What is a Data Platform?

The term “data platform” refers to technology that is used for collecting and analyzing large amounts of structured and unstructured data for business purposes. Data platforms can be used for multiple purposes such as storage, management, analysis, processing, visualization, and sharing across an organization or company’s network infrastructure.

A data platform can be a single tool or application, or it can encompass multiple components — depending on the size of your team and the scope of your project. A larger organization may use multiple applications or tools to support their data science workflows. However, several vendors offer all-in-one data platforms as well.

Data platforms provide the infrastructure to bring together all the needed data points in one place. A wide range of companies, organizations, and individuals are adopting data platforms as a way to access valuable insights. The rapid growth of digital data has made it increasingly difficult for companies to manage their own data effectively.

A data platform can also be viewed as a service or product that is used to connect various types of large datasets. It can also be defined as a hosting solution where analytical queries are executed against a database. Data platforms are designed to enable the extraction of meaningful information from large datasets with the goal of improving business objectives.

Data platforms can be customized based on what kind of analysis needs to be done and what the company goals are.

Components of a Data Platform

Platforms are made out of layers. The data platform is no different. There are three main layers:

Data Infrastructure Layer - this layer is analogous to the hardware and the software that runs on top of the hardware that enables the storage, movement, transformation, and retrieval of data.

Data Engineering Layer - this layer is a collection of tools and technologies that enable developers to efficiently build out their pipelines at scale without having to reinvent the wheel every time. This includes connectors for extracting data from various sources, transformations for manipulating data, schedulers for automating pipelines, and monitoring tools for tracking the health of these pipelines.

Data Science/Analytics Layer - this layer consists of a collection of tools and technologies that empower analysts and data scientists to explore and derive insights from data in an efficient manner.

If you’re like most companies, you have many different data systems. Your e-commerce team is running a CRM system, your marketing group has its own marketing automation software, and your customer service system generates yet another set of data. You might even have a machine learning or artificial intelligence system that adds to the pile.

All of this data exists in silos, creating an information maze that makes it hard for your company to efficiently operate. In fact, one study found that executives spend more than 40% of their time looking for information or tracking down colleagues who can help them find it - a serious drain on productivity. The right data platform can prevent this drain.

Choosing the right Data Platform

Because of the robust needs of businesses and their reliance on consistent, well organized data, there are a plethora of data platforms in the market to address almost all of your needs. Choosing the right tool for you is dependent on the volume of data your organization works with, who's accessing your data, what you're using your data for, and what your data governance principles are.

Some aspects you should consider when choosing a data platform include:

• Your current data stack. What tools do you use on the day-to-day? Where do you currently house your data? Understanding the capabilities of your current stack and your prospective tooling is essential for a seamless onboard or transition.
• What data you're collecting. The features and permissioning capabilities of your prospective tool likely depend on what data you're storing and how sensitive it is. If, for example, you're expecting to collect and store medical records, you'd want to search for a data platform that has principles of the legislation surrounding medical records in the product.
• Who is interacting with your data. If you're sharing your data with many members on your team who are not data-literate, you'll want to consider a platform with robust documentation capabilities, and likely with a user experience that is aimed towards a non-technical audience.

Examples

Secoda offers several compelling reasons to use it as your data platform:

1. Streamlined Data Workflows: Secoda simplifies the development and management of data pipelines, making it easier and more efficient to work with data. This streamlining saves time and reduces the complexity of data engineering tasks.
2. Enhanced Collaboration: Secoda provides a centralized platform for data teams to collaborate effectively. It offers version control, documentation, and sharing features, promoting seamless teamwork and knowledge sharing among team members.
3. Data Quality and Governance: The platform includes data validation and cleaning features, ensuring data quality and reliability. It also offers robust security measures and auditing capabilities, helping organizations maintain data compliance and mitigate risks.
4. Cost Efficiency: Secoda's efficiency and collaboration features can lead to cost savings by reducing development and maintenance time, minimizing errors, and optimizing resource allocation within data teams.
5. User-Friendly Interface: Secoda's intuitive interface makes it accessible to a wide range of users, from data engineers to data analysts, reducing the learning curve and enabling quicker adoption.
6. Scalability: Secoda is designed to scale with your organization's growing data needs, ensuring that it can accommodate increased data volumes and complexity as your business expands.
7. Flexibility: Secoda supports a variety of data sources and integration options, providing flexibility in connecting to different data systems and platforms.
8. Cloud-Native: Being a cloud-native platform, Secoda seamlessly integrates with popular cloud providers, such as AWS, Azure, and Google Cloud, allowing organizations to leverage the power and scalability of the cloud.
9. Data Documentation: Secoda offers robust data documentation capabilities, making it easier to understand and manage data assets, which is essential for data governance and compliance.

In summary, Secoda enhances data engineering and data management processes by providing a user-friendly, collaborative, and efficient platform. It promotes data quality, governance, and cost-effectiveness, making it a valuable choice for organizations looking to maximize the potential of their data.

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