Embedding data quality checks in your batch workflows to improve stability, quality and meeting SLAs

DvSum-Embedding data quality checks in your batch workflows to improve stability, quality and meeting SLAs

Improve the Data Portal of an Organization by Entrenching Data Quality Checks with Batch Workflow

A Workflow is a pack of multiple duties connected with the start-task link and activates the correct sequence to execute a process. While implementing a workflow, it ignites a starting task and parallels connected tasks in the workflow. One can create a workflow automatically and manually using Workflow Designing tool the offered by the Workflow Manager.

Embedding Data quality into batch workflow processes is a novel and innovative idea to manage the data workflow in a disciplined manner. But before that, one has to know about the traits of data quality checks also.

Data should be seen as a tactical corporate tool, and data quality needs to be considered a strategic corporate responsibility. The corporate data cosmos is constituted by a wide range of databases that are coupled with countless real-time and batch data feeds. Data is an ever relentless movement, and changeover, the core of any rock-hard and flourishing business is high-quality data services which will, in turn, make for resourceful and ideal business success.

However, the mammoth challenge here is that data quality, only on its own cannot develop. As a matter of fact, most IT processes have a damaging influence on the quality of data. Hence, if nothing is prepared, the quality of data will continue to drop until the verge that data will be seen as an encumbrance. It is also relevant to call attention to that data quality is not an achievement that can easily be attained and after that, you brand the undertaking complete and stack admirations on yourself for infinity. It is of utmost importance to develop the habit of monitoring data quality and hence, we are giving away certain data quality checks to implement in your batch workflows to gain stability and quality in data management.

DvSum-The most Significant Data Quality Checks to be attached to a Batch Workflow

Data quality checks are becoming progressively prevalent, and competent analysts are being sought after. Data-motivated decisions are designated to be accurate. A good grasp of data modeling and data quality checks into the batch workflow can assist QA forecasters with the pertinent information to draft an ideal testing strategy.

Following are some of the most practiced Data Quality Checks which can be implemented to improve your request handling processes in a Batch workflow. These are the core dimensions of the data quality standards.

1. Confirm Field Data Type and Length

Always validate the source and the target data types and character lengths. IF there is any disparity of the data type and length in the target table, it can be easily detected by these checks. It is a decent practice to authenticate data type and length homogeneousness between the source and target tables.

2. Verify Not Null Fields

Null fields in a batch workflow data is a waste of an empty carrier. Substantiatenull values in a column which features a not null limitation. When a source is equipped with this constraint, it should not display all values. This helps in filling all the data carriers with useful values and avoid useless spaces in a database.

3. Quality Checks for Duplicate Records

If your pipeline data depicts duplicated records, then your business related strategies will be mistaken and erratic. Poor data that is spoiled with incorrect and duplicate records will certainly not aid stakeholders to properly predict business targets. A Quality Assurance team should develop data quality checks to make sure that source data files are confirmed to spot duplicate records and any mistake in data. In the same way, you can assimilate automated data testing by adopting DvSum to authenticate all data rather than just checking an example or subset.

4. Avoid Orphan Records

A condition where there are child records with no corresponding parent records, then such records are labeled as Orphan Records. The business should invest in the framework of Strategic rapport rules amongst parent and child tables. Sometimes mysterious data justification is indispensable to make sure that the correct changes are made as planned for value is put into code. For example, suppose during the conversion process, if there is a logic to encrypt the value “Male” as “M”, consequently, the Quality Assurance team should double-check the gender column to maintain that it does not display a different encoded value.

5. Check  for Missing Values

Every dataset formed by the company database should have according to values filled in it properly.  Empty datasets create a huge mess while referring to the data. In data quality, unfilled or incomplete data is considered as a formidable sin. The QA team should ensure no datasets are unfilled or incomplete.

6. Data Completeness

Datasets in organizations may be an array of large data values. Data completeness refers to the adequacy of data. It decides whether there is enough information to come to a conclusion about the data. It also points towards the data authentication by verifying that enough individuals returned to it to confirm representativeness.

7. Quality Checks for Data Consistency

No matter what amount of data is garnered by the database of an organization, the reliability of that data can be questionable. Data consistency can be termed as a data quality check for the extent to which data is composed using the same technique and procedures by individuals doing the collections and in all locations over time. Consistency of data answers the questions related to documentation in collection methods, policies followed during the direction of data collection procedure, and training of data collection methods.

8. Data Accuracy

By following all the above-mentioned quality checks, can your data manager assure you of accurate and useful data?  The answer is NO. Data accuracy is a chartered data quality check in the syllabus. Although the above considerations hold your data to be optimum, they cannot guarantee a truthful data sheet. Data accuracy examines whether the data is at liberty from substantial errors. Accuracy also looks for the validity of the numbers coming out of that data to be useful or not.

9. Data Verifiability

As mentioned earlier, data quality checks enable you to improve your data handling methods; but only when the said data is able for an inquiry. Data verifiability empowers the data to be answerable to certain quality-related problems. It reflects the amount to which data analyzers have traditions to verify that data was poised and testified according to the predefined procedures.

10. Data Timeliness

Data quality encompasses infinite dimensions, but when it comes to enlisting core dimensions; the relevance of data is inevitable. It happens a lot that organizations boast of containing the entire records of all the previous clients and assignment completion documents, but is it relevant anymore? In pursuit of improving data quality, incompetent analysts sometimes invest a lot of resources and fortune in unwanted pieces of information. It is very important that the stored data in a data pool is relevant and efficient to giveaway juices in terms of numbers. Data timeliness refers the notch to which data embodies the genuineness from the requisite interval.

Looking at Data Quality as a Service

Data Quality Checks may be involved in data sketching, corroboration, scrubbing and stewardship services. Earlier, these were done in active systems like Order Management, CRM, Billing, etc., whenever an error befalls during new programs presentation or system upgrades, due to indecorous testing of the program logic or due to errors and unanticipated exceptions. It also used to occur while porting old application version of the data not assembling with the more stringent metadata necessities of the new version. Unifications of old-fashioned systems with the new system also invites data quality issues. This is also valid for mergers of organizations that in due course, result in system integration concerns.

When Data Warehouses were primarily intended to distinct transaction assignment from reporting and analysis assignments, data quality took the role of the cornerstone in pre-processing the data before the representation level alterations could be done to load them in Data Warehouse from transaction systems. Data Warehousing is now implemented with data quality checks to improve the stability and integrity of batch workflow.

However, now with data quality checks, we suggested here, these issues are solvable. DvSum offers a large variety of data quality services to ensure correct and accurate data for batch workflows. This improves your Service Level Agreements (SLAs) with the client and benefits your overall business strategies. No matter whether you operate in a MySQL database, Excel, or Oracle framework, DvSum data quality checks can plug-in and make your data warehouse a place ideal for making business decisions. We offer a bouquet of data handling services and tools in order to improve your target strategies and your client satisfaction ratio.

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About DvSum

DvSum’s cloud platform enables a disruptive approach to not only validate and align your data, but also actually fix it. The patented technology scans, checks and compares multiple data sources simultaneously - without moving or consolidating the data. DvSum's engine leverages machine learning and artificial intelligence to auto-discover and solve issues proactively. Along with the socially driven rules library, companies are able to connect and be fixing live data within hours.

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