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In the first main part of our report Fidelity Capital Management (FCM), an asset management company based in Brussels, is analyzed in terms of their process structure, IT-strategy and business model (the information for this analysis was gathered through a qualitative interview on the 23rd of september). FCM is, in contrast to many asset management firms in the market, specialized in an extreme long-term value creation for their clients. To enable this long-term value creation, FCM’s analysts take intuition based decisions on a regular basis aiming for investing in stocks which are generating continuous profits over a long period of time with as little risk as possible. Therefore, the decisions making approach, the analysts of FCM are taking, is one of the key process steps, because taking wrong decision will influence the fund performance and the amount of capital managed by FCM. Furthermore, FCM is rather pragmatical in terms of the use of IT, not using new technologies in communications or decision support.

This is where our idea starts. Due to the fact that data analytics become more and more popular even in small and medium sized companies, we proposed a software for tracking the decisions of FCM. This idea and its strategic, financial and technical feasibility were presented in the second part of the report. The proposed software allows FCM to track every decision made and rationales on which they were based. FCMs’ analysts can then see all decisions on a dashboard, which indicates how specific decisions performed over time, enabling FCM to learn from past  decisions and therefore increase the quality of future decisions. This learning process may later be supported through a pattern-spotting machine learning module. In addition, the proposed software should allow FCM to increase the quality of their decision without adapting their current organizational structure and most important their basic way decisions are made.

The third part of the report described important aspects of the implementation, which will be crucial for the success of the proposed decision tracking system. We recommended a three step rollout approach, making sure that the analysts of FCM will be introduced to the software step by step. This should help to motivate them to use the software on a regular basis. Additionally, we addressed an issue of a careless use of the system, that could influence the intuition based decision making process, which is the key factor of FCMs’ success in the last decades. The software should rather serve as new source of information than a new approach for making decisions.

Team 48:

Boy Curry-Lindahl (463357)

Mariusz Dudek (463235)

Fabian Fleischmann (462362)

Moritz Grassy (460051)

5/5 (1)

The best-selling book Moneyball shows how the data analysis and statistics revolutionized and changed baseball forever. It shows how these two things changed an average team with poor revenues into the one of the best teams of the season. Certainly the power behind the numbers and analytics is enormous.

This year analytics has come to an e-sport industry with the introduction of the Mobalytics platform. The start-up founded by the Ukrainian entrepreneur, Bogdan Suchyk, has won the grand prize of $50,000 at the Startup Battlefield competition, a part of TechCrunch Disrupt event in San Francisco.

Mobalytics is a platform for competitive gamers which tracks and analyzes player’s in-game performance and gives him a personal Gamer Performance Index (GPI). Calculation of GPI is based on a set of machine learning algorithms that measure and evaluate the performance of the player in a few key areas. Based on that score the system defines the game skills of the player and provides him with a detailed breakdown of his strengths and weaknesses on the user-friendly visual interface. What is more, thanks to its pre-game and post-game analysis tools, the system not only shows the areas for improvement, but also gives the gamer advice on how to improve in order to become a better player.

The company’s main target group of customers are the competitive players who want to improve their gaming skills, but have no access to coaches, who are usually only available to the top professional players. Mobalytics aims to be a personal trainer, that will show the way of becoming an exceptional gamer to every player willing to use it.

As according to the newzoo.com report, the e-sport market is expected to reach $463 million this year, a year-on-year growth of 43%, and to surpass the threshold of one billion US dollars in 2019, the opportunities for Mobalytics are plentiful. Although, the company currently tests its solution only in the League of Legends, a game that has dozens of millions of active players on a monthly basis, it plans to expand to other e-sport games, for example Overwatch, DOTA 2 and exceptionally popular Counter Strike.

The win at the Startup Battlefield, 13,000 signed beta users and booming e-sport market – these are the things that indicate a bright future for Mobalytics and show the interest in the solution the company provides. But, will Mobalytics make the e-sport more data oriented? Will it change the gaming industry as the data analysis and statistics have done with the conventional sport? Time will tell.

 

Sources:

1. http://www.uadn.net/2016/09/15/ukrainian-game-analytics-startup-mobalytics-wins-techcrunch-disrupt/
2. http://mobalyticshq.com/
3. https://techcrunch.com/2016/09/12/mobalytics-is-a-coach-for-competitive-gamers/
4. https://www.fusecrunch.com/mobalytics-is-competitive-gamers-coach.html
5. https://newzoo.com/insights/articles/global-esports-market-report-revenues-to-jump-to-463-million-in-2016-as-us-leads-the-way/

 

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“Information goods” are broadly defined as “anything that can be digitized” (Varian, 1999). The information good that we want to take a closer look at today is market data. We will look into two companies who have managed to create a business out of data gathering: Hootsuite, and Premise.

Hootsuite
Social media is predicted to be used by almost 3 billion people by the end of 2020 (statista.com). Platforms such as Twitter, Facebook, Instagram or Linkedin are known for generating an extraordinary amount of data, which can be highly valuable to enterprises.

However, engaging across many different social media channels, and analysing the outcomes, can be overwhelming for enterprises. Hootsuite offers a tool designed to make social media activity manageable and measurable by organising the different streams into a single customizable dashboard. By digging into Big Data and Machine Learning, Hootsuite allows its customers to have deep insights on every aspect of their social media.

Hootsuite’s biggest strength is certainly its large customer base. However, customer communication on social media may not reflect overall buying behaviour and customer satisfaction. Hootsuite will find lots of opportunities in the market for social media management, which is relatively new, still growing and far from saturated. Nevertheless, social Media are known for changing rapidly. If Hootsuite does not keep up with these changes, it may quickly be overcome by competitors.

Premise
Crowdsourcing is the process of obtaining needed services, ideas and content by seeking contributions from a large group of external people. If done right, crowdsourcing brings immense opportunities to businesses which decide to make a use of it.

Premise is a US-based research company that uses crowdsourcing in order to gather the data especially within emerging markets. Global contributors are asked to take photos of specific requested data with their smartphones, and upload them into the Premise database via a mobile application. The collected data is then aggregated and used to create alternative macroeconomic measures which are sold to businesses, governments and NGOs worldwide.

Premise finds its strength in the size of its network. As for its weaknesses, premise hasn’t made any profit so far. Opportunities for Premise are far from scarce, since there is still a tremendous amount of uncollected data all around the world. However, a long-term threat that Premise might face is a rise in the price demanded from their contributors, which may cause their paid crowdsourcing model to fail.

Conclusion
After analysing the two businesses we would like to finish by giving three predictions for the future of market data gathering:

Internet of Everything
As digitalization goes on, objects once far away from IT will now generate vast amounts of data through implemented sensors.

Machine learning
Machine learning will be an essential element for preparing and analysing data in the future.

Unpredicted new sources of data
Just as Hootsuite or Premise would not have been imaginable 15 years ago, there will probably be new sources of data gathering that we are not able to think of today.

 

 

Sources:
Varian, H. R. (1999), Markets for information goods (Vol. 99). Institute for Monetary and Economic Studies, Bank of Japan

<https://www.statista.com/statistics/278414/number-of-worldwide-social-network-users/>

<https://hootsuite.com/>

<https://www.premise.com/>