Enterprises across the world are increasingly reliant on the cloud technology when undergoing a digital transformation. At the same time, the complexity of cloud platforms is increasing, making it difficult for IT professionals to keep pace with these advances.
Many data management startups claim to have the next big thing, but the exponential growth and development of these services means that choosing the right provider can be an arduous task. There are numerous options to choose from, with as-a-Service provisions becoming increasingly popular. One such service is Data-as-a-Service (DaaS), which we will discuss further.
DaaS has become increasingly popular. This platform aims to provide a centralized data repository, along with numerous third-party integrations, to simplify data management in the enterprise. The term was first coined in 2015 when companies started integrating different functions from various first- and third-party services to create a single unified service.
Let’s take, for example, the Google Jobs service. It’s integrated into the main Google search engine, though it does not host any jobs of its own. Instead, the service curates job listings from across the internet and delivers them as a unified search result. It also integrates with Google Maps to offer estimates on commute times, making it a great example of “web mashup” services.
DaaS can be applied to a range of business applications, including customer relationship management (CRM), enterprise resource planning (ERP) and other in-house software applications. The result - easy access to mission-critical business data, regardless of geographical location and organizational barriers.
With DaaS, you can effectively manage a multitude of data from various heterogeneous sources. This includes both internal and external datasets, offering a competitive advantage in the form of improved speed, reliability and performance with data-reliant workflows.
Some benefits of a DaaS strategy include:
Agility – Most DaaS providers build their hosting infrastructure on a service-oriented architecture (SOA), which increases speed and flexibility when accessing your datasets. The premise for SOA is facilitating seamless communication between various business services, regardless of the platform or coding language. This is what allows for integration between your various business applications.
Cost-effective – With DaaS, the provider can quickly deploy applications that facilitate data delivery, primarily through extract-transform-load (ETL) methodologies. This increase in deployment speed results in a reduced mean time to delivery (MTTD) for data processing, which they pass down to you in the form of cost savings.
Data quality – Your DaaS provider will have a system for ingesting data, which effectively cleanses and structures data ready for analysis and use with business applications. This also automates quality control workflows, ensuring that your data is accurate, and any duplicates are merged or removed to minimize storage requirements. You also reduce compliance management risks by centralizing your data points within a single source of the truth (SSOT).
As with most cloud computing strategies, DaaS has its shortcomings. Most of these will also apply to other cloud solutions, but the risk is amplified when storing sensitive business-critical data.
When you hand over your data to a third-party and allow them to manage it on their systems, you are reliant on the security of their platform. This applies to all forms of cloud computing, where the management of hardware is out of your control.
To combat this, you can use identity access management (IAM), which allows you to control access to data using account-level rules that restrict access depending on the security clearance of individual employees or departments. You would be liable for properly configuring this, but your DaaS provider will be liable for any hardware problems or internal network configurations. It would be wise to discuss this with prospective providers and make sure they have a service level agreement (SLA) in place that offers reassurance against data security issues.
Trianz is a data management consulting firm with over a decade of experience in helping our clients secure and manage their data. We collaborate with you to assess the effectiveness of your data management strategy. After a thorough analysis, we then help you decide on a tailored solution with leading DaaS providers like Snowflake.
Get in touch with our data management consulting team and migrate to a better data management environment with Trianz today.
Contact Us Today
What are the Differences? Though often used interchangeably, data pipelines and ETL are two different methodologies for managing and structuring data. ETL tools are used for data extraction, transformation, and loading. Whereas data pipelines encompass the entire set of processes applied to data as it moves from one system to another. Sometimes data pipelines involve transformation, and sometimes they do not.Explore
One Unified Dashboard In the past, most enterprises would have used a legacy business management system to track business needs and understand how IT resources can fulfill these needs. The problem with these legacy systems is the manual data collection process, which introduces the risk of human error and is much slower than newer automated solutions.Explore
Intelligent automation in the workplace is becoming more relevant in the modern market. As automation technology becomes more refined and smart business models allow business owners to optimize their workflow, more and more are turning to intelligent automation for their internal and client-facing processes alike.Explore
What is a Hybrid Data Center? A hybrid data center is a computing environment that combines on-premise and cloud-based infrastructure to enable the sharing of applications and data across physical data centers and multi-cloud environments. This allows organizations to balance the security provided by on-premise infrastructure and the agility found with a public cloud environment.Explore
Leverage Your Data to Discover Hidden Potential The amount of data in the insurance industry is exploding, and the number of opportunities to leverage this data to achieve large-scale business value has exploded along with it. Rapid integration of technology makes it possible to use advanced business analytics in insurance to discover potential markets, risks, customers, and competitors, as well as plan for natural disasters.Explore
Increased Use of Data Lakes As volumes of big data continue to explode, data lakes are becoming essential for companies to leverage their data for competitive advantage. Research by Aberdeen shows that organizations that have deployed and are using data lakes outperform similar companies by nine percent in organic revenue growth.Explore