Over the past few years, several trends have appeared on the scene thanks to underutilization and the complexity of managing growing data sprawl. Among those trends is Data-as-a-Service (DaaS). A relatively new concept, DaaS is a cloud-based model that allows our customers to access data on demand, regardless of their own location.
Importantly, it represents an opportunity for improving IT efficiency and performance through centralization of resources. With the development and implementation of such technologies as data virtualization, data integration, Master Data Management (MDM), service-oriented architecture (SOA), business-process management (BPM) and Platform-as-a-Service (PaaS), DaaS strategies have increased dramatically in recent years.
As the DaaS trend continues to accelerate, so we have answered ourselves the the questions around it: How do we deliver the right data to the right place at the right time? How do we “virtualize” the data often trapped inside our applications? How do we support changing business requirements (analytics, reporting, and performance management) despite ever-changing data volumes and complexity?
Our Enterprise DaaS strategy & Infrastructure is a core focus area for us. Here’s why:
- Enterprise Data Warehouse (EDW) strategies are increasingly moving to cross-enterprise DaaS strategies
- Structured and unstructured data growth is forcing the evolution to DaaS
- As data in application silos move to a centralized corporate/enterprise asset, DaaS infrastructure becomes critical
To do any form of real-time enterprise analytics, you need DaaS in place first.
In the early years of this market, most DaaS was focused primarily on the financial services, telecom and government sectors. However, in the past two years, we have seen a big jump in the number of sectors adopting DaaS, namely healthcare, insurance, retail, manufacturing, e-commerce and media/entertainment.
What is Data-as-a-Service?
We already know that DaaS promotes the concept that data related services – aggregation, quality, cleansing and enriching data and offering it to different systems, applications or mobile users – can be provided and accessed from a centralized location. In addition, DaaS is the major enabler of the Master Data Management (MDM) concept.
Master Data is the Holy Grail of enterprise data management. Most companies focus on a single version of the truth, or Golden Source “Product”, “Customer”, “Transaction” and “Supplier” data. Why? Fragmented, inconsistent product data slows time-to-market and creates supply-chain inefficiencies, resulting in weaker-than-expected market penetration and an increased 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.
Here’s where DaaS solutions come in. We as getsix provide with our DaaS Services the plumbing that enables MDM, and have the following advantages:
- Agility (and time-to-market) – Our 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 our customers require a slightly different data structure or have location-specific requirements, the implementation is easy because the changes are minimal.
- Cost-effectiveness – We can build the base with our data experts and outsource the presentation layer, which makes report and dashboard user interfaces more cost-effective. It also makes change requests at the presentation layer easier and 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 the 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.
DaaS Use Cases
Organizations are looking to solve tough data and process-integration challenges as they start to invest in new business capabilities again. As they explore new opportunities, they also have to make choices that will help both streamline and propel the enterprise forward. DaaS is making geographic or organizational separation of provider and consumer an obsolete notion, while the emergence of Platform-as-a-Service, or PaaS, along with service-oriented architecture (SOA), is rendering the actual platform on which the data resides irrelevant as well.
DaaS has many use cases for the enterprise:
- Providing a single version of the truth
- Integrating data from multiple systems of record
- Enabling real-time business intelligence (BI)
- Processing high-performance scalable transactions
- Federating views across multiple domains
- Improving security and access
- Integrating with cloud and partner data and social media
- Delivering real-time information to mobile apps
- Conducting an enterprise-wide search
Say a client decides it’s time to take the next step. Where does that client begin to enable MDM strategy and build a data-as-a-service offering for the rest of the organization?
These are the elements a company needs in order to take that next step:
- Data acquisition – It can come from any source, including data warehouses, emails, portals, third-party data sources
- Data stewardship and standardization – Boil it down to a standard manual or auto-magic
- Data aggregation – Stick build data warehouse for acquisition. This has a strong service and technology-driven quality-control mechanism, which is very different from “let’s write 100 ETL programs”
- Data servicing – Via web services, extracts, reports, etc. Make it easy for the end user to consume the data, either via machine-to-machine or directly through the reporting universe
All of these capabilities come together around the data logistics chain. The last few decades have seen a dramatic shift in how companies handle data. Increasingly, they are shifting away from hierarchical, one-dimensional enterprise data-warehouses (EDW) with fixed data sources to a fragmented network of strategic partnerships with external data sources. Not surprisingly, this phenomenon causes ripple effects throughout the old data logistics network. DaaS at its core can address this problem of fragmentation.
As a combination of applications and technologies, DaaS consolidates, cleans and augments source enterprise data, and synchronizes it with all applications, business processes and analytical tools. The goal behind it is to achieve significant improvements in operational efficiency, reporting and fact-based decision making. To that end, key requirements of any DaaS strategy include domain knowledge, application knowledge, people/talent, processes and technology platforms.