Think of a four-letter word that is at the core of a multi-billion industry. It is worth spending a fortune trying to protect it and has the potential of striking wars amongst different groups at global platforms.

Those who are in possession of this in its uncontaminated form are endowed with true wealth and are going to be the last ones standing against the stringy times in the industry. Two consonants, one vowel; the present era is all about “data.”

Having highlighted the importance of data as the operational essence of a modern corporate world with Pantone shades of drama, the discussion on data quality and its management is compulsory. Before we proceed to the best management practices let us all that one needs to know about data quality.

Data quality and its management

Present day business scenario is highly data-dependent. The field of data acquisition and analytics emerges at a pace like never before. Quality statistics help businesses build insights into their product design and delivery techniques. In addition, they prepare a target customer base for direct marketing purposes. Data quality management brings the virtue of continuous analysis and improvement throughout the flow channels. With a quality database of pertinent facts and feedbacks, the customers and their needs are realized during earlier stages. Hence, there are low chances for the companies to make mistakes in their service offerings. Industry vendors identify the far-reaching wondrous impact of the reliable set of qualified variables on the overall process outcomes. This motivates them to increase the resources channelized to maintain previous benchmarks as well as create new ones in terms of data quality.

The three cornerstone practices

1) Allow clients to set quality parameters

When catering to multiple business environments, defining quality parameters could be a complex task. One might fail to include each of the factors that are considered necessary by the company. To ensure that both the data providers and users are on the same page, it is suggested that companies get to define the set of parameters used to judge the quality of data being used. In addition to the basic sorting factors, the third-party data providers get insight on prioritizing the parameters when the business purpose is conceptualized at a deeper level. This helps target the exact profiles for marketing and sales purposes.

2) Invite market insiders on your team

Mark the words. The industry perspective of the recruited data quality maintenance executives matters above all. The hiring process of the data generation and nurturing companies must test the advanced interpretation skills for the candidates. As a matter of practice, the leading operators must hold interactive sessions that educate their executives on the latest developments within the industry that reflect directly on their client requirements. Though the best practice is to get a business insider on-board, for no one else would have a better insight on the nuances of trivial as well as complex factors.

3) Employ a robust governing system

The highest advantage comes from a standard step-wise data governing system. The system accommodates both human and technical resources that actively locate any flaw or inconsistency at their root. Every node in the process is continuously monitored and effectively trimmed to minimize the chances of irregularities stepping up along the data flow. With relevance to business marketing activities, this bears the potential to breach the trust between the service providers and their clients in the worst case scenario. However, with an effective data governance team in place, remarkable time and efforts are saved during live campaigns.

These three basics go a long way in maintaining the relevance, consistency, and quality of the data built for your clientele and their portfolio offerings. Comment with your valued opinion on which other practices work well for B2B service providers.


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