Companies providing analytical tools are moving up the value chain by offering industry-specific solutions instead of the traditional analytical tools for analyzing data or performing specific functions like forecasting etc., which are specially designed to address the business needs of industries such as hospitality, automobile, among others. These solutions use data from business systems to enable analytics-based decision-making. Revenue Management Systems (RMS) for the hospitality industry would be a good example, wherein enterprises use data to make business decisions like pricing the rooms. However, after implementing these industry specific solutions, there could be challenges in understanding the new data conditions or complex decisions from these systems. Analytical support operations can help resolve this.

Build the right balance between domain experts and the analytics experts
Analytics is all about understanding real-life business processes and converting them into mathematical equations to achieve a business objective like maximizing profits or reducing costs. As a function, it must have domain experts—who understand and can relate to the customer challenges—in its support teams for making communication successful.Once you have the right level of domain expertise in the team, the next step is to hire the analytical talent who can analyze the data and propose solutions using analytical tools or techniques. It is not the question about domain experts or analytical experts as the real answer is both. Team members from these two areas must work together to resolve the client issues.

An important attribute to look for in analytics support professionals is their ability to communicate effectively with the clients. The focus should also be on designing and delivering a blended learning curriculum—that has the right mix of classroom, online and on-the-job problem-

solving learning—to accelerate the learning and development of the new team members.

Focus on building right tools and processes for scalability
After having the right support team to start the operations, the next step should be to scale the operations. It is extremely crucial to have the right tools that will process the data and present it in the required format ready for data analysis. If not, it becomes difficult to reduce the cycle time.

Analytics is all about understanding real-life business processes and converting them into mathematical equations to achieve a business objective like maximizing profits or reducing costs

However, the process is easier said than done as analysts prefer to consider every data condition as a unique situation. It could lead to “Analysis Paralysis” thereby impacting the client issue resolution time. To mitigate this, the role of process developer comes into the play. The process developer is responsible for documenting the investigation steps for each stage of the process and supplementing the standard answers to expedite the resolution. For example,a user might find forecast or decisions from the system as aggressive for few days. This is a probable question that many users might ask. This is where there is a need to standardize the issue investigation process and provide right data as well as graphs in the solution to answer client concerns.

The audit process follows next, to check the final issue resolution for accuracy. At the same time, it is also important to implement matrix systems that measure the efficiency, execution accuracy, and client management skills of the analytics support teams.

Nurture the talent on a continuous basis
It is important to nurture the talent. This can be done by either enrolling the team members for advanced analytical courses pertaining to the support processes or participation in industry events like analytics conferences.

There are few client issues which cannot be resolved by support teams and need the guidance from Research and Development (R&D) team, which is an effective way of grooming the talent. Also, programs focused on improving the communication skills, on an ongoing basis, go a long way in nurturing the talent.

Internal opportunities to work in Analytics Quality Assurance, Product Development and Research and Development teams will help to retain this talent within the organization. One success story of such internal transfers can go a long way to boost the morale of the ten other analytical support professionals. These internal transfers also create a unique advantage for the company by enabling team members across the organization to understand and relate to the common problems faced by users while using the solution. This, in turn, helps to build cross-team collaboration, resolve client issues fasterand develop a robust product that generates lesser issues for the clients.