As the velocity, volume, and variety of data increases, many businesses struggle with unreliable data that doesn’t offer valuable insights, leading to significant losses. In fact, a Gartner survey found that companies attribute an average of $15 million in losses each year to bad data.
Data as a service, or DaaS, helps solve these challenges by putting the task of data quality in the hands of the experts. This approach offers several advantages similar to those provided by database management systems, such as improved data consistency and enhanced data security.
By selecting a partner whose sole focus is delivering accurate, insightful, and reliable data, go-to-market leaders can turn their focus to the strategic and tactical decisions that will make a difference for their business.
Ready to learn more? Here’s everything you need to know, including common challenges, DaaS use cases, and how DaaS fits into the overall business puzzle.
What is Data as a Service (DaaS)?
Not to be confused with “desktop as a service,” data as a service (DaaS) is a cloud-based system that allows users to access essential business data anytime, anywhere, and through a wide range of platforms and applications.
DaaS eliminates the need for on-site data management. Instead, data as a service offers flexible, scalable, and cost-efficient data storage, processing, and delivery services. Users can interact with data services via APIs or web portals as a way to pull in data and manipulate it to enhance decision-making and drive revenue.
Some key components of a DaaS offering are:
- Data services
- Data management
- Data storage
- Data processing
- Data integration
- Data analytics
With reliable DaaS platforms, companies can make better use of the first-party data they collect and any third-party data they purchase. It’s an efficient way to build predictive go-to-market workflows and results.
What is the Difference Between SaaS and DaaS?
SaaS (software as a service) and DaaS (data as a service) are both cloud-based services, but they serve different purposes and operate at different layers of a common B2B tech stack.
SaaS provides users with access to software applications over the internet. Users don’t need to install or maintain the software, which is managed by the service provider. Examples include Salesforce, Microsoft 365, and Google Workspace.
DaaS provides users with on-demand access to data. This service allows users to store, manage, and retrieve data over the internet without worrying about data management infrastructure.
What Types of Data are Typically Offered Through Data-as-a-Service Platforms?
A comprehensive DaaS solution includes two interconnected layers:
- A data access layer: Delivers the data points from which teams can draw insights
- A data management layer: Provides the maintenance and enhancement services needed to make data work across a company.
Data Access Layer
The data access layer draws on several types of data, including:
Firmographics: Fundamental attributes used to define a business, similar to demographics for individuals. These may include the company name, website, revenue, employee size, location, and industry.
Parent-child hierarchy: The relationship between companies, sites, and structures. Examples include site locations, global parents, domestic parents, subsidiaries, franchise identifiers, and brands within a company.
Technographic data: The applications and software infrastructure used by a business, such as AWS, HubSpot, Salesforce, and ZoomInfo.
Intent data: Behavioral information about a company, such as content consumption, that provides insights into interests and potential buying signals.
Scoops: Actionable leads that are sourced through surveys and ZoomInfo’s in-house research team. Scoops identify projects and leadership moves to share with users for timely outreach.
Location: Detailed address information, including satellite offices, temporary locations, and more.
Contacts: Professional contact data, including work email, direct dial phone number, and office address.
Advanced insights: Additional information about a business that provides a more detailed picture of a company, such as its level of marketing sophistication.
Data Management Layer
The data management layer ensures the right data ends up in the right place. It requires a series of complex operations, including:
Cleanse: A systematic way to automate database health, including processes like record deduplication, data normalization, standardization, and customer segmentation.
Multi-vendor enrichment: A system with flexible, rules-based logic that enriches a database with multiple data providers and ensures that data is standardized and segmented to unique business requirements.
Route: Automatically route any type of data into a CRM based on any designated field or related object field. Creates simple or sophisticated routing workflows to quickly and accurately assign leads for fast follow-up.
APIs and webhooks: A comprehensive suite of search, enrich, and subscription APIs that seamlessly integrate and update B2B data and intelligence directly in any system and workflow, in real time, at scale.
A DaaS solution also includes data services for teams that have custom requests, advanced analysis, and larger-scale data delivery needs.
Data cube: An entire database of continually refreshed firmographics and technographics, delivered as a flat file or via services such as Snowflake, Google BigQuery, and AWS. ZoomInfo Data Cubes are built to support your master data strategy or run advanced analysis and modeling.
Custom enrichment services: Offline matching resolves any blanks left by real-time matching. Uses the same core technology as real-time matching, but it happens periodically in mass batches rather than instantly.
Modeling & scoring services: Lookalike regression and custom models are used to identify net-new, cross-sell, or upsell opportunities for product lines, divisions, and go-to-market teams. Understand your ideal customer profile (ICP) better by scoring any object based on any attribute. Leverage multidimensional scoring along with scoring based on behavior, intent, and fit.
Data can get pretty granular depending on your goals. Only the best DaaS platforms are equipped to provide data like advanced insights or intent data.
Capital One was able to use ZoomInfo’s data services to modernize its GTM strategy, ditching spreadsheets and manual processes and creating a more productive self-service data workflow for all its sales reps.
My job is to bring the information to our sales teams as easily as possible. Now, instead of 100 different sales teams coming to me with requests, we put the data in one place. We give them a report, and they can get it themselves — it becomes more of that self-service model.
Andy Ruffles, director of sales operations and strategy at Capital One Commercial Banking.
The Challenges of Data as a Service
One of the major challenges of implementing DaaS lies in dealing with unreliable data. Go-to-market, operations, and master data management teams often grapple with inconsistent and inaccurate data.
This directly affects strategic planning, tactical decision-making, and ultimately, the bottom line. That’s why ensuring data reliability, accuracy, and consistency is a critical part of successfully implementing and leveraging DaaS.
Additional data challenges include:
Data Security
As data becomes increasingly vital for revenue operations, ensuring its security is a top priority. The challenge lies in protecting sensitive information from breaches, leaks, or unauthorized access.
Solutions often include robust encryption methods, secure access controls, and regular data security audits. Trustworthy DaaS providers typically have stringent security measures in place to protect data.
Data Governance
Data governance involves managing data availability, usability, integrity, and security within a company. The challenge lies in maintaining high-quality data and complying with relevant laws and regulations.
Common solutions include implementing a comprehensive data governance strategy, which includes data stewardship, data quality management, and clear data policies and procedures.
Data Silos
Isolated pockets of data held by a single department or unit within a company, known as data silos, aren’t accessible or shared with others. Data silos lead to a lack of transparency, inefficiency, and missed opportunities.
Eliminating data silos requires a robust DaaS strategy that emphasizes data integration and promotes a culture of data sharing. By consolidating data from different departments into a unified, accessible platform, DaaS helps break down these silos, unifying GTM efforts.
What are the Main Benefits of Using Data as a Service?
Let’s delve into more specific benefits:
Speed: By delivering real-time access to data, DaaS can supercharge a company’s ability to react quickly to market changes and business opportunities.
Sales messaging: DaaS provides granular customer data, empowering sales teams to craft messages that resonate on an individual level, increasing conversion rates.
Revenue growth: DaaS enables the identification of hidden trends and opportunities in market data, creating new revenue streams and driving business growth with proper data orchestration.
A deeper understanding of your ideal customer profile: With DaaS, businesses get a clearer, data-backed picture of their ideal customer’s habits and preferences, enhancing their ability to tailor products and services.
Cost-effective operations: DaaS eliminates the need for costly data storage and management infrastructure, freeing up resources that can be better used elsewhere in the business.
Data-driven decision making: By offering accurate and timely data, DaaS forms the foundation for sound, data-driven decisions that steer the company toward success.
Artificial intelligence, machine learning, and predictive modeling: Accurate data supplied by DaaS services is the lifeblood of AI and machine learning systems, enabling more precise predictions and smarter automated processes. Generative AI projects are particularly reliant on large volumes of high-quality, trustworthy data in B2B applications — without quality data, generative AI apps can quickly veer off-target and “hallucinate” results that threaten to spread mistakes farther and faster than human error.
High-quality data when and where you need it: DaaS providers specialize in delivering clean, well-structured data, saving businesses from the time-consuming task of data cleaning and preparation.
How is DaaS Used? 3 Example Use Cases
Companies use DaaS in a variety of ways to drive go-to-market success:
1. Using DaaS to source accurate data on small businesses
For businesses that rely on physical address information — like shipping or freight carriers — having accurate location data is mission-critical, yet quite challenging at scale. The task is even more difficult if your customer profile includes small businesses.
With DaaS, teams can leverage third-party data alongside their own internal customer records to accurately cover even the most difficult addresses, like warehouses, small business storefronts, branch offices, and satellite buildings.
2. Using DaaS to profile the ideal customer for a niche market
If a product serves a niche market, prioritizing new customer segments can be challenging. Sometimes a company’s best accounts are not easily defined by traditional firmographics, like employee size or annual revenue.
Teams can leverage DaaS to pair nuanced company and contact attributes (such as decision-making authority, industry classification, and online behavior) with internal customer data (like time-to-close, deal size, and app download history) to uncover new industry segments with strong candidates for their solution.
3. Using DaaS to understand granular details about your target accounts
Every revenue team wants to know more about its target audience in order to segment and prioritize accounts. Segmenting target account lists by industry is a common practice, but sometimes a default industry classification, such as “technology” or “manufacturing,” can be too broad.
With DaaS, companies can select a handful of ideal accounts and plot their relevant terms or keywords onto a company semantics graph. This reveals related companies in new or adjacent industry segments that are potentially well-suited for what’s being offered.
Get the Most Out of DaaS With ZoomInfo Data
ZoomInfo Data provides a comprehensive suite of products and services to help companies build effective data-driven go-to-market engines.
Many of today’s leading brands trust ZoomInfo Data to deliver the high-quality business data they need, directly into the systems their teams use every day, saving time and optimizing performance.
Ready to do more with DaaS? Talk to a data specialist today.