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Marketers challenge in Data Scrubbing

While looking into the horizon of data and diving into lake of information, marketers are prone to acquiring useless data. The probability of getting a qualified data is relatively low than getting unwanted data (ComScore 2014). The statistics will show it all but would you mind keeping old data rather than giving them up easily. Now, these are the quality of a lead specialist that those of traditionalist are harder to let it go. Similarly saying a transition should be implemented in terms of scrubbing of data which only cause you more money allocation in keeping them. Let this be a starter for your data cleansing management once you decided to implement this.

  1. Alteration and Data Improvement: Altering repetitive and invalid entries in data cleansing has been the one of the most challenging and tedious problem. Most of these altered and corrected entries have been left no supply of right entries. Keeping the altered data insufficient to support the whole data entry is the problem. Talking about the return of investments, this alteration of data if not controlled will lead to much more costly situation.
  2. Preserving cleansed data: The time and the money on investing to data cleansing is expensive. So after paying the job done and now you have error-free quality data in your hand, one would want to keep it that way as far as the time can. The value of error-free data is half the same as the new ones. One would want its value free from errors. To preserve its value, the same process should be done only for those values that have been change. Because preserving the whole database requires big amount of data gathering and management techniques.
  3. Scrubbing in a cyber environment: The scrubbing of data in a cyber environment just like IBM’s DicoveryLink requires real-time data cleansing if ever accessed, which on the other hand can decrease the traffic results.
  4. Data-cleansing Scheme: Predicting the process of data cleansing in advance is impossible even with the guide of widely used graph. Due to this makes data cleansing a constant process that involves continuous data gathering and error detection and elimination to have a complete data audit. This can be as well related to data processing step such maintenance and validation.

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