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Data science and business intelligence can be emplaced to solve a myriad of business challenges and if properly run can be substantially beneficial. All too often, however, organizations have fallen victim to some of the following common mistakes, which could have detrimental consequences.

Utilizing Data to Validate Old Affirmations, Not Discover New Insights

The greatest thing about data extraction and analysis, at its most fundamental core, is that it can be used to gain insights and discover useful and meaningful information that can be put to use in a variety of beneficial ways – from boosting sales and improving efficiencies to forecasting market patterns and learning more about target groups. Unfortunately, when data results are used to confirm previously held biases rather than uncovering new findings, the full potential of what data can be used to achieve cannot be actualized.

Failing to Act on the Data That Has Been Gathered

Some organizations are all too keen to extract and analyze their accumulated data sources but once this is done, they simply sit on the insights they have gleaned. Data does grow to become outdated and the same is true for insights. The longer an organization sits on its data, the higher they run the risk of having that data becoming obsolete. To make the maximum use of their data and insights, companies and businesses should act as quickly as possible in order to gain the best outcome and benefits as well as return on investment.

Piecing the Entire Picture Together at One Go

Think of Big Data like a jigsaw puzzle. It is impossible to piece it together all at once as data pools are often enormous in volume. To solve this puzzle, your team will have to work on your data bit by bit until you form the entire picture. Otherwise, your team risks being overwhelmed and drowning in the tsunami of data at hand. Start small, make it manageable and your team will be better able to deal with their Big Data challenge. The experience gained from this method can then be applied and further refined down the road as the Big Data project ramps up in scale. Roadmaps are often helpful in this respect.

Not Being Able to Differentiate Causality from Correlation 

In data science, it is wrong to simply assume that correlation is causation. Just because they seem related to one another, does not automatically mean that one caused or is causing the other. This mistake is often made due to an eagerness in wanting to claim that a relationship exists between two random variables and is not merely a product of coincidence. Big Data would be put to better use comprehending the correlation between two variables rather than making the assumption that “cause and effect” is taking place as it would lead to making wrong forecasts and ultimately, problem solving and decision making.

Disregarding the Importance of Cloud Storage and Data Security

As Big Data deals with great amounts of data, it is crucial to consider an appropriate infrastructure for storage – one that is easy to access, cost-effective and secure. With Cloud storage, the amount of data you can store online is virtually limitless. What’s more, should disaster strike, Cloud storage allows you to retrieve and recover your data without suffering any losses. Cloud storage also allows you to seamlessly synchronize data across all of your devices without the hassle of exchanging data between desktops, mobile devices and pen drives.

Overlooking Business Intelligence Experts

Even after gathering the necessary data, it needs to be correctly interpreted and explored. While fully capable of analyzing the data, it would be better for the overall project if the data scientists worked hand in hand with a team of business experts who are knowledgeable and experienced in the particular field they are exploring. Combining the expertise of both parties would ultimately lead to improved outcomes and superior quality of insights gleaned.