Disastrous consequences due to poor data quality

Poor data quality causes many problems for organizations. Thus an entire field of Data Quality Management (DQM) is now an essential branch of Computer Sciences and specializations such as Data Quality Analysts, Data Owners, Data Custodians, and many more.
If nobody knows that,it could have disastrouse consequences for the company.


Data Quality refers to the methodical approach, policies, and processes by which an organization manages the accuracy, validity, completeness, timeliness, uniqueness, and consistency of its data in systems and data flows. Unfortunately, often these are not fulfilled and data becomes poor in these attributes.

Here are the problems caused by low-quality data in data-driven organizational models.

  • Complications in Data Reconciliation.

Due to poor quality data, it takes more time to reconcile data. To overcome this, scripts need to be written which adds to developmental and maintenance tasks. Increased reconciliation demands stray the skilled employees from the main tasks of generating ingenious automation eventually.

In contrast, a vendor-provided data quality solution such as by BiG EVAL requires less cost and time overheads for companies.

  • Extra Costs.

As companies become more data-driven, they consume more data sources. Additionally, as the number of data sources grows, they need more manual reconciliation which leads to an exponential increase in costs. Thus, automating this task as a role-based reconciliation controls the costs.

  • Duplication Issues.

Data quality management (DQM) centralizes and organizes data sources to rationalize the process based on the needs of departments and organizations. Without this DQM step, departments would replicate the work due to the lack of coherence and a lack of common data grounds. Duplications are the biggest cause of resource and time wastage in organizations.

  • Loss in Revenue.

So far, it is clear that data needs to be managed more effectively. The bigger problem in organizations regarding this is to downstream implications of low-quality data. Decisions based on low-quality data can be disastrous and ineffective. These causes organizations’ budget going in the wrong direction altogether.

  • Delays in deployment.

Unfortunately, new system deployment delays are very common issues caused by low-quality data. These delays put havoc on productivity, deadlines, and annual plans. They eventually impact service levels if the delay is involving around customer’s stakes. This can lead to hurting the company’s reputation and competitiveness in the market. This is a bigger issue in public companies as investors become less interested and stock prices drop considerably.

Take the example of Target Super Market, when they failed the expansion plan to Canada due to a problem in their ERP product data. This put out items at 30% increased rates, putting out wrong information on company websites and flyers thus Target lost its reputation in Canada.

Overall, the downstream effects of poor data are huge and can be disastrous for a company if not taken care of. The good news is,
data quality solutions exist, such as:

  • Frequent  data quality assessments of data-set.
  • Using an alternate source of data.
  • Using data resolution techniques such as using mean/mode/median instead of wrong data.
  • Using estimations.

Data quality management is supported by dedicated organizations that work on data quality improvement such as BiG EVAL, which generates technological tools and support to overcome this issue.

The automation solution by BiG EVAL DQM supports its clients in all tasks regarding data quality management. Our customers become capable of automatically applying ongoing quality checks onto their enterprise data, it provides them a quality metric, and supports their quality problem-solving processes.

 

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