What is a Data Platform?

Your business is built on data. Data is involved in every analysis, decision, audit…

So obviously, you will need high quality and reliable datasets that isn’t only accessed and understood by data engineers.

A data platform will manage and process your data from the source until the end of its life cycle.

What is a data platform?

A data platform is your business’ data centre. It holds, manages, processes and interprets all of your company’s data.

As this is a very complex system, you’ll need data stack that is  built from several different vendors who specialize with every aspect and integrate with one another.

As businesses grow and become more complex, the amount of documents, information, and security increases exponentially, so it is crucial to build a secure web that can evolve with your data.

Your data platform should have 2 sides to it:

• Upstream data: Data is ingested. cleaned, transformed, and made usable. Upstream data is analyzed and transformed by data engineers.
• Downstream data: The transformed data is now structured and usable. This data can be used by marketing and sales teams in the form of dashboards, product features, or ML models, for example.

Layers in a data platform

As mentioned, your data platform should be an ecosystem with several different products that work together to get the most out of your data.

But what elements should you have in your data platform?

Data observability

Your data observability tool essentially watches over your entire data platform.

It is a software designed to monitor the health, quality, and reliability of your data pipeline and dataset.

Your data observability tool will be able to automatically detect any issues and anomalies, such as missing data or unexpected patterns (among many others) and notify your data engineers so the problem can be solved quickly and efficiently.

The best data observability tools will also be able to prioritize issues so that data engineers are able to attend to each problem in the correct order and offer the necessary attention.

A data observability tool offers 3 technologies to maintain the quality of your data:

1. A data catalog

This catalog essentially stores your metadata. All the information about your current data y stored and analyzed to provide context to your tool.

By saving metadata, your data observability tool is able to provide context and insights so your tool knows how to spot anomalies or abnormal patterns.

1. Data monitoring

This will allow your data observability tool to watch over your data and alert your data team when there is an anomaly.

Additionally, Sifflet will also suggest what parts of your data need closer monitoring so you can focus on sections that tend to crash or break.

1. Lineage

Lineage allows your data observability tool to build a detailed map of how data flows through your entire data pipeline, showing the raw source and moving through every step it takes.

In case of an anomaly, this will allow your team to spot where the issue occurred and see how and where there has been an impact, so that measures can be taken effectively and, most importantly, quickly.

Lineage is essential to trace any issues to is root cause and maintain reliable data system by offering detailed visibility of your data ecosystem.

Data observability tools:

• Sifflet
• Monte Carlo
• Bigeye

Data ingestion

Raw data has to be imported or transferred into your data ecosystem. A data ingestion tool will help you bring in your data from one system into another, while keeping its quality and integrity intact.

For example, data from your CRM.

That data will then be stored into a data warehouse, a data lake, or a database.

Your data ingestion tool with extract the raw data from its original source and load it into your destination system following an ETL or ELT process.

It is essential to include a data ingestion tool into your data platform, because if your data is not imported correctly, every step in your data ecosystem will be faulty.

Your data ingestion tool can collect data in batch processing or real-time processing.

Batch processing is the most common type of ingestion, since it collect large masses of data, while real-time processing is more suitable for smaller amounts of data as it requires constant monitoring to constantly update.

Data ingestion tools:

• Fivetran – Managed, ELT-focused tool with pre-built connectors.
• Stitch – Cloud-first, lightweight tool for moving data into warehouses.
• Kafka (for real-time ingestion)

Data storage

All the data you bring into your data ecosystem will have to be stored, managed, and processed for a long period of time. To do this safely, you will need a data storage tool.

Some storage tools are optimized for transactional processing, while others are designed for analytical queries across massive datasets.

Effective data storage solutions support scalability, reliability, and integration with data processing and analytics tools, enabling organizations to retrieve, query, and analyze their data efficiently.

Your data can be held in a data warehouse, a data lake, or a database.

A data warehouse is designed to store large volumes of structured data, particularly optimized for SQL queries and analysis.

• Example: Snowflake

A data lake holds large volumes of all types of data including raw, unstructured, semi-structures, and structured data to enable its analysis.

• Example: Amazon S3

A database holds structured data that is organized and optimized to access information quickly.

• Example: MongoDB

Data transformation and modelling

Your business’ raw data will have to be cleaned and structured in order to offer accurate and reliable insights for analytics, reporting, and machine learning, that can be understood across different departments.

This is where a data transformation and modelling tools comes into play.

Although the tools tend to be used interchangeably, they have different features.

A transformation tool will clean and summarize your data, convert data types, and merge datasets.

On the other hand, a modelling tool will take your data to organize and structure it to build metrics.

Data transformation and modelling tools:

• Apache Spark → data transformation
• dbt (data build tool) → Data transformation and modelling
• Dataform → Data modelling

Business intelligence

A business intelligence tool will offer the “end product” of your data.

Once your data has gone through the entire data pipeline and other layer in your data platform, it is transformed into clear dashboards, charts, graphs, or reports that each department can use for analysis and reporting.

Business intelligence tools will allow teams to uncover trends, anomalies, and opportunities and make data-driven decisions concerning sales strategies or marketing approaches, for examples.

A Business Intelligence (BI) tool in a data platform is a software application that enables users to visualize, analyze, and report on data to support decision-making.

Business intelligence tools:

• Tableau
• Power BI
• Looker (Looker Studio)

Types of data platforms

The type of data platform a company adopts often depends on factors such as data scale, use cases, infrastructure preferences, and desired outcomes.

There are 4 different types of data platforms that are built to adapt to your type of business and your needs.

Enterprise data platform (EDP)

An enterprise data platform is a unified and centralized system built to store and manage an enterprise’s data. It’s main goal is to make all information accessible across all team members.

Originally this information was stored on-premises, however, now it stores structured data in a combination of data warehouses, data lakes, and business intelligence tools.

EDPs have a high capacity to handle large volumes of data that can be used and analyzed in many different departments, such as Sales, Marketing, Finances, etc.

By centralizing all structured data, an EDP guarantees to improve data accessibility, consistency, and data quality so an enterprise can work effectively as a unit.

Big data platform (BDP)

A big data platform stores and processes large amounts of real-time data. These datasets can be structured, semi-structured, or unstructured data.

BDPs can be found integrated in SaaS products or DaaS and are distributed across several platforms such as storage systems, processing engines, and data ingestion frameworks.

Its goal is to offer deep and up-to-date insights into analytics, predictive analytics, customer behavior, operational optimizationand even fraud detection in order to solve complex problems.

By offering large amounts of real-time data, a big data platform allows businesses to make data-driven decisions quickly and accurately, leading to improved performance and innovation.

Cloud data platform (CDP)

A cloud data platform is a scalable data infrastructure hosted entirely on the cloud. As there is no on-premises system, it reduces the cost of staff and can host big data platforms, enterprise data platforms, and customer data platforms.

CDPs allows businesses to handle large volumes of data and complex analytics without added costs, while offering high availability and scalability.

CDPs are usually integrated with data ingestion tools, datalakes, data warehouses, ETL/ELT processing, machine learning and business intelligence tools.

Modern businesses tend to rely on cloud data platforms as they are secure, unlimited and guarantee easy access to all members within a company.

Customer data platform (CDP)

A customer data platform essentially builds a unified and centralized profile for each customer.

Data for each customer profile is gathered from several different sources such as emails, social media, CRMs, etc. This profile can then be used to build customized marketings strategies and user experiences.

Customer data profiles typically offer built-in tools for data ingestion, identity resolution, audience segmentation, and integration with marketing and analytics tools. This allows marketers and customer experience teams to deliver more relevant, consistent, and timely interactions.

Benefits of a data platform

A data platform will require its own team of data engineers and an intricate web of different tools working together.

And while, yes, building your data platform will take time and money, the benefits to your business are endless.

• Increased efficiency

By integrating several tools into your data platform, data engineers and IT teams won’t have to manually gather, understand, clean, distribute, and fix raw data.

This allows data to flow seamlessly through the pipeline and reach other teams quicker and more accurately.

• Increased team collaboration with centralized data

Different teams and departments tend to work in parallel when they should be working as a whole.

Data is the basis of any business. With a well-built data platform, all teams have easy access to data models, dashboards, and other tools in order to make informed decisions about more specific strategies.

• Higher data quality

As we have mentioned earlier, building a data platform (particularly with real-time data), will allow businesses to access up-to-date and accurate data.

With integrated data governance, validation, and transformation tools, data platforms help maintain consistent and high-quality data across the organization.

With a data observability tool, data engineers are able to quickly spot an error, solve it and trace its impact without having to waste hours.

• Scalability

As your business grows, so will the volume and complexity of your data.

With data platforms, you can avoid constant updates in infrastructure. Specifically with cloud data platforms, you can hold massive (if not unlimited) amounts of data without collapsing your system.

• Better insights and analytics

Accurate, complete and updated data will offer much more detailed insights and deeper analytics.

Data platforms provide the foundation for advanced data applications such as machine learning, predictive analytics, customer behavior, and real-time insights, helping businesses stay competitive.

• Security and compliance

Data platforms include built-in security features such as encryption, role-based access control, and audit logging, helping organizations protect sensitive information while being compliant to security regulations.

How to build your data platform

Your data platform is essentially the ecosystem in which your datasets will flow and be stored.

It is essential to build a data platform that is adapted and unique to your business and your needs.

You can build your data platform by following these 4 steps:

1. Define your needs and goals

The first step is to analyze the type of data your business will receive and what type of datasets you’ll need to prioritize.

Additionally, you’ll need to assess the problems you will be solving with the data that you will ingest and its sources.

You’ll also need to define the level of security your data needs. Banks will typically need to choose a hybrid data platform, where some data can be stored on the cloud and the more sensitive data will be stored on-premises, and e-commerce can store all data on the cloud.

2. Design your data platform’s layers

Decide what layers you’ll need to make the most out of your data platform.

• Data observability: Analyze your entire data pipeline and spot issues quickly.
• Data ingestion: Gather information accurately and securely.
• Data transformation and modelling: Structure your data
• Data storage: Store you data in data warehouses, data lakes, databases, etc.
• Business intelligence: Analyze and interpret your data.
• Data governance: Manage metadata, access control, and data quality tools.

3. Choose the right tools

Once you have decided the layers you will be including in your data platform, you can start choosing tools that adapt to your budget, team skillset, and data needs.

Make sure you check integrations with your current tool stack in order to make the most out of your data.

4. Implement data governance and security

Security breeches and data leaks will make your business unreliable and can lead to legal issues.

To implement data governance and security start by defining clear roles and access policies to ensure that only authorized users can view or manipulate sensitive data.

The platform must also be designed to comply with data privacy regulations such as GDPR, HIPAA, or industry-specific mandates. This includes managing user consent and data retention policies.

Finally, it's essential to monitor data lineage so you’re aware of where your data has been and any possible security breeches.

And lastly…

A data platform is formed by a stack of different tools built to store, manage, process, and analyze all of your company’s datasets.

Your data platform is the foundation for any organization, for advanced capabilities like machine learning, predictive analytics, and personalized customer experiences.

A data platform will guarantee:

✅ Accessibility

✅ Quality data

✅ Accurate reports

✅ Successful strategies

✅ Scalability

✅ Efficiency

If you’re looking for a solid data observability tool that watches over your entire data pipeline, spots and prioritizes errors and predicts shifts, try out Sifflet.

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