I was facilitating a development workshop for a team of business analysts who were consulting with a global telecom giant. The managers had invested in the recent past in a key data analytics certification course for the en-tire team, but they were disappointed to see continued underperformance in the team. The general belief was that they were demotivated. Personally, I had found that a bit strange. An Ivy League certificate would any time be a motivation booster! So, why this underperformance? And at this juncture, I decided to host this workshop with the team.
I started off querying about the nature of their work. They described it as drawing insights about the phone, messaging and app usages during the weekdays and week-ends. I followed up with the next question, "Why do they want these insights?" Initially the team in the room was silent, and then emerged a plethora of conjectures. No one had asked the customer team a question as to why do they need this data and how do they use these insights! Hypothesizing that there might be hesitation from this team based on the seniority level of the clients that they interacted with, I asked them to describe their stakeholders. From the client-side, every level of hierarchy was engaged in conversation with the team here they had periodic check-in meetings, clarification call, all kinds of checks in place.
Curious to know more about how the team coped with so many levels of communication, I asked them about how they prepare for these different stakeholders. The team beamed an 80 slider presentation, loaded with data and analyses, to showcase their work. In fact, I observed the maximum energy in the room when they were taking me through the rigors of statistics that they have followed in checking their various hypotheses. The very skillset that they prided themselves for, was the basis of their unhappiness and consequent demotivation! Most of their stakeholders were Marketing and Sales Managers, Customer Account Managers, Program Managers, Digital Officers, there were hardly any statisticians on the customer team. The pains in their work went totally unappreciated by their clients and worse still, they were alleged of missing expected standards as the clients did not get their expected crisp insights.
This is often the case with any niche skills like data analysis, where people get engrossed in multiple webs of data extraction, analyses and interpretation, without being mindful of their target consumers for the insights. The data analytics skillset needs to be complemented by the 2Cs Customer Understanding and Compelling Content Creation.
The first recipe for success lies in Understanding the Consumer...who is he/she? What are their professional backdrops? What insights would excite them about the data? How are they likely to use these insights? What are the words and topics that resonate best with them?
And the next piece of success lies in Creating Compel-ling Content. This means presenting crisp insights, with the right graphics that are easy to understand and retain. The insight is to be made as much tangible and a story had to be weaved around it to give it a visual appeal.
The 2Cs are often not emphasized enough in the Business Analytics courses. The only recent development is the emergence of courses like Storytelling with Data, but the domain knowledge is still something that needs to be emphasized as On-the-Job learning.
Furthermore, given the spate of information spun out through increased connectivity and all-pervasive IoT along-side data privacy debates, it is also time that we think about the competencies and specializations that we need as we pursue a career ahead in business analytics. Given the present complexities, the competency areas could be:
1. Data Wrangling This requires cleaning the data and transforming datasets to improve data quality and usability.
2. Exploratory Data Analysis This involves systematic iterative investigation of structure and relationships in datasets; essentially domain knowledge expertise should be built into this segment. If required, intercultural orientation is must-know expertise. In the example cited, mobile usage data in European countries have different patterns from Indian context, given the different cultural norms of relaxing and living lives.
3. Visualization and Reporting This refers to de-signing graphics and reports that communicate the key in-sights drawn from data; aesthetics and creativity are good skills to have for this role.
4. Machine Learning and Predictive Modeling Given the present market, this segment would require a strong knowledge of algorithms and mathematical modeling, rather than just data analytics
Data analysts should become aware of their innate skills and strengths early-on in their careers, which would help them project their talent best and seek the best placement in the industry. For instance, the team whose story I had shared at the beginning, were expected to specialize in Visualization and Reporting. They were stuck at the level of data wrangling and exploratory data analysis, without having much domain intelligence. And to motivate them, their Ivy League certifications had been completed in Predictive Modeling!
We definitely want talented data analysts to be engaged and happy in their roles and jobs. And hence, this appeal to all educationists in this field to ensure that we help our budding talent to think through their Digital-Data-Business appetites prior to signing up for their courses. Also, we must guide them with the rigor of 2Cs to perform best in their jobs.
Nilanjana Bhaduri, Director - Learning & Development
An alumnus of Jawaharlal Nehru University and former research scholar of Indian Institute of Science (IISc), Nilanjana Bhaduri is passionate about connecting the dots of Organization Development, Leadership Development and Technology - all of which can make the new-age transformation happen. Presently working as global learning and development director in HARMAN International Nilanjana is leading the learning and development interventions for HARMAN.