Data scrubbingis an error correction technique which relies on background tasks for the purpose of periodically inspecting the primary memory for errors. Post detection, the errors are corrected through the use of different checksums, typically considered as redundant data.  Primarily, data scrubbing slashes the possibility of accumulation of single correctable errors, thereby resulting in reducing exposure to uncorrectable errors which pose a risk.

The integrity of data is of paramount importance in computer storage and transmissions systems, related with the reading, writing of processing data in such systems. It is important to note that, presently, a small number of systems accord adequate protection to users against the corruption of data. Data scrubbing software fills this gap by routinely checking data for inconsistencies of any nature. This pre-emptive action, eliminates or reduces the risk of failure in hardware or software.  The software makes use of this feature of scrubbing as a mechanism to detect errors and trigger corrections in memory and file systems etc ,

To put this in perspective, a simple comparison is in order. The human eye and mind has the power to discern variations between data records and understand that such errors are as a result of inconsistencies.  Data scrubbing software identifies, and weeds out data that is either inconsistent or incomplete data, thereby fixing the errors.

The process of computing, by virtue of the very nature of computing information that is input from various sources has inherent issues, and this data is often, labeled as ‘dirty’.  While the debate about the tag of ‘dirty’ may continue for long, what is indisputable is the fact that such errors are likely to occur for ever. It is herculean to eliminate errors in input when the whole purpose of processing data is to collate and work on the collated information. However, the need to weed out errors is certainly mandatory.

It is this necessity of ensuring hygiene of data that has acquired importance globally as a result of the large number of corporations that rely on CRM – customer relationship management systems for operations. Such organizations typically establish data warehouses to club and collate disparate data and information sourced from multiple points of origin and platforms.

In the absence of data scrubbing, IT department of organizations have the unenvious task of working with corrupt or incomplete data.  While, this problem may appear as trivial for the uninitiated, it is important to understand that this seemingly small problem can get compounded as a result of countless erroneous or inconsistent data to wreak havoc in operations.

All of this begs the question – how does dirty data come into existence in the first place?

  • The first point of input, i.e. data entry is the single biggest contributor to this dump of dirty data. This, in other words are typos, transpositions, and inconsistencies in nomenclature.
  • Database fields left empty.
  • Non compliance with data coding standards
  • Databases that form part of a large database are often structured differently from each other and from that of the unified database. This lack of a uniform structure causes a sea of problems.
  • Attempts to use data captured from older systems which may result in transferring the inconsistencies in the source – such as improper documentation and obsolete nature of the data.

Data scrubbing software eliminates errors and redundancy, with an avowed goal to make various data sets consistent regardless of the compulsions of creation of these data sets as a result of differing business aspirations and processes. Here, data scrubbing is critical in nature because of the simple fact that data sets that are not scrubbed or fixed will be of little or no use in a warehouse which is suppose to disseminate business intelligence feeds across organizations, and such inconsistent data will have a result that .is the exact opposite of its intended use.

Data Scrubbing, in simpler terms is the process of cleaning records, to ensure that the data is fully free from defects and is rendered fully fit for use by a business.  Some of the more common data defects are duplicates, incomplete email IDs, address fields that are formatted in disparate ways, and wrongly keyed in telephone numbers. And the main reason for these errors is incorrect data entry which could, in turn be as a result of incompetency or poor briefing.

When data is integrated or migrated, it must be made consistent with the master database, which should remain duplicate free and give you a single version of the truth. Clean data makes a world of difference in helping you to make the best business decisions, manage your inventories, improve your order tracking, delivery accuracy and ensure your direct marketing and customer relationship activities are effective. All of this comes from the reliable data achieved by maintaining quality data with Data Scrubbing Tools.

Data Scrubbing Software developed over the last two decades or so is designed to seamlessly integrate in organizational requirements, and this includes CRM and various operational systems.  Scrubbed records reside in CRM Systems as accurate and trustworthy sources, apart from transforming Marketing and BI strategies into highly successful ones. Organizations will witness the reduction data waste and will be better equipped to take decisions that have greater rates of successful outcomes.

Data Scrubbing Software is effectively, a set of intelligent tools that rely on data verification to bring about the scrubbing process. The process hinges on user-defined rules and the properties of data, wherein the program will generate reports reflecting the condition of the database or will involve a process of data correction.  Effectively, the program works on rules – one related to verification and the other related to the cleaning aspect. Here, the first set of rules spells out how correct data should appear, while the second set of rules spells out the issues that are possible with data values. These two sets of processes will help an entity to decide on the most suitable action for correcting the problem.

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