Informative Decisions Derived from Qualitative and Quantitative Data

New tools, platforms, and technology advances abound, bolstered by a shift in industry discourse. The hot new trends of neuroscience and automation have taken over conversations that were previously focused on agile and mobile research.

It inspires me to see how adaptable the insights industry is - continually scanning the horizon to see what technology breakthroughs are available and how they might be implemented. This approach involves a lot of questions and examination. What impact will this have on data quality? Will it make data collection more efficient? Is it going to make analysis easier? Is it possible to join as a new participant?

However, it appears that one point is frequently left out of these discussions: how will it effect the decision-making process? This is an admittedly difficult question to answer. It is, however, a significant one. If research were reduced to a single, united goal, it would be to help people make better decisions. As a result, any new technical breakthrough, procedure, or trend should be evaluated in terms of how well it contributes to this goal.

Quality, Relevance and Speed

Quality and speed are sometimes shown as polar opposites on a scale. Research can be quick or thorough, and decision-makers must pick between the two. It's simple to see where this viewpoint comes from, because research expectations can be unrealistically high. This metaphorical scale (which can also incorporate cost) is a useful tool for getting stakeholders to evaluate priorities.

But, in my perspective, this scale has become a stumbling block; a rod for the insights industry's own back.

To be honest, high-quality research should always be the goal. Making decisions based on bad data is worse than not having any data at all. Research must also be presented in a timely manner. The appropriate speed isn't necessarily rapid, but it's dictated by business requirements. A timeline should be built on an open discussion on when a decision must be taken. It becomes easy to suggest research techniques and procedures that might be used to collect quality data before the deadline from here.

However, there is a third consideration that must be made: relevance. Researchers must choose who will make the choice based on their findings and ensure that it is accurate.

We should all be seeking to increase our ability to execute on all three of these basic tenants as an industry comprising agencies, independents, and client-side researchers. In this, technology, process, and culture all play a part. The most crucial initial step, though, is to remember that the purpose is not to complete a research project, but to use the results of that endeavour to make a decision.

Turning Data into Decisions

Whatever data gathering and analysis methods are used, there are a number of measures that can be done to guarantee that data informs decisions, all of which revolve around how the story is conveyed. Journalists, probably more than anyone else, have mastered the art of narrative. Peter Cole of the Guardian wrote an article on the foundations of news writing over a decade ago that not only remains relevant today, but also gives researchers food for thought.

Peter begins by advising that a hierarchy of information be built before writing a single word. Information should be sorted from most to least relevant with the intended audience in mind, or in the case of the researcher, the choice and decision maker. After that, think about the decision maker's projected level of understanding and vocabulary. A post that the reader can easily comprehend and that is relevant to their prior knowledge will always be more effective and interesting.

When it's time to put pen to paper, make advantage of all the data kinds available to you and think about where you want to put them. Does an emotional vox-pop qualitatively corroborate the trend revealed by a survey question's graphical representation? Is a customer quote accurate in describing what's going on in a picture? Is it possible to use a heatmap to explain the results of a larger poll? As journalists are well aware, the way information is integrated frequently creates the most compelling story.

Finally, Cole's article underlines certain essential newspaper writing norms that are probably certainly applicable to the research sector. An active tense is more quick and speedier than a passive one. Stories that describe what is happening rather than what is not are more interesting. Official jargon, acronyms, and officialize irritate people (unless of course it is the language a decision maker is most accustomed to). Finally, adjectives should only be used if they contribute useful information; otherwise, they can create more questions than answers.

Creating a Culture of Insight

So, what do I suggest as a next step? Any researcher trying to improve their effectiveness should consider their impact on decision-making processes. Look for methods to improve how insight is applied across the board. Collecting more data, enhancing the quality of information (or perceived quality), sharing it more quickly, or developing more entertaining methods to convey it are all possibilities. New technologies and business developments may play a role, but it could simply be as basic as examining very human processes and culture.

Creating a culture of insight is no easy feat, but I believe there is a real chance to develop highly competitive, responsive organizations by focusing on decision-making processes.