Poor data quality negatively impacts business by generating significant cost overruns directly and indirectly, both short and long term. Not surprisingly, data is part of the entire organization in the most different ways., and that the omnipresence, in turn improved by new technologies, can't stop bill when the data quality is poor due to errors or deficiencies of any kind.
Even though each organization is different and generalizing is not always a success, it can be argued that poor data quality often costs companies a lot. Conversely, the Quality of the information, understood as the absence of erroneous data, duplicates, obsolete, inadequate or difficult to access, facilitates company performance at all levels, from the internal operational level to the relationship with customers or suppliers.
Companies of all sizes and sectors have a competitive need of having quality data, especially currently, characterized by an exponential increase in information from a wide variety of sources. Its improper handling will carry risks, thus increasing the probability that it will translate into cost overruns as a consequence of very common problems in low data quality contexts, as the following:
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Decision making mistakes: Poor data quality prevents good predictive or simply enlightening analytics to help make decisions. Logically, the use of faulty or inadequate data for decision-making may entail an extra cost due to lack of vision or foresight, at the same time preventing us from taking advantage of possible business opportunities.
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Increased administration costs: The lack of agility and functional efficiency implies an extra cost in human resources, unnecessary investments and expenses, as well as in the treasury area as a consequence of possible accounting imbalances or, as an example, ineffectiveness in detecting fraud.
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Deterioration of the corporate image: Bad data leads to poor customer management, which inevitably damages the image of the company and also translates into greatly improved marketing results.
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Non-compliance with regulations: Mismanagement of data involves legal risks that can translate into cost overruns.
It is, in summary, a loss of overall competitiveness as a consequence of this constant loss of money and resources. Therefore, the lack of a data quality system leads to operational inefficiency that no company can afford in today's extremely competitive environment.
The relevance of a data quality project
Assuming any company wants maximize your productivity At the level of workers and processes, in order to develop high value products or services that can compete in the market in the best feasible way, tackling poor data quality issues is revealed as a priority. Exactly, one way to know the return on investment in a data quality system is by evaluating the real business impact of these additional costs.
The evaluation of cost overruns can also be carried out as a forecasting mode for a future project, as an example, a marketing campaign or the design of a new customer service department, Let's say. Y, whatever the case, bear in mind that cost overruns can occur both actively and passively when an initiative is carried out with poor data quality or not, respectively.
The establishment of quality rules through the pertinent procedure of continuous improvement of the quality of the data will guarantee us a information optimization employee, making it suitable to our needs. Finally, only implementing a data quality project tailored to the company's requirements will allow find the balance point between the resources used for its improvement and the demands of its use.
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