Today, every industry uses Big Data as a powerful tool to serve various different purposes.
It is very crucial for them to grasp the possibility that if deployed carelessly, the negative impact of its real possibility. This series shall discuss the negative impacts, major threats, and risks and how it can affect a business’s performance.
If there are pros to big data, there are cons too. The negative impact of big data is subtly hidden in the trail of digital traces we unknowingly leave. Anything that is unmonitored leaves an opportunity for exploitation. The same is the case with big data. While big data is not bad in itself, it can have undesirable effects if the people involved in its use have malicious intentions. It is time that individuals and organizations become aware of the value personal data and information holds and adopt a more transparent approach.
Bad Data or Poor Data means false information or inaccurate data that can be created by duplication of data, wrong formatting or by an uncomplicated error of typos. Bad Data can turn into an expensive mistake for your business. It can be a difficult problem to deal with. A recent survey shows that bad data costs companies an average of $9.7 million per year. Bad data and the problems that come with it are here to stay for now, and the only thing that we can do is deal with it.
Technology evolves at an alarming rate, so much so it seems almost pointless to make a push for the leading edge. By the time you adopt new technologies and systems into your business environment, there’s something new and more efficient coming along right behind it. Applications or devices receive an official update, and you just have to take the time to configure. It’s easy to get caught up in either scenario, resulting in the regular use of outdated, inefficient technologies. But what you may not know is that this issue can have a profound impact on your business, productivity and workflow.
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Introduction Access to and control of data is one of the biggest challenges faced by data analysts and data scientists. Creative, persistent analysts find ways to get access to at least some of this data but doing that efficiently in a way that is also standardized and centralized for everyone on the team is difficult.
Introduction: Prediction is a tricky business. You have to step outside of your comfort zone, your fainted vision of the world and see it thorough across all possible dimensions. In this series, we will discuss the future of “AI”, applications that are yet unexplored.
Introduction: Humans are wired to make tough decisions bringing all the context and principles to bear. Similarly, can devices apply the available information to make the right judgment calls? In this series, we shall discuss some ethical dilemmas faced by emerging technologies.
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