Data Visualization Final

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Link to Presentation

There are conflicting opinions on what exactly is a successful company acquisition. Tomi Laamanen’s analysis of 111 small tech-based companies suggests that when a company acquires another, it is usually for a need for complementary resources. Barney and Turk, on the other hand, suggest that it is in when you combine unrelated resources into the operation of the company, it creates value in society. As for factors that identify acquirers, Blonigen and Taylor’s research shows substantial negative correlation between companies that ranked high in research and development with a high change to acquire. Bueller and Drori state that acquisitions can enhance the gradual development of a company’s capabilities and strategy. As for how acquisitions impact a company, Thatcher and Oliver’s research shows that increased productivity depends on the fixed size of the company and the size of their market.

Our visualization shows the progression of company acquisitions over time. This tells a story about the history of each company, the types of companies they are interested in, and paints an overall picture of where the tech industry is going. The visualization is relevant and unique because we haven’t seen a visualization that tells this story yet and we were curious to see what the data on the excel sheet would show.

The dataset is from and shows over 8,500 company acquisitions, dates, and the names of acquired companies from the Crunchable database. It is significant to us for a couple reasons. First, it gives us an idea of where the industries are going. Second, it gives us insight into the strategies of the individual companies. We had many issues we had to work through to get the data to work and tell a story. To make things more interesting, we went through and manually searched for the categories for the acquired companies because this would be the most interesting thing to visualize.

We used R to analyze the data and it quickly answered some of the questions we were asking ourselves. We found out which companies had the most acquisitions over time, including the months and years that the most acquisitions happened. More specifically, 2011 was the year with the most acquisitions (1487), with May being the month with the highest acquisition traffic (807). This helped us iterate through our ideas about the stories we could tell through the dataset.

We created a web visualization because we wanted people to interact with the different companies and get information like the name of the acquired company on mouseover. We brainstormed through some 4D implementations of the data and thought it would be interesting to have a tangible element in the future when we have more time to extend on the concept.

Finding the right form to tell this story was challenging and it required iterating through many concepts that didn’t work out. As our sketches show, we we started very broad and with time, trial and error, we narrowed it down. We initially wanted to show the relationship between companies and time through a circle with that had networked lines that connected based on what you clicked on.

The visualization came together when we begin limiting the information we used to tell the story. As much as we wanted to show the whole picture, we did not want to overwhelm the user. We have the ability to choose where to begin and end the story and made the decision to narrow the scope by only including companies with 10 or more acquisitions. We used D3 to implement this visualization, specifically a library called Gantt Chart. Once the user understands what colors correspond with what categories, they can play around with the different companies and get a sense for where they are headed.

The only related project we found online was a visualization called “The Age of Acquisitions” on TechCrunch. This visualization focused on the amount companies were sold for and used size to represent the amount. The visualization was also implemented through D3 and the categories they used made it easy for us to narrow down our own categories.

This visualization is for anyone interested in the field of technology, its past and where it’s going. It is also useful for startups looking at different acquisition options, and could be a good complement to the TechCrunch visualization so they can get a picture of what the companies have paid for other acquisitions.

In future iterations, we would like to incorporate information from the Crunchbase API, once granted access. A multi dimensional graph that gives users the ability to switch on and off what information to display would revel more information and allow the user to play with the data. It would also be interesting to see this information on a map to see the locations of the companies that get acquired.

Works Cited

B., Barney J., and T. A. Turk. “Superior Performance from Implementing Merger and Acquisition Strategies: A Resource-based Analysis.” The Management of Corporate Acquisitions: International Perspectives (1994): n. pag. Print.

Blonigen, Bruce A., and Christopher T. Taylor. “R&D Intensity and Acquisitions in High-Technology Industries: Evidence from the US Electronic and Electrical Equipment Industries.” The Journal of Industrial Economics 48.1 (2000): 47-70. Web.

Brueller, Nir N., Abraham Carmeli, and Israel Drori. “How Do Different Types of Mergers and Acquisitions Facilitate Strategic Agility?” California Management Review 56.3 (2014): 39-57. Web.

“Gantt-Chart.” Gantt-Chart. N.p., n.d. Web. 09 Dec. 2015. <>.

Laamanen, Tomi. “Option Nature of Company Acquisitions Motivated by Competence Acquisition.” Small Business Economics 12.2 (1999): 149-68. JSTOR. Web. 09 Dec. 2015.

Thatcher, Matt E., and Jim R. Oliver. “The Impact of Technology Investments on a Firm’s Production Efficiency, Product Quality, and Productivity.” Journal of Management Information Systems 18.2 (2001): 17-45. Print.

“Visualizing 15 Years Of Acquisitions By Apple, Google, Yahoo, Amazon, And Facebook.” TechCrunch. N.p., n.d. Web. 09 Dec. 2015. <>.