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Understanding Insight in Analytics: A Leader’s Guide

3-4 minutes reading time

As leaders we would like analysts to provide us with insightful findings. As analysts we want to prove our worth by providing insightful results. But what is “Insight”? Have we got this all wrong? Lets unpack its meaning.

Insight

(the ability to have) a clear, deep, and sometimes sudden understanding of a complicated problem or situation (Cambridge Dictionary, Online).

Having read numerous definitions of Insight, I found the above most clear. It’s important to note there is no reference to Insight needing to be unique, i.e., synonymous with ground breaking, or scientific advancement. Insight is therefore, subjective, and dependent on the individual.

In this definition, Insight is the result of understanding of a “complicated problem or situation”, which gives significance not just to the outcome, but effort involved to get there. It occurs to me, for something to be insightful, at minimum, two perspectives coexist:

(1) As a requester (or recipient) of insight, such as a product owner, you make a request for something that you know will be of great insight to you, where the result would give you “clear, deep and sudden understanding” in relation to your product.

(2) As a doer, such as an analyst, you present new insightful findings of a “complicated problem or situation” that is either requested or a result of discovery.

In either case, the other party may not agree that the findings are insightful, because either: (a) the doer already has the information the requester asked for (prior knowledge), (b) the recipient knows the answer because they have been presented with similar findings previously (prior knowledge), (c) the recipient or doer are not interested in the findings – this does not mean the findings are not insightful.

In summary, as a leader, it is not productive to expect insightful work to happen without some guidance as to what insight means to you. As analysts, some of the most insightful work comes from exploration and discovery, which should be encouraged; however, do not expect outcomes to be insightful to your initial target audience. Don’t let good work go to waste mind. Share findings with others – ideally, with help from others – you may enlighten someone else. Most importantly, an improved view of what insight means can be gained from two-way conversation between the doer and the recipient/requester, with an agreed understanding of what insight means to you both.

Data – Popular Terms Defined

Below is a list of the most popular topics associated with Data, ordered and ranked according to the highest number of Google and Google Scholar search results.

AI

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Artificial intellegence (AI) returns the most google search results of all the data related subjects listed here, although ranks ony 2 for Google Scholar search results.

J. McCarthy, (2004) defines AI as “the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable”.

Rank: Google 1, Google Scholar 2

Machine Learning

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Machine Learning ranks as the highest Google Scholar search result of subjects listed here, suggesting it is used or researched more in academia than AI up to this point in time (August 2022).

IBM define Machine learning as “a branch of artificial intellegence and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.”

RANK: Google 2, Google Scholar 1

Data Science

Data Science is the use of scientific methods to obtain useful information from computer data, especially large amounts of data”, (Cambridge Dictionary, 2022).

This definition is simple enough, but in contrast to its broad use, especially in job roles and responsibilities. Data Science encompass all, but not limited to the following: gathering, cleaning, analysing and storing data; analysing, interpreting and visualisation data using mathematics, statistics and computer science, often through the use of programming languages.

Rank: Google 3, Google Scholar 6

API

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Application Programming Interface, API’s “are mechanisms that enable two software components to communicate with each other using a set of definitions and protocols. For example, the weather bureau’s software system contains daily weather data. The weather app on your phone “talks” to this system via APIs and shows you daily weather updates on your phone”, (AWS).

Rank: Google 4, Google Scholar 8

Big Data

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“Big Data refers to data that is too large or complex for analysis in traditional databases because of factors such as the volume, variety, and velocity of the data to be analyzed”, (Microsoft).

Rank: Google 5, Google Scholar 4

Data Analytics

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“The identification of meaningful patterns within large bodies of data through the use of computers, and the prediction of future patterns, in order to gain insights that improve organizational decision making”, (Oxford Dictionary, 2022).

Rank: Google 6, Google Scholar7

Deep Learning

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“Deep learning is a subset of machine learning, which is essentially a neural network with three or more layers. These neural networks attempt to simulate the behavior of the human brain—albeit far from matching its ability—allowing it to “learn” from large amounts of data. While a neural network with a single layer can still make approximate predictions, additional hidden layers can help to optimize and refine for accuracy”, (IBM).

Rank: Google 7, Google Scholar 5

Data Mining

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Data Mining is the process of extracting useful information from an accumulation of data, often from a data warehouse or collection of linked data sets. Data mining tools include powerful statistical, mathematical, and analytics capabilities whose primary purpose is to sift through large sets of data to identify trends, patterns, and relationships to support informed decision-making and planning”, (SAP).

Rank: Google 8, Google Scholar 3