{"id":6117,"date":"2021-11-25T09:47:37","date_gmt":"2021-11-25T09:47:37","guid":{"rendered":"https:\/\/47billion.com\/?p=6117"},"modified":"2024-12-23T05:16:33","modified_gmt":"2024-12-23T05:16:33","slug":"managing-your-data-between-a-perfect-storm-and-the-broken-window","status":"publish","type":"post","link":"https:\/\/47billion.com\/blog\/managing-your-data-between-a-perfect-storm-and-the-broken-window\/","title":{"rendered":"Managing Your Data Between a Perfect Storm and the Broken Window"},"content":{"rendered":"\n

(How small neglected issues in data lead to the systematic decline of the organization as a whole & Importance of having clean data at source for effective data analytics)<\/p>\n\n\n\n

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\u201cIn God we trust, all others bring data.\u201d \u2014 W Edwards Deming<\/p>\n<\/blockquote>\n\n\n\n

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\u201cWhere there is data smoke, there is business fire.\u201d \u2014 Thomas Redman<\/p>\n<\/blockquote>\n\n\n\n

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\u201cWar is ninety percent information.\u201d \u2014 Napoleon Bonaparte, French Military and Political Leader<\/p>\n<\/blockquote>\n\n\n\n

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Data! Data! Data! I can\u2019t make bricks without clay! \u2014 Sir Arthur Conan Doyle
(Sir Conan Doyle\u2019s famous fictional detective, Sherlock Holmes, couldn\u2019t form any theories or draw any conclusions until he had sufficient data.)<\/p>\n<\/blockquote>\n\n\n\n

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The goal is to turn data into information, and information into insight. \u2014 Carly Fiorina, Former CEO of HP<\/p>\n<\/blockquote>\n\n\n\n

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\u201cData is a precious thing and will last longer than the systems themselves.\u201d \u2014 Tim Berners-Lee, inventor of the World Wide Web.<\/p>\n<\/blockquote>\n\n\n\n

So many famous quotes from so many different people, who belong to different eras, still, everyone is essentially saying the same thing. It is a fact that data was important, data is important and data will remain important in every era. Nearly every organization, regardless of industry or sector invariably collects a lot of data from many sources, be it business communication, or legacy software practices, or industry trends, or ambiguous sources all the time.<\/p>\n\n\n\n

The big question however is how to extract value from such large amounts of data. Analysis of this data can reveal loads and loads of critical information, be it business acumen, or future course navigation for the organization, cross-selling opportunities, streamlining an existing process to extract more value, and many more, opportunities are practically endless. Each data mining cycle will result in the output insight becoming more and more realistic.<\/p>\n\n\n\n

It is said that disorders relating to the mind can be treated effectively only if the subject is made aware of the situation and there is the self-realization of something amiss. Once the realization is there of a disorder, then the subject will respond to counseling and cooperate with the psychologist to resolve the problem. Having said that, it is more important to have a clear definition of the problem. The same principle applies to data science, data cleaning is a very costly affair, which means first and foremost the availability of data has to be established, and whether its current form is raw, unstructured, or structured doesn’t matter at this stage. What is important here is that the data has to be as cleaner as possible at the source. Efforts and resources need to be engaged in the early stages to avoid huge penalties of time, scope, and cost.<\/p>\n\n\n\n

Once data availability is confirmed, the analysis stage begins, now the data form will change multiple times, from raw to unstructured to structured data to give meaningful insights. It is imperative to engage an iterative process of cleansing data at each stage, such practice would be of enormous help in later stages of analytics, as meaningful and complete data will have a multifold impact on the final insights. And finally, the predictive stage begins, wherein measurable insights are mined from the refined data to make informed decisions. Once in the predictive stage, all efforts spent in earlier stages towards generating cleaner data would repay handsomely in terms of direct, clear, and definitive results<\/p>\n\n\n\n

So, in this blog, we are going to discuss an interesting take on a very famous criminology theory with relevance to data cleansing, problem areas identification, and building highly effective resolutions. This theory will also highlight why data analysis should be an important aspect of any organization right now and in the future.<\/em><\/p>\n\n\n\n

\u201cThe Broken Window Theory\u201d<\/h3>\n\n\n\n

\u201cThe broken windows theory<\/strong> is a criminological theory that states that visible signs of crime, anti-social behavior, and civil disorder create an urban environment that encourages further crime and disorder, including serious crimes. The theory was first published in a 1982 article by social scientists James Q. Wilson and George L. Kelling.\u201d \u2013 Wikipedia<\/p>\n\n\n\n

In a simpler explanation, the theory states \u201cIf there is a broken window in a house, and the owner doesn\u2019t take any affirmative action to repair it, more windows would be broken, still if no action is taken,  after some time the door too shall be broken, still not repaired, criminals would acquire the house, then anti-social and criminal activities would begin and slowly spread to other houses in the area if still no corrective action is taken, then the whole neighborhood would become a criminal base.\u201d<\/p>\n\n\n\n

When in fact none of this would have happened if the 1st<\/sup> broken window would have been fixed at the correct time. It would not have given the impression that everything is accepted and the premise is neglected.<\/p>\n\n\n\n

We are pretty sure that by this time, many of you would have started to think \u201cHey wait for a second!! What does a criminological or sociological or psychology Theory about the civil disorder, crime, or effective policing methods have to do with data analytics or data science?  Right?<\/em><\/p>\n\n\n\n

Believe it or not, it is relevant much more than you might have ever imagined! Let us see how.<\/em><\/p>\n\n\n\n

The essence of The Broken Window Theory is that if small problems are identified and fixed with proper clarity when required and in time, the bigger problems are less likely to occur. The same principle can be applied to data science, data analytics<\/a>, and data management. So now let us see for ourselves some of the most common \u201csmall things\u201d from traditional data which are more often than not ignored!!<\/p>\n\n\n\n