Almost everyone is familiar with the abbreviated phrase GIGO: garbage in – garbage out. This implies that the limitations of machine learning is dependent only on the quality of the data, algorithms, and human experience of the inputs to the system. Yet, the best of the results can be meaningless and considered to be garbage if no one can see and realize the value of the output.As with many more things, in data analytics, a picture is said to be worth a thousand words and can be more easily understood. Thus, the importance of visualization in data analytics, often the most critical and overlooked step in the analytics process. Visualization is the means by which humans understand complex analytics. As the quantum and complexity of the data increases, the complexity of the final model also increases, making effective communication and visualization of data even more cumbersome and crucial to end users. DATA VISUALIZATION IS ESSENTIAL TO GAIN A CLEAR PICTURE BASED ON WHICH CONCRETE ACTION CAN BE TAKEN Visualization permits one to take intricate observations and deductions and project them in a manner that is appealing, holds attention and is yet informative to the target audience. But then, a strong understanding of data science is a pre-requisite for that visualization to be successful. It must be kept in mind that, ultimately, it is people who can be thought of as the ‘consumer’ of the ‘product’ of all artificial intelligence or machine learning efforts. Thus, it must be our endeavor that the results are presented as executable, effective insights which can be acted upon in business and in life. Many different aspects of consumer behavior are determined by more than just two or three events but the human brain is only able to process two to three pieces of information at a time implying that one is required to use statistical modeling and advanced analytics to accurately predict consumer behavior and Key Performance Indicators for businesses.AN ACCOMPLISHED COMMUNICATOR IS A MUST IN EVERY DATA TEAMThe primary focus of most companies on starting a department or initiative for big data is the actual data or the intellectual capital needed to analyze that data. A large number of data scientists are of the opinion that the more data they possess to work with, the better the model, and that usually becomes the prime focus. Skilled data scientists would definitely be capable of assisting with this process, but one also requires a person with good domain knowledge of the business, and who has the capability to effectively communicate information back to the end users. The huge quantum of data available to consumers and businesses can often be daunting; this is bound to only spiral upwards in the coming years, making the task of locating accurate, granular, and relevant data through the messy chaotic data more difficult and important.A fine example for effective use of big data is the weather industry. A great deal of data is utilized by weather prediction models and the form in which a consumer finally receives the weather forecast is often the combined output of several models. Due to the ever-increasing complexity of forecasts for weather and businesses, the ability to take a model result and present that information in a manner that the target audiences can comprehend and act upon quickly, is essential for success.Now, having once obtained those results, how do we explain them? When we have as many as, say 10 to 20 different components going into a model with interactions, lags, and non-linear relationships, how do we explain that to users in a fashion that makes it simple and easy for them to act upon? THE EFFECT OF INTEGRATION OF MULTIPLE SOURCES OF DATAThe weather is something that shows how one single data source can be utilized for a variety of purposes. Almost every single moment of the day in people’s lives is dependent and determined by the weather. The genre of music they listen to, the beer they drink, the number of steps they walk, and even the time taken to drive down to work! This is where visualization comes into the picture – Quantifying and communicating how weather affects people in their daily lives. With the aid of the correct graphics, a user can easily and quickly absorb multiple pieces of complex information. This is especially important in the case of weather since it is highly dependent on geography. Every climate zone has different weather events and different reactions to weather. For instance we are all aware that that six inches of snowfall in Chicago will have a much different effect than, say, six inches of snow in Dallas, Texas. Now this was pretty simple! Now what happens when we’re looking at thousands of locations? How do you communicate properly? How are those complex relationships communicated properly? Capabilities in GIS are the answer to this. Notwithstanding how appealing the visuals are, the quality and granularity of the data influences the accuracy of model results and the pertinence of the end results.The most complex and accurate predictions possible may be created, but those results also have to be scalable to a wide audience and available through multiple delivery channels. The correct set of skills and resources are required to take in multiple data sources from all parts of the world and serve it up to a large global audience in a way that is responsive and accurate.Just think about the impact this new age of analytics will have and what it could do for your business, and while you are at it, remember that there is a need for smart people to be involved at all levels of the process. This will help deliver the perspectives and insights you and your customers need to make informed decisions that will impact your life and the bottom line.
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