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Tuesday, February 27, 2018

If Data is King, Data Analytics is the Queen

In concluding one of the project management classes, where I had to assess the understanding of the process stability using control charts with some design of experiments requiring data collection and data analysis, it became quickly apparent to me that amidst the many types of classifying and categorizing data, the fundamental characteristics of data itself is not adequately understood. While people look at objective and subjective data of details collected from the documentation, surveys, interviews, and observations, there is not a strong understanding of the classification of data as continuous or discrete, categorical or numerical, nominal or ordinal, interval or ratio, etc. On top of it, the datatype as mentioned in programming and database systems, such as the integer, float, double, boolean, string, varchar, identity, and datetime have added enough murkiness for many middle management roles to delegate their responsibility of understanding the data to subordinates, such as the data analyst, business analyst, etc.  

As I pondered over this dilemma, I found out that even professionals were unavailable to differentiate the three critical things any descriptive data analysis. The measures of central tendency, symmetry, and dispersion were not readily coming up in one of my talks touching on the house of quality. If the business is collecting so much data, how can the lack of understanding of the classification, categorization, and type of data be taken for granted? If the information contained in data is meant to be serving the management to make decisions, then, how can the lack of basic data analytics be an optional criterion in the job descriptions for roles managing products, projects, programs, and accounts? 

According to Standish Report (Hastie & Wojewoda, 2015), the number of failed projects is decreasing, but the number of projects challenged from inception to closure has increased while the number of successful projects has remained flat. The same story is entirely different if we take the size of the project into account where a higher proportion of large size projects embrace failure. With so many tools available for middle management, such as earned value metrics, failure mode effect analysis, and six sigma, why is the attention to detail missing? While such a detailed review is beyond the scope of this particular blog article, brief research on data and business intelligence (n.d.) from QGate provides a compelling summary of data may be speaking but we may not be understanding.

We don't stop at addressing this complexity with more mandatory education and training on understanding data and using data analytics for business solutions. Instead, we compound it further by introducing big data with its own value, volume, velocity, veracity, and variety. Press (2014) notes twelve different definitions for big data that one can choose from which doesn't address the fundamental requirements of how to systematically identify the right data to look at, analyze it, and make proper decisions using hypothesis testing, regression analysis, etc. 

There is a world outside with data analytics focusing further on multivariate analysis, ANOVA studies, factor analysis, and selecting various distributions to choose from based on how the samples represent the population. While such advanced requirements may be referred to data analyst professionals, can we not mandate a good understand data (classification, categorization, and type) and fundamental data analysis (central tendency, dispersion, and symmetry)?

After all, if we all agree data is King to business, data analysis is the Queen of business operations. Both the King and Queen are critical to making the right decisions, sustaining the proper business operations, and maximizing the right business opportunity. With the explosive growth of data due to the advances in technology, as noted by Ginni Rometty (2016), CEO of IBM in her speech to World Health Congress, digital [data] is becoming the foundation and data analytics so is the basis for subsequent cognitive understanding. 


Data and Business Intelligence (BI) (n.d.) QGate. Retrieved February 27, 2018, from
Press, G. (2014, September 3). 12 Big Data Definition: What's Yours? Retrieved Feb 18, 2018, from
Hastie, S. & Wojewoda, S. (2015, October 14). Standish Group 2015 Chaos Report - Q&A with Jennifer Lynch. Retreived February 24, 2017, from
Rometti, G. (2016, April 12). Ginni Rometty Speech Archives. Retrieved January 31, 2018, from

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