Analytics overload: Why data optimisation needs to be balanced
By Joanna Schloss, Dell Software
The healthcare industry generates large amounts of data on a daily basis and without the predictive insights from analytics, it will find it difficult to keep up with the vast amounts of information available.
As far as must-have capabilities go, advanced analytics is right at the top of the list.
The danger is that often, when organisations over analyse, they become obsessed with optimisation at the expense of innovation
But there’s a difference between going all-in on analytics and going overboard. In the business world, where analytics is more established, over-rotating on analysis seems to be on the increase and ends up negating many of its primary benefits. There is a danger of this also happening in the healthcare industry.
The drawbacks
In order to better understand the potential drawbacks of excessive analysis, you first need to recognise why data analytics is so valuable to the healthcare industry in the first place.
Data analytics, especially those that are predictive in nature, enable forward-looking insights. A great example of this is the University of Iowa hospitals and clinics where the surgical team has harnessed the power of analytics to help predict which patients undergoing colon surgery are most susceptible to surgical site infections. By merging historical records with live patient data, and applying advanced analysis, they have achieved a 58% reduction in infections stemming from colon surgeries while decreasing the cost of care.
Optimisation versus innovation
Optimising processes is a vital part of modern-day healthcare success and requires ongoing data analysis. But, while optimisation is certainly a critical part of what analytics should deliver, so too is innovation that leads to improvement. The danger is that often, when organisations over analyse, they become obsessed with optimisation at the expense of innovation. When that happens, analytics becomes exclusively about finding ways to cut costs when it should also be about finding ways to make improvements.
Focusing too much on optimisation can have the unintended effect of making organisations afraid to take on any risk. When done properly, with equal focus on optimisation and innovation, analytics should have the opposite effect. Good data analysis should be what gives leaders the justification and confidence they need to be bold and take risks with an eye on the future.
Good data analysis should be what gives leaders the justification and confidence they need to be bold and take risks with an eye on the future
At the Instrumentation Laboratory (IL) in the US, which is a leader in the development, manufacture and distribution of media devices and related technologies, consistent analytics is saving the company hundreds of thousands of dollars through avoiding scrap and rework. This is great news, but is not the company’s important goal. The analytics also enables IL to improve its regulatory compliance and reduce routine matters, freeing-up the company’s engineers and scientists’ valuable time and thereby enabling them to focus more on innovation.
Finding the right balance
So, how do you get a balance? How do you achieve desired levels of optimisation without becoming so risk-averse that you lose your ability to innovate? The answer is to create processes and a culture in which analysis is about innovation as much as it is optimisation. And this is where the healthcare industry can learn from the business world.
The answer is to deliberately separate the teams that analyse data for the purpose of process optimisation from the teams that analysis data for the purpose of innovating and uncovering new ways of doing things. A great rule of thumb is to organise your analytics efforts into three distinct categories. First, create a team focused on near-term efficiencies. This team’s job is very simply to analyse data to find ways to optimise operational processes in the here and now. But, and this is crucially important, teams focused on optimisation cannot and should not be given authority to pull the plug on projects implemented by teams focused on long-term innovation.
There’s nothing wrong with using data analysis as a means to protect short-term budgets, but keep in mind that at its heart, analytics is about always empowering innovation and that over analysis at the expense of innovation is a snowball that rolls downhill quickly and can be hard stop once it gets going
Next, create teams focused on intermediate-term innovation. These folks are ideally tasked with analysing data to identify emerging trends that don’t impact the organisation today, but are very likely to do so in the next three to five years.
Lastly, build teams that are empowered to use data in order to go forth and innovate, with no specific time horizon to focus on, and no concerns over the ROI their work will deliver that quarter, that year, or perhaps even that decade. This is a team that should feel empowered to look 10 or 20 years out and predict the type of changes that lay ahead for the organisation and the health industry at large.
Stop the snowball
There’s nothing wrong with using data analysis as a means to protect short-term budgets, but keep in mind that at its heart, analytics is about always empowering innovation and that over analysis at the expense of innovation is a snowball that rolls downhill quickly and can be hard stop once it gets going. The important thing, when looking to take advantage of the benefits of analysis, is to partner with an IT vendor that understands your particular healthcare industry-specific requirements.
Written By Joanna Schloss, Dell Software
