Choosing to implement a Data-as-a-Service (DaaS) strategy can be a daunting task, especially when deciding amongst the numerous cloud DaaS providers in the market. Handing control of your data to a third party involves some inherent risks, which makes it important for you to make the right choice for the long-term data security of your business.
A DaaS strategy can offer considerable benefits to the wider business, especially with data-reliant workflows. Access to real-time information can significantly improve the agility of your marketing teams, and thanks to the centralized and structured storage of crucial data points, the insight generation will also improve.
There are several key factors to consider when choosing a DaaS (cloud-based) provider:
Security – The biggest concern with DaaS is handing your sensitive customer and B2B data over to a third party. While the security of IT and network infrastructure lie with the DaaS provider, you will still need to properly configure and maintain the data you store on their cloud.
With cloud platforms, you will have access to identity access management (IAM), which allows you to control access to data based on the security clearance of an employee. It’s also important to consider data encryption, both in-transit and at-rest, as any data transfers between your network and the DaaS provider are your responsibility. Maintaining high standards in these areas is vital for maintaining compliance with regulations like GDPR, CCPA, HIPAA and PCI-DSS.
DaaS providers like Snowflake offer automatic encryption of data upon ingestion, using a minimum of 128-bit AES encryption. They also offer end-to-end encryption (E2EE), which encrypts data-in-transit with the same AES method. Such automatic data security management can reduce the burden on your IT staff, making it an interesting proposition to consider.
Usability – While not commonly discussed, the usability of your DaaS solution is important to consider. With complicated systems and unintuitive management tools, your IT staff could quickly become overwhelmed with data management workloads, compromising the integrity of your data management strategy.
You should choose a DaaS provider that offers software tools fully compatible with your existing IT assets and infrastructure. Key considerations here include client operating system compatibility, the type of structured query language (SQL) version that is being used and the cloud hosting platforms on which the service can run natively.
Integration – With a DaaS approach, the aim is to create a centralized data repository from which all your existing software tools will reference. To do this, you need to ensure that your DaaS solution is compatible with your systems and integrates well with your overall infrastructure.
For example, consider whether your data analytics platform can interface with your chosen DaaS provider’s system. With website management and media hosting, check whether your DaaS solution can integrate well with your existing CRM system.
Snowflake uses an extract-transform-load (ETL) approach to offer integration with several data integration tools. This approach involves pulling data from your source database before augmenting it for use with another system and pushing it to a new target database. You can integrate Snowflake with numerous third-party tools, such as Talend, Stitch Data Loader and Tableau.
Trianz is a leading data management consulting firm with decades of experience in helping our clients to maximize the value offered by their data. We work closely with you to assess your existing data management strategy, before helping you decide upon and implement a best-of-breed solution tailored to your business model. DaaS significantly increases data agility, and we can help you maximize visibility and insight generation by implementing these services.
Get in touch with our data management consulting team and begin centralizing your business data with Trianz today.
Contact Us Today
What Is an SQL Query Engine? SQL query engine architecture was designed to allow users to query a variety of data sources within a single query. While early SQL-based query engines such as Apache Hive allowed analysts to cut through the clutter of analytical data, they found running SQL analytics on multi-petabyte data warehouses to be a time-intensive process that was difficult to visualize and hard to scale.Explore
A Winning Base for Successful Digital Transformations When it comes to developing a successful digital strategy, it is not just corporations planning to maximize the benefits of data assets and technology-focused initiatives. The Government of Western Australia recently unveiled four key priorities for digital reform in its new Digital Strategy for 2021-2025.Explore
Engage Your Workforce with a Modern Employee Intranet Solution The employee intranet has changed significantly since it was first introduced in the early 1990s. What started as HTML-based static portals have now evolved into intuitive communication tools complete with search engines, user profiles, blogs, event planners, and more. Today, many organizations are taking a second look at employee intranets to bridge gaps between teams, build company culture, centralize information, increase productivity, and improve workflow.Explore
Adopting emerging cloud technologies, consolidating resources, and improving processes is the key. “IT no longer just supports corporate operations as it traditionally has but is fully participating in business value delivery. Not only does this shift IT from a back-office role to the front of business, but it also changes the source of funding from an overhead expense that is maintained, monitored, and sometimes cut, to the thing that drives revenue,” said John-David Lovelock, research vice president at Gartner.Explore
Deliver Powerful Insights Instantaneously with Federated Queries - No Matter Where Your Data Resides The concept of federated queries isn’t new. Facebook PrestoDB popularized the idea of distributed structured query language (SQL) query engines in 2013. Over the years, AWS, Google, Microsoft, and many others in the industry have accelerated the adoption of a distributed query engine model within their products. For example, AWS developed Amazon Athena on top of the Presto code base, while Google’s BigQuery is based on Cloud SQL.Explore
What is Unstructured Data? Almost 80% of the data that enterprises and organizations collect is unstructured - data without a set record format or structure. Unstructured data includes data such as emails, web pages, PDFs, documents, customer feedback, in-app reviews, social media, video files, audio files, and images.Explore