Garbage In, Garbage Out: Why Bad Data is Worse Than No Data

May 10, 2019
Stanford Silverman

Insights into Economic Policy and Strategic Planning

This blog explores the critical issue of data quality and its paramount importance in business decision-making and policy formulation. It argues that bad data can lead to misguided decisions that are worse than decisions made with no data at all. The narrative underscores the hidden costs associated with incorrect, outdated, or misleading data, including financial losses, damaged reputations, and misguided strategic decisions that can have long-lasting effects. The discussion emphasizes the necessity of implementing robust data governance practices to ensure data integrity and reliability. It calls for stringent data management strategies which include regular audits, validations, and updates to prevent the infiltration of bad data into the decision-making process. The blog also highlights the role of technological advancements, such as artificial intelligence and machine learning, in enhancing data accuracy and processing. By advocating for a culture that prioritizes data quality within organizations, the blog suggests that businesses and governments can avoid the pitfalls of bad data and make more informed, effective decisions that drive progress and efficiency.

Since the 80s or 90s, computers have grown in importance not just in a personal sense but in a business one as well. While technology has made life easier, it’s still powered by man (for now!) and therefore is not entirely infallible. You simply can’t trust your insights when you can’t trust the inputs.

How does this concept relate to the education industry? Mainly, through hardware, sales software and analytical marketing tools: while the leap from sales binders to Excel spreadsheets may have made enrollment and sales data more streamlined and convenient, the results ultimately depend on the data inputted rather than the vehicle.

With human error occurring more than we want to admit, false or faulty data can still leak into a document or calculation and contaminate outcomes, resulting in misaligned marketing strategies, increased costs, and business instability. The problem becomes amplified when large and varied sets of big data need to be analyzed to help an organization make informed business decisions. This is the often a complex process of examining large and varied data sets to uncover information including mystifying arrays, undiscovered parallels, market developmental cycles and buyer biases that help administrations gain valuable insights, enhance decisions, and create new products. The relationship between bad input leading to bad output can be summarized by this phrase: garbage in, garbage out.

The evolution from Rolodex to a spreadsheet or even smartphone app has certainly streamlined collecting information, but it hasn’t entirely eliminated user error. Innovations in hardware and software have made it uncomplicated and cost effective to amass, stockpile, and evaluate copious amounts of sales and marketing data. If good information is input, then good data will be spat back out and vice versa, which may significantly affect planning, buying and selling decisions. In education marketing, user error makes it more difficult to know the client. In essence, bad data is as good as no data and perhaps even worse.

So, what can we do? While adherence to data integrity and entry along with correct set-up ensures the best and most accurate results, human error will always be a constant. Bad data input will always occur, but controlling for bad data, and engineering procedures to supervise data integrity successfully will help eliminate issues in decision making and avoid increased cost and organizational miscues. The best solution is to detect the ‘bad’ early and locate the problem before it gets worse. Fortunately, we can do something about data quality. No one wants to find out a pipe is clogged by the time their basement is flooded. Admitting that you have a data quality problem is the key to the solution.

Tune in to my next article to find out how segmenting data based on audience, system of controls, implementing a tiered tracking system and management oversight can help keep data on track. I’ll also provide an important warning about overanalyzing data that can save you great turmoil and stress.

Disclaimer: This article discusses certain companies and their products or services as potential solutions. These mentions are for illustrative purposes only and should not be interpreted as endorsements or investment recommendations. All investment strategies carry inherent risks, and it is imperative that readers conduct their own independent research and seek advice from qualified investment professionals tailored to their specific financial circumstances before making any investment decisions.

The content provided here does not constitute personalized investment advice. Decisions to invest or engage with any securities or financial products mentioned in this article should only be made after consulting with a qualified financial advisor, considering your investment objectives and risk tolerance. The author assumes no responsibility for any financial losses or other consequences resulting directly or indirectly from the use of the content of this article.

As with any financial decision, thorough investigation and caution are advised before making investment decisions.