Communications operators are among the most data-rich of all businesses, with opportunities to capture and store a vast array of potentially valuable customer data.
Yet to date they have not capitalised on their “big data” assets as fully as they could. The underlying reason lies in the difficulty of turning an operator’s diverse mass of data into useful insight – a task that depends critically on having data that’s trustworthy, timely, and of consistently high quality. To achieve these attributes, operators need to build a data quality competency – a dedicated, specialised organisation responsible for maintaining, safeguarding and ensuring the quality of data across and beyond the enterprise. In a world where business decisions are increasingly dependent on data, building such a competency is critical. In this article, we examine the practical steps that operators can take to get their data fi t for purpose.
Gartner estimates that more than 40% of business initiatives fail to meet their objectives due to poor data quality.
5 challenges around deriving true analytics-driven insights from technology and data:
- Data as an afterthought
- Messy data
- Data skills gap
- Technology gap in a world of streaming data
- A failure to exploit cloud and open-source
PwC has defined three specific metrics through which data quality can be measured accurately and consistently
These are:
- Availability – is the data readily accessible? Do the data sources have high up time?
- Completeness – when the data is accessed, does it arrive in the correct formats in terms of datasets, structures and fields? Are the “rows” all present and are the “columns” all present and well formatted?
- Timeliness – how quickly does the data become available to be accessed, processed and analysed? What is the lag time between event creation and when the event is available for analysis?
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