Report on Data Visualization and identification of breach in Data Integrity

Introduction:

Data visualization stabilizes the requirement of visual derivatives and integrity plays a major role in the same. It is very vital to maintain sanity and balance in the interpretation of the data in order to make it trustworthy and in lieu of the construction of the governed system dynamics.

a.    Choose your data visualization:

Discover a Data to Visualization on the Web:

Data visualization issue:

Though the first instance, this chart from Sky News portraying the average UK life expectancy in 2018 doesn’t look highly misconfigured the allocated height of the bars creates quite a striking factor. They are majorly disproportionate to the evaluation they’re surfacing and show an abrupt rise in places they shouldn’t be. On further verification of the illustrated date, few abnormalities were genuinely highlighted like the bar showcasing male life expectancy in Scotland as 77.1% is actually set to a disproportionate height of 60% (roundabout). This kind of critical misinformation creates a different interpretation of the data than it should be and misleads the target audience into thinking of a multitude higher than it actually is (Morgan, 2019). The data visualization objective herein was a significant issue and it is expected that the common audience would adhere by the same and such miscommunication is fatal for the audience. In this very case, the concerned communities belong from the UK (England, Scotland, Wales & Northern Ireland). A bad data visualization omits the three most important issues expected out of an original visualization:

·        Meaningful to the audience

·        A story to the audience that uses languages and ideas that they understand

·        An ethical design involving patterns that would serve as a key to our future

Out of multiple issues, this data visualization has a huge discrepancy with regard to data integrity. Data serves as a key in order to respect the original source and make an influential study out of the same, for growth and development of concern or resolution. Hypo or hyper indexing of data equivalence creates a confusing scenario that often offends the transparency in the underlined study. Data integrity often refer to as the maintenance of assurance of the efficiency and resemblance of data over its complete life-cycle is a critical aspect with its design, augmentation, resourcefulness, and retrieval. The term is as broad as its context which generally surrounds its adaptation on the basic quality, validation, and serves as a repository to multi-functional needs. Data integrity in terms of visualization should ensure possibilities (inclusive) while the actual set has been collected and, on every occasion, avoid human interruption leading to intentional changes to information. While human error serves as data fatigue, it is always recommended to automate the best of data in order to maintain secrecy, accessibility, and safe from malfunctions. (Dan Clark, 2018)

Further, there are other major issues in this data visualization pertaining to the obscurity shown with respect to the ethical issue and procurement of the correct data and later producing it to the audience. The data must have been facilitated by a Government body as they are the sole keepers of the National statistics and any infringement in the procured data after Governmental procedures can be taken into account of major malfunction due to unperturbed human intervention. This has resulted in a confusing outlook of data visualization. Other significant disregards with respect to this forecasted data visualization can be majorly attributed to the loss of sincerity in providing correct and unilateral information to the common audience of the United Kingdom and its counterpart territories. This would include the essential loss of homogeneity in the data that has been resolved and can be administered under deceptive methods used to forge the actual imagery of the Data Analytics and(or), Perceptual/Colour Issues. The color coding used to analyze the kinematics and smoothly describe it in bulletins plays a pivotal role in data resolution and the provision of good data visualization and any breach in the same creates a lot of confusion. Moreover, the deceptive tools that calibrate the tables not do well in data visualization as their prime motto get debriefed in the middle of establishing firm statistics. 

The data herein used while normalizing the average UK life expectancy has been correctly adapted from verified Govt/private sources with skilled distributions maintained at every stage of procurement and analysis. National life tables, UK records the trends in the average number of years people will live beyond their present age measured using period life expectancy and systematically analyze using age and sex for the UK and its constituent counterparts. The office of National statistics maintains this pool of data and agencies/mediums can acquire them post following legitimate steps. In this case, this data visualization report contributes to the average life expectancy in the UK depending upon real-time variables that are non-regressive and highly non-redundant.

b.   Deconstruct:

Identification of the Objective of the data visualization and the Target Audience:

While we come across this data sets referencing a stipulated module responsible for a significant cause that the common public is bound to acknowledge, it is expected that the plots serve the visual attribution while maintaining integrity to its rightful sources. Any reported breach in the data vestibules can create a congregation of non-actual ideas that can affect the audience mentally and physically. Any data put to use in reconstructing a critical database should be clean of operational errors, malicious intent, and unexpected hardware failures. There are recorded cases where the loss of business-critical database has caused catastrophic loss of human life in life-critical systems. The target audience for this data visualization is the common public, data centers operating on real-time data, hospitals, and healthcare operators, National Census Operators. (datalabsagency.com, 2020)

The challenges encountered with data integrity in this graph corresponding to the average life expectancy in the UK must be suffering from physical and logical integrity. While physical integrity can counter to the errors in correcting memory hardware chips use of file transfer protocol (FTP), use of clustered file system (CFS), non-redundant hardware, the interruptible supply of electricity and recurring RAID arrays, it is advised to take intermediate steps to keep off from all these errors. The logical adversaries can be majorly attributed to the low hanging irrationality, referential integrity, correctness, entity integrity in a relational database. Or in some cases irrevocable ignorance pertaining to impossible censored data in robotic systems. Challenges involving constraints, bugs, flaws, and human interruptions impose run-time insanity and compiled-time errors in verified data. (Wikipedia, 2015)

The three most significant problems that could be improved or fixed:

The three major issues that can be improved in the data sets used to accommodate a real-time analysis portraying the average life expectancies in the UK are as follows:

·        Bar graph might not be the right solution to showcase such a vast population because a crisis in data integrity majorly adheres to the use of bar plots. Thus, the patterns that bind the relations between the underlying data and the overlying graphical interpretation would see more human nature involved in the same. A good data analysis, unlike this, splices the data in a variety of ways constituting patterns that grow over time, across space or between relevancies. The mobility personified in the visualization patterns instinctively incorporates a large share of eyeballs and has been an influential way of distributing data sets to the common audience for hundreds of years

·        There could be a better way of incorporating numbers into the story as a clear and convincing voice is better accepted by the crowd in general. Data visualization used to communicate something to the general audience ascertains either a view, a need for change or supply of enhanced information since this particular narrative of life expectancy is a compelling human emotion, a better story-telling outlook must have served as an integral part of the visualization portrayed

·        Data is nothing but numbers. One can play with words but numbers will always give one a hard time. Contextualizing numbers into adaptable visualizations is a connotation to establish its credibility and make it smooth for the understanding of the audience. Basically, there is no excuse for bad data visualization: there can be just a bad narrative to cover up for human errors. If this would have been a good data visualization, it wouldn’t have been just convincing, but also functional for use in a data-driven culture. While our eyes look for things that stand out on the basis of design-abstract shapes and colors create an additional impulse on our neurological systems. Visual cues that can be used as caps and markers in an explicit design entailing a story about the average life expectancy in the UK, can be seen as largely missing in the data visualization provided by Sky News.

c. Reconstruct:

Sourcing the Original Data:

The actual data that can be attributed to the average life expectancy in the UK and its constituent countries can be obtained from the office of National Statistics, National Life tables, UK. For the period of 2016-2018, life expectancy at birth can be recorded as 79.3 years in males and 82.9 years in females. This has seen a slight improvement as observed over the period of 2015-2017 by a margin of 3.7 weeks and 4.2 weeks for males and females respectively. Deeper research has shown that the UK has among the lowest life-expectancy improvements for both males and females when compared with similar countries. The functional probability of reaching the 90s remained the same during this period as observed in the earlier ones where one in five males and one in three females born between 2016-2018 are likely to celebrate their 90th birthday. (McDowall, 2019)

Reconstructing the Data Visualisation wherein problems have been fixed:

Recording actual indices for purposes pertaining to data verification and subsequent use of the date, this adaptation certainly rules out data integrity wherein the correct height for a segment of the bar graph has not been depicted. By changing the height to what it should be, the graphical representation might not look so appealing but adheres to correct informational content and nullifies every chance of miscommunication. The average UK life-expectancy in Scotland recorded as 77.1 years for males and 81.2 years for females has been correctly shown in the graph shown below and thereby all such misinterpretations in the graph with respect to England, Wales, and Northern Ireland have been rectified. This visualization might not be very interesting or striking as the original and malfunctioned one, but is at least way more accurate. Data consolidated in the form of graphical adaptations are mainstream highlights to infirm the common audience about sociological, political, demographical, topographical, and psychological perspectives and therefore a strict commitment to actual data sets in terms of analysis, referencing, design and social sharing must be maintained. (learnaboutgmp, 2016)