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All You Need to Know About Building Your Future Data Platform*


** cloud / modern and all other synonym


You probably have heard that term more than once before Data Platform / Modern Data Platform / Cloud Data Platform and some more variations of the same concept.

There is much buzz going around this topic, and I'm here to put some order and provide tips to build it right.

--> Here is how slowly but surely the data platforms' awareness grows...



At its core, a data platform is a central repository for all data, handling the collection, cleansing, transformation, and application of data to generate business insights.

For many organizations, building a data platform is no longer a nice-to-have option but a necessity, with many businesses distinguishing themselves from the competition based on their ability to glean actionable insights from their data.

So data platform is a thing, especially if you have experienced massive data growth (scale) and got swamped with ad-hoc requests and dashboards requirements.


As companies - many of the data groups thriving for a data-driven culture and data platform is a massive enabler

*Scroll down for two cloud data platform examples*


So how should we think of data platforms?

In the same way we see data itself as a product, data-first companies like Uber, LinkedIn, and Facebook increasingly see data platforms as products too, with dedicated engineering, product, and operational teams.

Despite their pervasiveness and popularity, yet, data platforms often turned up with little foresight into who is using them, how they’re used, and what engineers and product managers can do to optimize these experiences.


Before you start - check those topics to make sure you'll reach your future goals:


Align Your Product’s Goals with the Company Goals

When you’re building or scaling your data platform, the first question you should ask is, how does data map to your company’s goals?

To answer this question, you have to put on your data-platform PM hat. Unlike specific product managers, a data-platform product manager must understand the big picture versus area-specific goals. This is because data feeds into the needs of every other functional team, from marketing and recruiting to business development and sales.

Gain Feedback and Buy-in from the Right Stakeholders

It goes without saying that receiving buy-in up front and iterative feedback throughout the product development process are both necessary components of the data-platform journey. What isn’t as widely understood is whose voices you should care about.

While developing a new data-cataloging system at a leading transportation company, one product manager we spoke with spent three months trying to sell the vice president of engineering on her team’s idea, only to be shut down in a single email by the VP’s chief of staff.

At the end of the day, it’s important that this experience nurtures a community of data enthusiasts who build, share, and learn together. Since your platform has the potential to serve the entire company, everyone should feel invested in its success, even if that means making compromises along the way.

Prioritize Long-Term Growth & Sustainability over Short-Term Gains

Data platforms are not successful simply because they benefit from being “first-to-market.” For instance, Uber’s big data platform was built over the course of five years, constantly evolving with the needs of the business, Pinterest has gone through several iterations of its core data analytics product, and LinkedIn has been building and iterating on its data platform since 2008!

My suggestion: choose solutions that make sense in the context of your organization, and align your plan with these expectations and deadlines.

Sometimes, quick wins as part of a larger product development strategy can help with achieving internal buy-in—as long as it’s not shortsighted.

Rome wasn’t built in a day, and neither was your data platform.

Sign Off on Baseline Metrics for Your Data and How You Measure It

It doesn’t matter how great your data platform is if you can’t trust your data, but data quality means different things to different stakeholders. Consequently, your data platform won’t be successful if you and your stakeholders aren’t aligned on this definition.

To address this, it’s important to set baseline expectations for your data reliability—in other words, your organization’s ability to deliver high data availability and health throughout the entire data life cycle.

Setting clear service-level objectives (SLOs) and service-level indicators (SLIs) for software application reliability is a no-brainer. Data teams should do the same for their data pipelines.


Start small

Focus on a small-scale project to start — proving the efficacy of working with a data management platform is the best way to encourage wider adoption in your organization.

Make sure those projects are part of a larger plan (roadmap) and provide holistic approach to the end game solution.


Think big

Data is extraordinarily powerful and can be useful to every part of a business.

Make sure that the platform you choose can be used wherever data can be useful — throughout your organization.


Build a data culture

Making data analysis accessible to your organization is half of the equation — you have to create a culture that leads with insights from data.

Have data and analytics teams collaborate across the business to help empower users to seek their own insights.




AWS services for cloud data platform
AWS services for cloud data platform

Google services for cloud data platform

There is much more on that topic ahead!

Stay tuned.





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