For data scientists, adding digital data to an existing or new data science initiative is a struggle. Why? Because organizations expect data science professionals to work with outdated data capture systems that can’t keep up with today’s fast-paced digital world.
The challenges data scientists face working with outdated systems are all too familiar:
- Tag-based solutions don’t work. Inflexible and prone to errors, tag-based solutions struggle with session, device, and identity stitching. They require advanced configuration to ensure the micro-interactions within a page or experience are captured and tied to the session and the individual. For data science, this is a core challenge because many of these micro-interactions are required as signal inputs to the models being developed.
- Data elements are missing or inaccurate. It’s impossible to build a unified customer view without individual-level data that’s accurate, complete, and available in live time.
- Data is unstructured and unavailable in a usable format. Without a relational structure, data is essentially useless. This becomes painfully clear when trying to connect data from a MarTech stack to an external system or vendor. Manipulating, transforming, and joining data to be useable in downstream systems requires a lot of heavy lifting for data scientists.
What to look for in a data capture solution
Wrestling with incomplete, unstructured data is difficult for data scientists. Aside from the process being tedious and messy, it forces them to spend more time on data preparation and less time focusing on what really matters — delivering data analytics and reporting that enables organizations to make better decisions.
Better data science begins with an advanced data capture solution. Here’s what to look for in a data solution to ensure data scientists have what they need to overcome typical challenges.
Manual tagging is time-intensive, prone to error, and challenging for data scientists. Look for a tagging-free data capture solution that's deployed across all sites and can capture consumer interactions without needing extra data layers or tags. This not only eliminates the challenge of “we forgot to tag for it,” it also solves for inaccurate and missing data.
Instant access to accurate data
With a live-time data capture solution, data is processed and delivered in milliseconds. This improves the accuracy and usability of data and enables data scientists to build models and predictive analytics that are based on the most recent, up-to-date consumer information. Quickly understanding digital behavioral data reduces time to value. A solution that uses pre-built connectors for data science and machine learning out-of-the-box lets you connect your data in the format you need.
Live-time identity stitching
Capturing identity and identifying channel visitors is always a struggle for data scientists because most solutions can’t identify users or persist identity due to cookie and browser restrictions. Even when they do, the data is disjointed and must be stitched together after the fact. A solution that automatically captures and stitches data across domains, sessions, channels, and devices in live time will deliver a detailed picture of consumer behavior. It’s essential to use a platform that captures individual digital profiles for all visitors to your digital properties – even anonymous ones.
Complete data model / schema
Having a data model and schema readily available from your data capture solution is one of the most important factors to look for. Capturing data from multiple sources in a highly structured, lightweight format to feed downstream applications makes it faster and easier to use. Look for a solution with an extensible data model and schema that’s processed in milliseconds for downstream use. With a structured data model, data scientists can spend 80% less time prepping data to feed models and more time on analytics.
With so many regulations and compliance requirements, it’s time-consuming for data scientists to ensure the data they’re using is handled and managed the right way. A data capture solution that provides built-in compliance with all browser regulations, such as ITP, will ensure data persists beyond seven days.
The future of data science is bright
Digital data holds an incredible amount of value, yet many organizations still rely on outdated data capture systems to fuel their data science efforts.
If you can’t capture data in live time, you’re missing the mark on delivering personalized, relevant, in-the-moment customer experiences. An advanced data capture solution not only delivers live-time data for instant reporting, it also prioritizes customer privacy and compliance regulations to safeguard your organization’s reputation.
Until organizations invest in the right data capture solution, data science will suffer.
Tag management is dead.
Manual tagging is a common practice to support digital marketing, but it's time-consuming and prone to error. Say goodbye to tag management and hello to a tagging-free data capture solution.