Do you want to make Data Science an important part of your advanced data quality program? Well, data and its quality in particular is one of the most pivotal parts of business productivity. Bad data has a huge impact on making decisions, poor data connectivity and can have a high cost to fix resulting issues.

It is extremely essential to understand the importance of implementing advanced practices of data quality, which utilize the metrics of data quality, in order to identify areas that require serious improvement. it also ensures quality data integrity, especially in large environments, where small wins are also capable of adding up to large savings.

In order to offer the most effective scenario to our clients, we make use of the following key elements:

  • Identifying the Key Problem
  • Pinpoint the Effects Associated with the Problem
  • Building a Model for Data Reprocessing
  • Data Reintegration
  • Updating All the Reports

To give a clear illustration of this point, here is an example: one of our customers needed to continuously download and evaluate large quantities of data. The data was created and accumulated through different customer activities from a wide range of broad spectrum services from on-site equipment, which was offered by the company to their customers.

As a result of the quantity and complexity of the data processed on a daily basis, processing of data resulted in missed service level agreements (SLAs), which in turn resulted in their inability to support and fully understand their clients’ needs. It was extremely critical for the customer to build effective and efficient data management and a consistent reporting solution for all the various sources of data.

This, therefore, gave us an opportunity to extensively address the aspects of various individual items. For example, data modelling that includes the following:

– The prediction of database capacity

Predictive analysis mostly utilizes the historical data and statistical techniques to facilitate predictions regarding future capacity. The most common methods utilized by predictive modeling are known as linear regression. Regrettably, the application of regression has proved to be a challenging task because of the associated behavior changes; i.e., System administrators are likely led to change of retention policies or simply delete data. This may, therefore, result in poor predictions. Significantly, the most accurate models were secured through the finding of an optimal subset of clean data for every database and applying the linear regression to a subsection of the data.

-The automated Anomaly Detection

Since huge data requires the most effective anomaly detection, proposed enhancements were affected that facilitate real-time identification of the anomaly. By use of R and PostgreSQL, we have designed an alarm system, in order to closely monitor jobs from the schedule, that allow the users to be able to react decisively to various issues. The alarm system makes use of the storage, Shell and Model script, in order to perform various checks and later raise an alarm when an anomaly is detected. Basically, these alarms are meant to monitor the lower and upper thresholds for the kick-start and duration of modules in every weekday.

-Uniform Dashboards

Through the use of Tableau to facilitate the reporting, we have been able to build uniform dashboards for all the systems, which make it easier to understand the correlations between various metrics. The following could be discovered from the report:

  • KPLs Metrics getting into unacceptable ranges
  • Unexpected changes and trends
  • Variance on the data metrics
  • A data sliced by the host, geographical tags, cluster etc.

With our implemented quality program, our clients can successfully save substantial costs during the processing time. There are factors like complexity of the environment, data volume, and the general amount of effort required in the processing of the data that are vital for the processing time. So, data quality, besides being vital for reporting and process efficiency, can be an incentive for impressive cost optimization.

Author: CoreValue Services is a Global Software and Technology Services company, providing Cloud based implementation services, Data Science and Machine Learning powered solutions, and Web & Mobile application design and development services to industries such as Finance, Pharmatech, and Healthcare. Among our Clients are investors, funded startups, mid-sized enterprises and Fortune 50 through 500 companies.


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