Standards
Background: The promise of Big Data Analytics has been in news as early as 2010 and with McKinsey report on Data in 2011, the companies
across the world started embracing the Data Analytics in a rapid pace.
However, from early 2014 onwards, there have been numerous reports such as Gartner, Forbes, Information Week, Oreilly, McKinsey on failures
of big data analytics projects and initiatives across the world from organizations. (reference links given below)
Big Data Analytics is way different than implementing bespoke IT projects which are far more certain and deterministic in terms of outcome with least of experiments and research needed. The measure of accuracy (and errors) is also different in data analytics with concept of false-positives and specificity/sensitivity.
This proposed standard addresses the above constraints by developing a process management framework for Big Data Analytics which determines how, when and to what extent the components of data, architecture and technology process should be implemented with respect to stakeholder`s needs to minimize hurdles in data analytics projects at an organization level. This is to ensure long term sustenance and maximize business value.
The first Working Draft of the standard was presented in Japan plenary (2019). The work for next stage which is Committee Draft ( CD) is in progress.
References:
- https://www.gartner.com/smarterwithgartner/how-to-prevent-big-data-analytics-failures/
- https://www.forbes.com/sites/metabrown/2016/02/28/how-to-avoid-data-analytics-failure/#48d06923155c
- https://www.informationweek.com/big-data/big-data-analytics/8-reasons-big-data-projects-fail/a/d-id/1297842?
- https://www.oreilly.com/ideas/recognizing-and-rescuing-a-failing-big-data-project
- http://analytics-magazine.org/the-data-economy-why-do-so-many-analytics-projects-fail/
- https://www.mckinsey.com/business-functions/digital-mckinsey/our-insights/making-data-analytics-work-for-you-instead-of-the-other-way-around
Big Data, Data Analytics are high impact areas that are rapidly growing and getting adopted (either done internally or offered as a service) widely to gain a competitive advantage in various fields.
The various aspects of Data Analytics, its deployment on the ground, the procedures, life cycle processes are understood by experts in their own ways. A consensus cannot be arrived at without the aid of a common standard. Currently, most of the work and consensus gets built on case studies at best and mostly on success stories that talk about results with limited information on methodology.
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A standard on data analytics needs to have the following information:
- Key processes which are required for arriving at the desired outcomesof a data analytics project/ engagement
- The relationship of the key processes which are required in the lifecycle of an analytics engagement
- A clear description of the process outcomes, the standard practices that are followed, supporting work products
- Capability indicators that indicate how well a process has been performed or an organization performs
- Maturity scale to indicate repeatability in terms of performance of a group of such capability indicators
Data Science Foundation has conceptualized, socialized and is proposing an analytics maturity framework in the International Standards Organisation (ISO) through the Bureau of Indian Standards (BIS), to establish standards in this regard. The preliminary work has been shared with members of more than 7 countries in WG9 (working group of Big Data Analytics under ISO) meeting in Beijing and Washington DC in 2015 and a detailed discussion was held in February 2018 in New Delhi among the international members. The resolution has been passed in Dublin meeting and will go for final balloting The response has been very positive and work is progressing with International participation. International Standards proposition have been approved by more than 17 countries post SC 42 Artificial Intelligence plenary meeting at Dublin in 2019.
