Top 10 Factors to Consider When Building a Data Platform in 2024

Updated September 29th, 2023
things to consider when building a data platform

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Building a data platform addresses the critical challenges facing modern businesses in managing their data assets.

The struggle to centralize data from disparate sources, the inefficiencies tied to manual data handling, and the delays in deriving actionable insights are alleviated when a comprehensive data platform is in place.

By serving as a consolidated repository and processing hub, a data platform streamlines data governance, analytics, and machine learning.

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In this article, we will explore:

  1. 10 Things that you must consider when building your data platforms
  2. Steps to building your data platforms
  3. Qualities of a good data platform

Ready? Let’s dive in!

Table of contents #

  1. What is a data platform?
  2. 10 Things to consider when building a data platform
  3. How to build a data platform: Here are 10 steps!
  4. What are the key qualities of a good data platform?
  5. Summary
  6. Related Reads

What is a data platform? #

At its core, a data platform is an integrated technology solution that allows companies to consolidate, manage, and analyze their data from multiple sources in real-time.

It’s like a central nervous system for a company’s data, ensuring information flows seamlessly across departments and functions. With the right data platform, companies can harness the potential of their data in ways they never imagined.

In a nutshell, building a data platform turns data from a mere operational byproduct into a strategic asset, allowing for real-time decision-making, enhanced customer experiences, and faster innovation—all within a secure and compliant ecosystem.

To know more about data platform, check out the article -> What is a Data Platform? Understanding its Components, Tools, and Evolution

Now, let’s look at the top ten things to consider when building a data platform.

10 Things to consider when building a data platform #

Implementing a data platform is not a decision that should be taken lightly. Here are nine things to consider when implementing a big data platform:

  1. Understand your data needs
  2. Scalability
  3. Security and compliance
  4. Integration capabilities
  5. Data processing speed
  6. User-friendly interface
  7. Customization
  8. Cost
  9. Support and maintenance
  10. Future-proofing

Let’s delve deeper into the nuances of each point when considering the implementation of a data platform.

1. Understand your data needs #


Before diving in, ensure you have a clear understanding of the data you’re handling. Whether it’s customer data, sales metrics, or operational insights, each will require different tools and structures.

Moreover, understanding the frequency of data updates—whether real-time, daily, or monthly— can influence storage and processing decisions. By clearly identifying your data requirements, you can ensure that your platform is tailored to provide the most value for your specific needs.

2. Scalability #


Always anticipate future growth. Future growth and increased data volume are inevitable. Ensure that your data platform can scale without hitches.

Whether it’s handling more data points, more users, or more complex queries, the platform should adapt without compromising performance.

Moreover, scalability ensures that as your business evolves, your platform won’t be a limiting factor but a facilitating tool. Properly scaled platforms can also lead to cost savings in the long run, as they can efficiently manage increased loads without the need for frequent overhauls or replacements.

3. Security and compliance #


Navigating the digital realm safely is no longer just a luxury, but an absolute necessity. A platform’s integrity lies not only in its capabilities but also in its commitment to safeguarding the data it handles.

Ensure your platform adheres to GDPR, CCPA, and other relevant data protection regulations. In 2023 alone, businesses lost an estimated $5.4 billion due to data breaches and subsequent fines.

The ramifications of inadequate security protocols extend beyond financial losses; they erode trust and can irreparably damage a brand’s reputation. Prioritizing stringent security measures and adhering to compliance standards is not only a proactive approach but a foundational aspect of any robust data platform.

4. Integration capabilities #


Integration is paramount to ensuring a cohesive data ecosystem. Your platform should not only be robust in its stand-alone capabilities but also flexible in its ability to connect and synchronize with other critical systems.

A seamless integration capability ensures that data flows uninterrupted, minimizing silos and offering a more holistic view of business operations. Specifically, your platform should easily integrate with other systems like CRM, ERP, and marketing tools.

5. Data processing speed #


In the age of real-time analytics, a lag in data processing can set you back significantly. The platform should be able to handle high-speed data ingestion and processing.

Moreover, as businesses evolve, the need for instant insights becomes paramount, making processing speed a pivotal aspect of any data platform.

Slow or inconsistent processing not only hampers decision-making but can also lead to missed opportunities in fast-paced market scenarios.

6. User-friendly interface #


A system that’s difficult to navigate can discourage its use. Prioritize usability. When tools align intuitively with user expectations, it fosters confidence and encourages frequent engagement.

Remember, the best technology solutions feel seamless, making tasks easier rather than introducing new complications.

7. Customization #


Your business is unique, and so are your data needs. The platform should be flexible enough to adapt to specific requirements.

With an ever-evolving business landscape, having a customizable interface ensures that as your objectives shift or expand, your data platform can change in tandem.

Moreover, a platform that allows for customization can be tailored to align with your brand’s aesthetics and terminology, offering users a familiar and cohesive experience.

8. Cost #


Balancing the financial aspects of implementing a data platform is crucial in any business decision-making process.

While it’s essential to invest in a robust data platform, ensure you’re getting value for your money. Evaluate the cost versus the benefits.

Remember, an effective platform will provide long-term advantages that can significantly outweigh initial expenses. Consider both upfront and ongoing costs, and weigh them against potential productivity gains, data insights, and competitive advantages that the platform offers.

9. Support and maintenance #


A good vendor will offer excellent post-sales support. Ensure there’s a dedicated team to handle any issues or updates.

This not only safeguards your investment but also instills confidence in the system’s reliability and the vendor’s commitment to your success.

Remember, in the world of technology, it’s not just about starting right but sustaining that momentum, and a robust support system plays an instrumental role in this journey.

10. Future-proofing #


The tech world evolves rapidly. Choose a platform that is forward-looking and can adapt to new technologies and methodologies.

This proactive approach not only saves potential future costs and transition efforts but also ensures that you remain competitive and innovative in your industry. Investing in future-proofing is investing in sustainability and growth.

How to build a data platform: Here are 10 steps! #

Building a data platform is a complex undertaking that requires meticulous planning, strategic decision-making, and a keen understanding of the business’s current and future data needs.

Here’s a step-by-step guide to building a comprehensive data platform:

  1. Assessment and planning
  2. Select the right technology stack
  3. Data ingestion
  4. Data storage
  5. Implement data processing tools
  6. Develop data pipelines
  7. Integrate analytics and BI tools
  8. Implement security protocols
  9. Test the platform
  10. Training and onboarding

Let’s explore each step in detail.

1. Assessment and planning #


Begin with a thorough audit of your existing data infrastructure. Identify gaps, redundancies, and areas for improvement.

This initial assessment will not only spotlight the weaknesses in your current system but also guide the direction for your new platform’s development.

By understanding where you stand, you can chart a more informed and strategic path forward, ensuring that the upcoming changes align perfectly with your organization’s data goals and operational needs.

2. Select the right technology stack #


Whether you’re leaning towards cloud-based solutions like AWS, Azure, or Google Cloud, or considering on-premises setups, the choice should align with your business needs.

It’s essential to consider factors like data security, integration capabilities, cost, and future scalability.

Not every solution fits all, so it’s crucial to evaluate each option based on its merits and relevance to your organization’s specific objectives and growth plans.

3. Data ingestion #


Determine how data will be fed into the platform. This could be through batch processing, stream processing, or a hybrid approach. The chosen method should align with your data’s volume, velocity, and variety.

It’s imperative to ensure a seamless ingestion process, as this lays the groundwork for all subsequent data operations and analytics.

4. Data storage #


Choose a robust database system that caters to your data type and volume. It could be relational databases, NoSQL, or data warehouses.

Ensure that the chosen system aligns with your long-term data strategy and offers the flexibility to adapt as your data needs evolve. Moreover, considering data retrieval speeds and redundancy measures can significantly influence performance and reliability.

5. Implement data processing tools #


Tools like Apache Spark or Flink can help process vast amounts of data quickly.

Incorporating such tools can dramatically enhance real-time data analytics capabilities, enabling businesses to derive actionable insights with greater efficiency.

Additionally, these tools also offer scalability, ensuring that as data volumes grow, processing times remain optimal.

6. Develop data pipelines #


Create workflows to manage the flow of data from ingestion to storage and analytics.

These data pipelines ensure consistent data movement and transformation, optimizing the accessibility and reliability of your data assets.

Additionally, well-constructed data pipelines can enhance real-time processing capabilities, allowing for timely insights and informed decision-making.

7. Integrate analytics and BI tools #


Tools like Tableau, Looker, or PowerBI can help visualize and derive insights from the data. By seamlessly integrating these tools, you can unlock a more dynamic and interactive way to present your findings.

This not only enhances decision-making processes but also facilitates a deeper understanding of complex data patterns for stakeholders.

8. Implement security protocols #


Encrypt sensitive data, use secure data transmission methods, and ensure regular audits.

Implement multi-factor authentication to prevent unauthorized access, and maintain up-to-date firewalls to shield against potential threats.

9. Test the platform #


Before full-scale deployment, test the platform for any glitches, vulnerabilities, or inefficiencies. A comprehensive testing phase ensures smooth operations when the platform is live and minimizes potential disruptions.

Moreover, gathering feedback during this phase can provide valuable insights for refining and optimizing the platform further.

10. Training and onboarding #


Ensure that the team knows how to use the platform efficiently. Provide regular training sessions and updates.

It’s essential to have a structured onboarding process for new members, ensuring a smooth transition and immediate productivity.

Continuous refresher courses can also address any evolving platform changes, keeping everyone up-to-date and adept in their roles.

What are the key qualities of a good data platform? #

In the digital age, a data platform stands as the backbone for businesses striving to derive insights, make informed decisions, and innovate. As the bridge connecting raw data to actionable intelligence, it’s paramount that this platform is robust, agile, and efficient.

Here, we delve into the key qualities that set apart a good data platform from the rest.

  1. Scalability
  2. Security
  3. Flexibility and integration
  4. Real-time processing
  5. Data quality assurance
  6. User-friendly interface
  7. Cost-efficiency
  8. Robust data governance

Let’s look at each of these qualities in detail:

1. Scalability #


A top-notch data platform should be able to handle growth effortlessly. As data volume and velocity increase, the platform should scale horizontally or vertically to accommodate this surge without compromising on performance. This ensures that the business remains agile and can adapt to changing data needs.

2. Security #


In the era of cyber threats, securing data is not just an option but a necessity. A good data platform provides robust security measures, from encryption at rest and in transit to regular audits, ensuring that sensitive business data remains protected from unauthorized access and breaches.

3. Flexibility and integration #


Modern businesses use a myriad of tools and technologies. The data platform should seamlessly integrate with these systems, allowing for smooth data flow across the ecosystem. Furthermore, it should support a diverse range of data formats and sources, fostering adaptability.

4. Real-time processing #


To make timely decisions, businesses often require real-time analytics. The platform should be equipped to handle real-time data processing, allowing stakeholders to glean insights as events unfold, rather than relying on batch processing which might introduce lags.

5. Data quality assurance #


Raw data is often messy. A competent platform provides mechanisms to cleanse, validate, and transform data, ensuring that the analytics and insights derived are based on high-quality, consistent, and reliable data.

6. User-friendly interface #


Not everyone in an organization is tech-savvy. An intuitive user interface, complemented with comprehensive documentation and support, allows users of varying technical prowess to access and harness the power of data without steep learning curves.

7. Cost-efficiency #


While businesses understand the value of a good data platform, it shouldn’t break the bank. The platform should offer competitive pricing models, allowing businesses of all sizes to capitalize on data-driven insights without incurring prohibitive costs.

8. Robust data governance #


Managing and cataloging vast amounts of data requires robust data governance tools. A good platform offers features like metadata management, lineage tracking, and access control to ensure that data is not only secure but also organized and easily accessible.

In conclusion, as data continues to be the gold of the digital age, the right platform can help businesses mine this gold efficiently. Keeping these key qualities in mind during selection can ensure that the chosen platform serves as a reliable, powerful, and enduring ally in the data journey.

Summary #

Building a comprehensive data platform is no small feat, but the rewards are significant. In a world dominated by data, a robust platform will not only give you a competitive edge but also streamline operations and drive business growth.

By understanding the importance of such a platform, considering essential factors, and following a systematic approach to building it, businesses can stay ahead of the curve. Given the increasing reliance on data, it’s clear: those who invest in a sophisticated data platform today will be the leaders of tomorrow.

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