When deploying analytics for your company or your customers, you can feel stuck between the twin poles of “What data is available?” and “What do we want it for?” The key to coming up with the best insights lies in delving deeply into both questions; the answers you discover can help you get even more out of your data. The experiences of Measuremen, an international consultancy that helps organizations optimize facilities use, illustrate this point.



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<h2 class="wp-block-heading">Determining data goals, making a plan</h2>



Since 2005, Measuremen has helped its clients study and optimize facilities use. Founder and CEO Vincent le Noble started out studying and logging when, how much, and why employees used chairs, desks, conference rooms, and corporate amenities. Armed with data, he could advise companies on which facilities costs were good investments and which weren’t.



Even now, Vincent says, his engagement with his customers starts with a conversation about their goals:



“Each and every one of them begins with a change that they want to make to improve their workplace, so we try to get that as clear as possible. What is the change that they want to facilitate, and what data elements can help them in deciding how to improve or facilitate that change?”



Those client-generated objectives are the jumping-off point for Measuremen.



“So that&#8217;s what we start with,” continues Vincent, “and then we [add] all these <a href="https://www.sisense.com/glossary/data-discovery/" target="_blank" rel="noreferrer noopener">data collection methodologies</a> that will gather that information needed to make those insights.”



<h2 class="wp-block-heading">Multiple methods to collect maximum data</h2>



After writing out a properly scoped, well-defined set of goals and a way to collect the needed data, Measuremen begins its research period. That could entail surveys, studies, and app-based workplace sampling or involve a limited period when Measuremen observers personally monitor workplaces and record data.



When clients want a more permanent, passive method of collecting data, Measuremen places sensors in meeting rooms or at workstations. Many organizations want to understand the human element, too: How do their employees feel about the environment? What improvements would they recommend?



After that, says Vincent, Measuremen invites the client to a key insight meeting, “where we show the data … in an interactive session.” Measuremen can visualize the data in its portal, evaluate current trends, and recommend changes. Later, Vincent adds, “we&#8217;ll do another study to validate that the changes that they&#8217;ve made have resulted in the expected results they were aiming for.”



<h2 class="wp-block-heading">Applying data to goals</h2>



After engaging end users about their goals, it’s time to<a href="https://www.sisense.com/blog/building-data-models-to-empower-self-service-users/" target="_blank" rel="noreferrer noopener"> shape data models</a> based on their responses. And as the process continues, further refinements take place.



“Our customers … had a really good idea of what utilization and occupancy numbers would mean for them,” Vincent says. But soon their examinations of the data would result in questions like, “I can see there&#8217;s a low utilization of this meeting room compared to the other meeting rooms. Why is that?” and similar inquiries they couldn’t answer with the data they had on-hand. This would steer the research team in the direction they needed to go and tell them what data they needed to collect.



According to Vincent, there’s important work to do with the data after it’s collected. It’s crucial “to be able to slice and dice and go into that detail as you go along because not only do you want to provide information on a holistic view or a high level, but you want to be able to dive deeper.”



Not all data journeys start that way, though. Some organizations have only a loose idea of what the data could do for them or of what data they need to track to see the desired outcomes.



But don’t let a lack of data — or a lack of a clear direction — stall your <a href="https://www.sisense.com/blog/data-decisions-actionable-insights/" target="_blank" rel="noreferrer noopener">search for usable intelligence</a>. Sometimes it’s best to simply start with the data you have and see what you can get out of it.



Charles Holive, Sisense managing director for data monetization and strategy consulting business, says this:



“Sometimes I meet customers or prospects, and they would say, ‘I don&#8217;t have enough data.’ And I always reply, ‘Start somewhere.’ And then build from there, because there&#8217;s always enough to get started … asking the right questions, finding those high-value questions.”



<h2 class="wp-block-heading">Failure to launch: Avoiding analysis paralysis</h2>



Just as often, companies have the opposite problem: too much data. So much data that users can’t make sense of it. It’s the challenge that cloud software provider Quali had and<a href="https://www.sisense.com/case-studies/quali/"> </a>overcame through disciplined <a href="https://www.sisense.com/case-studies/quali/" target="_blank" rel="noreferrer noopener">data sifting and visualization</a> with an intuitive dashboard.



Whether there’s too much available data or not enough, whether you’ve got a clear direction or only a hazy idea of where you want data to carry you, data-driven insights infused into workflows are a must-have for every organization. The time to start asking questions is now. Think about the data you have (and need) and start experimenting with it; you’ll be surprised what you uncover.



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Former Sisenser Mindi Grissom has over 5 years of experience in the technology industry, helping thousands of organizations transform their business with data and analytics.

Experimenting to Win with Data

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