In a perfect world, every business aiming to build a data platform – meaning an infrastructure for storing, processing and managing data at scale – would have a team of skilled data scientists and analysts already on hand, ready to implement the platform of its dreams.
In the real world, of course, few businesses enjoy this luxury. Most need to approach the design and deployment of data platforms with smaller teams and fewer resources than they would ideally have.
The good news is that, as this article explains, it’s possible to build an effective data platform under these conditions. You just need the right strategies for working around the limitations you face.
What is a data platform, and why would you want one?
A data platform, simply defined, is the infrastructure an organization uses to work with data. Data platforms facilitate the efficient processing and management of information, which in turn means that the organization can take greater advantage of the data.
Data platforms are important because the typical business owns vast reams of information of diverse origins. By default, this information is often siloed, making it difficult to correlate different types of data to drive effective analysis. The data may also contain errors, inconsistencies or other issues that make it challenging to process.
Data platforms mitigate these challenges by providing a centralized repository where the business can store, clean, analyze and secure its data.
The challenges of building a data platform
Although data platforms play increasingly important roles in helping organizations take full advantage of their data (which is especially valuable for deploying new types of data-dependent technologies, like AI), they’re also complex.
Building a data platform requires addressing challenges such as:
- Designing the right data architecture: Choosing how to integrate data from disparate sources requires a nuanced understanding of the data types you’re working with, as well as which use cases your data platform needs to support.
- Selecting a data infrastructure: Various types of infrastructure solutions, like data lakes and data warehouses, are available to help implement data platforms. But selecting the right offering is not always easy; it entails not just understanding each solution’s technical capabilities but also the pros and cons of varying vendors.
- Governance needs: In addition to consolidating data, modern data platforms need to enforce governance rules related to security and privacy. To create and enforce these policies, teams must understand which data governance and compliance mandates they must meet, as well as how to implement the technical controls necessary to enforce them.
- Cost implications: The cost of building and operating a data platform can be significant, and poor design decisions could lead to overspending. Implementing the most cost-effective platform requires the consideration of a variety of factors, such as the pricing models of data tools and platforms, the costs of data storage and data transfer and the implications of varying data architectures for operational costs.
For the typical organization, managing these challenges tends to be difficult due, above all, to lack of the necessary in-house data expertise. Many companies don’t have dedicated data experts at all, and those that do may find that their data teams are not large enough to tackle the implementation or management of a complex data platform on their own.
How to build a data platform – even without the ideal resources
But just because your organization doesn’t have a dream team of data experts on hand doesn’t mean you can’t implement an effective data platform. Using the following strategies, you can build the solutions you need to manage data in the way you want.
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Assess in-house data skills
A first step is to create a competency matrix that identifies which data engineering and data management skills currently exist within your organization. You may find that, in some cases, staff who don’t work primarily in data-centric roles do possess expertise that can help with data platform design and implementation.
For example, developers who have extensive experience working in the cloud may be able to help deploy a cloud-based data warehouse, even if they are not data engineers per se.
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Identify upskilling opportunities
In addition to determining which data skills your people currently have, evaluate opportunities for staff to acquire additional expertise via upskilling. There may be employees who haven’t yet worked with the specific technologies you’ll use to build a data platform, for instance, but who have the basic background knowledge necessary to acquire the skills they’ll need to support those technologies.
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Assess and address data skills gaps
Once you know which data skills your organization currently possesses or can reasonably acquire in the short term, you’ll also know which ones you don’t have – and therefore need to cultivate.
Closing the gap between the skills your team has and the skills you need could involve hiring additional staff who possess the requisite expertise. It can also come in the form of working with outside consultants to help fill in your organization’s skills gaps – but keep in mind that the ideal role of outside experts is not merely to stand in for engineers you wish you had on staff but do not. Instead, consultants should take the time to transfer the necessary skills to your staff so that they become capable of managing the data platform on their own, without making you reliant on consultants indefinitely.
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Leverage data tools strategically
Choosing the right data tools can also help to mitigate the challenge of limited in-house data engineering expertise. Modern data management platforms, like Snowflake and Databricks, offer a variety of built-in capabilities for consolidating data, managing data quality and handling parts of the analytics process. The more work these tools do for you, the less your team has to do on its own.
This isn’t to say that implementing a data platform is as simple as adopting a data warehouse or lakehouse and calling it a day. It’s not because, as I mentioned, you need to make a variety of decisions related to data architecture, data governance policies, data analytics workflow configurations and so on. But choosing the right data infrastructure platform makes these tasks easier.
Conclusion: A pragmatic approach to data platforms
Modern data platforms unlock a wide range of powerful benefits for businesses. Choosing not to build one because your organization doesn’t have the perfect data engineering or data science team means missing out on those benefits and losing ground to competitors who are able to take better advantage of modern data management.
Instead, organizations should pragmatically assess the capabilities they do have for building a data platform, find ways to supplement the skills they lack and, over time, use the resources available to them to implement a data platform aligned with their needs. They should also, of course, ensure that they have the team resources necessary to maintain their data platform over time – but I’ll save that topic for another day.
By Igor Beninca
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