Faculty Article
Analytics Beyond Data: People, Process and Culture
Dr. Sunil Reddy Kunduru Assistant Professor IIM Amritsar
Let's get one thing very clear first. Data analytics cannot be done without data. Analytics is fundamentally data-driven. In a sense, data is necessary to do analytics. This article is a commentary on the sufficiency of data in analytics. Of course, I would look at data in a broad sense to include the methods of analysis as well. I would also look at the question of sufficiency in a relatively loose way. An obvious question first. If data is necessary but not sufficient for data analytics then what are the sufficient ingredients of data analytics? One of the obvious ingredients is the methods of data analysis. Data is only as good as the availability of appropriate methods that can help gain insights from the data.There are two types of methods that help gain insights from the data: computational and statistical methods. Computational methods that help you store, manage and retrieve data efficiently and reliably. On the other hand, you have methods of data analysis that includes statistical, mathematical and other heuristic methods. Data and methods taken together is widely considered as the full landscape of analytics today. Undeniably, they constitute the bulk of what is needed for data analytics. As a result, the emphasis of the industry has been on collecting as much data as possible and building capabilities, in terms of both human and physical resources, to extract insights from the data. Once the focus is firmly on data and the methods of analysis of data, it is probable that you miss the big picture. Analytics becomes an end in itself. You hire the best talent that can work with data and draw insights from it. While all this might seem fine to many, the argument in this article is that we might have swung too far in this direction because we ignored the smaller, yet vital parts of what constitutes the analytics space. This is what we call analytics beyond data.
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Now that we have established that we are looking at analytics beyond the actual data analysis, lets break it into familiar levels of understanding. We will look at analytics from the perspective of people, process and culture. This is not a new way of looking at things in any measure. In fact, it is just an application of knowledge gained from research on organizational adoption of information technology over the last three decades. Adoption of IT does not end at procuring and deploying IT. It needs transformation of people, processes and culture of the organization. I will revisit these time-tested principles and the possible adaptions required when applying these principles to adoption of data analytics. We need to train people to create data, work with data and take decisions based on data. Here, when I say people, I don't mean just the data analysts and data scientists. Data can be captured from every activity taking place in an organization. Of course, not all data have equal value. But value in data is not inherent, it has to be derived through working with data. Everyone in the organization must be trained to capture data, organize it and use it to gain insights into their own work. This is not as easy as it sounds. It can even backfire if one is not careful to recognize the fact that not every aspect of a person's work is codifiable. There is an element of tacitness to every activity performed in an organization. While training people to capture, analyze and use data in decision-making, it is vital to remember that a significant part of the decision-making comes from the tacit knowledge gained through years of experience. Further, if we force people to take decisions only based on data, it is possible that they would not develop the soft skills that we learn from experience. In the long run, all the decisions will be mechanical and the
