Research Paper on Data Visualization Tools and Programming for Data Analytics

Introduction

Data is playing a significant role in the world of today, making it so powerful. It proves extremely difficult to understand certain things by looking at statistics and loads of numbers. To facilitate easy understanding, there is a need to classify and process data such that the human brain can easily internalize the information (Stukowski, 2009). Science has proven that the human brain is good at processing images than it can process plain texts, and this explains why data visualization remains of great importance. When we talk about data visualization, it is nothing complicated but rather the representation of data in visual form. These could be graphs, charts, maps, or lists, among others. The representation of data in these forms makes it easy for people to understand the magnitude of the information. On the other hand, data analytics is the method used in the analysis of sets of data, either structured or unstructured so that they can be useful insights for the drawing of datasets conclusions. Many organizations are using data analytics technologies and techniques today.

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Today, data has grown to become a vital source of competitive advantage, and companies and businesses are doing their best to find the contingent methods for identifying and analyzing the data they generate in their day to day activities. Most of the decision makers in companies are today aware of pie-charts, intuitive graphs, and other visualization forms that try to instill sense into their revenue, sales, and other company operations components (Vesanto, 2017). Nevertheless, it is important to note that these data visualizations are as useful as the effectiveness of the data itself, or the process of using the data to arrive at the conclusions. Therefore, to formulate an effective data strategy, it is critical to come up with a balanced approach in data analytics and data visualization.

There is a confusion by many companies on data analytics and data visualization. In both, the user is allowed to make sense of the available data and acquire the essential metrics that will enable them to make decisions that are appropriate to their operations (LaValle, Lesser, Shockley, Hopkins, & Kruschwitz, 2011). Today, the information overload had increased enormously, and data generated has increased tremendously over the past three years. As a result, the interpretation of such data has become the need of the hour as it needs much more accurate and sophisticated systems to classify and analyze the data. Apart from that, there are also these projections and forecasts suggesting that exponential recto ensure growth in the coming years for the big data software market. However, the confusion originates from the fact that data analytics and visual,alization denote data in visual interfaces (Schroeder, Lorensen, & Martin, 2004).

Whereas a considerable overlap exists among the two, we need to understand that data analytics, unlike data visualization, deals with data at a much deeper level. In an end-to-end business intelligence solution, there are not only front end dashboard transforming the data into contexts that can be seen, but there are also algorithms and tools at the backend.

Difference Between Data Visualization and Data Analytics

Data visualization is the representation of the data in a visual context such that the patterns and trends inherent in the data can be explicit. In text-based data, such trends and patterns may not be precise. Most of the tools used in the visualization of data enable the filtering of the data so that it can be manipulated as per the requirement of the user. The use of traditional forms of visualization like the tables, charts, column charts, and line graphs have today been ousted by highly perceptive 3D visualizations (Sander, Freyss, Von & Rufener, 2015).

On the other hand, data analytics has gone a step deeper by discovering and identifying the patterns and trends that exist in the data. Although data visualization enables the users to make sense from the data, it does not provide a complete picture. The effectiveness of visualization relies on the data that is used in the preparation of the visualization in the first place. When the visualization engine is fed with incomplete data, the resulting visualization will be half-baked, erroneous, or obsolete (Raghupathi & Raghupathi, 2014).

Additionally, the data used by enterprises in the world of today are gathered from various sources, stored in multiple repositories, silos included. As a result of such complexities, it becomes challenging to gather comprehensive data for visualization. Whereas most of the visualization tools use unstructured and raw data, end-to-end analytical tools uses the algorithms of data mining in the cleansing of data, evaluation of the cleansed data through the use of various models of evaluation and software tools, renders it to algorithms, and then finally makes a decision on how the result should be displayed (Tansley & Tolle, 2009).

Data Integration as the First Step of the Process

The important requirement of effective data analysis is the consolidation of all the data in one place for the analytics to be effective. Whereas there are analytical engines able to collect data from multiple sources, it is important to consolidate all the data in one place as it enables a single version of the truth, hence preventing contradiction and duplication of data from interfering with the visualizations. Many companies have until recently been manually aggregating data, on an ad-hoc basis, since this was the easier way as compared to investing effort and time in a solution for the same (Russom, 2011).

Nevertheless, the manual aggregation has been rendered impossible in the recent past due to the sheer increase in the data volume. There are several software platforms and tools that have come up in rescue of the situation at hand, and they are catering for this need through the provision of automated solutions as opposed to the manual interventions where one has to conduct a manual intervention which in most cases was inaccurate and time-consuming. The benefit of add-on of the automated solutions is the cleansing of the data to eliminate messy, outdated, and misnamed data, mostly in an environment where there is an existence of disparate users and sources.

Data Analysis as the Second Step of the Process

After the aggregation and cleansing of the data, the following logical step is to subject such data into performing calculations or analysis on the data. Due to the increased complexity in the today business environment, there are also complex calculations involved in data analysis. There is an introduction of multi-stage formula due to the need for speed, and these formulas can simultaneously perform several calculations (Chen, Chiang & Storey, 2012). The function of the visualization tools is to focus on the reporting of data and not analyzing it, and as a result, there is a limitation in most of the tools, with restrictions installed in the possible aggregations per formula.

As opposed to that, the users can come up with formulas through the use of end to end analytical solutions, when they work in separate resources. There is an automatic undertaking of the pre-required calculations by the software, and this completely makes life easy for the users. For a business to thrive in today's world of the highly paced business environment, they need to have analytical tools that update their data and at the same time facilitating their collaborations in real-time. Some of the leading analytics tools that are available in the market today like the IBM Cognos are key in playing such needs, through the streamlining of the available data and leveraging of the plug-and-play interfaces to come up with colorful dashboards.

The companies that are in the retail sector have already increased the ability of data analytics in the streamlining of their business process, and this has resulted in the maximization of their revenue. Through visualization and analytics, they have managed to discover actionable insights and patterns about the behaviors of their customers hence helping the managers to plan and come up with initiatives. Retailers are harnessing data analytics to aggregate customer data for emphasizing profitability and efficiency.

Extensive business insight investigation suites offer prescient displaying, and different kinds of cutting edge examination dependent on complex calculations aggregated utilizing dialects, for example, R and Python. Propelled information representation, information warehousing, and dashboards make up a portion of the key advancements utilized by business knowledge stages presently. The best arrangements offer unmatched adaptability to the client, with the capacity to join information anyway the client requires or likes.

Additionally, the most recent scientific stages apply present-day apparatuses, for example, normal language preparing (NLP) and chatbots, making it simpler for clients to play out the required estimation or info their questions quickly (Hanwell, Curtis, Lonie, Vandermeersch, Zurek & Hutchison, 2012). The most recent advances, for example, area-based knowledge expands the capability of investigation and noteworthiness of the experiences in a significant manner.

Data Analytics or Visualization: Which Comes Last?

Whereas the best visualization is based on data subject analytics, it is not always necessary that visualization be the culmination of the project or the end of the process. Data analytics is adopted in many situations, and visualization is a cycling spree. They are looking at the case of Zao, running a host of predictive modeling applications and machine learning to measure the success of targeted email campaigns. The visualization of data takes place very early into the process. Through the pulling out of specific variables by the analysts into a graph to identify any possible correlations, or to spot metrics like median and mean averages, standard deviation metrics, data spread, to get a sense of the data scope.

Both data analytics and visualization deals with data. For the visualization tools, they come up with reports that are beautiful and easy to comprehend, but only robust backend capability, handling the messing data and processing it to through the application of advanced algorithms, giving an accurate report. The complete picture is offered by data analytics, whereas visualization only provides a summary of the available data in the best way possible. To obtain the best solution, we need both. The data are growing at an exponential rate. From the data insights, the managers and business owners could make decisions that could turn around the face of their business.

Conclusion

When it comes to the needs of an enterprise, there is a definite strike between the difference in data visualization and data analytics. Besides, it is also clear that even though visualization is essential, it cannot solely the problem of data processing, and that it needs both data visualization and analytics to draw sound conclusions for a business. The selection of the visualization tools and analytics tools varies from one organization to another, and this is majorly affected by the type of data it handles as well as the organization's size.

Both the data visualization and data analytics tools are essential business intelligence tools to harvest the potential within an organization's large variety of data. When the two are placed to work hand in hand, they can deliver the most actionable and impactful insights to enable the key stakeholders to make decisions. However, data visualization is only as good as the analytics behind it.