What Is a Data Platform? Understanding the Benefits, Challenges, and How to Build One

Your essential guide to data platforms: what they are, the benefits, how they work, and the different types. We also dive into the key components of a data platform and how to craft a data platform strategy in 8 steps.

Processing, storing, moving, and managing data is pivotal to the continued success of any organization. To streamline these actions and carry them out as efficiently as possible, enterprises need to use a tried, tested, and effective data platform. 

In this guide, we will discuss everything that’s worth knowing about data platforms. We will discover how the entire infrastructure of an organization can be positively influenced by the successful introduction of a data platform, as well as different types of platforms, the core components, the layers involved, and hurdles you’ll want to overcome.

What is a data platform?

what is a data platform

A data platform is a comprehensive end-to-end solution that encompasses every part of the data management lifecycle. It allows a company to centralize its data solutions and enable efficient, autonomous management via one focal point.

Rather than relying on a series of individual solutions to focus on certain parts of the data lifecycle, a dedicated platform allows an enterprise to concentrate the management of all their important data in one area. 

That means all of the following aspects of data management can be supported by one unified platform.

  • Data storage

  • Data ingestion

  • Data transformation (like normalization and ETL)

  • Business intelligence

  • Data observability

Data ultimately exists for companies to draw insight and information from. Having a platform in place that optimizes this process will lead to a variety of positive effects for any institution.

Why do you need a data platform? Understanding the benefits

It’s tempting for any organization to rely on their existing data solution framework. After all, these singular management systems are familiar, and employees are likely to have a strong understanding of how to use them. While it sounds like a logical thought process, the reality is that these outdated approaches to data management could be costing the company time and money. 

Data platforms maximize efficiency across the board, while also allowing enterprises to pursue new avenues of growth. Here are some key reasons you might need to adopt a data platform:

Remove integration issues

A unified platform ensures that potential issues relating to the integration of data are a nonfactors. Moving, warehousing, and converting data can be time-consuming and complex. These traditional barriers are removed when a platform pulls all tools and resources for data solutions together, enabling conversion to occur seamlessly.

Ease the process of data access

Similarly, accessing data is easier when it is stored in one location. A user only has to interact with one system to draw on the information they’ll need, reducing admin and time spent on infrastructure, and heightening the speed at which users can begin drawing insights from their data.

Employee retention and hiring

Outdated data tooling and siloed data can leave a company struggling on two fronts. It increases the time needed to process and exploit information, while also making it more challenging to hire or retain employees who have been left frustrated by continuously compounding issues. Using a modern data platform that makes day-to-day work easier is alluring to prospective hires and reassuring to existing team members.

Enable future data exploitation

One of the most important uses of data is its role as a predictive analytics tool. Knowing what future market trends might look like is a powerful weapon for any company. A data platform makes trends easier to predict, with insights drawn from a wide array of existing internal databases to form measured estimations for the future.

Increase confidence in governance programs

A centralized data platform greatly reduces risks concerning data governance and the management of personal data. As long as the data was initially uploaded with the appropriate consent measures accounted for, everything within your platform will be safe provided that you do not download the data to an external sheet.

How does a data platform work?

data platform workflow process

Just as with any utilization tool, a data platform operates via a specific workflow process. This allows for a routine approach, ensuring no key factors are missed at any stage. The process can be categorized as follows:

  • Collection

    The first step of the process sees data actively drawn from sensors, weblogs, social media, SaaS sources, databases, or anywhere else relevant information can be accessed.

  • Storage

    When all the data has been gathered, the next step is to store it in a data repository. Good examples of data storage solutions include Amazon S3, Amazon Redshift, and Google Cloud Storage.

  • Processing

    Processing is possibly the most intensive stage of the data platform model. It is the point where you filter, standardize, clean, aggregate, or transform your data to enhance its use as part of your work tasks. To do this effectively, you need a strong data platform with a focus on decision intelligence.

  • Analytics

    Following its processing, data is then run through analytics tools to help provide clear and valuable insights for enterprises. The results generated at this stage will determine future strategies and approaches.

  • Governance

    Once data has been secured in a platform, it is cataloged, traced, and securely managed. This ensures personal identifiable information (PII) remains private.

  • Management

    Some platforms allow companies to back up or archive their data. The data remains within the platform itself, preventing future governance issues.

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What are the different types of data platforms?

Data platforms centralize your solutions – but that doesn’t mean they offer a one-size-fits-all approach. Depending on your exact needs, the size of your company, or the type of data that you want to process, you may need to shift your approach to a certain platform type. Data platforms can be categorized in a number of different ways, depending on what they provide or how they manage data. Here are some of the most common types. 

By deployment type

Some platforms are built with data deployment in mind. This means they’re primarily focused on using predictive analytics and technologies to process and better understand business user needs before deploying a tool or product. These platforms help ensures a business is positioned as perfectly as possible to take advantage of their consumer base.

Examples of this type of platform are:

  • On-premises data platforms. As the name suggests, these platforms are found on-site. Often, they’ll be used within purpose-built hardware that allows a company to remain totally autonomous over their data resources. They’re popular, as they offer full control of each data layer, make it easier to manually preserve data assets, and usually have lower latency levels owing to proximity. However, they tend to cost more and are often harder to scale due to the restrictions of being a single-tenant platform.

  • Cloud data platforms. Any platform that solely uses cloud computing and data repositories can be categorized as a cloud data platform. These platforms ensure adherence to the highest-level security protocols, and that factors like software, infrastructure, high scalability, and backup are stored and managed within the cloud platform. Sharing and analysis are also often a possibility. Latency and price variances can sometimes be issues, though, depending on how often you rely on the tool.

  • Hybrid data platforms. This type of platform takes elements of both on-premises and cloud data platforms. This blend means that organizations can remain in control of on-premises hardware, while being able to benefit from the scalability and growth that cloud data centers provide. These tools are often easier to use, less expensive, and offer better support for remote workforces.

By usage

Platforms that fall into this section use data from existing consumers to help organizations make in-the-moment decisions that affect how they approach their marketing and business strategies. Insight is usually collected through surveys and analytics tools, before data analytics is used to create a full report on existing consumer behavior.

Examples of this type of platform are:

  • Enterprise data platforms. An EDP can integrate and communicate with both internal corporate applications and communications from third parties. Tools and methods of data collection, preparation, and analytical reporting are all included within an EDP, helping to provide a single, harmonized image of a company’s data that can be managed entirely internally.

  • Customer data platforms. If you want to refine your focus specifically on the customer base or clientele of an enterprise, a CDP might be the best approach. It works by creating a comprehensive consumer profile for every customer, drawing on PII that they’ve consented to sharing. This helps to provide a strategy and roadmap for marketing to specific individuals, boosting the chances of a successful campaign.

  • Data analytics platforms. This type of platform allows a user to execute specific and intricate queries on large volumes of data. This is the most effective way to find insight on a focused subject. With its emphasis on data analysis, this could be a good platform for any company that needs to adapt and evolve in a fluctuating sector.

Type of data they process

As part of any analytics program, you may also want to consider the type of data that a platform can use. Different platforms will perform better depending on the type of data that’s being processed. Consider which is best for managing the data, whether that data is structured, semi-structured, or unstructured.

What type of processing they support

Sometimes, you need to make in-the-moment decisions to capitalize on live opportunities. At other times, a more measured or tailored approach could be beneficial. Different platforms will rely on real-time (streaming) and batch data. The former is used in immediate operational analytics, while the latter is primarily used for larger, more complex analytical tasks.

The latest approach to data platforming

A modern data platform (MDP) can both batch process and support data streaming in real time. These were once mutually exclusive capabilities of a data platform, but with an MDP approach, the unification provided allows for versatile and agile management of all information. Furthermore, automated machine learning can execute complex operations using structured, semi-structured, or unstructured data.

What are the layers of a data platform?

Most MDPs consist of six primary foundation layers. These layers metaphorically stack on top of each other to create a finished and comprehensive data platform. But what are the six core layers?

1. Data sources

A data platform does not have the capability to generate its own data. It needs to come from an initial source. Structured, semi-structured, unstructured, and streaming are the types of data that most data platforms can access. 

Structured data (which is already stored in a database) is the easiest to transfer. Semi-structured data like CSV and XML files are also flexible additions to a platform. It may even be the case that unstructured data such as plain-text files without a preestablished data model, or schema files, will have to be housed here.

2. Data ingestion

This layer focuses on the extraction of the data from its original sources. In the case of Extract Transform Load (ETL) and Extract Load Transform (ELT), this is known as the extraction stage. This will either be done via full data extraction (everything taken at once) or incremental data extraction (where only data subsets with altered changes are extracted).

3. Data processing

This layer sees existing data taken and modified, then placed in storage for later analysis. This layer needs to be able to read and apply transformations to data in the platform, working on factors like  data cleaning, formatting, and normalization.

4. Data storage

Once the information has been properly processed, it can sit in storage, waiting for its chance to be used properly as part of an analytics campaign. A wide variety of data warehouses can be incorporated within the platform. The data warehouse you require depends heavily on the type and size of data that you need to store.

5. Data analytics

At its core, data exists to be used for predictive and preventive measures. The analytics layer ensures that data which has been correctly processed, cleaned, and stored, can be used to make insightful and accurate estimations about the future behavior of a wider market or customer base. This is typically carried out via reports to a dashboard, but might also include analysis like diagnostic, predictive, prescriptive, or automated analytics.

6. Data visualization

The data lifecycle concludes with discovery. This business intelligence is often presented in a visualized form, such as a dashboard, graph, table, graphic, or even a written report. This “data storytelling” is designed to provide a clear and concise way to digest the insight drawn from your platform.

In addition to this linear approach, data platforms can include other layers designed to heighten the experience for users. Other layers you might find in a robust platform include:

Data access

This layer makes it easier to gain access to data stored in persistent storage of some kind such as an entity-relational database. In essence, it provides a standardized interface for systems and applications to engage and interact with any data stored within the platform.

Data publishing

If the data you’re using needs to be shared through or to external channels, you will need a data publishing layer. This is an important step for any enterprise that needs to share data from a platform so that it can be used by an external application.

Key components of a data platform

A good data platform approach needs to address the complexities and demands of modern data ecosystems. To do that successfully, the platform must include a series of important components. They are:

A user-centric design

A good data platform is nothing if users can't easily easily interact with it and get the results and insights they need for the company to thrive. The key here is for users not just to consume data, but also contribute to the data landscape. The platform also needs to be flexible, allowing for customization of reporting and analytics techniques. 

Scalable data integration

Most modern approaches to data platforming make it easier than ever to draw on a wide array of data sources, with architecture that can adapt to various data volumes and types. Furthermore, it’s usually possible to connect the platform with legacy systems, making it easy to migrate and transition relevant data from your outdated warehouse.

Self-service analytics

A good data platform puts the power to carry out comprehensive data analytics in the hands of everyone within your organization. By having more eyes on good data, a company heightens its opportunity to use and maximize the efficiency of data-driven decisions. As well as this, it also makes it possible to serve the many different analytical requirements of varying departments.


In its purest form, automation takes manual, time-consuming tasks away from workers and drops them into the lap of artificial intelligence (AI). Data platforms are able to rely on AI to carry out analytical tasks, while also serving as real-time systems for red flags.

Challenges with a data platform

Data platforms are complex systems, so it’s natural to experience a certain level of resistance during different stages of a data platform lifecycle. Here are some of the primary hurdles that an enterprise might have to overcome. 


Prioritizing the wrong requirements


It’s vital to set up a data platform with a clear end goal in mind. Ultimately, the platform doesn’t exist solely to serve as a dumping ground for data, which may or may not be called upon when needed. Unfortunately, it can sometimes be tough to know exactly what your priorities should be. Focusing on the wrong things is just as damaging as having no clear focus at all.


Ensure your end goals are clearly defined and made an intrinsic part of the setup of any platform.


Failing to take data legacy implications into account


Integration between existing data solutions and a new platform aren’t always seamless. This can prove to be a major hurdle if existing systems or processes don’t immediately blend with the technology used as part of a new platform.  


Fully understand and audit what your existing data processes mean for any potential platform. Preparedness is essential to mitigate any risk associated with an integration between legacy software and your new data approach.


Some lack data governance


The safe and careful handling of sensitive PII is paramount to any strong data platform approach. Some data platforms lack the initiatives to store this information safely, missing the robust elements needed in a framework to protect personal data from leaking.


Ensure that any data platform software you use puts data governance at the forefront. You need a clear and concise system to manage and protect data at every stage of the pipeline.

How to craft a data platform strategy in eight steps

For a data platform to have the desired impact, it must be set up correctly. To make sure that happens, organizations can take several key steps that will help them obtain insight faster and make informed decisions more confidently.

1. Define objectives and use cases

First and foremost, it’s vital to understand what the end goal is for any enterprise looking to introduce a data platform. The key issue the platform will address needs to be identified, as well as what kinds of activities will be performed on it and by whom. The type of data processing and analytics should also be taken into account at this stage, as well as the data governance and additional policies that should be followed.

2. Current data architecture and infrastructure

The existing data structure also needs to be assessed. This might involve carrying out a data audit or inventory. The end result of this should be a clear and concise view of how data is currently being handled within the organization, as well as what types of data can or need to be stored. Understanding this will make it easier to implement a platform that seamlessly blends with legacy systems.

3. A roadmap that aligns with the organization's objectives

Any enterprise will work to specific targets and objectives. A good data platform will pinpoint these key moments and ensure a robust rollout corresponds with them. Establish goals and a timeline of desired events, then use a platform to monitor and analyze the success of these metrics. 

4. Selecting the right technology stack

This is an important step toward ensuring an enterprise’s data needs are accommodated both immediately and in the future. The platform needs to be adaptable to the existing data infrastructure, while also ensuring scalability is not overlooked as a company grows.

5. Data governance and security

Without the proper data security measures in place, sensitive breaches of PII and cyberattacks become possibilities. That’s why clear policies and procedures for managing this kind of data need to be put in place during the setup of any data platform. It may be necessary to assign or even hire a dedicated compliance officer. Their role would be to ensure all data management adheres to all relevant regulations, while also training others in how to store, analyze, and transfer data safely.

6. Data integration and interoperability

The combination of data from various different sources helps to provide a rounded, insightful, detailed, and accurate overview. Typically, different internal databases can include social media, events, sensors, website interactions, and call center logs. These data sources need to be identified early on, with the specific requirements for each worked out at this stage. The volume and complexity of the data available will decide what kind of integration approach to take.

7. AI initiatives

Modern data approaches involve the heavy use of automation and machine learning to allow for immediate and flexible alignment with an organization’s core objectives. At this point, it can be valuable to assess where using an AI helper might speed up processes, automatically flag risk factors, make data-driven insights, or predict future market trends.

8. Building a data culture

Once the core of the data platform has been plotted out, the final phase is to implement a strong data culture within an enterprise. This means educating everyone on the importance of safe and effective data management, making training and education programs a part of this process. It's even possible to use specific KPIs and metrics to track success.

Once a data platform has been implemented to its fullest capacity, a strong monitoring and analytics strategy should be established to ensure that insights and data performance are optimized.

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