The Butterfly Effect in Data Quality - 1

The Butterfly Effect in Data Quality – 1

 
Let us start today with a definition – ‘The sensitive dependence on initial conditions in which a small change in one state of a deterministic nonlinear system can result in large differences in a later state.’ Do you know what I am talking about? While the definition might be confusing, the concept is well-known to most of us. The above words are how Wikipedia defines the ‘butterfly effect’. In simple terms we would say that a minor event in a system can lead to huge changes down the line, just like a butterfly fluttering its wings can cause a tornado on the other side of the globe due to the chain reaction it starts.
 
But why are we talking about butterflies and chaos theory in a blog dedicated to material master data management? The answer is quite simple – just like that tiny butterfly and its even tinier wings can wreak havoc halfway across the globe, similarly, a small error in enterprise data can lead to much bigger problems down the line. In the following series of posts, I shall try to expand on this idea and how to avoid this situation.
 
Data quality is of prime importance for any organization and it is very easy for errors to creep in, especially if we are dealing with large companies and huge amounts of data to begin with. Let us see what a small error can lead to –
 
In a plant with no data quality/data governance system in place, two employees are creating entries for a new type of air filters that have recently been delivered. Employee A enters the name of the product as ‘Air Filter’ while employee B names the same product ‘Filter, Air’. Whatever you may think about the taxonomy used, the problem is self-evident. Confusion is bound to occur when a third employee (Employee C) tries to search for the filters sometime down the line.
 
There are three ways such small errors can affect your data quality & your organization.This includes –
 

  • Operational Efficiency – Time and resource are sure to be wasted when someone tries looking for the filters – first in searching for them and then in correcting the mistake by standardizing the nomenclature. Such incidents affect your organizations operational efficiency and are sure to pinch in terms of hard dollars.
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  • Revenue – Speaking of dollars, you can clearly see that these errors will hit your revenue. Even small errors will affect your ability to cater to your clients in time and this will directly affect your bottom line. In a hyper competitive world, the clients have a plethora of options and anyone not up to the mark is sure to be left behind.
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  • Compliance – This is something that we sometimes do not pay much attention to, but which is critical. You need to comply by state and federal laws and regulations and data quality errors can hamper this ability. The worst part is that you will come to know of such errors only when you have been hit with a heavy fine.

These are the big problems that a small error in data can lead to, but how can you void this? These tips and techniques will be covered in the second part of this series. In the meanwhile, tell us your experiences of data errors cascading into bigger problems – head to the comment section below.
 
Further reading –

Blog – Tips and strategies for enterprise data management processes

White Paper – Empower end-users with Master Data Management

Case Study – Material Master Data Management for a leading Oil and Gas Company

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Vipul Aroh

Vipul Aroh

Vipul Aroh is a part of the Marketing team at Verdantis. Although relatively new to the field of master data management and data governance, he is fascinated by the topics and is becoming more passionate about them by the day. Vipul holds a Master’s in Business Administration from Sydneham Institute of Management, Mumbai.

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