Working with large amounts of data can be overwhelming. At least, that’s the conclusion I came to after my college statistics course and a brief foray into business intelligence while working at HubSpot.
However, data management platforms can help turn those seemingly nebulous pools of numbers into organized, actionable insights that can help you make better business decisions.
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What is a data management platform, how can it help you streamline your business, and what are the best options for 2024? Look no further because I’ve compiled everything you need to know about data management platforms below. Let’s dive in.
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What Is a Data Management Platform (DMP)?
A data management platform (DMP) is software designed to store, organize, and interpret customer segmentation and advertising campaign data across multiple sources. DMPs provide businesses with actionable, data-driven insights to help make informed strategies and decisions.
Data management platforms make sense of vast amounts of first-, second-, and third-party data so businesses can deliver personalized content to the right customers at the right time. DMPs aren’t a silver bullet, though.
The key to success with your data management platform is clean, organized, high-quality data. The adage “garbage in, garbage out” applies to the mileage you’ll get from your data management platform in 2024.
Now that I’ve covered the basics of data management platforms, let’s check out the best DMPs on the market. After testing various tools, I’ve compiled this list of the top 10 DMPs to help you make informed decisions in 2024.
The Best Data Management Platforms of 2024
- Amazon Redshift
- IBM Db2® Hybrid Data Management Console
- Google BigQuery
- Lotame Spherical Data Management Platform
- Cloudera Data Warehouse
- Snowflake
- Adobe Audience Manager
- Microsoft Intelligent Data Platform
- SAP HANA Cloud
- Permutive
1. Amazon Redshift
Amazon Redshift is a cloud-based data management tool integrated into the Amazon Web Services (AWS) platform. I was impressed with how easy it was to get set up and how fast the data processing was due to their AI-powered massively parallel processing (MPP) architecture.
What I like: Amazon Redshift integrates with other popular AWS offerings, such as Amazon S3, AWS Glue, Amazon Quicksight, and more than 170 different services.
2. IBM Db2 Data Management Console
IBM Db2 is a fully managed cloud-hosted SQL database. It’s a scalable solution with plans ranging from free to enterprise, and I like that IBM allows you to choose the server‘s physical location where your database will be hosted. By selecting from one of IBM’s many servers worldwide, you can reduce latency and comply with your country's data regulations.
What I like: IBM is a trustworthy brand in the space, and you can expect end-to-end security and redundancy to ensure your data remains safe.
3. Google BigQuery
Google BigQuery is a scalable enterprise DMP that lets you manage and query your data without worrying about the underlying data infrastructure. BigQuery’s serverless design lets your team focus on data processing and analytics while Google handles the database's provisioning, scaling, and maintenance in the background.
Are you interested in data visualization but not sure where to start? Download our Data Visualization Guide to learn how to create charts for internal teams and stakeholders.
What I like: I like that Google’s billing is based on actual usage and not infrastructure, which makes it an excellent solution for organizations of all sizes. I’m also a big fan of Google’s Gemini AI, which is baked into the platform to help you intelligently design queries and uncover patterns in your data.
4. Lotame Spherical Data Management Platform
Lotame’s data management platform excels at collecting data from various sources such as social media, websites, and email. As a result, I find Lotame’s DMP particularly suited to marketers looking to create hyper-targeted marketing campaigns using actionable data insights.
In my product demo, I was impressed with Lotame’s Global Data Exchange, a library of pre-packaged customer data segments to supplement your existing data.
What I like: Lotame allows for real-time activation, so you can instantly act on insights generated from your data analysis with personalized ads and tailored content.
5. Cloudera Data Warehouse
Cloudera is a robust data management platform that empowers analysts to query their data and gather insights without jumping through any hoops or dealing with red-tape from admins or IT. Cloudera describes itself as a self-service analytics platform, allowing you fast-paced access to insights and analytics in a cloud environment.
I tested Cloudera DMP and found the interface to be attractive and intuitive. If you’re familiar with Saas products, you will feel right at home. I was particularly impressed with the drag-and-drop data visualization capabilities, where I could spin up and customize charts with just a few clicks.
What I like: Cloudera’s SQL AI assistant feature is a powerful tool for building efficient, simple-to-maintain queries specific to your organization's SQL dialect. You can use natural language to prompt Cloudera’s AI to create complex queries, which is especially helpful if you are learning the ropes.
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6. Snowflake
Snowflake is a DMP that offers a single platform for accessing your organization's data, including unstructured, open-format, or third-party data. Using their Cortex AI assistant, you can quickly create custom AI apps to manage and process your data as needed.
One of the things I like about Snowflake is that its architecture separates data storage and processing, drastically decreasing processing time and increasing performance. I think Snowflake is an extremely powerful (albeit expensive) product that is quickly becoming a top-of-the-class DMP solution.
What I like: Snowflake takes data security and protection seriously. You can set your data storage to comply with regulatory guidelines such as HIPAA, PCI DSS, SOC 1, and SOC 2, and adjust security levels as needed.
7. Adobe Audience Manager
Adobe Audience Manager is a data management platform that focuses on end-to-end audience management and activation. This DMP is particularly suited for marketing teams looking to create unified customer profiles and deliver more targeted and relevant campaigns.
One thing I like about Adobe Audience Manager is its emphasis on cookieless marketing. Third-party cookies are being phased out due to privacy concerns, and as a result, this DMP focuses on first-party data collection and real-time audience activation.
Pro tip: I researched people’s experiences with Adobe Audience Manager and found many reviews stating a steep learning curve to get used to the product. Not many people are experts at using this DMP, so it can be a valuable skill to have.
8. Microsoft Intelligent Data Platform
Microsoft's flagship data management platform is a cloud-based solution for removing data silos, simplifying governance, and bringing clarity to large, complex data sets.
One feature I liked in my experimentation with this DMP was Microsoft Fabric, a brand-new analytics platform within the DMP that integrates various tools across data lakes and warehouses. It features tight integration with the Microsoft ecosystem and powerful AI features for quickly creating reports and crafting SQL queries.
What I like: Microsoft’s DMP integrates tightly with Microsoft Teams so that you can share reports directly with your team without email.
9. SAP HANA Cloud
SAP HANA Cloud is a robust database management platform best suited for enterprises with large, complex datasets. SAP HANA can handle structured, semi-structured, and unstructured data, allowing for complete data unification so developers can easily create intelligent, adaptable data apps.
I toured the product, and I must say I wasn’t particularly impressed with the user interface. It felt slightly dated compared to other more modern SaaS UI I’ve been accustomed to seeing in my testing. However, this does not reflect the platform’s powerful analytics and data processing capabilities.
What I like: According to multiple online reviews, SAP HANA plays nicely with existing on-premise data storage systems, allowing businesses to seamlessly connect legacy systems to the cloud without any headaches.
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10. Permutive
Permutive is a real-time data platform that connects advertisers to their audiences. It seriously emphasizes protecting consumer privacy and user consent, making it an ideal choice for brands prioritizing data security and alignment with regulations.
Permutive enables real-time data activation and enhances customer segmentation without relying on third-party cookies, which I think makes Permutive an excellent choice for the future since third-party cookies are being phased out.
What I like: I love that Permutive takes advantage of edge computing processes, meaning that data is processed closer to where it is generated (like on local devices or nearby servers.) As a result, audience data is processed locally, which allows for privacy-friendly, real-time audience targeting without dependence on centralized cloud servers, allowing for a fast, responsive user experience.
Delivering on Data Potential
Working on this piece taught me much about data management and understanding your company’s unique data needs. Some platforms excel specifically at audience segmentation and personalization.
At the same time, other DMPs are powerhouses that can house and unify your enterprise data and enable much more than just personalized activation. The right choice depends on your company’s needs, budget, and available resources.
I’ve learned firsthand that there is no one-size-fits-all data management platform. Instead, I recommend careful evaluation and testing to uncover which DMP can scale with your business and help you garner the most relevant insights and strategies from your data.
In addition, I believe consumer data privacy and regulations will only become more relevant over time, so I highly recommend future-proofing your organization with a DMP that meets regulatory guidelines and prioritizes the safety of customer data.
Editor's note: This article was originally published in December 2018 and has since been updated for comprehensiveness.
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