The Role of Data as a Service in Digital Transformation

The future market insights report, states that the global data as a service (DaaS) market will grow from USD 20.8 billion in 2025 to USD 124.6 billion in 2035 at a CAGR 22.8%. This cloud-based model provides the architecture to gather, process and distribute enterprise data. Data as a service offers a single interface to support cross-departmental analysis and decision-making based on strategic data. It facilitates standardization of data, improves scalability, and offers a cost-effective infrastructure which offers data security.

This article explores the role of DaaS in digital transformation, use cases of data as a service for different industries, and how data management services offer specialized expertise that in-house teams lack. Let’s dive in!

What’s the Role of Data as a Service in Digital Transformation?

Breaking Data Silos

At an organizational level, big data is available across multiple fragmented systems. These siloed databases contain information, but are inconsistent and unorganized, making it difficult to extract meaningful analytics. The diverse data systems in organization marketing data, sales data, CRM, IoT sensor data—individually store the information required for operational decisions. DaaS is the cloud-based repository that centralizes big data by collecting and aggregating it in one place—acting as the single source of truth.

There are various other benefits that an organization via leveraging data as a service, such as.

• By being a cloud-based infrastructure, DaaS offers real-time access to the databases. It leverages Application Programming Interface (APIs) for seamless data access. APIs also enable customization and flexibility to extract data. For example, the customer service might need access to last week’s escalation, while a sales team member might need the customer billing data.
• Since data as a service not only collects and aggregates the data, but standardizes it as well, it reduces the inconsistencies across the entire database. It enables different departments to leverage data that is uniform, eliminating discrepancies.
• The centralization of these databases enables cross-departmental collaboration. Since every department has a single source of data, it doesn’t leave room for guesswork. This also drives different departments to collaborate more, which potentially leads to more strategic insights.
• The workflows become more streamlined, increasing operational efficiency.
• The comprehensive dashboard provides a 360-degree view of the customer journey. For example, in eCommerce, it brings data from multiple touchpoints at one place, such as the navigation time of the customer, recently or most viewed products, saved products, abandoned products in cart, and the final purchase. This offers comprehensive insight into customer behavior, enabling the organization to deploy behavior modeling and predictive analysis. This enables the organization to offer personal recommendations, enhancing customer experience (CX).

Enables Data-Driven Decision Making

Once the data has been integrated into a unified dashboard, it enables the organization to analyze it for predictive analysis. With DaaS, integrating data in real-time allows organizations to be proactive. Moreover, the scope of cross-departmental collaboration brings more strategic insights into the analytics dashboard. The comprehensive view of the customer journey makes it even easier to make informed decisions.

A few key areas where data as a service fosters strategic decision making are.

• Market Research & Analysis: For any organization, market research is the key to long-term sustainability. DaaS acts as a single source interface for data collected from multiple sources, such as competitor analysis, current market trends, social media, etc., and facilitates the decision-makers to analyze trends and patterns.
• Fraud Detection & Prevention: When the entire data is aggregated in one dashboard, it facilitates the detection of patterns and anomalies. This helps the organization to counter potential fraud. This is particularly useful for a few organizations, such as insurance and banking, where financial transactions are frequent.
• Business Performance Monitoring: In large-scale enterprises, tracking and monitoring the key performance indicators (KPIs) and business functions across multiple departments can be a hassle. The integration of data as a service model creates a seamless workflow for the management to track all activities.
• Product Development & Optimization: In the case of product-based enterprises, continuous optimization based on customer feedback is crucial for better market positioning and long-term sustainability. It also allows businesses to identify market gaps and optimize them accordingly.
• Supply Chain Management: One primary advantage of data as a service is that it enables efficient inventory management and, therefore, helps businesses to avoid overselling. This ensures continuous production and streamlines manufacturing processes.

Scalability

The role of data as a service in digital transformation is that it fits the demand at various organizational levels, from small businesses to established enterprises. This cloud-based model offers scalability according to the fluctuating needs of the organization. It enables seamless integration of data and on-demand resource adjustment. Moreover, the scalability is real-time, enabling quick integration of data streams.

Cost-Effective Data Storage

Storage of big data might pose additional overhead for enterprises. It requires capital investment at multiple stages, including infrastructure, resources, maintenance of integrated workflows, and continuous operational costs. Data as a service is available on a subscription-based model(pay-as-you-go); therefore, it reduces the cost factor. This not only reduces the unnecessary expenses but also allows the usage of resources without making long-term, hefty investments.

Improved Data Quality and Validation

Modern cloud-based models have integrated machine learning in order to automate data cleansing and validation. This integration leads to scalability as well as a crucial factor to managing big data. The process involves automatic detection of inconsistencies, errors, and duplicate entries. This not only improves the quality of the big data, but it also ultimately leads to improved strategic decision-making. Along with this, data cleansing is real-time, eliminating the need for ad-hoc cleansing processes.

Enhances Data Governance and Compliance

To avoid potential penalties, sustain brand reputation, and mitigate the risk of legal action, Data as a Service implements role-based access for users. This ensures data security through data encryption, which is particularly crucial in high-stakes industries such as healthcare and finance. DaaS further adheres to regulatory compliance standards, including the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA). Another method apart from data encryption is data masking through which it ensures data security. By enhancing data security and implementing regulations, it automatically facilitates risk mitigation.

Industry-Specific Use Cases of Data as a Service

Data as a Service for eCommerce

The eCommerce landscape is heavily reliant on customer behavior and continuous optimization. Customer experience is influenced by multiple factors, including personalized recommendations, product data, and stock levels.

Data as a service enables businesses to.

• analyze patterns in customer behavior across multiple touchpoints,
• keep track of stock levels more efficiently,
• optimize pricing strategies according to competitor analysis,
• personalize recommendations and offers based on behavior analysis,
• fix discrepancies in product information across multiple channels,
• undergo customer segmentation to create targeted marketing campaigns.

For instance, an online retailer detects, using the DaaS dashboard, that the top-searched products have lower conversion rates than in the previous quarter. He ran a competitor analysis leveraging the real-time analytics and visualization features of the data as a service model of the product listings and found that competitors have listed the product at lower prices. He optimizes the pricing strategy, and it results in a conversion boost by X%.

Data as a Service for Healthcare

In healthcare, a patient record—from names to medical history—is crucial for effective treatment. Data as a service allows healthcare institutions to build a comprehensive customer profile by aggregating the data from disparate systems, such as electronic health records (EHRs), patient feedback systems, and IoT health sensors. DaaS also offers compliance with regulations such as the Health Insurance Portability and Accountability Act (HIPAA) and GDPR for sensitive healthcare data.

Data as a Service for Finance

Risk factors are always high in the finance sector due to consistent cash flow. DaaS facilitates risk management by leveraging real-time monitoring. This model facilitates financial institutions to detect fraud, conduct credit analysis, provide monitoring of real-time transactions, and personalized service offers to the customers. Data as a service provides centralized access to multiple datasets, making it easier to track patterns such as unusual spending habits, anomalies, and errors.

Data as a Service for Marketing

In marketing, datasets are fragmented not just across different channels but also across formats such as audio (podcasts, voice search data), video (campaign video ads, demos, webinars), and text (emails, social media posts, blogs). DaaS enables consolidating this data in a centralized interface and then leveraging it to extract meaningful insights. The unified interface enables comprehensive audience segmentation, campaign attribution, and cross-channel insights to drive effective marketing campaigns.

Data as a Service in Retail

The manufacturing sector works on efficient inventory management, accurate forecasting, and personalized customer experiences. Data as a service enables businesses to analyze sales data, customer behavior, trends, and to undergo competitor analysis.

For instance, a food store chain leverages DaaS to track sales for predictive analysis, enabling it to optimize stock levels and prevent overselling. It also ensures they are stocked with the most purchased products across all the stores.

Why Businesses Struggle to Leverage Data as a Service In-House?

Lack of Infrastructure and Resources

The process from collecting big data to ongoing maintenance requires significant infrastructure. It’s a capital-intensive process that requires businesses to invest in advanced software, servers, and cloud infrastructure. This not only leads to overhead but also diverts the resources from core business operations.

Complexity of Regulatory Compliance & Data Security

In-house teams often lack specialized expertise with regulatory compliance, such as GDPR and HIPAA. In high-stakes applications such as finance and healthcare, where data security is paramount, this can lead to penalties for data breaches.

Lack of Scalability

Due to resource constraints, the data operations are not flexible to scale with fluctuating demand. The process would require additional infrastructure and staffing, resulting in increased overhead. In big data, scalability is essential because the volume of data keeps changing. Then, in the case of

Unstructured Workflows

When in-house teams lack streamlined processes for effectively managing and analyzing big data with DaaS, this leads to overburdened staff, disrupted operations, and high turnover rates. A lack of established workflows potentially causes added overhead, since it lacks the infrastructure to manage capital investment.

Adapting to Evolving Technical Landscape

In-house teams are often not specialized in adapting to technological advancements. They lack the time and expertise, especially when their focus is on core business functions. In such cases, there are high chances of them using outdated tools and methods, leading to ineffective data management.