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Big Data - What Can Go Wrong? Featured

Big Data - What Can Go Wrong? Sigmund

Tech experts are heaping all types of praises on big data, some even saying it is perhaps the next big thing after the advent of the internet.  Although stating that big data is the greatest invention after the internet is arguable, it is cannot be disputed that it has changed many things in modern organizations. It allows employees and business owners to sift through data intelligently, use data to learn through machine learning and employ analytics tools to inform decision-making processes. While it is a source of success to many organizations, people must understand that this technology is a double-edged sword that can help or wreak havoc if not used properly. Building the right data culture is what can drive a positive change in an organization. Therefore understanding what may go wrong when using big data is critical. Here are areas that may go wrong in big data:

  • Phantom data

Overwhelming data is real. Massive amounts of data that sit in different locations and in diverse forms, most of which organizations are not aware of, make things complicated for businesses. This data is confusing because no one knows their status, whether clean or not, whether it is accurate, usable or is manageable. Such amount of data may have been entered manually by human employees and therefore has many human errors. During further processing of such data, errors may be amplified, leading to adverse and unprecedented outcomes. Big data operates in the principle of GIGO, i.e Garbage-In-Garbage-Out and users must be careful on the data they feed to the analytics tools.

  • Statistical dependence

There are too many statistical models, some of which are too complex and overwhelming. These models look at the past behavior and use the past data to predict the future. These models, while they may be useful, can complicate things as some might not be true as expected. This can skew output leading to bad decisions. As such, data scientists and analysts are required to look beyond numbers and use their experience in data science to ensure that the outcomes are useful. This is always a difficult thing that may slow decision making.

  • Paralysis of analysis

Big data initiatives are still new to many organizations. This means that policies, laws and regulations that govern this technology are not known well. As such, many businesses jump to big data initiatives without the full knowledge of what they are getting into. This leads to stalled projects and wastage of resources that would have helped an organization significantly if it had been used well. As such, it is useful to start big data initiatives with small manageable steps.

  • Trading security with “innovation”

Security is one of the most critical aspects of big data initiatives. However, when working with big data, this is often the first area that is overlooked as many big data projects fail to mitigate security concerns associated with it. For a solution to be found, there is need for a multifaceted approach for securing data and the organization at large. This will lead to proper understanding of data being processed, enabling control on the privileged users. Organizations should ensure that big data security is done appropriately.

  • Investing on complex tools

Businesses that have smaller datasets often get into big data solutions, some of which may strain their budgets significantly. As such, if these tools are to work as required, organizations should budget for what they can and avoid what may strain their budgets. Businesses must understand that not every issue requires the use of sophisticated big data tools. In fact, some traditional approaches can solve some of these problems and therefore unwanted expenses can be avoided.

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Scott Koegler

Scott Koegler is Executive Editor for Big Data & Analytics Tech Brief

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