Blending analytical and research capabilities

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Blending analytics and research empowers the business decision-making and represents one of the recent trends in the Analytics & Insights industry.

Companies from different industries are actively sharing and learning the best practices from each other – with FMCG companies becoming more focused on the development of its analytical capabilities, and technology industry bringing more consumer and user studies to its agenda.

However, speaking about the most enhanced projects, professionals from FMCG industry continue to focus on the blending qualitative and quantitative research methodologies, while technology companies are mostly proud of the new ways of application of the big data analytics.

I believe that the golden middle is somewhere in-between.

The reason is simple – all of these data and research methodologies have their limitations, while their combination allows to develop more holistic insights and viable business recommendations.

High speed of the data access and production means that companies have more opportunities to test and pivot. At the same time, the big data analytics and machine learning aren’t the magic box and they require a lot of data structuring, preparation and control of the data quality.

This means that the big data analytics ends up to be not as quick and efficient as expected. An approach that is focused on the development of the scalable analytical solutions becomes critical for driving ROI behind the big data projects.

That’s why keeping the final framework (or model) in mind along with the business questions is so vital here. Comparative analysis that can be done based on these frameworks have much stronger business power than one time analytical effort.

Importantly, very popular dashboards are relatively easy to develop but they are not that easy for the insights generation and identification of the further research questions.

bestinsightsphere_analytical_frameworks

To address the further research questions, blending analytical solutions with the additional research and external data can be highly beneficial and might allow to develop a holistic picture on the market and consumers. 

Importantly also to remember that a lot of in-depth insights can be generated from videos, images and data from connected devices which are not yet enough exploited due to limited analytical capabilities. However, they are important for the everyday consumer lives. Accessing these sources is still very challenging especially talking about big scale projects as it requires a lot of efforts for machine learning and manual coding.

To conclude, regardless the industry in which you are working I recommend to consider how your business questions can be answered with a combination of analytical and research capabilities. It’s also worth to allocate sufficient time for the big data analytics and ensure that the scalable solution is one of the end objectives for the analysis.

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