Data Modeling Protect Data Integrity

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Data Modeling Protect Data Integrity

Data modeling protects data integrity

Data architecture is used to ensure data integrity by establishing the policies, rules, standards and models that define and govern how data is collected, accessed, stored, integrated and kept secure. Data modeling protects data integrity by illustrating through diagrams, flowcharts and text how data should be organized to flow toward an achievable goal; the diagrams or charts describe the optimal architecture for that particular computing process. Data modeling improves data integrity by weeding out inappropriate, weak or irrelevant data from the relational database, so the final processing configuration uses only high-quality information that is directly relevant to the targeted goals.

Organizations that fail to use data modeling to develop their information architecture often experience data integrity challenges.

Not all data is the same

The explosion in the number, configuration and complexity of electronic health records offers lessons in why data modeling is a critical step toward establishing a sound database populated by high-integrity data. For example, a 2008 RAND report indicated that the U.S. health care system could save over $4.5 billion per year if its records management systems could successfully weed out millions of duplicate patient entries. At the time, master patient indexes opened duplicate files on a single patient when there was more than one identifying variable contained in more than one medical record. Millions of duplicate patient records that divided relevant patient information into more than one file burdened the system and its medical providers. Had the health care system used a data modeling technique before opening the database to multiple external data banks, it would have identified the duplication concern before creating those millions of unnecessary records.

Data modeling reveals gaps

Data modeling reveals the gaps in data integrity that can affect any enterprise which relies on data for decision-making and other corporate functions. Whole organizations can be affected by Big Data that lacks integrity, for example when it contains irrelevant or erroneous data mingled with appropriate information. A 2015 PricewaterhouseCoopers study revealed that only four percent (of responding corporations) were successfully managing their data, suggesting that those that didn’t remained engaged with databases that had no inherent information integrity. Extrapolating from that reality, we see that CRM and ERP systems that don’t model for data integrity are vulnerable to the excessive costs and wasted effort that occurs when critical corporate activities are undertaken based on inappropriate information.

Ensuring the integrity of value chains

Every company has a value chain that feeds its data banks with relevant and irrelevant data. By applying a data modeling process to establish the relevant controls for a value chain, corporations can identify which incoming data is actually relevant to its operations, and which is wasting money and time. The resulting database can reduce costs and improve productivity by refocusing corporate attention on those value chain activities that are truly integral to the company as a whole.

Contact the Data Integrity Solutions Corporation or email for more information regarding Data Architecture and Data Modeling training and consulting services.

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Data modeling protects data integrity

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“Data integrity” is another name for “information security.” Attacks on digitized information compromise its integrity.

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Data modeling assures data integrity