That’s why avoiding data quality issues is so important. The cost of bad quality data is often higher and more unpredictable than anyone anticipates – the US economy alone loses $3.1 trillion per year due to poor data quality. So, taking a proactive approach to data quality is always a huge plus. Here are 3 things to keep in mind while doing that.
#1 Anomalies Scale With Data
Finding logical patterns in huge datasets has been one of the greatest challenges data scientists have had to face this decade. Today, we can all agree that data doesn’t always follow a logical pattern, and one of the main reasons behind that is what we know as anomalies.
I like to believe that anomalies (also known job function email list as temporary fluctuations) constantly show up in data patterns to keep us sharp and on the move. And, no, we’re not talking about the seasonal fluctuations due to well-known events like Christmas or Independence Day here. We’re talking about the seemingly-random and short-lived patterns that threaten the decision-making process.
At the early stages of growth, manually investigating and adjusting for outliers is feasible. But, as your company grows and collects more data, you’ll start running more and more often into new anomalies, and you’ll need the help of more powerful tools. Most companies today make use of custom machine learning algorithms through software development services that allow them to automate the most difficult tasks of this process. Getting powerful technology on your side is certainly a good way to prevent anomalies from snowballing into your decisions.
#2 All Data Models Have Volume Thresholds
As proud as you might be about the nike’s house of innovation is one of the brand’s current data model you’re using in your company, know that no data model is made to last forever. There’s always a certain volume of data that will exceed the capabilities of your current data model and introduce tons of inefficiencies. In other words, most data models will begin to break down as data volume increases.
This isn’t necessarily the consequence of a faulty data model—that’s just how things work right now (or at least until quantum computers bring some groundbreaking magic to the table). All that you can do is accept the truth: most organizations will run into data quality problems as they grow, and they won’t become apparent until the data volume reaches a certain threshold.
#3 Data Can Be Easily Wasted and Misused
I believe most companies today are whatsapp database philippines collecting data and using it to inform decisions in one way or another. That’s just a standard of competitiveness today. However, I also believe that most companies don’t actually have a data strategy in place, which could lead to a lot of wasted opportunities.
One of the great things about data is that it is filterable and reusable.
