Home Business Insights & Advice How bad data hurts your business and how to fix the issue

How bad data hurts your business and how to fix the issue

by Sponsored Content
28th Feb 22 1:25 pm

When any aspect of a business makes a decision, from the CEO right down to the customer, data is generated in incredible quantities. In fact, about 2.5 quintillion bytes of data are produced every single day, yet over 73% of this data is never used by businesses. While some of this comes down to the quantity meaning not everything can be produced, there’s the added barrier that some of this data is bad data.

The harm that bad data inflicts on organisations isn’t a secret, with 95% of companies stating that their inability to structure bad data is holding them back. In this article, we’ll be drawing back the curtain on this data issue, explaining the difference between good and bad data and then displaying how you can ditch the latter and cultivate the former.

Let’s get right into it.

Why is good data important for your business?

Analytics, once a fairly underrepresented field, is now one of the leading factors that help businesses to make better decisions. While there is more data around than ever, quality data is slightly harder to come by. Yet, once data infrastructures have been put in place, engineers will be able to transform large databases of numbers into direct insights for your business.

By acting upon these insights, data gives business leaders a solid ground of facts to base their decisions on. Instead of turning to guesswork and hoping they’re making the right decision about a certain campaign, strategic shift, or implementation, they’ll simply be able to look at the data and know which is the more beneficial outcome.

Data is applied across all business fields, impacting everything from healthcare to eCommerce. With good data, your business will be able to harness statistics and gain actionable insights that can be put to use towards building a stronger organisation.

Unfortunately, these useful business benefits are only accessible to those that have access to good data. Currently, more and more organisations are suffering from ‘bad data’. Let’s break this down further.

What is poor business data?

Bad data refers to any form of data that your business generates that is unstructured. Whether that means it is inconsistent, has duplicated figures, is incomplete or is simply inaccurate, any form of unstructured data can become a huge problem.

Shockingly, bad data costs the United States $3 trillion every year. This figure accommodates for the fact that bad data is both expensive and time-consuming, as workers will have to spend time hunting for data sources, correcting any inconsistencies, and confirming any data that looks a little off.

There are several core reasons why your data could be bad:

  • Inaccurate – If the COVID-19 pandemic revealed anything about global data sources, it’s that they’re often completely incorrect. False data or data that is simply inaccurate caused major panic around the world, leading to confusion and difficulty reacting to the situation. When data is inaccurate, your business cannot draw the correct conclusions from it, meaning that it loses efficiency and cuts its potential ROI from this data.
  • Inconsistent – While sample size is indicative of great data, pulling from multiple sources at once can lead to inconsistencies that pose as an issue. Whether this is baseline inconsistencies, like pulling from a UK website that lists temperature in celsius and a US site that lists it in Fahrenheit, or something more serious, this can frustrate the process and cause your streamlined data pathways to become clogged.
  • Too Much To Process – A data scientist will spend 80% of their time at work simply locating the correct data. As the potential data pool that they’re pulling from increases, so does the time it takes them to track down usable data. As your business scales, be sure to stay wary of the demands on data scientists.
  • Slow Data Pipelines – Data infrastructure becomes outdated and obsolete fairly quickly. If you don’t have data engineers that are continually streamlining your data acquisition process, you’ll be wasting time, ensuring that less data is processed and leading to shallower information sets that your business can act upon.
  • Duplicated Data – Not only does duplicated data mean that an unknowing data scientist can spread double the time on a data set than needed, but it also leads to clogs in the data pipeline. With data coming in from a range of different sources, like cloud services, local databases, data lakes, and more, it’s easy to get wires crossed.

Bad data comes in all shapes and sizes. To avoid your company falling prey to the malevolent reach of one of the above forms of bad data, you’ll have to do a lot more than wondering which data warehouse, clickhouse vs druid, is best for you. Luckily, going beyond baseline tools, there are actually ways that you can ensure the data your business collects is more efficient.

With this, your organisation can then draw meaningful insight from your data, transforming the way you do business by acting upon quantitative statistics and insights.

How to ensure data your business collects is quality?

When collecting raw data, there is a lot that can go wrong. Wrong someone typing in a misspelled name to a badly formatted collection scheme, any number of accidents can happen.

In order to overcome these issues, there are three central methods that your business should start to employ:

  • Reanalyse your data collection structures
  • Treat data as important
  • Update your toolbox

Let’s break these down further.

Reanalyse your data collection structures

When those that monitor data in your company come across errors, they shouldn’t assume that this is a one-off occasion. Bad data has come from somewhere and was caused by something, meaning that it can be fixed.

When examining your organisation, be sure to trace where bad data has come from. Instead of just fixing the surface-level data point error, make sure that your data engineers dig deeper and uncover what caused the error.

By taking a more holistic approach to data, your business will be able to whittle down the different errors that it makes, ensuring that more of the data you collect can be processed and then analysed without any hassle.

Treat data as important

While ‘Data’ is definitely an all-encompassing term that is perhaps a little too broad, it is something that cannot be overlooked by modern businesses. The internet has given way to a continual stream of information that is going to be wasted unless your business treats it seriously.

By reconfiguring your business to take data seriously and putting in place the systems and data engineers that will be able to handle it, you’ll be able to boost the productivity of your whole business. In fact, businesses that use insights generated by their data routinely perform better than those that don’t.

The CEO of eCommerce company From You Flowers, Michael Chapin, demonstrates that before using analytics, his business was growing at an average rate of 10% per year. After he restructured his business around the value of data, he then saw a continual 30% growth year on year. This trend continued for five years straight, demonstrating the potential that data unlocks for your business.

Instead of letting bad data take control of your analytics, get ahead of it by implementing the necessary systems that can process, handle, and restructure your data into something advantageous for your organisation.

Update your toolbox

Data science is no longer a job that is managed by an engineer with a passion for numbers. Modern data requires modern tools that will help you manage the vast quantities that your business is currently receiving.

Instead of relying on age-old tools and legacy solutions, you should restructure your data collection and processing with a more updated set of tools. With modern tools that are able to handle the increased demand that modern data places on systems, you’ll be able to process more of the data that comes your way.

With this, you’ll reduce the amount of bad data that your business is using, with many tools being able to restructure and monitor your data during the process. Instead of guessing if something is right or wrong, a modern data architecture will have all the tools to deliver your business detailed analytics that help drive your progress.

Final thoughts

In the modern business world, data is a make-or-break tool that any organisation in any industry can take advantage of. Acting as a personalised pool of information that will point to probable ways that you can streamline your business, boost profits, or decrease expenditure, data is an invaluable resource.

Yet, there’s a lot more bad data out there than good data, with businesses that lack the correct tools and structures often falling into the trap of data that harms more than it helps.

By restructuring businesses around the value of data, implementing comprehensive data structures and systems, and ensuring that data scientists in your company have what they need to excel, you can start to turn the tide on your own data.

Instead of worrying about good or bad data, you’ll be able to start drawing insight from the data your business is producing, creating an action plan that will lead you towards success.


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