Building a Better Analyst
I participated recently as a panelist in a Business Intelligence Summit at the University of Akron as they had identified a gap that their graduates had when entering the workforce; they had little or no analytical skills when it came to data. The faculty was exploring the idea of adding business and data analytics classes to the curriculum for business majors to fill that gap. We were there to discuss Business Intelligence and Analytics and impress upon them the opportunities that exist in the job markets, the fields those jobs were in and the skills needed to compete.
I participated recently as a panelist in a Business Intelligence Summit at the University of Akron as they had identified a gap that their graduates had when entering the workforce; they had little or no analytical skills when it came to data. The faculty was exploring the idea of adding business and data analytics classes to the curriculum for business majors to fill that gap. We were there to discuss Business Intelligence and Analytics and impress upon them the opportunities that exist in the job markets, the fields those jobs were in and the skills needed to compete. Christina Rouse, Chief Architect for Incisive Analytics, presented a series of slides that explored the skills needed by analysts from 5 specific areas:
- Inductive and Deductive Reasoning
- Business Fundamentals
- Data Concepts
- Data Quality
The Skill Set
Analysts need math skills like fish need water, and without them they won’t be analysts, or fish, for very long. There are a set of specific areas within mathematics that are needed for analytical examination. There are obvious ones like understating changes and fluctuations in both relative and absolute terms, peer (competitive) and periodic comparisons and ratios come to mind as the basic skills. Businesses, however, are starting to become more advanced in their analytical view of their data as volume increases and quality improves. Things once reserved for pure statisticians such as acceleration rate of changes, trending and projections, statistical examinations like z-scores, correlations and standard deviations, and data normalization have made their way in to business analytics.
Reasoning seems to becoming a lost art, but it is an important part of analytical study. The ability to observe data and see patterns and from that patters develop a hypothesis and theory of what is occurring and why is important in finding hidden trends or opportunities for growth. Inductive Reasoning takes a curious person to sit down with a large data-set, with no specific question to answer, and parse through it to see what they can find. Deductive Reasoning is a bit more traditional, in cases where you know what is happening, but you need to understand why it is happening.
Business fundamental may seem like an obvious skill, but there are cases where students are coming out of school without understanding business fundamentals from an analytical sense. Students need a strong understanding of accounting principles like credits and debits, balance sheets items versus income statement items and cost and managerial accounting. Marketing is an important area to understand when it comes to analytics, and understanding areas such as market basket analytics, customer loyalty, and market share versus wallet share as well as how to measure campaign effectiveness are essential. Another area is supply chain management and concepts such as fill rates, lead time and consumption rates.
There is a better than average chance that the last three items I touched on are covered in a good school of business, but basic data concepts are lacking. Without understating concepts like cardinality, parent-child relationships, mutual exclusivity, as well as basic data warehousing fundamentals like facts and dimensions, star schema methodology and table relationships they can be behind the eight-ball on analytical capabilities.
Data quality also falls in to the areas that are not covered in a traditional business school. Without understanding data population, data validity, consistency and completeness of data they may make assumptions on an incomplete or invalid data-set.
The Final Piece; Data Intimacy
The last point to make would be that of data intimacy, which only comes with exposure to the company’s data. An analyst can’t be afraid to dive in, dig through and ask questions so that they can understand every twist, turn, and variation the data may go through or hid in. As an analyst, you have to know the data you are dealing with inside and out.
What does all this mean?
Data volumes are growing at an increased rate, and it is projected that we will not have enough qualified data and business analysts in the very near future based on the demand these data volumes are creating. Higher education must act now to meet the demand of the “real world”, or students must take it on themselves to step outside their schools required curriculum and take class to prepare themselves for the challenges they will face when they enter the workforce. For that to happen, we must educate those students in what the true need is, and not what is prescribed by an outdated curriculum. By taking the right classes, and preparing themselves from a business and technical sense, students can position themselves to compete in a very high demand job market once they graduate.
Don’t believe that the demand is there? Check out all these articles on The Emerging Role of the Analyst.
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