Better data, better insights, better results: Helping brands create a data flywheel
Introduce total quality management and enforcement into your customer data pipeline with new Data Master features and Calculated Attributes. With Data Master and Calculated Attributes, establishing a source of reliable customer data that will create a customer data flywheel, where the data quality and data’s impact on the product cycle will continuously improve over time.
Today, the best customer experiences are built on real-time personalization to resonate with customers’ unique needs and preferences. The foundation of these experiences is built upon a clean and reliable customer dataset. Without this, product, engineering, analytics, and marketing teams may find themselves trying to cobble data from multiple sources and systems into customer profiles that end up being outdated, incomplete, or just outright inaccurate. And this only becomes more complex as more data is created and consumed, moving to and from more sources, more systems, more applications, and endpoints. In fact, this issue is so common that it's estimated that only 3% of organizations' data meets basic quality standards.
Today, we're excited to provide an update on two critical features to help you introduce total quality enforcement into your data pipeline: data validation and data planning. Together with the newly-released Calculated Attributes, these new platform features can help brands create a compounding effect around data quality. It begins with data design, and establishing and enforcing the desired data schema with validation around data accuracy to ensure accuracy and hygiene. Then, gaining greater insight into consumer behavior over time through dynamically updating event and attribute calculations. With Data Master and Calculated Attributes, establishing a source of reliable customer data that will create a customer data flywheel, where the data quality and data’s impact on the product cycle will continuously improve over time.
A better data foundation
Data Master was designed to help you create a solid data foundation to achieve more success. Data corruption and inconsistency is the fastest way to erode customer trust, and spend excessively on marketing tech tools. By using mParticle to protect data quality, you’re not only able to deliver better and more consistent experiences along the customer journey but also ensure that you’re getting more value out of all the tools within your growth stack.
Data planning: Create your data foundation
With mParticle, teams can create Data Plans to validate and enforce data collection based on a shared agreement across teams and functions. Data planning ensures that all developers, product managers, and marketers have a common understanding of what data is available, in what format, and how data is consumed. It helps teams increase their trust and ultimately confidence in their customer data to spend more time on adding value. New updates around data planning allow you to have:
Customizable historical data imports
Data import lets you import past custom and screen view events, which is especially helpful for teams that build a data plan by subtraction. With this feature, you can see all the events sent and identify which ones are necessary, so you can keep costs down and make it easier to track your data. Once events to be added are identified, you can update your implementation to improve overall data quality.
Increased control over your data plans
Data plans support more iterative development of plans for data currently in production, while still allowing you to activate and deactivate new data plans easily.
Faster refresh times.
Latency can lead teams to missing opportunity, and with this current update you can immediately activate new data plans into production and begin validating your data instantly.
If you don’t know where to start with your data planning, an mParticle Solutions Consultant can help you accelerate your data planning capabilities providing on site expertise to accelerate your speed to market.
Data validation and enforcement: Protect your house
With these latest capabilities, various team members across function can easily and visually explore everything about their customer data—including data source type, business terms, and errors—in one place, with detailed data validation and schema violations statistics.
To help you avoid polluting production data, our enforcement capabilities allow teams to identify and address undesirable data that does not conform to expectations defined in the Data Plan. The types of errors that can happen as a result of mistakes during implementation, like improper formatting, naming, or coding of an array as a string for example, can now be easily mitigated and sequestered.
Customer data can be validated in two ways:
- Live Stream: Identify and resolve data validation issues quickly as its coming into the system.
- Data Catalog: View all of the data mParticle is collecting in one place to debug and maintain data consistency.
Leveraging Data Master our flexible identity resolution capabilities provides teams with an added layer of customer profile accuracy; as customers engage with your digital properties over the course of their journey, mParticle’s programmatically assigns data points to new or existing profiles, taking the operational pain out of creating data-driven customer experiences.
New validation features for this version of Data Master include:
Total control over violations
Control which attributes for an event are validated so that you only get alerted to violations that matter to you. Eventually, you can either choose to drop these data points or fix them and replay them into various vendors.
See event status in Live Stream
This new feature allows you to review if events passed, failed, or were not validated by any data plan as they stream in and out of mParticle.
mParticle already helps brands deliver exceptional customer experiences across every touchpoint in real time by providing an easy way to segment users, simplify data analytics, use APIs to improve CX, and more. But, sometimes you may need to dig deeper into historical data to understand your customers’ habits and better serve them through your digital properties.
Our newest feature, Calculated Attributes (now in early access), allows teams to create continuously updating user data utilizing any of the customer data points you have available to enrich audiences, drive increased personalization, enhance analytics, and improve speed to market. Calculated Attributes can be applied to dynamically track lifetime value, calculate aggregate values, map preferences or create propensity scores, user behavior lists, and time-based calculations. Calculated Attributes can be easily integrated into any of our 200+ Event integrations and/or our 100+ Audience integrations.
Calculated attributes are maintained by mParticle over time and include calculations such as:
- Event counts: Calculate the total number of times an event has occurred, like the number of logins in the last 30 days.
- Aggregate functions: Calculate sum, minimum, maximum, average, or median for an event attribute, like lifetime purchase value.
- Capture first or last occurrence: Calculate the first or last time an event has been seen, or the first/last value for an event attribute.
- User behavior lists: Calculate unique lists of values, unique counts of values, and most frequent values seen for an event attribute.
- Time-based calculations: Limit calculations to relevant business windows such as “Last X Days” or “Weeks,” or go as far back as your configured retention period.
Build your data flywheel today!
Data is a team sport and mParticle helps product, engineering, analytics, and marketing teams amass the most complete and accurate view of the customer journey. This data powers outstanding customer experiences, and better measurement of engagement throughout the journey which leads to better optimization and higher probability of success. With Data Master and Calculated Attributes, establishing a source of reliable customer data that every member of your organization can use to drive product and business decisions is easier than ever before. Put your team’s trust back into your data and get back to what’s most important.
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