Relativity and Data Analysis

Introduction

It must be clearly understood that people and some professionals sometimes are taken away by the technical noise used to describe terminologies that come and go. Data analysis had been there for years. What changed are the applications and tools used to enhance insights.

Tools for the Profession

It is interesting that people are failing to understand the difference between data analysis as a skill and skills for the tools used for data analysis. Data analysis is the ability to produce information from collated data to answer business questions. The information is then used to produce insights about resolving those business questions. Data analysis is not just producing information but is answering those questions that need to be answered about the performance of a business. If the business people do not ask good questions, the data analysis will not produce valuable information for the organisation. Imagine if you just ask how many clients did not pay their dues this month? This question is answered by simply using a query that focuses on payment done or not done. The real questions should be asking questions about the business performance? My observations are that organisations are more interested in people who know the tools and not searching for candidates who know how to ask the right questions for data insights. Knowing SAS, R, Tableau, SQL, etc, is not that important for me as an analyst, what is and should be important is to understand how data results are used to produce insights about the business operations. It must be also noted that not all data analyst from different industries and sectors must have same skill-sets. An academic data analyst can not be same as a data analyst from a manufacturing company, banking sector or entertainment business. What is common for all are the tools they use but the analysis and insights are different. What makes the company strategic is the difference of its people's skill-sets. This is why real strategic companies are hesitant to use consultancy for their core strategic business operations because they will not be competitive enough if using consultancy firms because the consultants will be using similar approaches for companies in the same industry.

Use of Technical Jargon to confuse People

In data analysis, individuals must be computer skilled in the tools needed for the data retrieval, preparation and organisation. They must understand statistics concepts, they must understand databases for storage especially to be able to do the preparation of the data. Then comes the analysis, this part requires a set of things. One needs to understand how to answer the business question and know the maximum and the minimum level of a good answer to a business question. A real analyst should be able to understand what is the purpose of that data analysis process. The insights needed from the data should be not known when data has been collated and analysed because that is mining. To put the procedures into perspective, people should not just use technical jargon as a way of complicating things. Up to this date there are few organisations that really understand what skills are required in a data analyst because they always follow what the recruitment companies consider and not really understand what they want. It is of no use to hire a data analyst who does not change the organisation way of thinking when it comes to data decisions. A company with a good data analyst should not say, "we think the problem was ..." , they must know for sure because the data will be there to give concrete evidence.

Conclusion

Data analysis is a skill that is unique and requires making sense from data and link it to business goals and aims. Knowledge of tools is important but one can be a good user of those tools and not producing the correct results. The blame will be given to those who will try to make sense of the data yet the best thing to do is to ask the correct questions and data is manipulated and analysed to resolve those problems. Those routine obvious questions are within what I term as the minimum or lower level of general knowledge about any business. This reminds me of my professor who used to say, "Ask the correct questions to be outstanding."