Maintaining Optimum Data Quality

Posted by Iksula on May 6, 2014 9:48:37 PM

Maintaining Optimum Data QualityCost plays a major role in the success and failure of any company. However, cost is not the only contributing factor that helps in the growth of an organisation. Today, there is lot of competition in the market and companies are striving to make their niche to stay on the forefront of this competition. For this, many companies have entered outsourcing markets with multiple vendors. This enables organisations to set-up seamless delivery processes and a higher ROI. Companies follow a full spectrum of evaluations to identify gaps in their business process. Improper understanding of these shortcomings may influence business decisions which in turn might negatively affect the company’s performance

Data quality and data integrity-related issues are common in many organisations. The intensity of quality issues depends upon the severity and volume of data issues. Let’s take a look at different scenarios that drive data errors and the ways that can help to minimize them for improving data quality.

Key contributors towards high quality

Any organisation needs to follow three things to ensure high quality output. These three contributors include resources, skill set and commitment.

1. Resources
Most organisations under-invest in human assets. This causes them to face lower ROI in outsourced engagements. A high quality delivery model can be achieved with a people to technology ratio of 8:1. With every fluctuation from this ratio, the output quality is affected. As such, the company should consider hiring more staff. This helps meet the needs of complex data and the ever-evolving nature of the business.

2. Skill set
Sometimes even a sufficient budget is not enough to hire the right set of experience and skills for good data maneuverability. The biggest challenge every organisation faces is getting specialised skills to predict, detect and fix the causes of inaccurate data. Every company must recognize the importance of data quality. It is a major responsibility of the IT department to ensure that the required data quality levels are met. Many organisations fail to understand the role of their technologists in processing data quality. It has also been observed that even well-established IT teams lack the expertise to identify the issue, due to which they struggle to achieve good data quality.

3. Commitment
In any organisation, the best data handlers possess the ability and skills to close the gaps in the process of data quality. As such, it is necessary for the organisation’s team performance plan to focus on data quality rather than other activities with higher perceived values. It is necessary that the resources give high priority to data quality over insights and automation. It is critical for senior management to develop a coordinated infrastructure. It is also essential to evaluate how to create and implement a good data quality strategy that is cost-effective and easy to execute.

Building a robust process

In order to ensure high quality data, on-going activities for process and quality improvement need to be performed after the initial implementation of a full-fledged process. These on-going activities are aimed towards ensuring the required level of data quality. It is preferable to engage a reputed third party such as Iksula that can provide the essential skills, focus and rigour to build a strong foundation for quality data. There are three general areas that can be outsourced to such partners in order to drive data quality and value.

1. On-going process audits

Nowadays, new pages, site sections, features and content are used to keep the website updated, and to popularise it. For this, it is essential to engage with an experienced third party and establish the discipline of on-going audits. This is possible with the help of automated site scanning, debugging tools, and customised manual checks. With these tools, the company can identify missing data, uncover errors and create a close-to-real-time control of the entire site.

2. New implementation QA

On-going audits are useful to discover most of the problems associated with existing product pages of the website. However, it is recommended to execute a QA process with appropriate site mechanism before the pages go live. Most of the sophisticated organisations use a web development environment for their websites to test and validate web content integrity.

3. Troubleshooting

There is a possibility that the data handling mechanism may still produce an unexpected output even after striving to ensure accurate pre-go live QA. There is a possibility of questionable data being uncovered at regular intervals during site analytics dashboards. The steps to be followed to correct this data include:

  •  For a systematic drill down on a specific problem, segment or perform filter analysis.
  •  Use trend analysis to identify when the change occurred.
  • Use a debugging tool to check the data that is passed to the staging server.

With budget constraints, it can be a daunting task for companies to find out ways to achieve good data quality. However, most companies have realised the importance of data quality and operations. They now focus on specific directives that can help them to structure the organisation in a cost-effective manner, within the stipulated budget. The three core directives to help them do just that are:

  •  In order to deliver valuable, business changing insights, core teams need to focus on high value.
  • Technology teams need to put emphasis on the development and implementation of high value applications and systems.
  • To deliver effective functionality and insight, firms need to have a motivated and productive workforce.

 

For more information, download the White Paper on Data Quality here.

Tags: Data quality, Generic, optimum data quality

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