Five Question Blitz: Data & Analytics

December 31, 2020

We are creating a new information series answering five questions related to Data & Analytics. Topics will be broad and answers will be simplified. Our goal is to promote common definitions and increase the general knowledge of individuals with interests in Data & Analytics.

In celebration of the New Year the two co-founders of SimDnA, George Earl and Ion King, will be sharing their answers for each of the five questions. Even though our answers are different the core messages are complimentary and share common threads.

What is Data & Analytics?

Ion – Data & Analytics is the process of using data to facilitate scientific research for the purpose of learning and knowledge.

George – Data & Analytics are a fundamental discipline utilized in the business world to assist organizations in learning and decision-making.

What purpose does Data & Analytics serve?

Ion – Data & Analytics serves the purpose of education. Through scientific research of data, knowledge is acquired that can be useful in a number of outcomes. These include forecasting, modeling, segmentations, business intelligence, product design, root cause discovery, remediations, performance measurements, quality assurance, intelligent algorithms, machine learning, and many more.

George – Data & Analytics serve two primary purposes in the business world. First is the quantification and measurement of customer behavior. This has become critical in an economy that involves so much remote interaction. The second is to create a science and discipline around organizational learning and decision-making.

Who performs Data Analysis?

Ion – Individuals that perform data analysis can be viewed across a broad spectrum defined by mathematical skills and analytic tools proficiency. Mathematical skills range from basic mathematics and algebra through to advanced calculus and Probability & Statistics. Analytic tools range in proficiency from a spreadsheet tool like excel through to coding languages such as SQL, SAS, HiveQL, Python, or R. As an individual’s skills increase they move across the spectrum towards more advanced analytic positions such as forecasters, modelers, machine learning engineers, and data scientists. More junior roles at the opposite end of the spectrum might include interns and entry level analyst roles. One other factor that weighs heavily in the spectrum is experience. As an analysts experience grows, and experience doesn’t always correlate with time; they become more potent in the sense of delivering value beyond their stated role or position.

George – Data analysis can be performed by any data-driven professional with the proper tools and access. However, it is better performed by a centrally resourced function within the company that has dedicated significant time to developing this unique and challenging discipline.

How is Data & Analytics used by companies?

Ion – The basic purpose of Data & Analytics is to inform decisions. The process of performing analysis determines how companies will use Data & Analytics. Companies with an efficient process will have well informed decision makers. Companies with broken and/or faulty processes will suffer from no information and/or incorrect information.

Data & Analytics is a process with many steps. The foundation is the collection of data which then goes through a number of transformations and segmentations to create an architecture that supports a specific company/business/outcome. The quality of this process dictates a company’s capabilities in using Data & Analytics.

George – Data & Analytics could be metaphorically described as the art of storytelling through facts and statistics. So while many focus on more mundane outcomes of data & analytics like reports and models, the real power is its impact on helping employees to truly understand their business. Humans learn best through stories and data & analytics is the discipline of learning.

What is the biggest challenge for Data & Analytics?

Ion – The biggest challenge for Data & Analytics is overcoming the misconceptions and artificial hype generated by Big Tech success stories (FB, Google, Amazon). The romanticizing of Data Scientist, artificial intelligence, and machine learning has created widespread recognition of “Data Science” but without providing a true and accurate definition. Optically Data Science has been removed from the spectrum of Data & Analytics to a level that makes other analytic positions appear devalued. As a result many organizations target Data Scientists as sole analytic resources setting up both the company and Data Scientist for failure.

Data & Analytics requires knowledge of the task at hand along with knowledge of the skills sets required to achieve the desired goal.

George – There are two overwhelming challenges in the field of data and analytics. The first is data. It is disorganized, dirty, and often poorly understood/documented. The second is bias - cognitive bias affects novice and expert alike. It encourages poor decisions, sloppy execution, and often results in lost opportunity and money.

Click here to read Five Question Blitz: Deep Dive into Data.

Header image by Image by Gerd Altmann from Pixabay.

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