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How To Maintain A High-Quality Big Data Company Posted on Jul 17 - 2017

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These days, accessing big data has become easier than ever due to reduced costs in storage and processing. Companies are collecting data anywhere they can from web traffic, mobile apps and IoT devices, along with many other third-party sources. We can categorize all processes in any big data company into the following:

• Collection

• Unified structure

• Processing

• Delivery

• Pruning

To guarantee overall quality, we need to focus on data -- the quality within each of these individual categories. Each key performance indicator (KPI) should be defined to measure the quality, enhancement over time and ways in which to improve a specific set of data. KPIs should also be broken down by different labels. Labels could be a date, department, version, data source, third-party partner or any other meaningful category to your business use case.

In the first part of this series, we will focus on collection and unified structure.

Collection

First and the foremost, the main part of any big data company is how to collect data from different sources. You cannot guarantee your company’s quality if you cannot make the collection of data as smooth as possible. Any interruption in the collection will affect your downstream processes. Some data also might need to extend with other sources, which is a great idea to include such processes during collection.

KPIs that can be defined in the collection phase can be tied to many factors, including the number of records received, missing fields, the uniqueness of values, stats on each value and the number of appearances of that value. View More

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