Poor-quality Data and What it Spells for a Business

Written by
4 Minute Read
Share Blog:

Data is an important if not vital part of any organisation as it underpins almost every department and operation imaginable.

In fact, data is what drives improvement as a lack of knowledge of the metrics that define one’s current status and the goal of where one is going, makes improvement almost impossible. Therefore, how can we extrapolate this into business and what are the consequences of having poor data to work with?

Data in Business

If data provides metrics to analyse how well something is functioning, then it stands to reason that it would be of enormous benefit to a business in every department and operation. And if it provides a sense of how well something functions, it also highlights poorly functioning areas, therefore giving a perfect area to invest in to facilitate improvement.

Data can be ascertained for every department in the business: Accounts and Receivables, Building Maintenance, Customer Satisfaction, Shipping and Handling, Resource and Inventory Management and even Human Resources. It is applicable to the entire business and therefore gaining strong and reliable data should be a primary focus.

Inventory Management and Data

Data is the very fabric that binds a good inventory management system together. It is impossible to properly keep track of inventory without information pertaining to what you have in stock, where it is located, order rates, lead times and shelf lives.

How do you get this data? Well, some companies may still be on the more traditional spreadsheet system and some might have dedicated inventory management software. Although both designed to facilitate data retrieval, organisation and management, the difference is in their automation, ease of use, transparency and mitigation of (human) error.

When data is goes wrong, it can go very, very wrong. And the rate of business in the current climate, means that possibly significant decisions or actions have been taken based on the poor data well before an error is detected. Therefore, doing everything possible to support impeccable data retrieval and management is of the utmost importance.

What defines good quality data?

There are five accepted standards that denote quality in data. Aiming for these standards will go a long way in securing success in your business.

Accuracy – ensuring all qualitative and numerical data (names and values) are recorded correctly.

Completeness – understanding correctly what data is required where and ‘answering the question’ in completeness.

Consistency – ensuring the same calculations or summaries are calculated or given in the same way, every time.

Uniqueness – everything you are entering must only correspond to one thing in ‘the real world’. For example, you should not have one item in your warehouse recorded with two different names in your data.

Timeliness – quality data is real-time data that reflects a current status as is relevant to business management and users.

What are the consequences of poor-quality data?

1. Inefficiencies in company operations

This can be directly attributed to poor data. Inaccurate, untimely, incomplete or inconsistent data will affect accurate ordering which will influence supply and demand.

The downstream effects of this are compromised customer satisfaction if it results in missed or delayed orders. If the opposite is true and poor-quality data results in the overordering of materials, bottlenecks in the warehouse can occur, compromising the storage and timely use of the materials and accounting for massive financial losses over time.

When this poor-quality data is inevitably surveyed and corrected, valuable company time is utilised and redirected from more worthwhile projects.

2. Erroneous decision-making

Erroneous decision-making is a result of poor-quality data. No amount of weighing up benefits and risks when making strategic decisions is going to be of use when the data upon which they are based is fundamentally flawed.

Quality data underpins quality decisions and the opposite is true where poor data is concerned.

3. A culture of mistrust

Creating a culture of mistrust is an unfortunate by-product of generating and perpetuating poor data. When the data cannot be trusted, resulting in incorrect or inaccurate decisions or actions that have direct or indirect effects on customers, mistrust is fostered and slowly grows. This can severely compromise your customer, supplier and internal relationships which will negatively impact your business.

What can you do?

Implement proper back-end support such as inventory management software that is designed to retrieve, populate and analyse data in an accurate, real-time fashion so that quality business decisions are possible.

But a new inventory management system alone cannot guarantee success. You need to create a culture of change where where your people want to support the generation and maintenance of quality data. This involves training your staff, providing incentives and insights into the necessity for excellent data management. Although inventory management is an obvious area where this applies, the reality is quality data really does come into play in all areas of your organisation.

More about the author:

Share Blog:
Melanie - Unleashed Software

Article by Melanie Chan in collaboration with our team of Unleashed Software inventory and business specialists. Melanie has been writing about inventory management for the past three years. When not writing about inventory management, you can find her eating her way through Auckland.

More posts like this

Subscribe to receive the latest blog updates