From the ease of deployment to tangible business benefits, DSaaS is more than building up machine learning capabilities faster.
Data Science as a Service offers on-demand access to an experienced data science team, using a mature methodology to deliver one-off analyses and production-ready predictive models. Data Science as a Service (DSaaS) embraces data science for business quickly. Whether or not organisations have an in-house data science team, DSaaS helps to build up machine learning (ML) capabilities faster so you can focus on driving business results. DSaaS offers a compelling operating model to develop a unique insight into therapeutic effectiveness while avoiding inequities due to social determinants.
In March 2021, AWS announced a partnership with Change Healthcare to offer DSaaS for healthcare analytics. And after the announcement, the adoption of the concept picked up the heat. Mobility solutions vendor Comviva announced its own DSaaS offering to help telecom providers with marketing. Data Services provider Calligo acquired data analytics vendor Decisive Data to expand its DSaaS offerings.
According to Anand Rao, Partner and Global Artificial Intelligence Leader at PwC, “Data Science as a Service means the outsourcing of data science activities to an external provider.”
DSaaS covers everything from analytics tools embedded into popular SaaS platforms such as Salesforce specialised vendors offering pre-built models for specific business applications which they can customise and manage for customers to standard consulting deployments.
Although there are barriers to adopting analytics in organisations, which include lack of proper technology and talented resources, poor data governance and lack of push from top management, as per Deloitte’s Analytics Advantage Survey, these challenges can be addressed by choosing DSaaS.
1. Ease of Deployment
Setting up an in-house server and application infrastructure with a dedicated analytics resource team is a daunting and expensive task. There is a massive gap in the demand and supply for data scientist resources that implies huge starting salaries and retention costs for top quality resources. Maintaining analytics infrastructure is not the core competency of most IT teams.
With DSaaS, the analytics applications, as well as a resource, are entirely outsourced. So you will be able to save on upfront deployment costs and cut down on time to adoption.
Also Read: The Rise Of MLOps
2. Ease of Use
Most of the cloud-based data science platforms are designed so that executives can use them without any specialised training or coding. A semantic layer is applied that uses the business terms as part of the interface and hides the underlying data sources, manipulations and calculations from the end-users. Insights are presented in a visually appealing and easy to interpret manner, cleanly and consistently which simplifies strategic as well as tactical decision making. This is extremely important to encourage adoption, not just at the executive level but at all levels within the organisation.
3. Improved Data Governance
Using a centralised analytics environment across the organisation will force the users to adopt data governance’s best practices. The IT department within an organisation is responsible for incorporating data from different teams in a clean and consistent manner for uploading to the analytics platform. It helps prevent the duplication of data and provides a single source of truth for business users across departments, geographies and functions.
4. Tangible Business Benefits
With low upfront investments and highly specialised analytical insights, DSaaS ensures a maximum return on investment. Cloud-based solutions are available across various business functions such as customer experience (CX), finance, supply chain and talent management. Industry-specific vertical solutions such as retail analytics, manufacturing analytics and so on to make sure that the important level of domain expertise is embedded in the design of data science solutions.
Also Read: Cloud Adoption Strategies For Enterprises
The challenges you might face
Like all other outsourcing models, DSaaS also comes with a set of problems, which needs to be handled as part of the decision making process. Relying on a black-box analysis of your business data can be risky. Business users need to be aware of the fundamental logic working behind data manipulation strategies employed by the DSaaS provider. These strategies need to be in sync with the strategic priorities as designated by the top management. Business users must keep a vigil on an increasing number of false positives or false negatives and continually strive to work together with the DSaaS provider to improve the quality of results.
The right selection of a DSaaS provider and the application platform is crucial. While evaluating DSaaS options you should consider the cost, skill sets and time required to transfer operational data in a consumable manner to the DSaaS provider. You should assess the vendor’s support for version control and metadata about datasets, models and analysis results.
All these aspects need to be considered for long-term manageability.
