What is Data as a Service (DaaS)?

Data is the foundation of business in the 21st century. But as the velocity, volume, and variety of data increases, even the most advanced enterprises are struggling with inaccurate data that doesn’t provide actionable insights. In fact, a Gartner survey found that organizations attribute an average of $15 million in losses each year to bad data.

Data as a service, or DaaS, helps businesses solve this costly problem by hiring specialized providers whose sole focus is delivering accurate, insightful, and reliable business data, allowing leaders to focus on growing their business.

Choosing a DaaS provider is a critical decision for any business. OperationsOS is trusted by world-class teams as the DaaS solution designed specifically for enterprise data and operations teams at growing organizations.

What is DaaS?

DaaS is a suite of products and services that create an end-to-end data management platform. Some key parts of a DaaS offering are:

  • Data services
  • Data management
  • Data storage
  • Data processing
  • Data integration
  • Data analytics

With a proven DaaS provider, organizations can make better use of the first-party data they have and third-party data they purchase, to build predictive go-to-market workflows and results.

A comprehensive DaaS solution includes two interconnected layers: a data access layer that delivers the data points to be woven together, and a data management layer that provides the maintenance and enhancement services needed to make data work across an organization.

Data Access Layer

It all starts with data. The data access layer draws on several categories of business-related intelligence, 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.
  • Technographics: The applications and software infrastructure used by a business, such as AWS, HubSpot, Salesforce, and ZoomInfo.
  • Intent: 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, normalization, standardization, and 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 brick: An entire database of continually refreshed firmographics and technographics, delivered as a flat file or via via Snowflake, Google BigQuery, AWS, as opposed to an API. Data bricks 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. Better understand your ideal customer profile (ICP) by scoring any object based on any attribute. Leverage multidimensional scoring along with scoring based on behavior, intent, and fit.

Why Do You Need DaaS?

Data quality remains an issue plaguing most organizations, with the biggest challenge being the sheer volume and amount of data sources to process. In an O’Reilly survey of data analysts and engineers, more than 60% reported “too many data sources and inconsistent data,” as their biggest hurdle, followed by 50% citing “disorganized data stores and lack of metadata”.

Common problems that organizations experience with their data include:

  • Erroneous information, particularly across an organization’s tech stack.
  • Incomplete coverage or missing records make it difficult to reach target markets.
  • Fewer data points can lead to missed opportunities and customer service errors.
  • Flawed entity resolution or a poor match rate can lead to duplicate records or lost information.
  • Outdated company hierarchy information creates go-to-market problems, such as bad lead routing and unassigned accounts.

Managing multiple sources of data across several systems is not easy. And the time it takes to manually clean, enrich, and unify data detracts from more valuable activities and hinders the work other teams do with that data.

For marketing and sales teams, bad data can cause disruptions in segmentation and lead routing. Operations teams dealing with data discrepancies will have to deal with extensive conflict resolutions. Master data management and analytics leaders can face fundamental problems with third-party data quality and coverage, leading to inaccurate modeling and even an unreliable master database. Bad, siloed, or missing data also leads to a disjointed customer experience.

How is DaaS Used?

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, especially for small businesses, is mission-critical, yet quite challenging at scale. 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.

4. Using DaaS to fill in missing information about your web visitors

Typically, the shorter your web form, the higher your conversion rate. To enhance the analytics of your website traffic and optimize lead conversion, real-time enrichment can automatically infuse each lead with missing business data, such as geographic region, annual revenue, or industry classification.

With DaaS, your web traffic data is automatically cleaned, enriched, and mapped to certain fields in the CRM, optimizing inbound lead workflow. Every department and sales rep receives the information they need and marketing qualified leads (MQLs) become highly trusted by sales

5. Using DaaS to evaluate the financial standing of small, private companies

Before extending credit to a new customer, many teams use industry classification codes to assess the level of risk involved. But industry codes only reveal so much about a business, especially if they’re generic and grouped together too broadly.

Advanced attributes from DaaS help transform unstructured business data into structured, usable signals and intelligence, such as a detailed view of the industry a company serves or how advanced their tech stack is. Advanced insights like a company’s tech sophistication rating or funding history can be strong indicators of creditworthiness.

What are the Benefits of DaaS?

Compared to traditional data management methods, DaaS offers significant value through better insights, improved trust, and stronger performance. Some of the benefits include:

  • Improved functionality: DaaS is delivered through cloud networks, which are more reliable than an onsite server, making data less prone to disruptions.
  • Enhanced artificial intelligence (AI), machine learning (ML), and predictive modeling: AI and ML are crucial for modern automation. Without accurate data, you can’t have successful AI.
  • Outsourced data gathering: Collecting data is time-consuming and unrealistic if you’re gathering large quantities of information. DaaS not only provides data but also handles the orchestration and enrichment of your database.
  • Updated, enriched data: Stale data has a direct impact on your bottom line. DaaS providers solve this with regular, automated maintenance and data updates.

Solving dirty data with ZoomInfo OperationsOS

ZoomInfo OperationsOS provides a comprehensive suite of products and services to help organizations build effective data-driven go-to-market engines. Powered by the world’s best B2B data and intelligence, OperationsOS provides a comprehensive set of features and services to accelerate end-to-end data management.

Many of today’s leading brands trust ZoomInfo OperationsOS to deliver the high-quality business data they need, directly into the systems their teams use every day, saving time and optimizing performance.