Information & Interpretation: Why Data Must Be Put To Work

Of all the trends that have made headlines over the last few years, none has had a greater impact than the rise of Big Data. Businesses, news agencies, police stations, nonprofits, nearly every organization you can think of are gathering and using data to change how they operate from day to day. Ideally, they’re using all this data to make better decisions, taking into account a larger and broader pool of information so as to avoid common sources of mistakes and biases.

But as valuable as Big Data is, simply having it is no guarantee that your organization will make better decisions. Data can help you understand what’s happening at your company and what it means for the future, but only if you have the tools to interpret it. Otherwise, you will be subject to a wide range of errors, biases, and obstacles, including:

Information Overload

The most common challenge associated with Big Data is simply interpreting an enormous load of information. The more data you gather, the harder it is to sort through it all and identify the specific information that is useful for your purposes. Modern organizations are already working with more data than human minds could ever handle, which is why they turn to computers for much of their information storage and analysis. But even computers often have trouble dealing with all this data. Given the linear processing methods that modern digital devices use, sorting through a large load of data requires considerable time and energy, and there is a risk that the equipment will overheat and break down. As a result, organizations that go through the trouble of gathering data on a large scale still have trouble using it.

The solution to information overload lies in developing computers that are not just more powerful than current ones, but that process information in a more efficient way. Current computers rely on a linear method of retrieving, interpreting, and processing data. But with the rise of artificial intelligence, computer developers are beginning to mimic the way that the neurons of the human brain handle information. Different groups of neurons will perform different cognitive activities in parallel with each other; the more often they perform those activities at the same time, the stronger the connection between them becomes. This allows for more efficient information processing, and it reduces the risk of overheating and other computing problems. By designing computers that are capable of the same parallel processing, the developers at Imaginea are making it possible to interpret even the largest loads of data efficiently and accurately.

Structuring Issues

AI devices must be trained to interpret information, and that training relies on the use of large, structured datasets. But producing such datasets from unstructured data is difficult, costly, and fraught with errors. Only highly skilled professionals can properly assemble and label a dataset, which they must do through a painstaking process. But such professionals are subject to a range of biases, which affects the way that they assemble this data. As a result, not only is it highly expensive for organizations to adopt AI and use it to interpret data, but even after that AI is in place, it proves biased and inaccurate.

In order to streamline the data structuring process and reduce the risk of biases, Imaginea is developing synthetic data tools. These tools use vast data repositories to generate and label data, producing enormous datasets that can be readily used for machine learning applications. Organizations without access to vast resources or skilled labor forces can thus take advantage of AI technology with ease. In this way, not only do synthetic dataset tools help organizations structure data for AI applications, but they open the door to a wide range of other data interpretation activities.

Sample Limitations

In addition to problems with how data are processed, there can be issues with which data are used. If data gathered is biased or incomplete, the resulting information can be misleading or hard to use. Only by incorporating data from a wide range of sources can organizations use it to draw accurate, reliable conclusions.

To illustrate this problem, consider one type of organization that is struggling to use Big Data: police stations. Using information gathered on patrols, police officers determine which locations of their city have the most serious crime problems. They then dispatch more officers to those locations. But because officers tend to patrol areas that they already think have high crime rates, they record more crimes from those areas than from other areas. As a result, the data they gather ends up overestimating the rate of crime in those areas compared to the rest of the city, leading them to focus on those areas more than they should.

The solution to this problem lies in gathering data from a wide variety of sources, thereby limiting the influence of biased sampling. Police officers, for example, must go beyond merely using patrol data, incorporating calls from citizens, social media posts, and other indicators as well. This requires tools that correlate large and distinct types of data with one another, producing a report that takes all this data into account. Artificial intelligence and other advances in computing will make it easier to design these tools, thereby allowing any organizations that want to use Big Data to do so as accurately as possible.

Imaginea promotes the development of artificial intelligence, synthetic dataset tools, and other technologies that improve the accuracy and accessibility of Big Data. To learn more about these developments or gain access to them for your own organization, visit our website today.