Representative End User Clients
Representative Automation Clients
Representative Software Clients
In almost every conversation we have with end users looking for predictive analytics solutions, they consistently raise a point of concern, indicating they, “don’t want to have to hire a bunch of data scientists.” Fair point.
In a time of “do more with less” thinking, adding head count, whether via full-time employees (FTE) or omnipresent contracted data scientists, isn’t palatable or sustainable. With that in mind, what should buyers expect when it comes to the roles and responsibilities that will need to be addressed during an advanced analytics project?
Below is a high-level look at the skills necessary to support a predictive analytics project. It also considers those same roles in the context of an organization looking to grow predictive analytics as a core capability within the organization.
For the purposes of this post, I will concentrate on the most critical element of predictive analytics: the development, training, and use of algorithms and data models:
| Role | Responsibility | Ownership |
| Data scientist | Selects the algorithms, builds advanced data models, and trains and deploys solution. Responsible for the “math.” | Typically delivered by solution provider. However, tools for data scientists are increasingly being incorporated into sandbox environments within solutions, though many of these still require users to have advanced mathematical skills. As these data science skills become increasingly valuable within the market, a premium will be placed on solutions that enable in-house data scientists to be more productive (versus an end user company having to hire new data scientists). |
| Subject matter expert (SME) | Provides industry/process-specific context for what the patterns identified by the algorithms and models mean. | Typically provided by end user. Some solution providers also have this as part of their value proposition, though it is sometimes delivered via behind-the-scene partnerships. |
| Citizen scientist (aka citizen data scientist) | Has data science-like skills, such as statistics, but not as advanced as a data scientist. Delves into new data and improves existing models. Creates and deploys additional basic models to gain added insight. | Many solution providers are addressing this role via services. Some are also embedding tools within their solutions geared for users that have experience with statistics and modeling, such as engineers, but don't require data science math expertise. As analytics initiatives move from pilot to become competencies within enterprise, this role will increase in importance for the end user company. SMEs will take on the roles of citizen scientists for advanced analytics as core job responsibilities, supported by an analytics toolkit. |
The growing role of the citizen data scientist
If you choose to build advanced analytics as a core capability within the organization, you’ll likely create a role of the "citizen data scientist." Given that the term can be a mouthful, I’ll refer to it as the citizen scientist.
In many instances, organizations can leverage internal skills to bring the basic data science expertise in house for advanced analytics while minimizing the burden on organizational resources. Engineers can fill the role of citizen scientists, bringing to bear their background in math, statistics, and modeling. They can use these skills to wring more value from the analytics solutions they are using, as well as dig more deeply and broadly into data made available to them.
Analytics providers are moving quickly to build out tools to support the citizen scientist. An example is rich data visualization, where data streams can be added, connected, and analyzed via drop and drag methods. These tools are powered by an underlying analytics engine, so it is easy for the citizen scientist to create more “ah ha” moments using data, algorithms and models.
This role will be key in determining what story the data tells as new parts of the operations incorporate analytics and results are shared across the enterprise—e.g. how different failures or inefficiencies across multiple aspects of the organization are related to overall corporate risk. However, citizen scientists should not be counted upon to manage the end-to-end process of analytics development, training, and use of algorithms and models. They lack the advanced mathematical expertise to do so.
Of course, individual business requirements will dictate the specifics of how internal resources are built and/or solution provider capabilities are leveraged. However, the chart above provides a reasonable idea of what to expect as you plan for an advanced analytics project or pilot and, mostly importantly, grow your capabilities.
ARC is clarifying the advanced analytics market
In our ongoing commitment to deliver vision, experience, and answers for industry, ARC continues to provide guidance on the topic of analytics. In the coming months, we will release a series of strategy reports on advanced analytics, one of which is now available for ARC clients on the ARC Client Portal. As well, we have built out a set of functional requirements to support supplier selection projects, a number of which are already underway. Should you need to leverage this requirements list for your RFX process, please contact us.
At our 21st Annual ARC Industry Forum in Orlando in February of 2017, we will host multiple sessions that will dig more deeply into advanced analytics. I will also co-present a workshop on how to determine requirements for an advanced analytics request for proposal (RFP) process. For information on any of the sessions, please contact your client manager or shoot me an email.