Defining better data

Better.

A six letter word with a broad meaning: of a more excellent or effective type or quality. But that’s just the dictionary definition.

Better is ambiguous. It is interpreted in many degrees. But doing better and being better are only relative when speaking in comparisons.

• Our concert seats are better than theirs.
• You are better at Candy Crush than me.
• The patient is feeling better than yesterday.

But maybe our seats are only one row in front of theirs. Maybe you’re on level 9350, but I am on 9349. Maybe today the patient is able to move their toes, but they still can’t walk on their own. Better is just positive progression from the previous state. It doesn’t necessarily mean it’s the best, but it’s a start.

Better gives hope- which is essentially the first step to reaching your full potential.

So what is better data? And how can we transform it into the best?

Is less always more?

There is a frequent debate of “big data vs. better data.” Think of it more commonly as quality vs. quantity. Are you the type of person who likes to have a lot of average friends, or a few close ones? Are you the type of company that casts a large net to get the most data, or do you narrow down your pool for the most relevant data?

Which angle is more effective?

The answer is neither and both. Big data is better data. You need to expand your data outreach to diversify the collection. Having more data gives insight of outcomes we might not have been able to foresee without the vast knowledge. The better data is within the big data.

The general purpose of data is to use collected information to make more-informed decisions.

What matters is where we find it and what we do with it. To promote data from being better to the best, it needs to do its most efficient capabilities to complete its purpose.

Within the big data, we need to narrow down better data; the stuff that’s relevant.

The data that matters.

How do we know what’s relevant?

This is where balance comes in. Having the data is half of the solution. The other half is knowing the needs of your audience. Whether that’s users, employees or competitors, you have to have an objective in serving your audience.

Optimizing real-time feedback loops and data queries enables direct communication. According to Jeremy Korst from Martech, “At the end of the day, data needs a human filter,” (Jeremy Korst, Better data vs. big data: The importance of taking a lean data approach, Maretch). Listening to the voices of the people that the data actually impacts is vital to its success. Having the data is important, but it’s ineffective if we don’t know what to do with it.

In addition to listening to what is being said by both the data and the audience, it is essential to push beyond that. Consider what isn’t being said. Silence and lack of information tells a story of its own.

Seeing the gaps gives us the opportunity to fill them. With clustered data points, we can easily identify where information is coming from. Using our experience as living people in the world, we can seek information beyond the clusters. This is where using our human experiences comes into play. Following up with outliers, reaching out to the underrepresented and aiming to be more inclusive provides more perceptive data for collectors and better service for users.

Be your best.

Why does improving “better data” matter?

Businesses, social media, the government, healthcare, etc. all use data in decision making. Whether it’s to sell you a new pair of shoes or promote a list of suggested friends, they rely on tracking your data to best serve what they think they can provide for you.

As data collection continues to advance, it is evident how interwoven the process is within our lives. Instead of trying to hide our data, we should take control to make it better. Or rather- the best.

If your data is already being tracked, why not make it more relevant to your needs? Letting the providers know what you need is beneficial for both parties.

Since the purpose of data is to drive logical decision-making, the decisions should be well informed and backed with reason. Oftentimes these decisions come with risk. Keep in mind that this goes beyond advertising and marketing trends. Data is used when developing products, writing legislation, and conducting medical research.

Having the best data matters, because it has the power to impact lives.

And if we are going to improve the world, it is crucial to know the condition of whom that includes.

Efficient data goes beyond.

Put things into perspective.

Let’s use healthcare as an example.

A patient is struggling with their physical therapy. They had knee surgery, but their recovery isn’t going as efficiently as they planned.

With accurate data on the individual, you can assess problems in their recovery. Lack of daily physical activity due to a remote job, past injury from high school sports, or underlying health conditions- better data can show us the problems.

And although that might be enough for some therapists to solve the problem in certain cases, it’s not the best data can do.

In the article The Future Of AI In Healthcare, Gil Press comments, “Physicians are susceptible to incorrect advice, whether the source is an AI system or other humans,” (Gil Press, The Future Of AI In Healthcare, Forbes). He points out that both people and machines are biased. This means that people and machines are only going to do and think what they are taught. Improving better data goes beyond that.

Clustering the data, diversifying the pool and navigating through its gaps improves efficiency and alleviates some of the bias. Patients and therapists can see outside the limitations of the individual patient.

While basic knowledge on the patient might show us the problems, having improved data can show us how to solve them. We can compare people in similar situations and analyze their outcomes to improve efficiency for current patients. Using the vast experiences of the diverse data set gives us the opportunity to connect the hidden dots that wouldn’t have previously been thought of as related.

In products like predictive health software, better data can develop efficient recovery plans that highlight risks and provide treatment alternatives. The experiences of others combined with patient backgrounds, genetics and lifestyles are all a part of being better data. Utilizing it correctly is what makes it the best.

Essentially all data is good data. But we can be “more excellent” than good. We can do better than good. Our data has the potential to be the best. Our data tells a story, and it’s our right as people to take control of what it says.

If you want to improve the impact of your narrative, check out Theralytics for a predictive software that strives for the best data.