Differences in data preparation
To standardize and purify data for new use cases, a number of data preparation approaches are frequently required. This is a manual, monotonous, and time-consuming job. When data prep teams working with data from separate silos calculate similar sounding data items in different ways, data quality issues might develop.
For instance, one team may compute overall customer revenues by deducting returns from sales, but another team may calculate it solely on the basis of sales. As a result, metrics in separate data pipelines are inaccurate.
Bringing diverse data taxonomies together
Business units inside a corporation or merged companies may have developed and fine-tuned their respective data taxonomies and semantics to represent how they operate. Private equity investments, for instance, can hasten the speed of acquisitions and mergers, resulting in the consolidation of several businesses into a single giant corporation.
Each acquired company's CRM, marketing automation, marketing content management, customer database, and lead qualifying technique data was often unique. Integrating various technologies into a single data structure in order to manage unified campaigns poses significant hurdles in terms of big data quality.
Consistency is important
Cleaning, verifying, and normalizing data can all pose problems with big data quality. For instance, one telephone firm developed models that connected network failure data, outage reports, and consumer complaints to see if problems could be traced to a specific place. However, some of the addresses were inconsistent, with "456 Second Street" appearing in one system and "456 2ND STREET WEST" appearing in another.
There isn't a plan in place to govern data
Lack of data governance and communication can result in a slew of quality difficulties. A solid data governance program that defines, manages, and communicates data policies, guidelines, and standards for successful data utilization and to build data literacy should be backed up by a big data quality plan. The principles and specifics of the data are understood and respected by the data community once it has been isolated from its source contexts.
Obtaining the ideal equilibrium
There seems to be a natural conflict between the desire to acquire all accessible data and the need to ensure that the data collected is of the greatest quality.
It's also crucial to comprehend the goal of gathering certain data, the techniques utilized to gather big data, and the organization's intended downstream analytics applications. Proprietary practices can often develop that are subject to mistakes, unstable, and unrepeatable.
Excessive data collection
Data management teams can become preoccupied on gathering ever-increasing amounts of data. However, more isn't always the best strategy. The more collected data, the higher the danger of data inaccuracies.
Before training the data model, irrelevant or faulty data must be removed, however even cleaning approaches can have a negative impact on the outcome.
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