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Big Data – improper use risk, says Gartner

Oct 15 • News, Technology • 2640 Views • No Comments on Big Data – improper use risk, says Gartner

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 By 2018, half of business ethics violations will occur through improper use of Big Data Analytics, says Gartner.

Analytics projects that utilize big data or advanced analytics are increasingly popular but present a heightened risk of failure, according to Gartner, Inc. which says analytics leaders can improve the likelihood of success by following five best practices.

“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,” said Alexander Linden, research director at Gartner who is  presenting on advanced analytics at Gartner’s  Business Intelligence & Analytics Summit 2015, this week in Munich, Germany. “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 have a number of unintended and highly impactful consequences. Those can include loss of reputation, limitations in business operations, losing out to competitors, inefficient or wasted use of resources, and even legal sanctions.

Gartner also predicts that, by 2018, 50% of business ethics violations will occur through improper use of big data analytics. Following key best practices will help analytics leaders to improve the likelihood of success, and they include:

  1. Linking Analytics to Business Outcomes through Benefits Mapping
    Analytics must enable a business decision maker to take action, and that action should have a measurable effect — whether the effect is directly or indirectly achieved. 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
  2. Investing in advanced analytics with caution
    Many organizations believe that Big Data automatically requires advanced analytics. However, the data-crunching power required to manage the big data characteristics of volume, velocity and variety does not inherently require any more sophisticated algorithmic processing. It is the complexity of the analytical question to be addressed that drives the need for advanced analytic tools, and in many cases desired outcomes can be achieved without resorting to more sophisticated analysis.
  3. Balancing analytic insight with the ability of the organisation to make use of the analysis
    Because analytics can only be beneficial in organizations that are willing to embrace change, it makes sense to limit investment in analytics to a level that matches the organization’s ability to use the resulting insights. Analytics may not be the most suitable approach if  pertinent data is absent, when there are high levels of ambiguity, or where there are entrenched opposing points of view.In these cases, scenario planning, options-based strategies, and critical thinking should also be incorporated into analytical approaches to better support the organization’s ability to take action.
  4. Prioritizing incremental improvements over business transformation
    Using big data and advanced analytics to improve existing analyses, or to incrementally update and extend an existing business process, is easier than using them to deliver business transformation, because there are fewer dependencies to overcome to ensure success. Care should be taken to validate the level of overall change required. In some cases, deep reform of the business strategy may still be necessary — for instance, when a new disruptive vendor enters a market, when technology innovation changes the business model, or when an organization has become dysfunctional.
  5. Considering alternative approaches to reaching the same goal
    Few objectives can only be achieved in one way. Statistical modelling, data mining and machine learning algorithms all provide means of testing ideas and refining solution propositions. Big data and advanced analytics help validate proposed hypotheses and open an even wider range of potential approaches to addressing corporate priorities. Not all problems even require a fully engineered analytical solution. Investment may be better targeted on human factors, re-education or reframing the problem.

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