Data integrity has become a major institutional challenge as big data analytics increasingly drives decision-making. To guarantee data integrity, organizations need to establish strong quality management practices that will help protect and maintain data during collection, processing and storage.

Data cleaning and maintenance:

Research by The Data Warehouse Institute (TDWI) reported that data quality issues can cost US businesses more than $600 billion annually because data cleansing efforts accounts for 30-80% of the preparation process of most big data projects. Yet decision-makers do not take action with their bad data until it manifests itself into high-impact costly problems. An essential first step in producing information that translates into business performance and profitability is data cleaning.

A data cleaning approach should satisfy several requirements. First of all, it should detect, eliminate or correct all errors and inconsistencies. It should also be a continuous process that supports system health in order to maintain data integrity. As a proactive solution, the Data Integrity Gateway (DIG) tool integrates with an institution’s information system and centralizes cleanup projects in a single repository. By automating processes, delegating tasks, and monitoring data cleanup, DIG helps maintain data quality throughout its life-cycle.

Data entry training & accountability:

Data integrity starts at the source – the user. Manual data entry can result in errors that compromise analytical results meant to guide business decisions. That’s why it is vital that staff members with system access are properly trained on data entry and upload protocols. There are several steps to consider when training:

Training should be an active, evolving process in response to operational needs.
An easy-to-understand document with procedures should be readily available for reference.
System administrators should assign correct level of access to users based on their training and role.
Auditing processes should be put into place so that individuals can be held accountable for any inaccurate data entered into the system.

Data validation rules:

Even with a proper training plan in place, there is always room for human-error when a company includes manual data entry in their operations. By using data validation rules, administrators can ensure data integrity by controlling and restricting the values that users can enter into their system. By protecting information from accidental alteration, validation rules provide additional security and data quality assurance – a natural requirement for accurate analytics.

Data Integrity Threats:

Data integrity can be compromised through human error or, worse yet, through malicious acts. Data that’s accidentally altered during the transfer from one device to another, for example, can be compromised, or even destroyed by hackers.

Common threats that can alter the state of data integrity include:

Human error
Unintended transfer errors
Misconfigurations and security errors
Malware, insider threats, and cyberattacks

Compromised hardware.

Build confidence in your data:

Data that is secure and trusted is essential in today’s world. IBM data integration software solutions can deliver clean, consistent and timely information for your big data projects, applications and machine learning.

Govern data in real time:

Flexible and real-time data governance is needed 24×7. The IBM data integration platform massive parallel processing capabilities help manage, improve and leverage information to drive results and reduce cost and risk of consolidation.

Consolidate and retire applications:

Multiple, disconnected systems or an outdated application infrastructure can negatively impact business and increase costs. IBM data integration software solutions automate manual processes, thereby improving the customer experience and business process execution.

Business challenge:

Aiming to double the value of its real estate portfolio in five years, it should decision-makers need timely, accurate insight into portfolio data scattered across many disparate source systems.


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