Unlocking the Power of Your Data: 5 Quick Wins to Transform Your Data into Actionable Insights

Are you adrift in a sea of data, yet find yourself struggling to extract meaningful or actionable insights? Perhaps it’s time to reconsider your approach. In this blog post, we’ll explore five simple and practical steps that you can take to help you unlock the full potential of your data and empower your team and organisation with meaningful insights.

Quick Win #1: Develop clear hypotheses to guide your efforts

According to Forbes, a staggering 90% of the world’s data was generated in the last two years alone. With the increasingly overwhelming amounts of data available to us, it is easy, or rather inevitable, that we sometimes lose ourselves in the torrent of information. Very often we end up “boiling the ocean” and wasting much of our efforts. It is therefore critical to ensure that you have clear hypotheses in mind before diving into the data. This will allow you to focus on the 20% of factors that are most likely driving 80% of your organisation’s value, promoting a more efficient and targeted analysis.

Example:?One company that has successfully used the hypothesis-led approach to focus its efforts and drive innovation is Zara, the fast-fashion retailer. Zara’s designers develop hypotheses about which styles, colours, and fabrics might be popular with customers in the upcoming season, and then test those hypotheses through small-scale production runs. This allows the company to quickly test the market and react rapidly, rather than relying on traditional market research that takes longer to complete and is likely to quickly become stale in the fast-changing world of fashion.

Top Tip:?While there are structured methodologies for deriving hypotheses, it is equally possible to land on “quick and dirty” hypotheses based on experience. Before launching into any data gathering and analyses, make sure to spend some time considering (and talking to others in the organisation) about the key questions you are trying to tackle. For instance, if you are looking to boost profitability, then make sure to have in mind key areas where the company is most likely underperforming and to focus on those areas first.

Quick Win #2: Incorporate a broad range of perspectives

Involving a wide variety of stakeholders with diverse perspectives tends to lead to improved outcomes, regardless of the endeavour, and this principle undoubtedly holds true when it comes to generating insights from your data.

Diversity leads to better outcomes because it increases the chances that potential blind spots and new opportunities are not missed, that unique perspectives which challenge conventional thinking are explored, and that biases, groupthink, and tunnel vision are mitigated.

The caveat of course is that you need the right people at the table, representing a valuable and diverse set of relevant experiences, rather than just having a large but random selection.

Example:?The multidisciplinary teams (MDTs) within the UK’s NHS are a good example of this principle at work. MDTs were introduced in the 1980s and brought together clinicians from different specialties as well as allied healthcare professionals such as pharmacists and nurses, to discuss the treatment and management of individual patients. For the first time in the history of the NHS, patients started to be treated as a holistic entity. A key benefit of MDTs is the fact that while individual complications or symptoms might stump or might be overlooked by one specialist because of his or her narrow focus, this is much less likely to be a problem when you have a diverse group of healthcare professionals examining the patient’s data. The same is true of data more broadly.

Top Tip:?Develop a RASCI (Responsible, Accountable, Supportive, Consulted and Informed) document for each major decision type, and use it to define who needs to be ‘at the table’. This also helps people to understand the expectations of them when it comes to decisions being made. Go one step further for complex and multidisciplinary issues and datasets — consider bringing in people that are known to be “connectors” or “synthesisers”, who are skilled at observing patterns or joining the dots between seemingly disparate pieces of information.

Quick Win #3: Focus on the right metrics

While the right metrics can tell you a lot about how the business is performing, the flip side is that looking at the wrong metrics can very easily lead you astray. Equally, metrics need to be adapted across projects and over time — the right metric for one project may not be the right metric for another project, while the right metric today may not be the right metric tomorrow.

Example A:?The US healthcare industry offers an example of how metrics can go awry. Surgeons are often evaluated by the frequency of complications or deaths in their surgeries, which impacts their reputation and insurance rates. Unfortunately, this has led many of them to stop taking high-risk or complicated cases, leading to an obviously poorer outcome for patients. A better metric for patients and insurance companies would have been to use a rating that takes also into account the degree of difficulty, risk and potential for complications.

Example B:?In their book Working Backwards, Colin Bryar and Bill Carr describe Amazon’s disciplined approach to managing its business with metrics. While initial key metrics are defined early on in a project’s life cycle, teams continue to monitor the linkage between these metrics and desired outcomes (e.g., volume of sales, profitability) and adapt them as new information is received. For instance, when Amazon first started its foray outside of books, it focused on selection and chose the “number of detailed pages” as its key metric. The belief was that having more detailed pages reflected a wider selection, which would in turn improve sales. However, this led to an expansion in new products but not necessarily ones that were profitable. The metric was then adjusted to “number of detailed page views”, then to the “percentage of detailed page views where the products were in stock, and finally to the “percentage of detailed page views where the products were in stock and immediately ready for two-day shipping”.

Top Tip:?When deciding if a metric is the right one to be using, consider if it is actually actionable, driving the right behaviours and has an impact on the desired outcome. If it does not, or the link is only tenuous, the metric may need to be adjusted. If the metrics are found to be no more than “vanity measures”, then they should be eliminated altogether.

Quick Win #4: Don’t overlook non-quantitative data

When we think about data we generally think about it in quantitative terms (i.e., data that can be counted or measured in terms of numbers). We sometimes end up overlooking qualitative data — data representing information or concepts that cannot be represented by numbers, but which are no less valuable. Examples include feedback from sales teams and retail staff, opinions in the form of customer quotes and documented perspectives from focus groups.

As humans we are built to emotionally connect with, and respond to stories. Qualitative data more effectively captures the human experience, which is invaluable in encouraging empathy and understanding of the impact of a situation on individuals and communities.

Example:?In the world of oil and gas drilling, a lot of time and resource is invested into predicting the materials and scenarios that a driller will come across during an operation. Yet an equal amount of weight is also placed on user experience when deciding what to do when a driller is faced with a particular scenario (e.g., complex geological formations, drilling into high-pressure zones, equipment failures). By combining the quantitative data that represents the operation at hand with documented experiences of other operators in given scenarios, operators have found that decisions are better informed and less likely to be impacted by biases.

Top Tip:?Insights which enable better, more informed decisions often come about when both quantitative and qualitative data are presented together. This may be as simple as displaying both quantitative and qualitative data alongside each other, for instance, an analysis of in-store customer experience scores alongside supporting customer quotes.

Quick Win #5:?Tailor the data to your audience

All too often we are presented with too much data, irrelevant data, or data that is generic in nature, often because it is the “easiest way out” for those who have generated the data. The user must then invest time and effort to pick out and translate the data into meaningful insights.

In the wise words of public speaking and presentation expert Lilly Walters, “The success of your presentation will be judged not by the knowledge you send but by what the listener receives.” Taking those extra few minutes or hours to tailor your data for your audience is an investment well made, and will help ensure that the efforts you put into data collection and analysis do not go to waste!

Example:?A monthly Prostate Cancer Pathway meeting at a University Hospital in the UK that one of us attended several months ago was a case in point. The team was discussing patient cases and was presented with patient data that included patients’ dates of birth. Knowing the age (or age bracket) of the patient is clearly valuable data, which when combined with other information, can influence future care options. However, team members had to frustratingly spend the first few seconds of each patient discussion to translate the date of birth into their current age. As basic as this appears to be, simply changing the format of the information presented to include the patient’s age, would have helped everyone to more effectively focus on the important information and decisions in front of them.

Top Tip:?Take time to understand your end user’s needs and follow that up by tailoring the availability and presentation of that data. This can be as simple as asking, “how do you usually use this data, and how do you prefer it to be presented to you?”.

Conclusion

Having reached this point in our blog post, you will now have a good idea of how to go about implementing these five simple strategies for unlocking the power of data within your teams and organisations.

The greater the complexity of organisational needs, and therefore applications and use cases for collected data, the more imperative it becomes to have in place a structured and comprehensive framework and approach for: (a) designing your data strategy, and (ii) deciding how data is deployed within your strategy. Should this be the case, we invite you to reach out to the authors for?a preliminary discussion?on the most optimal way for you to move forward.

Justin Tan?is a strategic thought leader and trusted advisor to senior leaders in organisations such as Merck, Beckton Dickinson, Holland & Barrett, and Bupa. He specialises in helping management teams to maximise their effectiveness and achieve strategic clarity.?Paul Teasdale?is an improvement practitioner who brings his experiences working in Formula 1 team McLaren, and other high performing teams, to help organisations accelerate their performance.