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5 Key Aspects of Data Smartness You Shouldn’t Miss

With data becoming a central aspect of Digital Transformation initiative, Data Smartness is a critical aspect of making better decisions faster. However, many a times, you will find that business managers are relying on their gut sense instead of such data insights and making decisions by tribal wisdom.

Data Smartness is more of a mindset!

The criticality of using data driven decisions across the organization is a central point of discussion across the globe now. The importance of data has been highlighted in multitude of research and surveys to the extent that data is being mentioned as the new oil.

The current pandemic has accelerated digital transformation across all sectors of doing business by many folds, changing the world around us exponentially. The rate of change is even steeper. At this juncture, where business must go through rapid changes, Data Smartness becomes a competency of decision makers to make more realistic decisions.

While most of the organizations have either created dedicated teams for data analysis or have outsourced it to tech partners, most of them find do not find enough traction of usage of such reports from the business decision makers and hence face a supply push in terms of the analyses, rather than a demand pull.

Obviously so, organizations will be able to get better value of all such data driven initiatives, only when a larger part of their organization will become Data Smart. Such leaders can balance their tacit knowledge coming from experience with the patterns and insights that they get from analyzing the data. Top key aspects of data smart leaders are the following:

1. They Don’t Ask “What can we do with the data”

Many a times, we have heard senior decision makers saying that we have now generated tons of data. What can we do with it? Wrong question! Data smart leaders look at the problem through a different lens. They appreciate that Garbage In Garbage Out holds for any analysis. Hence, they prefer to ask the question “What data would I need to have deeper insights for my decision making?” Basis this, they would then brainstorm with their data team to have a clearer understanding of their data requirement and would take a nuanced decision on what all data points can be possibly looked at initially before shaking the boat too much.

2. They are more experimental in nature

Data smart leaders are more experimental in nature. They are okay to do a small experiment with data to understand what comes out of the analysis. Instead of a big-bang approach, they would take a judicious call in starting small as an experiment. If they succeed, they are quick to scale it up fast, else they know how to move on. Doing many small experiments with data gives them an opportunity to look at a problem from multiple dimensions.

3. They can create a balance between complexity and impact

Data smart business leaders understand that data analysis interventions are complex in nature. Not only they are able to breakdown the complexity of such an initiative into multiple areas of decision making, but they are also able to come up with the possible business impact of such initiatives and are able to measure such value to be created using multiple metrics. Data smartness would mean that they would be able to start off with interventions with higher possible impact and lower possible complexities.

4. They can create a Roadmap for their Data driven initiatives

Data smart leaders use their experience in understanding that organization wide data driven initiatives are complex by their very nature. While many data scientists may get elated with the complexity with which they are able to use the data, Data Smart leaders are able to create a roadmap for all such initiatives, take a portfolio approach and are conscious about the possible business value that may get created in the process.

5. They have the habit of asking questions

Data smart leaders are curious, inquisitive and (often) argumentative. They have the habit of asking questions and not take any analysis on face value till they understand the validity, reliability, robustness, and recency of such analysis. They are never bogged down by hyper-technical terms like Artificial Intelligence, Machine Learning, Neural Networks or Python, but they make it a point to be updated with the perspectives of such terms and areas of applications in their own business. They are team players who work with their data analytics team to get the best possible analysis and then test it out in a small way in real life, before scaling it up in a big way.

COVID-19 crisis has accelerated data smartness initiatives in companies around the globe by many years and senior leadership in organizations have become serious about leveraging data more in the forthcoming days. Many organizations have taken up data smartness as a key competency for their decision makers.

Data smartness helps organizations to realign themselves quickly, become more realistic in understanding customer requirements and hence, would become more proactive and agile.

Prof. Rajnish Dass


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Prof. Rajnish Dass

Prof. Dass has served as a faculty for executive education and Regional Director for Cornell University, Samuel Curtis Johnson School of Management. He has taught as a faculty in the Information Systems Group and Business Policy area of IIM, Ahmedabad for around a decade. He has also taught in a visiting/adjunct mode in various academic institutes like IIM Udaipur, Chandos (at London), Academy of Scientific and Innovative Research etc.

He is engaged as an independent advisor and think tank in various areas impacting policy making at central and state government(s) in India as well as co-­alignment of strategy and IT for numerous private sector, public sector and not for profit organizations. His primary research and teaching interests are in the areas of Digital Transformation throughout its complete lifecycle (Planning, Justification, Benefits Management, Adoption and Communication, Project Management and Execution, Value Creation, Impact Analysis, Strategic Sourcing and Governance). He has received numerous scholarships and has published a number of research papers in forums like national and international research conferences, journals and as book chapters. 

Prof. Dass has been awarded as the Best Professor in IT Management in Asia in Asia’s Best  B-­School Awards presented by the World Education Congress in 2012 in Singapore and has been awarded the Glory of India in 2017 at the 43rd Annual Convention of the Indian Achievers Forum. Under his leadership, The CEEI has been received multiple awards and accolades as a next generation outcome oriented executive education initiative.

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