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Trusting Your Data is Non-Negotiable; Why?

Data has become a strategic asset in today’s business world.  Companies are striving to become data-centric as they seek to make more data-driven decisions. Having data integrity of the highest standards is a must and non-negotiable. Data integrity focuses on the trustworthiness and reliability of data throughout its lifecycle. Data integrity elevates the level of usefulness of data within an organization to make better business decisions. A Forbes survey shows eighty-four (84%) percent of CEOs say they are concerned about the integrity of the data they are making decisions on. Data integrity is a must have as it not only impacts decision making within an organization but also impacts decisions made externally by business partners, investors, analysts, and customers. 



Let us look at the importance of data integrity across some business facets and its critical role in maintaining stability and trust regarding the data consumed.  First let us look at accurate decision making. Data-driven decisions are the backbone of successful businesses. When data is accurate and reliable, leaders can make informed choices. Imagine a sales campaign based on flawed data, it could lead to wasted resources and missed opportunities. Customer trust and satisfaction is grounded in trustworthy data which builds confidence with customers. Whether it is personal information, order history, or service preferences, maintaining data integrity ensures that customers receive accurate and personalized experiences. Core to most enterprises are Operational/Process Efficiencies which rely on accurate data. From supply chain management to payroll processing, integrity ensures smooth operations. Imagine an HR system calculating employee salaries where accurate attendance and performance data is questionable. Many industries have regulatory obligations related to data integrity. Financial institutions, healthcare providers, and insurance companies to name a few are sectors with privacy laws which emphasize accurate record-keeping.  All enterprises assess and manage risk to the enterprise. Risk assessment depends on reliable data. Whether it is identifying market risks, cybersecurity threats, or operational vulnerabilities, integrity is crucial. Financial Reporting and Accounting is an essential element to all enterprises, large, small, public, or private. Financial data integrity is paramount. Accurate financial records and statements are essential for executives, investors, auditors, and stakeholders alike. All institutions manage their Brand and monitor their Brand reputation. Inaccuracies or data breaches can damage a brand’s reputation. Customers expect their data to be accurate and secure.   Therefore, organizations must invest in robust data management practices, ensure data accuracy, consistency, and context to achieve high data integrity to minimize negative impacts to the enterprise.  


Now let us examine a couple of examples of common practices that adversely affect data integrity and the impacts on an organization.


  • Manual processing and error-prone reconciliations. Many organizations still rely on manual processes for data reconciliations. Accounting teams often rely on complex calculations across various spreadsheets, which lead to errors and inconsistencies adversely impacting integrity.

  • Legacy systems and outdated technology. In this instance companies use outdated legacy systems to process core business functions and manage data. These systems are rigid and riddled with layers of old code presenting difficulty implementing business, compliance, and regulatory changes. These systems often lack the proper validation mechanisms to handle today’s data volumes and data types. Data inaccuracies, security vulnerabilities and operational inefficiencies are common impacts.

  • Varied and Multiple Sources of Data and Data Bases. Processing for many organizations involves a multitude of data sources, transaction logs, customer records and such, especially in the Financial Service sector. Processing is often reliant on multiple databases with different context and transactional data for customers and products. Ensuring data consistency across the array of data sources and databases is challenging.

  • Data Entry Errors. Data entry for master data and transaction data comes into an organization from many sources; call centers, online portals, Point of Service/Sales systems, and more. Mistakes during data entry such as typos, incorrect account codes, customer numbers, or order numbers, or missing details can compromise data integrity.

  • The impact and cost of poor data integrity is real. Impacts can range from customer trust to reliability of financial reporting.  


A cornerstone for good data integrity is data quality.  Data quality is a subset of data integrity.  While data integrity ensures overall consistency and trustworthiness, data quality checks whether data serves its purpose. Data quality is the fitness of data for its intended purpose. It encompasses a) completeness which ensures that all relevant data points are present, b) accuracy which defines the precision of the data, c) timeliness which speaks to having up-to-date data, and d) validity which ensures data adheres to defined business rules and constraints.  High quality data contributes to good data integrity. Accurate, complete, timely, and valid data enhances the overall trust of the data, aka data integrity.


Common data quality issues are inaccuracies, missing information, inconsistencies, and timeliness. Inaccuracies are errors in the data. Missing Information is incomplete data. Inconsistencies are contradictory data points within the datasets or databases. Timeliness is outdated data that does not reflect the current state. These issues impact accurate reporting, analysis and decision making.  A few key methods or actions an organization can take to improve data quality and hence data integrity are:

  • Establish data governance which defines and instantiates policies, procedures, and responsibilities for data quality management.

  • Implement data validation and verification processes to regularly validate and check data to ensure accuracy.

  • Implement automated data check processes to identify inconsistencies and errors.

  • Implement data cleansing procedures to remove duplicate records, correct inaccuracies and address missing data.

  • Educate the organization through training and awareness about the importance of data quality and their role in maintaining it.

Prioritizing and focusing on improving data quality is fundamental for an organization to achieve and sustain data integrity.


As data Integrity and data quality are inexorably linked, and data quality is fundamental to integrity, it is important to understand the interplay between them. In the area of decision-making and analytics, data quality ensures reliable insights. Accurate, complete, and consistent data empowers informed decisions. Data integrity safeguards the reliability of insights. Trustworthy data underpins predictive analytics, scenario modeling, forecasting and trend analysis. In the area of compliance and accountability, data quality supports regulatory compliance so that valid, relevant data meets and adheres to mandatory reporting standards. Data integrity maintains the audit trail providing transparency. Accountability and confidence hinge on unaltered records. Business confidence and reputation is anchored on data, which is transparent, error-free, and timely. Data integrity fosters stakeholder confidence and builds trust and reputation.


Data integrity ensures reliability, consistency, and trust of the data, while data quality focuses on usefulness and relevance. Good data quality improves the integrity of an organizations data and resultant information. Both are important for an organization that is moving to a data centric, and data driven decision making culture. Do not assume you have good data integrity; the consequences could be harmful.



About the Author:

Harry Hanelt is a member of HP Marin’s Executive office and is the firm’s CEO. He has over forty years’ experience in both Consulting and Industry, where he has held leadership positions in several large firms including KPMG, BearingPoint, SunGard, and Heublein. Mr. Hanelt also served as the Managing Director and President of HP Squared LLC, an affiliated Data Strategy Consulting Business. Mr. Hanelt has been a keynote speaker at conferences and has served as an Industry Advisor to the University of Connecticut’s School of Business.


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