Causing low quality of data

According to Gartner, through 2005, data quality-based IT business will be very effective in achieving improvement goals. To repair effort to succeed, senior management must recognize and adopt the data quality as a business priority.

The main factors causing low quality of data Are:

• data mismanagement

• Lack of data acquisition process

• Multiple silos of data to maintain the same data

• Redundant data across multiple channels

• Process data update is not effective

For years, companies tend to maintain silos of data that continues to expand with the wrong notes.
It is important to identify the weak sources in an enterprise from which flows the wrong data.
Because of intermittent network and integration capabilities are not enough, the data are more contaminated with false information. Inaccurate, exaggerated and missing data continues to grow until they become a set of databases that are too much fragmented and unproductive.

Different systems are programmed to access data from different database corrupted. CRM, ERP, BI and integration technology that failed in the companies that most often the result of substandard data. data quality metrics that only a road map for improving data quality. Establish processes to monitor results and set a goal of increase was the next important phase of the cycle of data quality.

Cost, effort and time spent on data quality processes must be able to provide business benefits, accordingly. Process improvement is a set of business cases and specific areas that need to be addressed. With local repair, objectives and priorities attached to each must be clearly identified. Personnel responsible for implementing each task clear improvement must understand the entire process of execution. Process improvement can be taken as a measure to gauge the future of data quality improvement.

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