The use of big data and advanced analytics is rising, but improper use of big data analytics will represent 50% of business ethics violations by 2018.
The forecast was unveiled at Gartner’s Business Intelligence & Analytics Summit 2015 in Munich, where analytics are discussing the best practices for big data analytics.
Alexander Linden, research director at Gartner, said: "Although big data and advanced analytics projects risk many of the same pitfalls as traditional projects, in most cases, these risks are accentuated due to the volume and variety of data, or the sophistication of advanced analytics capabilities.
"Most pitfalls will not result in an obvious technical or analytic failure. Rather they will result in a failure to deliver business value."
Failure to properly understand and mitigate the risks can lead companies to loss of reputation, limitations in business operations, losing out to competitors, inefficient or wasted use of resources, and even legal sanctions.
In response, Garnet has unveiled a set of key best practices to help analytics leaders build their strategies for the future.
First, the research firm said that businesses need to link analytics to business outcomes through benefits mapping.
It said that by linking analytic outputs to traceable outcomes using a formal benefits-management and mapping process can help the analytics team navigate the complexities of the business environment, and keep analytic efforts both relevant and justifiable.
This is followed by a warning to invest in advanced analytics with caution.
Balancing analytic insights with the ability of the organisation to make use of the analysis is another step to future-proof analytics businesses.
Gartner’s fourth best practice is for companies to prioritise incremental improvements over business transformation.
The company said businesses can benefit more from using big data and advanced analytics to improve existing analyses, or to incrementally update and extend an existing business process.
Lastly, the firm said that companies can consider alternative approaches to reach the same goal, highlighting that statistical modelling, data mining and machine learning algorithms all provide means of testing ideas and refining solution propositions.
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