As a generation growing up on science fiction, AI vs. homo sapiens always seemed like a near possibility. (hint – The Matrix). Will this be the next big face-off?
No, it's going to be humans with AI vs. humans without! Whether you are thinking of setting up internal analytics or using a hybrid/ outsourced model for analytics adoption, human intelligence is going to be at the heart of artificial intelligence. Now this is not just an HR headache; since your people strategy is going to shape your analytics strategy, and consequentially, your business strategy (that's how analytics interacts with your organization!). Here are some common talent-related challenges that you should know about to help you formulate a winning strategy.
Hiring and Retention
The Demand-Supply Dilemma.
The right mix of talent across technical expertise, project managers, business leadership, and functional expertise is needed to drive analytics. Due to high demand and lack of skilled resources, hiring a technically sound analytics team with the relevant domain knowledge and exposure can be challenging. Not only is the whole talent attraction process time- and cost intensive, but retention can also be an uphill battle. If teams are not big enough, the attrition of even 1-2 members can derail entire projects.
Workforce Dynamism
Avengers Assemble!
For the smooth and independent functioning of the analytics unit, resources across complementary skills (like data management, data engineering, visualization, machine learning, functional expertise, and stakeholder management) are needed. An initial investment is required to set up the infrastructure, recruit the right talent, and sensitize business stakeholders.
Further, as the analytics maturity of an organization evolves, a different mix of skill-sets is needed to execute and transition seamlessly between different kinds of projects, while continually interacting with cross-functional stakeholders. At this stage, due to fixed skills of the resources, there is an imbalance in the available resources and the project's needs. While this can be alleviated if the resources have more fungible skills, or by hiring additional resources, this results in cost escalation and resource utilization concerns.
Fast-Paced Environment
Keeping Up with The Statisticians.
Catching up with the rapidly changing business and technological environment can be challenging. Every day, new solutions are made possible by AI, better tools are developed, and existing tools get new functionalities. To stay in the game, continuous investment (in resource development, skill addition, maintaining state-of-the-art tech infrastructure) is necessary.
Realistically, only a handful of organizations (like Walmart or Apple) have been able to manage their analytics journey by pouring heavy investments into their analytical talent. For most players, roping in external partners with the right mix of business translators, up-to-date machine learning experts, and data-savvy teams already in place can achieve the much-needed head start.
Understanding the above-stated execution challenges has become extremely important as organizations take small steps towards data-driven decision making. An external expert can support businesses in this transition through a sturdy foundation of industry best practices and frameworks to handhold, train, and educate stakeholders in this journey of deriving value from data.
This article is the third in a series of six, where we discuss some of the most commonly faced obstacles in the adoption of analytics.
The content of this article is intended to provide a general guide to the subject matter. Specialist advice should be sought about your specific circumstances.