Along with the demand for big data, better data, and the need for greater insight into company operations comes the need for analytic professionals who can effectively leverage this data to maximize business benefits. But it’s not always easy to find and hire these types of people. Within the past few years, we’ve seen titles such as BI Analyst, Data Analyst, Data Scientist, and Big Data Engineer emerge in job listings as companies seek out much-needed expertise to wrangle their growing amounts of information. As a matter of fact, McKinsey and Company’s often-quoted 2011 report on big data predicted that by 2018, the US alone faces a shortage of 140,000 to 190,000 people with deep analytical skills as well as 1.5 million managers and analysts to analyze big data and make decisions based on their findings. The bright side of this situation is that job seekers with analytic talent and business acumen have a tremendous opportunity to make a positive impact for business teams. BI is a growing field that shows no signs of slowing down, so let’s take a closer look at what makes it attractive to job seekers as well as students deciding what course of study to pursue.
Decision-makers depend on insightful charts and graphs to help them make fast, accurate decisions. “Insightful” charts and graphs are easy to read and understand and are the right design for your data. The wrong chart type can throw everything off and make your dashboard unusable. For example, while pie charts are most often used to display the share or percentage of a total, they’re not good for comparing the relationship between two variables – a scatter chart is better for that. While bar charts are good for comparison, if you want to compare many categories of data over time, go with a line chart.
Are you tasked with creating the dashboards that are used every day at your organization? As dashboard experts, let’s take a look at a few practical examples of why certain charts are better suited to display certain types of data versus others. If you find this article useful, be sure to join us for More Dash Less Bored on September 8th, our popular webinar about the latest thinking in dashboard design, where we’ll give you more practical tips to create interactive, “go-to” dashboards. Reserve your spot here.
Sales data for the year may be best displayed on a bullet graph. This type of graph displays a fair amount of data in a small space, compares measures to enrich its meaning, and is generally a simple, uncluttered visual representation of data. With a bullet graph, the budgeted sales amount for the year is represented by the entire length of the bar; the actual value is represented by the thin bar in the forefront (blue on the sales bullet graph). The shaded grey areas (in our image) represent the values “poor,” “satisfactory,” and “good.” In our image, the sales data is satisfactory, approaching good. Stacking bullet graphs allows the user to compare values with ease – here, sales vs. costs. A bullet graph would even work well as a desktop widget since it can showcase an important KPI in a simple, space-efficient format.
Would a bar chart work for this same data?
A bullet chart is actually a variation of a bar chart. Even though bar charts are useful for comparing data as well, for our sales example, it may not be your best option. With a bar chart, you’d need to have 2 columns per month – one for actual and one for the target/budgeted amount. Alternatively, you could use a stacked bar chart, but that is basically what we have here with the bullet graph, only with even more data than a stacked bar chart typically offers. Because it would be difficult to track your progress over time, a standard bar chart isn’t the most efficient chart type for sales actual vs. budget data.
Candlestick Charts vs. Bubble Charts
EDIT 6/1/11: Click here to access a recording of this incredibly popular webinar!
Join arcplan on June 1st at 2pm Eastern for How To Be An Analyst, a free webinar on common data analysis techniques and their real-world application in business intelligence.
Many of the scorecards and dashboards you see today are quick glances into the rear view mirror of a business, but what most businesses need is a deeper look into the metrics that drive performance. The issue is not a technology problem – most modern business intelligence platforms can easily perform more advanced analysis. It’s a people problem, and it’s probably not your fault. Many business managers were never taught to be analysts, have assumed the role because of a staffing shortage, or simply like being self-sufficient when it comes to answering business questions. But the truth is, it takes time to understand all of the nuances of data analysis in order to be able to extract meaningful information from rows and rows of data.
This presentation is a primer on the art (and craft) of being analytical. It’s for managers who are new to data analysis or have simply forgotten what they learned in school. We’ll begin with overviews and use cases of the basic methods of analysis including:
- Sorting and ranking
- Comparative analysis
- Contribution and Pareto analysis
- Projection and regression analysis
Then we’ll apply these methods to real world business intelligence scenarios that you see on scorecards and dashboards, including:
- Sales rep performance
- Revenue forecasting
- Accounts receivable and the aged trial balance
- Financial reporting, P&L, and balance sheet
- Pareto analysis (the 80/20 rule)
- Above-and below-the-line performance analysis
Becoming an analyst is a journey. This presentation will set you off on the right foot in your quest to master some of the most common data analysis techniques.
|Date:||Wednesday, June 1st|
|Time:||2:00 pm Eastern (New York City time zone)|
|Presenter:||Dwight deVera, Senior Vice President|