Factors crucial for Data Visualization in Decision Making
Discuss about the Data Analytics and Visualisation.
A picture is worthier than thousand words, especially while some one is attempting to understand and gain insights from the data. For data analysis and decision making, some factors are very crucial. These are data visualization, transformation of data, information and knowledge into visual representations. Data visualization is very crucial to the users as it could suggest comprehensive insights for various analytical purpose. Commercial firms, non-governmental public sectors and other individual private companies require data visualisation with the help of graphs and figures. The big data analytics has been spread in a large domains alike marketing, finance, economy, politics and many others.
The corporate houses utilize different types of software tools and packages such as R, Excel, Tableau and Qlikview for business decision making. The report aims to present explorations and enhancement in knowledge for comparing the cutting edges of techniques of information visualization. For supporting effective decision making, the data visualization technique is necessary. Sophisticated analyses and more effective decision making can be performed by exploring data set.
Big data quality relies up on three types of categories that are data quality categorization, data manager and big data handling.
It is the conventional method to generate graphs like Histogram, pie-chart, scatter plots, line-bar-bubble charts as well as trend charts. Sometimes data flow diagrams, entity relationship (ER) diagrams and Venn diagrams are also used to be taken into consideration. However, in this era, the mainstream analytical visualisations are word/ text/ tag clouds, network diagrams, parallel coordinates, tree mapping, semantic networks and cone trees.
The data visualization effectively increasing the solution for challenges as the ability effectively supporting to visualize the data set. Volume, variety and velocity are the three key features of big data analysis. The proper categorization of created, provoked, transactional, compiled, experimental or captured data is necessary for authentic visualization. The quality of data effectively considers outliers and data outcomes. The factors that are pretty much needed for data visualization and interpretation are relevance, reliability, appropriateness and accessibility. The advanced software such as Hadoop, Splunk, Python and D3 are the most beneficial big data platform tools. With the help of these software, exploratory data analysis is executed.
R software is used by a large number of academic statistician. R is platform independent software that easily enables digging of dataset, comparison of data sets and contrast of profiling (Miller, 2017). Beyond the sophisticated modelling techniques, R supports the requirement of performing summary tables for determining data groupings. The typical tasks that fulfil data visualization are – identifying the fields of the data, enlisting of field attributes and statistics, reviewing field value distributions, null ratios, reporting minimum, maximum, averages and scatter ness of time series data. In R-programming language, the clearer outcomes and complexities are being easily visualised. Financial dash board making is easy in R. R software controls data science for both computer and non-computer scientists. Communication relates the involvement of both the presence of business and finance. Report formation, dashboard making, interactive web applications could be successfully apprehended by this tool (Source: Classycareergirl.com., 2018). Tactically, R tool explores inherent relationships within the data that lead to look through new and good ideas.
Types of Software Tools and Packages for Business Decision Making
In the big data world, Tableau helps to connect directly to local and cloud data resources for importing fast in-memory performance. Another target of Tableau is to perceive the self-service analytics and to mine big data with intuition. Effectively the data analytics uses point-and-click analytics in Tableau for analysing Real-time drag-and-drop cluster analysis, cross data source joining, mobile enabling, powerful data connectors and real-time regional data exploration. The internet-based data companies accumulate massive data sets for visualization with the help of Tableau. Small enterprises and retail sector uses Tableau as it provides the essential information. This software easily accomplishes the quick-iterative analysis and become an essential aspect of business information systems. Visualization in Tableau is more vibrant than MS excel. Scatter plots, altitude plots, dashboard plotting or heat mapping visualisation is easily available in Tableau. In Tableau, a business analyst can analyse spreadsheets, public data tools, analytic data packages with a larger variety of general-purpose databases and data cubes. Tableau unlike basic data software MS Excel can perform three tasks that are public domain data sets, commercial data services and cloud database platforms. Tableau connects several connectors such as Google Big Query, Open Data Protocol, Salesforce, Amazon Redshift, Google Analytics and Windows Azure Marketplace (Murray, 2013). Creation of data blends, network organization, colourful presentation of basic statistical graphs and trend charts have made Tableau more popular. Bullet graphs, packed bubble graphs, Histograms and Gantt charts are the most promising plots that could be executed as per Tableau. Framing and taming of data set in Tableau server can solve many technical problems of small industrial industry. Web-Tablet authorisation facility is readily available as a supplementary tool in Tableau. Dashboard making may provide a more robust environment for making a day-to-day decision. Embed java script code, iFrame, pathway analysis in Tableau provides effective analysis and action.
MS-Excel helps to find comparisons, contrasts, tendencies and dispersion of the common and traditional analysis technique. The complete profiling method in excel is displayed as-
Data analysis package and solver add ins help to analyse data in the advanced method. The 3D graphs and Maps could be executed in MS Excel. Power view Ribbon and data filtering help to analyse the data more specifically. All types of files such as .xlsx, .csv or .txt are accessible in MS excel. Data cleansing and data transformation are very easy to carry out by MS Excel. For visualization, excel also permits to manually create the cross-charts, graphs and charts too. Analysts can analyse the text ribbon, the context menu, text modifier, thematic reporting, glowing back ground and maps in power view with the help of data. Small enterprises shape and split the databases in excel. KPI descriptions and views in Navigator prospect the more authentic visualization in MS Excel (Aspin, 2016)). With the help of context filtration, Text filtration and merging, formatting of trend data, Management of perspectives and power pivot handling, data modelling is framed. Google Publisher and PowerBI in MS excel support the service sectors and business intelligence by making Dashboard presentations (Pileggi et al., 2012).
Different Types of Graphs and Charts used for Data Visualization
Qlikview is a trending business intelligence tools that helps to explore and visualize the data such as MS Excel and Tableau. Operational intelligence, Investigation and adjudication are the influential factors of data visualization. For business Case, data modelling, data load scripting and creating visualisation, Qlikview is a worthy tool to the analysts. Small companies may generate visualisation of their profitability analysis, inventory analysis and project management with the help of Qlikview tools. The software is capable to solve any kind of business related current and potential problems. In case of business intelligence, the software helps a company to comprehend on time and easily interpretable outcomes to the customers.
Qlikview application in the right way used to specify any kind of parameters. The associative logic of Qlikview offers the capability for conducting direct and indirect associative searches either within a single field or whole data set. Qlikview helps to load the data set quickly and create quick visualisation. The responsive deign and touch-sensitive visualization develop the data load script for sales analysis. Real values of sales, product promotions and allowances are easily visualised in this analysis. Creating dynamic expressions and dynamic dimensions are utilised for profitability analysis in Qlikview. The combo charts, waterfall charts, Mekko charts, heat maps and block charts are accomplished in Qlikview. Coding and looping could also be done with this tool. In this software, business-driven data discoveries with guided paths of analysis are highly customizable and tightly governed. Qlikview customises scripting and extend development with workbench. Therefore, for a business analyst, Qlikview is exclusively a system management and business application software that naturally intends to navigate complex information for accelerating discovery. The special feature that one researcher can observe in Qlikview but not in Tableau or Excel is its enterprise-level governance of data. The attractive aspects include Qlik NPrinting, Qlik Market, Qlik DataMarket, Qlik Connectors or Qlik GeoAnalytics (Troyansky, Gibson & Leichtweis, 2015). Its executive dashboard helps to create business analysis model for senior managers for monitoring performance in business. It helps analysts to drill down into the microscopic details of the business. As a result, a corporate organization becomes capable of manufacturing fast moving consumables using a reseller model for delivering its products across multiple local or global regions.
Conclusion:
The overall discussion display that MS Excel as well as Tableau and Qlikview are generally used for non-coding purpose. On the other hand, R is a complex and inconsistent coding language that are commonly made easy with the help of “tidyverse”, “ggplo2”, “dplyr” or “Lattice” packages. The difficulty level of R-tool is higher than rest of the three software. On the other hand, the visualizations of Tableau, R or Qlikview are far more qualitative than MS Excel. However, data manipulation, visualization, iteration, modelling and communication are very easy with the use of R. For business analysis purpose, R is more required than Tableau or MS Excel. However, for basic business objectives, MS excel performs many difficult tasks quite easily such as data entry and storage, accounting and budgeting, data analysis or forecasting. For business trend showing, R is the best software for visualisation followed by Tableau.
References:
6 Ways Tableau Can Transform The Way You Do Business. (2018). Hacker Noon. Retrieved 19 April 2018, from https://hackernoon.com/6-ways-tableau-can-transform-the-way-you-do-business-d44e2ba3e964
Aspin, A. (2016). High Impact Data Visualization in Excel with Power View, 3d Maps, Get & Transform and Power Bi. Apress.
Business-science.io. (2018). SIX REASONS TO LEARN R FOR BUSINESS. https://www.business-science.io/assets/ds4b_rating.pngwww.business-science.io/business/2017/12/27/six-reasons-to-use-R-for-business.html
Classycareergirl.com. (2018). https://www.classycareergirl.com/2016/05/business-uses-for-microsoft-excel/
Miller, James D. (2017), Big Data Visualization, Packt Publishing Ltd.
Murray, D. G. (2013). Tableau your data!: fast and easy visual analysis with tableau software. John Wiley & Sons.
Pileggi, H., Stolper, C. D., Boyle, J. M., & Stasko, J. T. (2012). Snapshot: Visualization to propel ice hockey analytics. IEEE Transactions on Visualization and Computer Graphics, 18(12), 2819-2828.
Qlik.com. (2018). Retrieved from https://www.qlik.com/us/products/qlikview
Troyansky, O., Gibson, T., & Leichtweis, C. (2015). QlikView Your Business: An Expert Guide to Business Discovery with QlikView and Qlik Sense. John Wiley & Sons.
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