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Understanding the Business Case for an Analytics-as-a-Service Partner


Whether it is managing an enterprise data strategy solution or building a reporting structure for marketing analytics, a trusted Analytics-as-a-Service partner gives you flexibility and the benefit of an organization that actively tracks industry trends and best practices to drive focused up-skilling and resource management. And consequently, has the ability to provide both industry agnostic and industry specific expertise capable of being allocated for the exact scope of your needs, and beyond. All this without having to bear the significant capital costs required to build it internally and the opportunity cost of the time it would take. An Analytics-as-a-Service partner can provide those resources and support those needs to the extent to which they are needed.

What is Analytics-as-as-Service (AaaS)?

Analytics-as-a-Service (AaaS) is the external solution relating to the active management and effective utilization of data at various stages in its lifecycle at your organization. AaaS is a practice by which an enterprise could leverage a long-term partner who could bring valuable team members, expertise, as well as data science tools/platform that could kick start and/or manage your analytics journey. Depending upon the organization’s analytics maturity, this is a way to partner with experts for handling the complete analytics load or outsource individual tasks. 

Any organization’s data strategy or IT strategy will inevitably reference cloud service modules that are provided “as-a-service,” including infrastructure, platforms, software, analytics and so on, all of which follow the same fundamental logic. Instead of incurring significant capital expenses in creating an end-to-end data ecosystem in-house, not to mention acquiring the cutting-edge skillsets to manage it, organizations can leverage a strategic partner to build and support parts or all of that ecosystemPartners can provide valuable team members, tools/platforms, and expertise to direct your organizations’ data initiatives.

Key Roles on an Analytics Team

An analytics team’s core structure includes positions, with varying degrees of utilization, that encompass data engineering, data analysis and data science and data strategy/architecture. The primary roles are as follows: 

Data Engineer: Typically coming in with a software engineering or computer science background, data engineers provide the backbone of this team. They work in a number of environments to build systems that collect, manage, and convert unstructured raw data into a usable form. Effectively creating the foundation for data scientists and analysts by creating the network from which they can connect the dots.  

Data Scientist: As mentioned previously, data scientists connect the dots from the network that data engineers build out. What separates data scientists from simply data-proficient individuals, is an almost artistic approach to capturing, interpreting, and presenting data. Leveraging a wide range of tools/skills in areas, data scientists use innovative techniques to experiment with and analyze data that can subsequently be used to build machine learning pipelines that drive decisions making in the future. 

Data Analyst: Data analysts function as the primary interpreters of data for your day-to-day purposes. Data scientists have a more dynamic and innovationfocused role. Data analysts are the ones engaged in routine analyses, reporting and data visualization that provides the bridge between the data and the business. These roles are more accessible to the wider organization and likely have a proactive, working relationship with various parts of the business. 

For those organizations without an analytics practice, the benefit is more obvious. Given than 81% of companies today operate on the cloud, your organization may already have some combination of data engineers, scientists, and analysts at the helm. But we do see many of our clients needing to fill gaps in one or more of those roles, which is where an Analytics-as-a-Service partner can enter the picture. Here are some use cases for the organizations that have some data and analytics organizational infrastructure, but benefitted from supplemental AaaS support. 

Use Cases

Take an instance of an organization that may not have enough data and analytics personnel to support a wide range of projects, especially those that are particularly time-sensitive and have an ROI that is difficult to define: empowering citizen data scientists. 

A citizen data scientist/analyst can be described as non-technical personnel who can perform sophisticated analytical tasks as power users of specific platforms. For example, a data scientist may investigate a retailers eCommerce data and determine that market basket analytics could drastically improve sales. A data analyst can then leverage a data engineers data pipeline to build out a dashboard. A citizen data scientist would then use the drag-and-drop features in the reporting platform to derive, investigate, and drive insights of their own. While they may not have the know-how to build the data pipeline, the proficiency in the specific platforms combined with business experience allows them to re-interpret the data through the unique lens of their own business expertise and find insights that a data team might not see. Projects like these need a concentration of resources in building out the pipeline and empowering resources to drive it independently. Following this, only light maintenance, and support on an ad-hoc basis is necessary. It would be hard to allocate critical in-house resources to train one subset of the business on how to leverage a dashboard tool for a project with an ambiguous ROI or test out new concepts. Outsourcing this to a Analytics-as-a-Service partner, on the other hand, would avoid the trial-and-error phases, ensure a full-time support to accelerated execution, and prevent pulling any resources away from critical items.  

For those organizations that are a little earlier on in their analytics journey, or those that have a smaller budget, it is likely that they have a very limited analytics team. While this may amplify their personnel’s value within the organization, they also present the risk of being single points of failure. Supplementing a limited team with flexible, ad-hoc resources will go a very long way as a cost-effective risk mitigation strategy. 

Analytics-as-a-Service Partner Examples

To bring this to life, consider the below client examples from Clarkston’s analytics-as-a-service practice:  

Example 1: One client started with the Analytics Activation program over two years ago. Our team remains with the client today, providing flexibility to provide analytics services, including data engineering, data analysis, building machine learning models, and formulating business recommendations. The success of this client has drawn from their vast curiosity and ability to ask the right questions that have meaningful business actions. Our team remains equipped to answer any business question based on the wide variety of skills analytics-as-a-service enables. In our partnership so far, we’ve built out: customer segmentations, a graph database for a 360 view of customers, data-driven new product launch insights, marketing optimization models, and a healthy backlog of new project ideas.    

Example 2: Another client partnered with us to grow their analytics function. They began with us with a single data resource and a data council that met on a volunteer basis to share knowledge. We helped them formalize their analytics processes, from the intake and prioritization of ideas from the organization to executing the work into production-ready solutions. By starting with a Proof of Value (POV) project, we were able to share the business benefit across the finance, supply chain, and sales and marketing teams and begin to evangelize analytics across the company. The POV project uncovered other areas for improvement; the organization wanted our team to stay on board and build out their data lake and supplement their team of one to begin building data pipelines for analysts. With each success comes another new gap to address, and our analytics-as-a-service team is able to drive that progress in partnership with their client to provide the data and analytics skills they need as their needs evolve.

Starting off with a relatively smaller, exploratory venture is the perfect introduction into what an Analytics-as-a-Service partner can bring to the table. This exercise also helps gauge the maturity level of your organizations analytics practice and understand where your in-house practice can benefit from an analytics partner. Clarkston’s AaaS offering is focused on partnering with clients to actively plan an impactful backlog of work and roadmap to ensure value-added projects, in addition to executing efficiently. With a quick ramp-up and lower operating cost, impactful analytics is not too far away.  

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Tags: Actionable Analytics, Analytics, Data & Analytics, Managed Analytics, Analytics Trends