GETTING COMFORTABLE WITH DATA AND DATA ANALYSIS: Let's Talk About Data

As a university student almost a decade ago, I recall listening to lectures and having to prepare lecture notes from the class sessions. I’d subsequently study these notes to prepare for tests and examinations. On the other side, the lecturer used his lecture notes to prepare test questions. Upon completion of a test or exam, the lecturer would mark, and students would obtain scores. These scores were processed such that each student’s score was attributed a grade e.g. A, B, C, D, E or F, which eventually fed into the student’s GPA. In this simple exchange, data has been extracted, processed, and refined into usable forms.

An accountant may need to record entries for the purchase of an asset and subsequently record entries to recognize monthly or annual depreciation depending on the company’s asset policy. As an external auditor, you may be on the other side (where I was a few years ago) and be required to audit financial records or financial statements. What is being recorded or evaluated in this instance is data- Financial quantitative or discrete data.

In energy exploration, data must be skilfully collected to find, process, transport and utilize new energy sources. From upstream, through midstream to the downstream sector of the energy value chain, having and using the right data could be the key differentiator between a successful campaign and a failed one. For an E&P company, this may be seismic data, well log data and so on, which when integrated with other forms of data can reasonably define new resource areas of interest. These are all data or instances where data is interacted with. Now, what is this data?

One definition of data refers to it as the atoms of decision-making. This definition implies that data forms the smallest units of information that can be used as a basis for reasoning, discussion, or calculation.?

Data can range from abstract ideas to concrete measurements, even statistics. Data are measured, collected, reported, analysed, and used to create data visualizations such as graphs, tables, or images.

Data types can be explained from different perspectives. For example, one may be evaluating qualitative versus quantitative data; discrete versus continuous or analogue data; or structured versus unstructured data. For this article, I will look at data from the perspective of structured and unstructured data.

Structured data in a simple context refers to data we find in tables. A key thing to note here is that data is organized into rows and columns. It can exist in standalone tables or may be contained in a set of tables related to one another by one or more fields. This set of tables is what is referred to as a database, or more precisely a relational database. Usually, the column header serves as a label or brief description of the data contained within that column and defines data within that column. Structured data within a relational database may also have different data types such as string, numerical, Boolean etc. There are more elements of structured data, but this is a good place to start for the purpose of this article.

Unstructured data as the name may already suggest implies that you do not have your data organized into a perceivable structure. Usually, the data is in its raw format. Examples are audio recordings, satellite imagery, internet activity etc.?The amount of unstructured data we have vastly outnumbers that of structured data. Advanced analytics approaches such as data mining and stacking are required to extract insights from qualitative data. It is why many businesses today employ the services of data analysts, data scientists, data engineers etc.

Now let us discuss sources. At this point, it may already be evident that data is all around us and can be obtained from several sources. Sources of structured data are databases, lists, tables etc. and data sources for unstructured data may include stories, people, objects, the internet, equipment, pictures, tools and campaigns, the earth itself etc.

In summary, most of the decisions we make in our personal lives or at work are driven by data in one form or the other. It becomes obvious that we even use data unconsciously! My next write-up would be on the interaction of basic statistics with our work, and how data analysis incorporates statistics.