While getting from data to insight is challenging, the final hurdle for creating value is about translating insight into actions. Data insight can help boost sales, customer engagement and critical business KPIs, but only if it is actually translated into action.
Insight to action is about people
Most organisations are sitting on huge and growing amounts of data, and rely on analytics technology tools applied by teams of analysts to extract value. This arrangement means the analysts finds themselves as an intermediary between the decision maker and the data. The hand off from analyst to decision maker is therefore at the heart of an organisation’s ability to drive impact through actionable insight.
Many organisations cannot take full advantage of their data because they simply do not have the people or the culture to get the process to work. While big data grows in abundance, people with the skills to get from this data to insight are in short supply. The global 2015 CIO Survey by KMPG found big data analysis and business analysis skills in most short supply affecting 36% and 29% of those interviewed.
The demand for social skills and strategic understanding
Analysts and data scientists attract a certain type of person, the same kinds of people who are attracted to computer science and mathematics, and who are in short supply across all industries.
At risk of stereotyping, it must be admitted that all too often this particular analytical skill set is associated with weaker of social skills. For example, an inability to ‘zoom out’ and see the bigger picture. Many of us who have worked with statistical analysts are familiar with the experience of trying to get the analysis out the technical details and into actual implications and inferences. This is the problem of the big ‘so what’ that plagues so much analysis. Despite many hours of work the question is still, ‘yes but what does this mean, what we should do differently?’.
Research confirms that the folks who are in the highest demand of all are those who combine both technical and social skills. A 2014 University of California study of the US labour market found an ability to combine cognitive skills like mathematics with social skills has become a key and growing determinant of career success.
A strategic view is also important, to understand the context of analysis. Strategic consultants often succeed where analysts fail because they approach problems from the top down and not the bottom up. They start with the big questions that the organisation needs to answer and bend the analysis to that. The risk is they go too far in the direction of over claiming based on weak data.
The data never speaks for itself
Somebody has to make the conceptual jump from analysis to implications, and then the organisation has to be ready to take action. The successful analyst has to be able apply themselves to the questions that the organisation needs to answer and make strong recommendations when the price of inaction appears greater. This means creating data stories, and communicating results, which requires much more than technical analytic abilities. For it to work, decision makers in the organisation need to do their part. Be ready to listen to what analysis is telling them and put preconceived notions and internal politics aside.
Easier said than done
A review of the current evolution of analytics capabilities by the Gartner consulting group defined prescriptive analytics addressing the question ‘what should we do’ as the highest stage that an organisation can attain (for example in this presentation by Gartner). They rightly identify analysis which drives action as having the highest business impact and requiring the most advanced skill sets, to get beyond descriptive and predictive analysis.
Getting from insight to action is easier said than done. The difference between success and failure is how well people can work together to align around what the data analytics means for action. Organisations need to hire the best talent and develop the right organisational culture.
A culture of data driven decision making means that a junior analyst should feel comfortable telling a senior executive that their product is not right for the market, or their ad spend is wasted, or even that there is no good data to know what the best course of action should be. There should be no fear of ‘shooting the messenger’. Any debate about the right action to take should be grounded in a robust predictive model of the likely consequences of action for KPIs.
Your organisational culture must accept that even imperfect analysis is preferable to flying blind. You will find unexpected insights and opportunities along the way and will learn the that process of trying to make data driven decisions is valuable in itself as everyone gets closer to the analysis.
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