Data Analytics and Insights

Data is a collection of facts which form the building blocks of insight. These can be observations, words, movements, numbers, patterns, or places. Data forms numerical and visual patterns that shows us where the gaps are, for instance, where markets are overserved or underserved and how changes in a business affects customers, markets and profit.

The data can be 'big' as in big data, the kind that businesses collect to determine the cause of problems, who is buying and when, and how to market more effectively. Data can also be small. Where big data is about machines, velocity and volume, small data is about people, taking time and answering the why question. Small data is critical for the insight that lies behind trends in big data and provides a more actionable way to market to target audiences.

Collecting the right data to answer your question is essential. In market research we collect different forms of data – qualitative and quantitative. These two different types of data provide different insight through different forms of interpretation.

Quantitative data is survey data. The data is more objective, numerical, structured and can be either continuous – as in tracking data points across time – or discrete to serve the needs of a particular study such as a market sizing study. Big data tends to be quantitative in nature but not all quantitative data is big data.

Qualitative data is drawn from focus groups, interviews or observational research. The data is subjective, interpreted through observation, listening and conversation, the reading of text, the watching of video and can also be either discrete or continuous – observing people over time. Small data is qualitative which allows for great depth and understanding.

The process of conducting the research relies on interpreting the data – either big or small. Throughout the data collection process (or fieldwork) you can be collecting insight as well as data. Sometimes insight can happen even prior to the fieldwork, by merely considering the problem or researching the right questions to ask. But the most important time in finding insight usually occurs at the time of analysis, where time is set aside to review the facts, observations, and data and consider what this all means in solving the problem at hand.

It is important to have your analysis plan or tools ready as the research proceeds, so that you can download important ideas, facts and considerations as you go. As insights can come at any time, do not wait until you have completed the research to write your plan. An analysis plan which is focused on answering the key objectives will help keep you on track.

Analysis paralysis is a term many researchers use to describe the feeling of being overwhelmed by data to the point where it is hard to get started, keep going or draw meaningful conclusions.

Creating an analysis plan will:

• Outline the objectives and how you want to answer these with your data.
• Ensure you have asked the right questions or made the right observations to answer your questions – and if not, can remedy the situation.
• Help you funnel data into themes and then ultimately insight to inform better decision making.
• Make you contemplate clearer and more accurate conclusions – for instance have we seen these patterns before, what could be influencing the patterns?

To get your data fit for inspiring insight, you need an analysis plan. Planning will help you focus on the things that matter and keep you out of the weeds. Inspiration can come at any time so remember to think about outcomes before you start the collecting process.