In my role as a Business Intelligence consultant, I often perceive myself as a data scavenger, adept at extracting insights from data. This leads me to question: if I can derive knowledge from data, what is the necessity for a data scientist? To address this, we’ll explore both the capabilities I possess and the limitations that necessitate a data scientist’s expertise.

First, let’s clarify what a data scientist is. Here are two comprehensive definitions: - Definition from mastersindatascience.org - Definition from Tech Target

In brief, a data scientist is an expert skilled in employing advanced mathematical and statistical techniques to analyze data, uncovering hidden patterns and building models for future predictions.

So, what can I achieve independently?

1. I can analyze historical data to understand trends over time by utilizing a time axis. This allows me to interpret changes in data through various reports.

   The table on the left illustrates current sales figures compared to the previous year's numbers. Meanwhile, the chart on the right provides a breakdown of sales by brand, offering a clearer view of business evolution.

   However, to identify factors that could enhance business performance, I require deeper insights.

2. I can identify patterns in my data through detailed analysis.

   For instance, after several years of strong sales, a noticeable decline occurs, followed by a year of resurgence. This fluctuation might be attributed to marketing campaigns or other variables, some of which I may be aware of and others not.

   Understanding customer behavior regarding product purchases is achievable with minimal effort. A scatter chart can visualize sales data effectively:

   The analysis shows Europe as an area with significant growth potential, given its lower sales figures compared to other regions. Notably, the brand “Fabrikam” displays a considerable sales gap across Asia, Europe, and North America.

   Additionally, I may need a new metric to assess profit margins for a clearer understanding of business success.

   This knowledge can inform targeted marketing strategies if necessary.

   Another illustrative example is the chart depicting CO2 emissions from 1920 to 1955, highlighting a decline post-WWII:

   While I can contextualize this decline with historical knowledge, lacking such context can hinder my ability to explain it, leading to potential misinterpretations.

3. I can leverage AI and machine learning tools to enhance data insights, such as decomposition trees and automated features in various BI platforms.

   For instance, using Microsoft Power BI’s built-in Key Influencer tool, I can uncover valuable insights:

   This reveals that categories like Audio, Games and Toys, and Music, Movies, and Audiobooks show the lowest sales figures. The next step is to utilize the segment analysis to dig deeper.

   The approach to deriving insights varies with the tools employed, but it sets a foundation for further exploration to boost business performance.

   Using data from Our World in Data, I can create another example. Power BI offers automated data analysis through Quick Insights.

   The chart below shows the interrelation of greenhouse gases:

   However, to fully grasp the implications of each greenhouse gas, I must acknowledge that methane is significantly more potent than CO2, warranting concern.

   The following chart illustrates GDP per capita by continent:

   The drawback of these charts is the absence of a temporal relationship, limiting their practical utility.

   Ultimately, the quality of insights derived from these tools can vary significantly. If data is mismanaged or mixed inappropriately, it can lead to misleading conclusions.

   It's crucial to invest significant effort and knowledge to extract meaningful insights from data.