As both the corporate and healthcare worlds get increasingly consumed with data and analytics, there are many preventable mistakes and lost opportunities that executives experience when determining everyday decisions about their customers or patients. A common scenario is when managers make ‘improvements’ to a product or service, only to find later that their new changes resulted in financial losses, and they don’t really understand WHY.
Often this is due to a lack of developing a deep understanding of customer insights from their own data or research. Commonly, what executives BELIEVE they know about their customers or patients, is largely at a superficial level. In fact, they often ‘don’t know what they don’t know’, and conscious or subconsciously refuse to acknowledge their lack of awareness of customer behaviour. Immature data management leads to a vicious cycle of poor executive decision making.
The proof of ineffective management decisions lies in the results such as poor customer or patient engagement, low sales volume or high numbers of patient complaints. Mostly these executives also only act when its too late, because they are not fully aware of the ‘preventative’ solutions available to them.
The real tragedy is that customer data is often readily available within an organisation’s own databases and data centers. However, many executives simply don’t know where their data is kept, who to get it from and how to connect the pieces strategically to solve everyday problems. The common excuses are:
Failure to utilise data effectively often translates into poor return on investment on staff time, budgets and resources. On an individual level, executives are penalised, reprimanded or even fired for underperforming and making costly mistakes that they should have picked up on. On an organisational level, it also leads to inability to solve problems or transform outdated business and care models – the lag indicator is when the Chief Financial Officer starts complaining about an unhealthy bottom line.
In our busy worlds, we are inundated with multiple channels of data and information from customer feedback, patient surveys, focus groups, social media posts, emails, website forms, call centers, mobile chat, etc. Many executives don’t know how to effectively make sense of all these valuable data sources particularly with the quantity, speed and variety at which data is coming at us.
In these more demanding environments where customer and patient expectations are greater than ever, what’s often missing is the ability to translate all the data ‘noise’ into meaningful insights and wisdom that changes executive decisions, actions and improve results. It’s often tricky to see the ‘wood from the trees’, particularly when an executive has been in a role for over a year.
Here are 16 of the common mistakes we’ve seen executives, even experienced senior managers, make over and over again. Can you relate to any of them?
1. We’ve found that most organisations vastly underutilise their existing data. Reasons for this can be lack of technical expertise, lack of awareness of available insights tools and methodologies or lack of time and money. Conversely, many executives spend excessive amounts of time and money implementing poorly researched projects or solutions, which could be greatly enhanced by utilising data and insights currently available.
2. There is often confusion that more volume of data = better insights. Whilst this can be the case, deep insights come from a combined analysis of quantitative and qualitative data focused on the solving a specific problem. In the current era of ‘big data’, higher volumes provide greater accuracy and new insights, but they can also cause more confusion if the right filters are not applied.
3. For those that are more data-savvy, there is a heavy reliance on ‘hard’ quantitative data to measure performance. However, in our interviews with leading CEO’s, many are starting to understand that measuring ‘soft’ KPI data measures like culture, compassion, staff attitude and customer frustration provide much better lead indicators to problems like patient complaints, high staff turnover and poor customer retention.
4. Once datasets and data sources have been aggregated and are easy to access, some executives think “its now time for the data to tell us what to do!” That’s an incorrect way to think about data; its far more effective to think “What are our major business problems or current priorities that we can now use this data to help solve”. This initial approach may then uncover unexpected trends.
5. Actually speaking to customers and patients also = data! Stories and text verbatim from face-to-face meetings, observations, and long answers in surveys are goldmines for WHY problems keep recurring. Whilst such anecdotal data shouldn’t be generalised, it also shouldn’t be ignored completely. These case studies are often the key insights to innovation and improving products and services
6. Executives often do not dig deep into the emotional analysis and root cause of customer frustrations and delights; they often deal with problems at a superficial level and implement quick, superficial solutions. As we move into an era of better understanding of human behaviour, deep emotional analysis of customer and patient experience is fundamental to transforming processes and systems.
7. Once data is organised, they should be used to inform an organisation’s measures of success and how incentives are allocated. However, it’s important to understand which data points are most appropriate for a measure, and that they are collected in a consistent fashion over time.
8. Some executives often extrapolate general industry behavioural data to its own customers – and implement solutions because “everybody else in the industry is doing it”. Customer segments can behave very differently e.g. Gen Y purchasers of health insurance have significantly different expectations to Baby Boomers. A mass-approach loyalty program may only be partially effective, without deep customer understanding.
9. It’s important to integrate perspectives (datasets) from multiple stakeholders to make the best decisions. Relying on purely on senior management instinct or front line staff feedback, may not give you accurate picture of what your customers/ patients think and feel (and vice versa).
10. When it comes to gathering qualitative data (from surveys, etc), many executives either ask the wrong questions or ask the right questions in the wrong way. The emphasis is often on closed questions or Likert scale responses, which are simple to analyse, rather than open questions with rich experiential information from customer narrative or verbatim patient responses.
11. Understanding security requirements for how data should be collected, shared and stored is a speciality in itself and most executives do not understand these aspects at all. Many do not really want to either. This is often the realm of the IT and Legal departments and even then it is a highly specialised space requiring specific knowledge of the organisation’s data security protocols. When in doubt – delegate (or outsource) to trusted experts, especially with regard to data security and compliance.
Actioning Data-Driven Decisions
13. When it comes to decision-making, the process of prioritising solutions to a problem can often be based on subjective insights expressed in a management meeting (e.g. loudest voice, length of tenure, organisational politics, who plays golf with the boss, etc). These decisions may ignore objective feedback from key stakeholders such as customers. This often hurts organisations when they have to look back and try to rationalise why things were done, without any evidence to back the decision.
14. Human beings are obsessed with predicting the future, and things are no different with data. Predictive analysis of data is helping many organisations make big strategic decisions such as ‘which new market do we do diversify into’ as well as tactical decisions such as ‘how to best deploy a new machine asset in our diagnostics area’. However, organisations need to build up to this capability and it is no sense having predictive capabilities if basic operations and customer service are not performing well. Basic operational performance is influenced by how culturally accustomed staff are at using data in their decision-making. This is a capability that can be nurtured over time.
15. Remember, that having valuable insights from your data is are critical starting points in a change journey, and one that need to fundamentally improve in the healthcare industry. Nonetheless, there is no substitute for courageous leadership and sound judgement that comes from experience, and knowing when to ‘believe’ there is adequate data and insights at hand to make a timely decision, and when to look for more information before making one. This is where it is important to be guided by a big picture, holistic view of the organisation and account for other problems or strategic priorities that can influence a final decision.
16. To keep up with the pace of change, health organisations need their data collection, analysis, and decision-making processes to be systemised in order to be effective over the long term. Obtaining useful insights from data is a better start, but having skilled, trained people to take the right actions is absolutely critical.
If you are experiencing challenges with data or would like to have a conversation with us about your business priorities, please contact us. I’d be happy to answer your questions or explore how we may be able to help you use your data to solve your healthcare or business issue.