Viewing Data as a Fundamental Element of Intelligent Analysis

data element intelligent analysis

Analytic solutions (intelligent data analysis) are no longer the future, but the present, even if the technology has not yet reached the mainstream level. However, it becomes increasingly clear that predictive analytics cannot be performed without data viewing, which is a new challenge for suppliers.

Predictive modelling and other advanced intelligent data analysis solutions are performed with powerful software applications specifically designed to run complex algorithms on large datasets. However, analysts and practitioners of such solutions increasingly support that much of the work done. Theoretically, it has to lead to useful information for decision-makers which is frequently based on viewing tools of data.

Far from being a minor component of analytics applications, viewing data covers some crucial areas during the analysis process. From the initial exploration of data and the development of predictive models to reporting on the analytical results of used models, techniques and data visualization applications are critical elements of any analysis. Without them, the data analysis teams are practically involved in an impossible mission.

The importance of data viewing tools is explained most directly regarding efficiency. The human brain has limits to encompass large volumes of information, and the only practical way to pattern constant observation in long strings is through vision. As such, the drawing of graphical maps for correlation observation is infinitely more useful than studying huge sheets of tabular data.

The current market situation shows that data predictive analysis programs are becoming more common in organizations, is partly driven by the growth of large data-type architectures and increased sales of machine-learning technologies. Consequently, predictive modeling and data viewing tools appear to become more and more inseparable.

The Efficiency of Intelligent Analysis

International studies show that this year’s visualization tools have been the cutting edge technology in business intelligence and analytics where organizations have invested money. In a survey conducted by TechData, for example, it appears that by the end of August 2016, 43.5% of nearly 3,000 global respondents said they had recently acquired modern data viewing tools. At the same time, predictive analysis solutions ranked only fourth (21% of respondents) on the list of advanced technologies acquired.

Things seem to change regarding the prospects for future intentions. Predictive analysis solutions were reported by almost 40% of respondents as an investment target over the next year, while 38% of respondents indicated the viewing tools. This result shows the apparent tendency of linking the two technologies in order to streamline intelligent analysis in the near future.

However, the most crucial area for data viewing is the reporting of results generated by predictive models. Why? If data analysts fail to show decision making in business leadership that predictive models deliver using business leadership information that has potential to improve internal decision-making and operational processes. It is very likely that financial support for expanding investment in such solutions to be finished and smart data analysis projects to be abandoned entirely.

Practically, viewing makes data accessible to much broader audience categories, and this helps to increase the culture of data analysis within the organization. Most data analyzed in predictive models and large data analytics projects are nothing but collections of one and zero. By themselves, such seemingly endless strings of data do not mean much. They need context and the viewing tools just do.

It remains to be seen what these international trends will mean. Especially since the local market is far from being mature in terms of intelligent data analysis solutions. Moreover, this can be an advantage since the simultaneous acquisition of such solutions, and modern visualization tools would only lead to real development of the local market. Click here.

The economic, social and political environment in which decisions are being made today is characterized by a clear and continuous dynamics in which advanced technologies become a significant determinant of the style of human life. The number of possible actions can be very high. The degree of uncertainty it can make is very difficult to predict and the consequences of making a decision could be disastrous due to the complexity of operations and chain reactions it might create.

The convergence of information processing with communication techniques illustrated eloquently by the exponential development of the Internet has led to the emergence of enormous amounts of data, information, and knowledge represented in the most diverse forms. This vast amount of data is continually enhanced not only by the ongoing web development but also by the aggressive emergence of technologies such as embedded systems, systems mobile, and omnipresent (universal) information processing systems.

It is, therefore, indisputably clear that the need to extract information and knowledge from these massively distributed data are primarily for assisting decision-making processes. In this regard, it is necessary to represent characteristics of explicit information that is no longer related to the abstract representation of real-world concepts.

Designing decision support systems is to mitigate the effect of a decision maker’s limit and resistance on solving decision-making problems. In the process of decision making, the central position is occupied by human intuition and judgment, and the methods used are based on the analysis of available data.

The main concepts and results in the field of computer-aided decision-making activities, which involve data analysis, came from online analytical processing and data warehousing as well as from data exploration and knowledge discovery data mining.

Prediction modeling, the Discovery of Knowledge in Databases (DCD) aims at searching for predictive information to support decision-making processes, using the machine and statistical learning methods that take into account the specificity of large data volumes.

The performance of a model, the result of a training method, is assessed by its ability to predict or generalize. Measuring this performance is very important for data finder because it allows the selection of an optimal model from a family of associated models the method of learning used, guides the choice of the way by comparing the selected models with each other and provides a measure of the quality or confidence that can be given to the forecast.

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