Three minutes a day takes bad business decisions away. Today\'s tip: The three most common mistakes I see made by digital transformation lea...
7/20 #ThreeMinutesADay: Why healthcare companies have a love/hate relationship with data
Three minutes a day takes bad business decisions away.
Today's tip: The reason healthcare companies have a love/ hate relationship with data is that they have access to a large quantity of it but such factors as interoperability, clean data, and understanding of data aren’t sexy but they are incredibly important for any digital transformation initiative. Embrace these concepts and you will start to love your data.
In a hurry? Just watch the video below.
Today. I would like to talk about a very contentious topic in healthcare: the love/ hate relationship healthcare companies have with data. This encompasses every aspect of data from data collection to how data is interpreted analytically to how data is used to create healthcare products. It affects every department of a healthcare company from the C-suite to product development to the marketing and PR department. It is one of the most polarizing topics in healthcare and something for which you hear just much praise as you do complaints. Here we will talk about 3 reasons companies have this love/ hate relationship with data.
Why healthcare companies have a love/ hate relationship with data
The first thing I want to raise is interoperability. This is a buzzword that you will hear at every single healthcare conference that you attend. You'll hear about it online and you will absolutely hear about it any time you listen to a digital transformation leader in the healthcare space give a talk.
The buzzword of interoperability comes up all around the healthcare space and yet, despite making some substantial progress over the last decade, even today we are nowhere near where interoperability should be in healthcare. Why? Well, there are a variety of reasons. The data needed for interoperability is often siloed or stuck in legacy systems that need to be upgraded. What it really boils down to, though, is the simple reality which is that, in order to be really interoperable, you need to make an investment. A big investment.
Oftentimes, this type of investment is not sexy. It is not the type of investment your shareholders will get excited about. That is why most healthcare companies simply do not do it. The simple fact is, everyone in the healthcare space talks about interoperability but no one wants to make the investment to make it happen.
The second point I want to talk about in relation to the healthcare space’s love/ hate relationship with data is the meaning and importance of “clean data”. Nowadays, it’s no longer enough to just have access to data, you need to have clean data. Clean data is data that has been through a “cleansing” process where corrupt, inaccurate, or irrelevant records have been either corrected or removed so the dataset or database you have is as accurate as possible.
When you talk to any healthcare company, they will tell you they have tremendous amounts of data on their patients, providers, doctors, or on anybody. If you are a digital innovation expert or executive in the healthcare space, your number one priority should be making sure that your company has access to all this data in the form of clean data. Why? What does clean data ultimately really give you? It gives you the ability to extract all data you have as a healthcare company and truly make sense of it. Clean data is the backbone of any true digital transformation initiative.
You need clean data for artificial intelligence, blockchain, chatbots, IoT technology, and even for marketing initiatives. Really, you need it for anything that is meaningful and that could revolutionize your company. Remember, if you’re a healthcare executive your primary goal is not to build new features. It is to have clean data. Clean data isn’t sexy but it is the backbone of any real initiative no matter what emerging technology you’re talking about and that should be enough to make you love clean data.
The final reason healthcare execs both love and hate data is that even if you have clean data, it is necessary you understand it. Only then can you act on the data in a way that makes the final digital product meaningful to patients, doctors, or anybody else in the healthcare space.
A perfect example of this – outside of the healthcare space – is the company Credit Karma. Credit Karma is a company in the financial space with a site and a mobile app that was recently sold to Intuit for a whopping $7 billion. When Credit Karma started it was doing the same thing all the other credit bureaus were doing: telling their customers, “We’re going to give you, the consumer, access to your credit report and nothing else.” Their only value proposition was that they gave this report to you every week for free.
However, the way they would eventually make money (and the way they got from just a small startup to selling for $7 billion) was by making sense of the data. They shifted their value proposition to, “We’re going to give you this credit report, Codrin. And, based on your credit report, we will tell you what credit cards, personal loans, student loans, or mortgage options are available to you, based on your report. We’ll also tell you the rates you might possibly get without actually having this company run a credit report.”
It was the understanding of the data that made Credit Karma so successful and profitable. That is how it ended up selling for $7 billion. It turned its business model towards making sense of the customers’ financial profile and offering suggestions about the financial products for which they qualified. In short, Credit Karma turned raw data into actionable data and advice.
Data is King. Data is incredibly powerful but only if it’s clean if you make data extraction a priority, and if you make sure that data can be understood and acted upon. You do these things and you will fall in love with data all over again and forget the hate.
Do you have a love/ hate relationship with data? If so, tell me why in the comments below.
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