Most of you would agree that hiring the right candidate for the job is one of the key challenges in the recruitment process, at the same time, developing this skill while working within the organization is another important aspect that every candidate looks for in the organization. Without a proper career path, sometimes it becomes challenging for the organization to retain such talents.

Hence data scientists and associates working in a similar area should have a very clearly defined career path which not only motivates then but also meets the organization’s objective. This point is very well explained in the McKinsey research which says how the organization should plan for accommodating different types of talent at a different level of their skill and work toward the objective of the organization.

Type of organizations may vary from industry but if we look at ground zero, we need someone who understands business and someone who understands technology. Getting such talent may not be easy but not impossible. It is very important for the organization to have a very clear vision of what they want to achieve with the analytics. company vision can help us drafting the vision of the analytical team, Lack of right vision or clarity in the expectation from the team may result in insufficient outcomes. edit right leader who can clearly define and help connect the organization’s different departments which can help them realize the power of analytical advancement, and how it can help not only their department but the whole organization when it is used in the right direction.

For instance, if we take an organization that has a very well-defined global presence. If you are a big firm with a presence in multiple countries, it is more important that we have some core team which can understand what are technological advances we can have with Analytics and one core team of Business which understand what type of information they would need to make the informed decision, and what information they can let machine feed to the rest of consumers and subsidiaries.

Each organization is using Analytics in some way or other, but some organization it’s a distributed department where each department has its own analyst, who is responsible for making sure the objective of their departments are met and only work towards getting data out of the systems in the landscape. This setup has its own pros and cons.

To discuss a few pros:

· It’s easy for the departments to enforce their objective and get the analysis done sooner and in a more personalized way

· The individual analyst has more understanding of the department’s business requirement most of the time

· The individual analyst would effectively suggest, improve and also automate the process to get the information over the period of time

· The ability of the individual analyst to address the business problem along with the ability to analytically see the data has an edge in making right decisions pertaining to the department’s ability to address the information and use them

· Such members can be considered as a key team member-based upon their contribution to the team

Some cons can be:

· An analyst might find it repeat it in order to solve the same problem repeatedly

· An analyst is not able to communicate out of his own department With his other fellow analyst on the problem where they might be solving the similar or same type of problem differently

· While walking in the virtual wall of departments you are always Constrained by the limitation of the objective of your department

· May not be easy to cross-train or utilize another analyst when somebody is not available

· In the big picture sometimes, this analyst might Miss on the other opportunities across departments our ability to innovate only because they are contained within the departments

· Sometimes you need more information from other streams of the business in order to effectively make a concise decision, and having a distributed analytics team as part of their own departments creates its own the dilemma of getting data or right contact person to collect such information on send rating the data feed which can provide such information

As Suggested having analyst in each department has its own advantage and disadvantage, but if we can pull these people into a group of experts and segment them by the ability to understand the technology and the business and put them at the center of the whole organization so that this group can help every department of the organization and also look for an avenue to further, improve any unknown or unforeseen business scenarios.

The below images show how one such setup can be structure in the organization.

Here CORE team is in the center of departments and has representation from each department to fill the knowledge gap or support the cross-functional transition.

The COE group may or may not possess all the knowledge of the given business, hence it would be good to have representation from each department (Should be someone who can make decision) . This setup would help in improved interaction from each department to make sure that any solution is not overlooking any of the department’s need , and build the trust in seeking future guidance. This follows very much like how a project is executed where you will connect with stakeholders before concluding the project, and then you run into multiple ‘sprint’ to slowly explore what all this project has to offer. This feedback from all the stakeholders helps in delivering the solution that would help them.

Few pros and cons of this method are as below:

· Initially, it may be tedious for other departments to work with such CORE department and it might look very confusing as they may feel like there is no uniqueness in the ability to view the information as if they were built by someone within their own department.

· Every delivery may reuse or overuse some or other the existing solution to meet the request from other departments and may result in lots of unstructured solution for many different problems.

· How secure is the data shared among the department, and how to manage the security of important data?

· There is a high possibility of not excepting or failure of deliverables if the required parties are not consulted well and the solutions are just imposed on the business

· Some department may feel sensitive information’s are at risk of being available to everyone, or it is shared among every other department which may not be accurate for the business

· It’s more than a change in behavior in addition to the change management for every other department to use such a group in order to facilitate all the business need.

This type of approach is not new we have an Analytical department in many the organization, they have their own analyst in their respective departments who use the data provided by the analytical department and then do the further analysis to support the business requirement of their respective departments. This is a very ambiguous situation and I was not able to understand why the Analytical the team was not able to give the information that is required by the departments in the way which can help them make the right decision in time. It was very easy to understand why the analytical departments and the other departments were not on sync because the analytical department starts working on a pre-defined templated approach to solving any problem instead of a more dynamic and business-centric design that can change or adapt to business needs quickly.

For example, this global pandemic has created many business changes in the product demand and shift in the corporate’s vision for this year ‘s sales. To give you an example including the company that started more focus on building a facemask or designer facemask and advertising and promoting such products. This change in the decision was not driven by any business change but this was the demand of the situation and Many organizations follow the trend. In this very dynamic situation, things are changing very rapidly, and you need to view and maybe sometimes sell something which you are not designed to or prepared to sell in the market. At this time any ability to quickly mobile or use your salesforce and make proper use of their presence across the globe would be very helpful if you have the right tool to capture all the key information and apply those to the business on the ground. A well-advised Analytica Team can play a key role in deciding and supporting with all the quick tools that may help businesses to reply to the situation more effectively.

Some ref examples where a similar or more controlled approach was followed, taken from McKinsey survey and research papers in reference.

One industry conglomerate addressed this scale requirement by starting with a centralized COE serving all business units. As the use and understanding of analytics grew across the organization’s companies, they demanded more support, and the COE was split into sub-groups that were fully dedicated to the largest companies. Over time, ownership of these groups was transferred to the “client” company—but not until they had built a sense of community and common methodology across the entire conglomerate. This sense of community was further reinforced by requiring all new recruits to spend six months at the COE and to go through specific AA training and networking events. Since fragmentation of the analytical talent across functions is almost inevitable over time, it is critical to start out with the appropriate processes and mechanisms to ensure consistency and community across these new profiles.

A leading pharmaceutical company developed an integrated talent strategy that merged business and analytics functions. The company recruited technology and analytics executives in key management roles and developed analytics career paths for them. Placing analytics professionals in key business roles enabled the company to identify and operationalize new analytics opportunities before their competitors could. The organization successfully embedded analytics in key elements of the business —for example, analytics on clinical trial data to enable more cost-effective data.

Let us know what you think, share your experience with us.

1. What was your experience when you had to work under who doesn’t know what you can do or your expertise has to offer?

2. Would you prefer a team of only data scientists or mix with business and IT?

3. Which department can best manage the Data Science team?

4. What is/was the career path for such a role in your experience?




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