Underutilization and the complexity of managing growing data sprawl have spawned several trends during the last several years. Data-as-a-Service (DaaS) is one such trend which represents an opportunity to improve IT efficiency and performance through centralization of resources. DaaS strategies have increased dramatically in the last few years with the maturation of technologies such as data virtualization, data integration, MDM, SOA, BPM and Platform-as-a-service.

Within the corner offices of business heads, data scientists and analysts several questions are being asked:

• How to deliver the right data to the right place at the right time?

• How to “virtualize” the data often trapped inside applications?

• How to support changing business requirements (analytics, reporting, and performance management) in spite of ever changing data volumes and complexity?

In the early years most of DaaS initiatives were limited to financial services, telecom, and government sectors. However, in the past 24 months, we have seen a significant increase in adoption in the healthcare, insurance, retail, manufacturing, eCommerce, and media/entertainment sectors. This is because of massive amalgamation of extracting continuous insights from structured and unstructured data, liberation of data restricted and protected within silos to the enterprise level and the express desire to conduct real-time analytics.

Businesses are looking to solve tough data and process integration challenges as they once again begin to invest in new business capabilities. Data as a Service (DaaS) is based on the concept that the fragmented transaction, product, customer data can be provided on demand to the user regardless of geographic or organizational separation of provider and consumer. Additionally, the emergence of PaaS and service-oriented architecture (SOA) has rendered the actual platform on which the data resides also irrelevant.

Data as a Service (DaaS) has many use cases:

1. Providing a single version of the truth;

1. Integration of data from multiple systems of record

1. Enabling real-time business intelligence (BI),

1. Federating views across multiple domains;

1. Improving security and access;

1. Integrating with cloud and partner data and social media;

1. Delivering real-time information to mobile apps

Data as a Service (DaaS) brings the notion that data related services can happen in a centralized place – aggregation, quality, cleansing, enriching and offering it to different systems, applications or mobile users, irrespective of where they were. DaaS is a major enabler of the Master Data Management (MDM) concept.

Master Data Management is the Holy Grail in data management.  The focus for most businesses is on the single version of the truth or Golden Source “Product”, “Customer”, “Transaction” and “Supplier” data.  This is because:

• Fragmented inconsistent product data slows time-to-market, creates supply chain inefficiencies, results in weaker than expected market penetration, and drives up the cost of compliance.

• Fragmented inconsistent Customer data hides revenue recognition, introduces risk, creates sales inefficiencies, and results in misguided marketing campaigns and lost customer loyalty.

• Fragmented and inconsistent Supplier data reduces efficiency; negatively impacts spend control initiatives, and increases the risk of supplier exceptions.

MDM provides the plumbing that enables DaaS solutions. This plumbing allows for:

• Agility & Time to Market – Customers can move quickly due to the consolidation of data access and the fact that they don’t need extensive knowledge of the underlying data. If customers require a slightly different data structure or has location specific requirements, the implementation is easy because the changes are minimal.

• Cost-effectiveness – Providers can build a base with data experts and outsource the presentation layer, which makes for very cost-effective report and dashboard user interfaces and makes change requests at the presentation layer much more feasible.

• Data quality – Access to the data is controlled via data services, which tends to improve data quality, as there is a single point for updates. Once those services are tested thoroughly, they only need to be regression tested, if they remain unchanged for the next deployment.

• Cloud like Efficiency, High availability and Elastic capacity. These benefits derive from the virtualization foundation —one gets efficiency from high utilization of sharing physical servers, availability from clustering across multiple physical servers, and elastic capacity from the ability to dynamically resize clusters and/or migrate live cluster nodes to different physical servers.

We find that there is a common process that is appearing within the mid-market and customer customers focused on enabling and MDM strategy. It is the data logistics chain consisting of data acquisition, data stewardship, data aggregation and data servicing.

There is a sudden and dramatic shift in how data is handled in businesses as they are shifting away from a hierarchical, one-dimensional enterprise data warehouse initiative with fixed data sources to a fragmented network. This phenomenon has caused ripple effects throughout the old data logistics network.  Data-as-a-Service (DaaS) at its core is addressing this problem of fragmentation soundly enabled by MDM.