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Customer data platforms (CDPs) are increasingly popular sources of customer insights. These systems are usually owned and operated by an enterprise marketing department and provide data for insights into customer trends, behavior, and customer 360 analyses. Unfortunately, one major challenge prevents many companies from realizing the full potential of their CDP. Let’s explore what it takes to drive the full value from this common data platform.
CDPs are useful but have many well-known problems that reduce their utility. They’re intended to offer a single source of truth for customer data, but they lack a fluid and flexible integration model. This leads to the following:
That doesn’t mean CDPs are hopeless. DataOS, from the Modern Data Company, can mitigate the problems associated with CDPs. As an operating system for your data stack, DataOS aids with every stage of the data lifecycle, providing automation, governance, and interoperability. It can improve the data quality, discoverability, and observability of the data in your CDP. The result? Full value from your investment.
DataOS is the only operating system on the market designed to work with every tool, app, and data source within your data ecosystem. This is what that means for your current CDP.
CDPs tend to be siloed in marketing departments — data comes through marketing channels, and there is very little connection with the company’s larger data ecosystem. CDP data remains cut off from data in other departments that could enrich the customer picture. Likewise, the enterprise as a whole has no access to the data in the CDP. In addition, all the usual problems associated with data silos (duplicate, incomplete, inconsistent, and out-of-date data) reduce data quality and user trust in the data.
A data operating system actively connects all data from different sources throughout the enterprise. Users can see what type of data is available for their particular query through a self-service portal, and administrators can view what data is used where and for what purpose. Because DataOS connects to everything in the data stack, no data goes unnoticed.
CDPs ingest data through both governed and ungoverned channels. These include internal data lakes, apps (1st or 3rd party), web SDK, internal or 3rd-party databases, and even spreadsheets generated within the marketing department. Without lineage or governance metadata, the quality of this data is anyone’s guess.
In addition, a CDP owned and controlled by the marketing department may not get the full attention of already overwhelmed IT engineers, becoming an afterthought regarding data governance. As a result, CDPs may be less secure than other data stores even though they hold a lot of customer PII.
A data operating system — specifically DataOS and its capabilities — can apply federated governance automatically throughout the data ecosystem, using data policies devised by the enterprise’s governance authority. It can apply metadata and track the lineage of CDP data to improve data quality. This happens quickly through a central administrator portal, and establishing or changing permissions causes minimal disruption thanks to complete observability.
The CDP is yet another point solution to be integrated into the data stack. It increases complexity in the data pipeline and creates yet another learning impediment for both users and SI engineers. Additionally, this complexity prevents the interoperability that modern enterprises need from their data ecosystem.
Tech stacks become too complex for a variety of reasons. There may have been no plan for integrating new platforms into the ecosystem. Perhaps turnover within the IT team (or a lack of a dedicated team) caused a gap in management that resulted in haphazard maintenance and task execution. It could be that an overall lack of standardization led to inconsistencies that add up over time.
A data operating system brings the data stack under a single, holistic view. It acts as a connective tissue for the organization so that data can flow to the right tools consistently and uniformly. DataOS can connect everything in the ecosystem to the CDP and ensure data quality, define lineage, and maintain governance no matter what tools are in the stack.
CDPs often end up as “data swamps.” Over time the lack of governance leads to poor data quality and incomplete knowledge about what data the CDP even contains.
Collecting more data isn’t always a good thing. Companies may struggle to access the true value of data, leading to frustration and disappointing ROI. Data swamps are breeding grounds for errors and inconsistencies, which profoundly affect a company’s decision-making and resilience in the face of disruption. Even worse, they can add dramatically to the cost of keeping data and lead to greater security risks.
A data operating system ensures timely and consistent data cleaning and monitoring. DataOS offers a data catalog and metadata to bring your data under governance and improve discoverability. New data is added to the catalog automatically upon ingestion. This removes the risk of human error and helps companies with even massive data stores keep up-to-date with all data maintenance tasks. No more data swamps.
A data operating system provides your CDP with the integration it needs to drive accurate insights from massive customer data. It helps eliminate the silos that prevent marketing teams and their tools from working with other departments. It ensures data quality without needing tech expertise and allows company leaders to create a single, federated governance policy.
To find out more about how DataOS can transform your CDP, schedule a quick call with one of our data experts.
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