Fragmented customer data | Define data redundancy | What is data inconsistency? | How to minimise data redundancy & data inconsistency
Fragmented customer data
Let's think about how the data is handled and managed by people in a business. We have the sales team, invoicing, tech support or customer service. These departments often manage data in silos, which can lead to two critical problems: data redundancy and data inconsistency.
As organisations grow, the costs of these issues grow with them. Not only can they lead to inefficiencies, but they can also directly impact decision-making, customer relationships, and operational costs.
In this article, we explore the real costs of data redundancy and inconsistency, and more importantly, how your organisation can prevent them:
- Define data redundancy
- What is data inconsistency?
- How data redundancy and data inconsistency can damage a business
- How to minimise data redundancy and data inconsistency
- Next Steps
Define Data Redundancy
Data redundancy means storing the same piece of data (or data point) in multiple places within a database or across systems.
This can be done to improve data availability and fault tolerance, ensuring that if one data source becomes unavailable or corrupted, the data can still be accessed from another location. However, it can also lead to increased storage costs, potential data inconsistencies and can lead to complications in maintaining data accuracy and integrity - if not managed properly.
Data redundancy - by accident. Poor database design or a lack of centralised control leads to this situation. Over time, this redundancy increases, adding to the operational load and storage costs of an organisation.
Case Study: Fast Moving Consumer Goods
Long-standing Australian confectionery manufacturer faced similar challenges as their outdated on-premise system struggled to support their data needs. Their legacy infrastructure contributed to data duplication across multiple systems, particularly in their sales and billing departments.
To address this, Notitia implemented Qlik Sense SaaS, which provided a unified, cloud-based data platform. The migration allowed the business to consolidate their data into a single source of truth, significantly reducing redundancy and improving overall operational efficiency.
What is Data Inconsistency?
Data inconsistency happens when the same data point presents differently in various parts of your system. Unlike redundancy, this inconsistency often results from different departments or tools collecting, entering, and using data in different ways.
Case Study: A regional hospital
A Victorian hospital approached Notitia to help them optimise their data management processes, as inconsistent and siloed data hindered decision-making at the executive level.
The hospital needed to streamline how their data was accessed and used across departments. Previously, inconsistent data across their systems made it difficult for the executive team to get an accurate picture of patient volumes and workforce resourcing.
Notitia helped the hospital centralise its data by implementing multi-layered reporting, Qlik Sense dashboards, and data literacy training, which ensured that all stakeholders had access to consistent, reliable information and enabled the standardisation of data inputs and outputs across the organisation
This solution provided data consistency, giving managers and executives real-time access to accurate and reliable information.
This transformation empowered the hospital’s executive and senior teams to make data-driven decisions that improved both day-to-day operations and long-term strategy.
How data redundancy & inconsistency can damage a business
Data redundancy refers to unnecessary duplicates of information scattered across various databases. Data inconsistency arises when there are conflicting versions of the same data point in your systems. For instance, a customer’s billing information may differ between the sales and accounting departments.
The consequences:
- Wasted storage and increased costs: Redundant data takes up unnecessary space, increasing server and cloud storage expenses.
- Inaccurate reports and misinformed decisions: Inconsistent data can lead to flawed insights, meaning your leaders make decisions based on incorrect information.
- Damaged customer relationships: Inconsistent data may cause customer service issues—sending duplicate emails or incorrect invoices.
How to Minimise Data Redundancy & Inconsistency
Step 1: Normalisation of Your Databases
Normalising your databases is a core method of reducing redundancy. The goal is to restructure your data so that no duplicates exist, and all information is logically linked.
Step 2: Implement a Centralised Data Platform
Adopting a centralised platform, like Microsoft Power Platform or Qlik Sense, ensures that all data sources feed into a single system. This drastically reduces the risk of redundancy and inconsistency.
Step 3: Appoint a Data Steward
A data steward is someone responsible for overseeing data quality and ensuring consistent standards across departments.
Step 4: Regular Audits and Clean-ups
Regularly auditing your databases for redundancy and inconsistency is crucial. AI and machine learning tools can help automate this process, allowing you to identify and correct discrepancies before they cause issues.
Implement a Centralised Data Platform with Qlik, Databricks, or Microsoft
When data is spread across multiple departments and systems, errors, duplication, and inconsistencies are inevitable. By adopting the right solution with a technology partner such as Qlik, Databricks, or Microsoft Power Platform, businesses can consolidate their data, ensuring consistency and accuracy across all departments.
Qlik: Provides a powerful solution for visualising and integrating data. By centralising data businesses can create a single source of truth, allowing decision-makers to access accurate, real-time information from across the organisation. Notitia’s expertise with Qlik helps organisations reduce data redundancy and inconsistency while improving the overall quality of their data.
Databricks: Offers a unified analytics engine that breaks down data silos and allows businesses to manage large volumes of data efficiently. This platform reduces the risk of redundancy and inconsistency, ensuring that all data sources feed into a central hub. As a Databricks partner, Notitia helps clients implement bespoke data solutions tailored to their needs.
Microsoft: For organisations looking for a flexible and integrated solution, Microsoft enables businesses to centralise their data, automate workflows, and reduce the complexities associated with redundant and inconsistent data. Notitia’s Microsoft solutions provide businesses with the tools needed to ensure high data quality and operational efficiency.
At Notitia, we specialise in recommending the right technology for your needs and integrating these technologies to help you overcome the challenges of data redundancy and inconsistency.
Next steps: Taking control of your data quality
The costs of data redundancy and inconsistency are not always immediately visible, but their impact can be felt across every department in your organisation.
By taking steps to normalise your databases, adopt centralised systems, and implement data audits, you can ensure that your data quality remains high.
At Notitia, we specialise in helping organisations overcome these challenges through bespoke data solutions and automation tools. Let’s chat about how we can help improve your data.
