keep in mind.



Key things to consider when building data infrastructure


To start building data infrastructure, there are key things to keep in mind that must be carefully designed to provide flexibility and space for the future. These key things include data accessibility and the amount of data.


The goal of building an organization's data infrastructure is to be able to organize data to make it usable, accessible, and applicable to future use. Data accessibility, therefore, is an important goal that must be considered from the beginning. Having Data accessibility means that everyone who needs to use the data can easily access the data they need. For example, a data analyst is able to retrieve the data they want at any time without having to pass through the hands of the IT team. This will make the work process more streamlined. Ensuring data accessibility is the first goal we need to keep in mind while making sure that we are not compromising security at the same time.


Another thing to consider is the amount of data. For now, our organization may still be small, with quite small data on hand. But every business is growing and the data is only increasing every day. Later when the organization is expanded, The amount of data circulating will increase and the existing data structure may not be able to cope with the increasing amount of data. As a result, we have to consider this fact from the beginning. We should build a data infrastructure that is scalable and flexible to changes.


We now understand two key things about building a robust data infrastructure. Let's start building one now.




1. Define your data utilization strategy


Before starting building data infrastructure, the first thing we need to do is to define a clear organizational data strategy. For example, choosing between a cloud or on-premise, and then setting the standard for how data will be stored. Determine the sources of data in your organization, evaluate the current situation of data to see whether it is organized and how strong is your security protocols, and determine which teams or departments have access to specific data. Establishing a clear data strategy will allow us to build a strong data infrastructure that really meets the needs of our organization.


2. Create a data model


The next step is to create a data model to help you see a clear picture of data in the organization. The data model describes the structure of the data, the nature of the data, and all relationships of the data. The data model will help us understand the complexity and limitations of all data in an organization in order to design an efficient data infrastructure.



3. Choose a data repository system.


A data repository refers to the place where our data is stored. It can be a single large database or a series of smaller databases connected together. Two popular types of data repositories are a data warehouse and a data lake. You can either choose one or opt for a hybrid approach. The decision is based on the characteristics of your data and operations in the organization.


A data warehouse is a large-scale repository mainly used for structured data. The data warehouse stores data from all sources in the organization in one centralized place, making it ready for data analytics or business teams to make further use of. A data lake, on the other hand, is oriented for unstructured raw data with the capacity to store a much higher volume of data. A data lake is suitable for users such as data scientists or data engineers whose tasks are to organize and apply the data to train a machine learning. A data lake also has a lower cost than a data warehouse. You can decide based on the nature of your data, usage, and goals.


4. Clean and organize data


The next step in building data infrastructure is to clean and organize data to optimize efficiency. This is because incorrect, inconsistent, or unstructured data can lead to inaccurate analytical results. Cleaning and preparing data involves reviewing and removing inconsistent or duplicate data, fixing errors in the data structure, and updating data. You can also utilize tools that automatically organize your data to save time and resources.



5. Build an ETL pipeline


Another essential step in building a robust infrastructure is building an ETL pipeline. ETL, as it stands for Extract-Transform-Load, will retrieve data from various sources, such as data from different teams' storages, transform the data into appropriate formats as specified, and load the data into the data warehouse or data lake.


Having an efficient ETL pipeline system helps ensure that the data is standardized, complete, accurate, and accessible. The design of the ETL pipeline system must take into account the challenges that may arise in the future, such as changes in data formats, unconnected data, or an increase in data amount. As a result, we need to design a pipeline that is flexible and scalable to support future use.


6. Define a data governance


After we have a storage and ETL pipeline, another indispensable step in establishing a strong data infrastructure is to define data governance, which is the standard on how data is managed and controlled, which will be enforced throughout the organization. Data governance frameworks deal with data collection, data usability, data classification, data availability, data accessibility, data security, and data elimination.


These frameworks will help control and manage ongoing data to ensure data quality and availability, and to create a single, clear data management standard that is comprehensive and practical in order to streamline the operations, increase flexibility, and effectively secure data in the organization.



Data infrastructure solutions from Sertis


At Sertis, we provide a full range of data infrastructure services from the initial design of the infrastructure to building an automated data analytics system using AI. Let's unlock the hidden potential of data with solutions designed specifically to meet your business needs to take a leap forward together. Our solutions include a wide range of:


Readiness Assessment

Assess the data situation in the organization and compare it to the standard of the overall industry to identify areas of improvement, see where to start, and how to design the strategy for maximum efficiency.


Data Mapping & Validation

Create a unified map of your organization's data to connect data from different sources and transform data automatically to ensure the correct format in order to speed up the data management system and enable data consistency and unity across the organization.


Data Normalization & Quality Assurance

Build a system that helps verify ongoing input data's quality and consistency by detecting errors or duplicate data to ensure that the input data complies with the standards and is updated to changes in structure.


Data Governance

Establish standardized data guidelines in the organization to ensure the quality of data storage, security of data management, and accessibility across the organization in order to build an efficient, practical, and scalable data infrastructure.


Data Integration

Build a system that connects data from multiple sources together and present them on one centralized platform to make data more convenient to use and easier to manage.


Data Lake Platform

Build a data lake platform for centralized storage of all raw data with the ETL pipeline to connect data and make them ready to use.


Data Analytics Platform

Build a data analytics platform for end-to-end data analytics, from data classification to automated analytics for real-time insights.


Learn more about data solutions from Sertis at: https://www.sertiscorp.com/data-preparation


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