This is the second segment of the fourth part in an ongoing series of Assessing Analytical Maturity. If you are new to this series, we recommend reading:
The world is grappling under the clutches of the COVID-19 virus and entire countries have been put under lockdown. The impact (on human life, social-cultural, and economic) is immeasurable. As governments plan for multi-billion-dollar stimulus packages, almost all businesses, (large, medium, and small) are attempting to pivot their strategies to adapt to the ‘new-normal’, sustain, and grow in these uncertain times.
Undeterred by the crises, the role of data analytics team from now to the post COVID-19 ‘new-normal’ world is expected to increase.  Increased focus to meet the growth of the digital consumer and the change of purchasing patterns, building redundancies to optimize the value chain, etc. will ensure survival is directly linked to the acceleration that companies can exhibit in becoming AI-driven. As recovery becomes the focus for companies, it will be crucial to keep the people at its core while doing so.
The first segment of this fourth part attempted to tackle the following questions as exhaustively as possible. We covered the first three and laid out the various roles and the subsequent interactions during solution development. We also took a sneak peek into what the analytics team of the future might look like.
• What are the roles required to build a great data analytics team? What are the skills needed?
• How do the various roles interact with each other during any solution development?
• What is the analytics team of the future going to look like?
• How does hiring keep up with the ever-changing needs to build the analytical teams of the future?
• What role does training, learning, and development play in helping the employees chart out a great career path?
• And finally, what are the other factors that play a role in ensuring a low employee attrition rate?
Let us take a look at some of the remaining aspects under the dimension of people and how they affect the analytical journey of organizations.
The first segment of this part referred to one of our earlier posts , which talked about analytics teams having been historically staffed by people expected to play a wide array of roles akin to a Swiss Army Knife. Analytically mature organizations are mostly responsive to customers’ evolving demand which currently is assuming a complete ownership and control over the solutions that they were provided with. This herald the advent of contextual AI which necessitates the hiring of professional with specialization on evolving technologies such as contextual AI. Great teams employ and then deploy people bringing in a specific skillset to play a specific role. This brings us to yet another one of our earlier posts  which delves into the details of hiring the ideal analytics professional.
To briefly outline the requirements, it is critical to not seek only the ‘unicorns’ (professionals with a strong grasp of mathematics, software/coding, and domain expertise). Hiring the people who have the penchant for learning and are quick to adapt to the fast and ever-changing demands of the analytics world prove to be far greater assets than previously imagined. To keep up with these needs, hiring teams need to be cognizant to not be swayed solely by the CVs and take into account the ability of the interviewing candidate to apply their learnings into other domains, to think quickly on their feet, and the potential of the candidate. Most importantly, it is critical to gauge a candidate for their communication skills and the ability to generate insights in the case of analytics professionals.
Having a hiring panel with a broad skillset for adequate and holistic assessment, and to test the candidate extensively through assignments and case studies with a section for psychometric evaluation, ensures organizations can build a strong and an effective analytics team of the future. Key consideration is also to not dilute the principles as scaling happens. Developing processes which can withstand the added pressures of hiring at scale can go a long way in ensuring longevity of data analytics teams.
Richard Branson says, “Train people well enough so they can leave, treat them well enough so they don’t want to.”
As organizations, people too seek growth in their field of work. As the years pile on and people grow in their respective roles and responsibilities, a significant motivating factor for people to continue in their line of work revolves around the skills they acquire and/or enhance. Again, in of our earlier posts,  we have explored in detail the various training philosophies that exist. The key takeaway is for organizations and leaders to focus on developing a program that caters to the needs of their analysts.
Contextualized to their needs, organizations may opt for teacher- or student- or society-centric philosophies or a hybrid of them all. The imperative is to ensure that all employees, regardless of their role and experience, are always engaged and more importantly, motivated to learn more. Strategies to motivate could include linking their learning goals to their annual goals and subsequently appraisals, it could also mean acquiring certifications, credits, or any other mode for them to showcase their learning and growth.
And while these may motivate the employees, it is equally important, if not more, to ensure they are provided with the right tools and platforms for them to achieve this. Referring again to the earlier point of following any of the training philosophies, it needs to be supplemented by laying the foundation of the right platforms for formal and informal exchange of knowledge and ideas.
The second part of the Richard Branson quote mentioned above encompasses a wide variety of dimensions and activities to maintain an attrition rate which is acceptable as per industry standards. People will leave organizations. It is inevitable. And it is unwise to even aim for a 0% attrition rate. However, keeping the following in mind can go a long way in retaining employees that include the data analytics team for longer, thereby, providing a sense of longevity to the entire analytical transformational journey itself.
• Build a strong hiring panel. People who are experts in their respective field and can adjudicate candidates effectively, in this case, a successful analytics team. Design a robust process that evaluates holistically and is set up to handle when the requirement for scaling teams comes up
• Hire the right people for the right role. Brief them in detail about the job profile and associated roles, responsibilities, and expectations. Guide them on how it would be to work for your organization. Gauge them on their ability to learn, adapt, and communicate
• Compensate them well. To acquire good and competitive talent requires a compensation package that is in par with industry standards and commensurate with the expected roles and responsibilities. It also ensures a good pool is available for the hiring team to target for future requirements
• Induct all employees regardless of their role and experience in a structured and systematic manner
• Develop Learning Programs which cater to all employees and factor in their personal goals as well. Motivate them further by aligning them to their yearly goals, providing credits and certifications, etc.
• Focus on continual interaction and building a career path for them. Employees wish to see the potential for growth. Align with their personal interests, inculcate their natural strengths, work on their areas of improvement. Build an organization that exposes them to a more diverse and varied set of activities. Build a rotation policy that maximizes exposure and aids the overall development of the individual
Employees should look forward to having a long-term career in your organization. Analytical transformation is not an overnight process and the people who will help in this journey need to be retained to make this happen. Excessive churn derails these efforts and in addition to slowing the process, may even set us back.
In our endeavor to help assess analytical maturity of organizations and nudge them along in their analytical transformation journey, we have covered the foundational layer of data, the infrastructural requirements from the tools, technology, and platform perspective. We have finally taken a look at the crucial aspect of people, the ones who make it happen. The next part in this series, will have us diving into the governance dimension, which aims at integrating analytics teams with the existing ecosystem to derive maximum impact. Stay tuned.
When not solving complex business problems for organizations, Vinayak loves to read and, has lately been intrigued by psychology. He loves to observe behavioral sciences at play in everyday life. He is also a movie enthusiast with (not surprisingly) LoTR and Christopher Nolan movies taking the top spot in his list.