You are not alone. The advent of Big Data has spelt the end of an era when data sources and research studies could be considered as separate entities, and rightly so: their combined power is so much greater than the sum of their parts.
But for those of us attempting to combine traditional survey data with newer survey sources such as Enterprise Feedback Management (EFM) and non-survey sources such as customer databases and in-house metrics, our experience may be less Big Data and more Big Headache. This is often because we want to link up our data sources, but they have not been designed with links in mind.
Research ecosystem. Each different research programme or study should serve a different role. By defining these roles and how they work together, multiple research studies can be combined to deliver holistic insight as efficiently as possible. This ultimately means greater return on spend.
It is this harmonious network of different research studies, supplemented by other data sources where available, that we call a research ecosystem. Each of these will be unique to the organisation that they reflect and to the team working with them (for example, the research ecosystem required by a customer experience team may look very different to that required by an advertising team).
It is when study roles are not defined or not respected that research programmes involving multiple studies can become confusing. This can make it hard to find space for new questions, or even to find the answers to existing questions, leading to a sense of drowning in data.
This is not a criticism of the organic growth or scope creep inherent in most ongoing research programmes: it is natural to want to get the biggest bang for your buck from any research study. By extension, the next step is to ask as many questions as possible in any given study. We are certainly not trying to stand in the way of this. But we are inviting owners of research programmes to step back and consider how each of their studies are used, and what the impact of longer questionnaires can be on the respondent experience and subsequent data quality.
What are the components of an ecosystem?
The goal of establishing a research ecosystem is to ensure that each of your questions is asked only where necessary; in the best vehicle for getting a response at a time that suits your business; and when the question is relevant to the respondent’s experience of the organisation, brand or product.
To achieve this, every research ecosystem shares the same essential components:
- All available data sources are identified
- A purpose is defined for each research study
- All stakeholders declare their data needs
- Owners/stakeholders for each data source are known and willing to share
Once these components have been established, an ecosystem can start to be developed. This approach carries five key benefits:
- It identifies knowledge gaps
- It boosts efficiency, saving costs and creating space
- It delivers better quality data by improving the respondent experience
- It facilitates data integration
- It provides a framework for current and future programme design
How does this help ensure my business questions are answered?
Framework and integration are key benefits that deliver this. The ecosystem renders explicit the relationship between different studies, allowing questions from one to be answered by data from another, simply by the juxtaposition of sources.
How is a research ecosystem established?
Three questions are at the heart of developing a research ecosystem:
- What do we need to know?
- When do we need to know it?
- Do we actually need to ask this?
What are the biggest challenges?
- For smaller programmes that include relatively few studies and stakeholders, the principles of ecosystem design can be quickly and relatively easily implemented in a session that may feel like little more than a standard account/ questionnaire review.
- For more complex large scale programmes with multiple, potentially multi-national studies, the simple logistics of getting stakeholders in one place to agree the purpose of each study can be time prohibitive.
- For some, the incentive of having a balanced, efficient research ecosystem will merit this temporary pain. For others, it will understandably come at too high a cost.
This does not, however, mean that the three key questions of ecosystem design cannot be used going forward, even if it is not possible to implement them retrospectively.
This can translate into a simple audit of existing research programmes, resulting in keep/lose/change/add recommendations at a study and/or question level. When combined with a more mindful approach about question placement going forward, these baby steps can lead to a balanced ecosystem in the long run.
Undoubtedly the proliferation of data – from survey or other sources – available to organisations today will continue to grow, and with it the challenges of managing and using that data. Already this has brought about a step change in the way insight is delivered: where once ‘more data, more detail’ was a constant mantra, now smart data that delivers specific insights easily, coherently and upon request is the Holy Grail.
We have therefore reached a tipping point where managing, mapping and sharing data is, for many organisations, as important – if not more so – than obtaining it. New technologies, with EFM just one example of these, offer a variety of ways of achieving this. But as a consequence, planning how to knit data sources together has grown in importance, otherwise these technologies risk simply presenting us with a wall of numbers.
This is where we believe the ecosystem can help: it is not a universal panacea for insight delivery, but by challenging us to step back and assess what we need to know and when, and then identifying what data is available from non-survey sources to supplement this, it invites us to map where and how our information needs will be met.
In this way it unites different data sources, and through their integration provides us with more holistic insight. But it does this in a transparent and controlled way – putting insight managers back in charge of their data.
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