Still, the line between CDP and privacy managers is usually clear: CDPs store customer data imported from other systems while privacy managers read the data in place. There might be a small gray area where the privacy system imports a little information to do identity matching or to build a map of what each source system contains. But it’s pretty easy to distinguish systems that build huge, detailed customer data sets from those that don’t.
There’s an exception for every rule. Skypoint Cloud is a CDP that positions itself as a privacy system, including data mapping, consent management, and DSR (Data Subject Request) fulfillment. What makes it a CDP is that Skypoint ingests all customer data and builds its own profiles. Storing the data within the system actually makes fulfilling the privacy requirements easier, since Skypoint can provide customers with copies of their data by reading its own files and can ensure that data extracts contain only permitted information. Combining CDP and privacy in a single system also saves the duplicate effort of having two systems each map and read customer data in source systems.
The conceptual advantages of having one system for both CDP and privacy are obvious. But whether you’d want to use a combined system depends on how good it is at the functions themselves. This is really just an example of the general “suite vs best-of-breed” debate that applies across all systems types.
You won’t be surprised that a young, small vendor like Skypoint lacks many refinements of more mature CDP systems. Most obviously, its scope is limited to ingesting data and assembling customer profiles, with just basic segmentation capabilities and no advanced analytics or personalization. That’s only a problem if you want your CDP to include those features; many companies would rather use other tools for them anyway. There’s that “suite vs best-of-breed” choice again.
When it comes to assembling the unified database, Skypoint has a bit of a secret weapon: it relies heavily on Microsoft Azure Data Lake and Microsoft’s Common Data Model. Azure lets it scale effortlessly, avoiding one set of problems that often limit new products. Common Data Model lets Skypoint tap into an existing ecosystem of data connectors and applications, again saving Skypoint from developing those from scratch. Skypoint says they’re the only CDP vendor other than Microsoft itself to use the Common Data Model: so far as I know, that’s correct. (Microsoft, Adobe, SAP, and others are working on the Open Data Initiative that will map to the Common Data Model but we haven’t heard much about that recently.)
How it works is this: Skypoint can pull in any raw data, using its own Web tag or other sources, and store it in the data lake. Users set up a data flow to ingest each source, using either the existing or custom-built connectors. The 200+ existing connectors cover most of the usual suspects, include Web analytics, ecommerce, CRM, marketing automation, personalization, chat, Data Management Platforms, email, mobile apps, data stores, and the big cloud platforms.
Each data flow maps the source data into data entities and relations, as defined in the Common Data Model or adjusted by the user. This is usually done before the data is loaded into the data lake but can also be done later to extract additional information from the raw input. Skypoint applies machine learning to identify likely PII within source data and lets users then flag PII entities in the data map. Users can also define SQL queries to create calculated values.
Each flow has a privacy tab that lets the user specify which entities are returned by Data Subject Requests, whether data subjects can order the data erased, and which data processes use each entity. The data processes, which are defined separately, can include multiple entities with details about which entities are included and what consents are required. Users can set up different data processes for customers who are subject to different privacy regulations due to location or other reasons.
Once the data is available to the system, Skypoint can link records related to the same person using either rule-based (deterministic) matches or machine learning. It’s up to the client define her own matching rules. The system maintains its own persistent ID for each individual. Matches can be either incremental – only matching new inputs to existing IDs – or can rebuild the entire matching universe from scratch. Skypoint also supports real-time identity resolution through API calls from a Web tag.
After the matching is complete, the system merges its data into unified customer profiles. Skypoint provides a basic audience builder that lets users define selection conditions. This also leverages Skypoint’s privacy features by first having users define the purpose of the audience and then making available only data entities that are permitted for that purpose. Users can also apply consent flags as variables within selection rules. Audiences can be connected with actions, which export data to other systems manually or through connectors.
Users can supplement the audience builder by creating their own apps with Microsoft Azure tools or let external systems access the data directly by connecting through the Common Data Model.
Back to privacy. Skypoint creates an online Privacy Center that lets customers consent to different uses of their data, make data access requests, and review company policy statements. It creates an internal queue of access requests and tracks their progress towards fulfillment. Users can specify information to be used in the privacy center, such as the privacy contact email and URLs of the policy statements. They can also create personalized email templates for privacy-related messages such as responses to access requests or requests to verify a requestor’s email address.
This is a nicely organized set of features that includes what most companies will need to meet privacy regulations. But the real value here is the integration with data management: gathering data for subject access requests is largely automated when data is mapped into the system through the data flows, a major improvement over the manual data assembly required by most privacy solutions. Similarly, the connection between data flows, audiences, and data processing definitions makes it easier to ensure the company uses only properly consented information. There are certainly gaps – in particular, data processes must be manually defined by users, so an undocumented process would be missed by the system. But that’s a fairly common approach among privacy products.
Pricing for Skypoint starts with a free version limited mostly to the privacy center, consent manager, and data access requests. Published pricing ranges past $2,000 per month for more than ten data integrations. The company was founded in 2019 and is just selling to its first clients.