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Why do businesses struggle to drive value from behavioral data?

Complex infrastructure, new processes and collaboration challenges all present challenges that Behavioral Data Management helps alleviate.

Challenges with behavioral data compound each other

We have identified common challenges that organizations face when trying to deploy advanced use cases to drive value and competitive edge from behavioral data

Enabling a self serve culture

As your organization scales, self-serve culture and freeing up data team time becomes more critical to success.

Having more than one source of truth

Without a single high-quality behavioral data set as your source of truth, trust in the data will erode.

Maintaining a complex data platform

You must collect and deliver the data in a robust, performant and assured manner.

This must all be underpinned by compliance with ever-evolving privacy rules and regulations

Challenge: deploying advanced use cases

Data teams we speak to want to focus on advanced use cases but time is taken up by ad-hoc requests and the data they have is unreliable and requires significant preparation

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Common technical challenges

  • Data in many different formats, from many different collection tools making it hard to consume.
  • Data is not delivered with a reliable latency.
  • Extracting data from packaged analytics tools is difficult.
  • The structure of the data is governed by the needs of the packaged analytics tool it was extracted from, rather than the need of the business.

Common organizational challenges

  • Your internal expertise is too busy addressing technical challenges and dealing with ad-hoc requests.
  • You have big aspirations but do not have the experience, available expertise or know-how to roadmap and plan advanced data projects.

With Snowplow, collect a single stream of event data from all platforms and channels, delivered at low-latency in a highly-expected event structure. We manage your data platform so you can focus on delivering advanced use-cases with our expertise helping to guide you.

Challenge: remaining compliant with privacy regulations

Data collected across different jurisdictions, under different circumstances and lack of ownership of your data make staying compliant difficult

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Snowplow helps you stay compliant by enabling you to obfuscate or remove PII entirely as part of processing, as well as enabling end-to-end auditing on your data. All processing takes place in your cloud environment; no third-parties have access, not even Snowplow so you own and control your raw data.

Challenge: enabling a self serve culture

Giving business teams assurance in the data and the skills to explore it themselves to answer key business questions is challenging with poor quality data

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Common technical challenges

  • Building tables to ingest into BI tool for self serve is time consuming because data is siloed.
  • Maintaining stateful joins in those tables is difficult, front end data contains few properties.
  • End users want data in their BI or visualisation tool of choice.

Common organizational challenges

  • Creating and maintaining trust in data quality amongst consumers.
  • Providing full transparency to end users as to what data is collected and what it means.
  • End users having to join multiple datasets with unknown freshness.
  • Lacking documentation on the available data.

Snowplow delivers a single, rich unified data set in real-time streams, to warehouses and to lakes. You can be assured of the accuracy and completeness of your data through lineage, observability and QA tooling. Up-front schema’ing mean your data is always documented at source.

Challenge: having more than one source of truth

Difficulties wrangling data across silos and reliable change management mean there are weak foundations off which to drive value with data

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Common technical challenges

  • The questions you ask across mobile and web are identical, yet the data you use to answer them is structured differently.
  • Different parts of the organization buy different tools; data silos forming, a proliferation of tools and SDKs.

Common organizational challenges

  • Eroding trust in your data; different teams coming to different conclusions.
  • Lacking processes and systems to gather requirements and evolve data collection as the organization evolves.
  • Lacking version control and change management across tracking and data modeling.

Snowplow SDK’s capture events across all platforms and channels in a single, commonly-structured data set, eliminating data silos and meaning every team is working from the same raw data set. Our schema’ing provides robust change control management as your data collection strategy evolves.

Challenge: maintaining a complex data platform

The time, knowledge and expertise required to run and maintain a complex data platform is sizeable and having to stay up-to-date with the latest technology advancements adds further burden

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Common technical challenges

  • Collecting a complete data set across web and mobile due to tracking prevention methods, ad blockers and cookie consent affecting third-party collection tools.
  • Delivering data at low-latency in a robust a performant manner.
  • Monitoring and observing the data platform for issues and red flags.

Common organizational challenges

  • Hiring in the right expertise as your data platform evolves.
  • Constantly learning and regularly experimenting with new technologies and platforms.

Snowplow’s open-core Behavioral Data Engine is robust, performant and low-latency technology that has been developed through years of learning and decisions. Our unique Private SaaS deployment model means all the benefits of it being deployed in your cloud while we maintain and upgrade it.

Snowplow solves these challenges at the foundation of your behavioral data

Find out how Snowplow BDP tackles these challenges

The most successful companies in the world use behavioral data to drive competitive advantage