Rick Delgado

In a world so powered by Technology, the ability to analyze data and use its results to help direct Business plans for marketing and innovation becomes imperative.

Unfortunately, collected data can be corrupted through malfunction of machine or simple human error, and when that happens, it can mean huge roadblocks for the business that needed that information in the first place. How can you prepare?

Understand That Data is Not Infallible

No matter what instance and situation data is being collected on for research, there will always be variables. An important thing for businesses to remember is that the results of data Analysis are never infallible. As such, any business should always have a backup plan in case the analytics come out faulty or the results are not what was expected.

Though programs for data collecting created by engineers are often very reliable, absolutely nothing is perfect and it’s important to remember that when working in businesses that effect people, society, and culture. It’s always important to have a plan B when statistics you were counting on don’t pull through as expected.

Don’t Rely Solely on the System

When it’s a smaller project being analyzed and implemented, it’s more tempting to let machines do all the work in figuring everything out. Enticing as it may be, it’s something to avoid. No matter how big or small the perceived results should be, it’s always hard to say how broad a scope the consequences can really have.

So whenever a business is working with collected data, it’s always important to have humans work on your side of that data to understand what the results mean and make sure the consequences and impact they’ll have fits the situation that is being looked into. Technology is incredible, but the slightest input error can have a drastically different impact, and having a few pairs of human eyes around to go over them can make or break any kind of study.

One Should Never Assume

Assumptions and predictions of different data projects can sometimes make sense, but in many situations, they backfire in a way no computer system predicts. Working data analysis based on preconceived notions and biases can sometimes result in getting trapped in the proverbial echo chamber.

A business projects the kind of statistic it thinks it will find, data can show some statistics that both agree and disagree with that way of thinking, but confirmation bias based on the initial opinion contaminates and clouds the results. In many ways, it falsifies the information gathered. For a business to go forward with those kinds of results can be extremely detrimental to both the success of the business and whatever aspect of society that business represents.

Broaden Your Data Analysis Horizons

In a similar way that preconceived assumptions can mess with data results, having a narrow range to work with can negatively impact studies as well. When it comes to technology and analytical methods of progress, two perspectives are better than one, four perspectives are better than two, and so on. When it comes to big data especially—when the results are going to impact a turn in the tides of business, society, or technology in general—the more data that can be recorded in all kinds of different ways on your computer server, the better.

You wouldn’t want to use only numbered statistics or just opinion polls from surveyed people, when combining both would get a much clearer picture in the long run. Getting the most accurate results from the data being collected is the best way to move forward for a business and keep progress moving.

Data Does Work

The world today stands on the brink of a technological revolution. Data analysis is proving to be incredibly important in business models from the biggest to smallest of scales. But technology trying to move forward won’t get anywhere if humans aren’t equipping themselves to handle it.

Businesses need to use all the data they can get to move forward, but when it doesn’t work, when wrinkles need to be ironed out, they need they people power to stay steady under pressure. Training people behind the businesses to understand aspects of the data they get back will make any result easier to deal with. And when those results aren’t what was hoped for, the people behind the scenes should be able to pull together to see the alternative path.