Blog

Engineering

Prevent data quality issues with these six habits of highly effective data

Maintaining data quality across an organization can feel like a daunting task, especially when your data comes from a myriad of devices and sources. While there is no one magic solution, adopting these six habits will put your organization on the path to consistently reaping the benefits of high quality data.

Growth

How BD myShopi improved personalization while protecting consumer data privacy

BD myShopi connects consumers with exclusive deals from Europe's leading retailers. Learn how they leverage mParticle to present consumers with relevant offers while also supporting data privacy.

Growth

3 Tips on Growing a Startup from Nick Warner at Route

The future of every business is dependent on improving the customer experience. The first step to improving that experience is to know your customer. We spoke to one of the rising stars in growth hacking to learn his strategy.

Growth

Connected, by mParticle Episode 3: Leveraging emerging platforms with Miguel Navarro

In this episode of the Connected, by mParticle podcast we welcome Miguel Navarro, digital transformation leader and patented inventor.

Company

Why I’m Here — I’ve joined mParticle to build the future of customer relationships

Access to accurate customer data, and the ability to turn it into actions that benefit customers, is the future of customer relationships. In this post, Katie Cerar discusses how she'll be helping brands build that future as Head of Product Strategy at mParticle.

Engineering

How to implement an mParticle data plan in an eCommerce app

This sample application allows you to see mParticle data events and attributes displayed in an eCommerce UI as you perform them, and experiment with implementing an mParticle data plan yourself.

Engineering

What does good data validation look like?

Data engineers should add data validation processes in various stages throughout ETL pipelines to ensure that data remains accurate and consistent throughout its lifecycle. This article outlines strategies and best practices for doing this effectively.

Engineering

Should you be buying or building your data pipelines?

With demand for data increasing across the business, data engineers are inundated with requests for new data pipelines. With few cycles to spare, engineers are often forced to decide between implementing third-party solutions and building custom pipelines in-house. This article discusses when it makes sense to buy, and when it makes sense to build.

Engineering

Three threats to customer data quality (and how to avoid them)

In this video, Jodi Bernardini, a Senior Solutions Consultant at mParticle, lays out three major threats standing in the way of customer data quality, and offers advice on how organizations can address them.

Engineering

Ask an mParticle Solutions Consultant: What is data quality?

In this video, Andy Wong, a senior leader on mParticle’s Solutions Consulting team, discusses what data quality means, why it is important prioritize, and the benefits of creating a centralized data planning team to oversee data quality.

Engineering

When to use a data lake vs data warehouse

Enabling teams with access to high-quality data is important for business success. The way in which this data is stored impacts on cost, scalability, data availability, and more. This article breaks down the difference between data lakes and data warehouses, and provides tips on how to decide which to use for data storage.

Growth

From spreadsheet to source code: Leveraging mParticle’s Data Plan Builder

Effective data planning is critical to ensuring that the data entering an organization's internal systems is accurate, reliable, and actionable. mParticle’s Data Plan Builder is available to help both mParticle customers and prospects with the key aspects of data planning: collaboration, implementation, and continuous iteration.