Research firm Gartner has disclosed that improper use of big data analytics can have numerous unintended and high impactful consequences. The study made by Gartner says that while big data projects are increasingly popular, they present a heightened risk of failure as well.
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,” explains Alexander Linden, research director at Gartner.
Linden feels that most pitfalls will not result in an obvious technical or analytics failure; rather they will result in a failure to deliver business value. He added that failure to properly understand and mitigate the risks can have undesirable results, including loss of reputation, limitations in business operations, losing out to competitors, inefficient or wasted use of resources and even legal sanctions.
Gartner predicts that, by 2018 i.e. in next 3 years, 50% of business ethics violations will occur through improper use of big data analytics. However, Linden says analytics leaders can improve the likelihood of success by following below said best practices.
Linking analytics to business outcomes- 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. By doing so, the business head can keep a track of outcomes using tools such as management and mapping process, which helps to navigate the complexities of the business environment, and keep analytic efforts both relevant and justifiable.
Investing in big data analytics with a caution- There is a myth among organizations that big data automatically requires advanced analytics. However, the data-crunching power required managing the big data characteristics of volume, velocity and variety does not inherently require any more sophisticated algorithmic processing. Generally, it is the complexity of the analysis which drives the need for advanced analytic tools.
How to balance the analytic insight with the organization’s intention to make use of analytics- All those organizations which are ready to embrace the change can enjoy the benefits of analytics. Actually, it makes sense to limit investment in analytics to a level that matches the organizations ability to use the resulting insights.
Note- Analytics indulgence may not suitable if pertinent data is absent; when there are high levels of ambiguity; where there are entrenched opposing points of view and in highly innovative or novel scenarios. In these cases, scenario planning, option based strategies, and critical thinking should be incorporated into analytics approaches to better support the organization’s ability to take action.
Prioritize 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, a business transformation prioritization can be achieved.
Alternative approach can be considered to reach the same goal- Statistical modeling; data mining and machine learning algorithms all provide means of testing ideas and refining solution propositions.
Alexander Linden feels that big data and advanced analytics help validate proposed hypotheses and open an even wider range of potential approaches to addressing corporate priorities. He added that not all problems even require a fully engineered analytical solution. Investment may be better targeted on human factors, re-education or reframing the problem.
Dynamic Network Factory (DNF) makes big data analytics usage professionally simple and highly efficient by preventing misuse of analytics in an enterprise environment.
The company believes that organizations won’t need to fund a department of data scientists and Ph.D.s to get powerful, insightful big data access. DNF with the help of its analytics partners offers the following capabilities for Big Data:
- All relevant data: Access, integration, and cleaning of sources of data as varied as Hadoop (including Cloudera&MapR) or NoSQL (MongoDB) and Excel or Teradata
- Fastest Platform to build analytics: Sophisticated but accessible predictive and spatial tools, combined in a simple, workflow design environment
- Simple sharing of Big Data analytics: Single click sharing of analytic applications that can be used by any decision maker
To know more call 510 265 1122 or register yourself in the following web page to let experts contact you.