The mobility ecosystem with user stories – used at BlockchainedMobility Hackathon 2018
“I can honestly say my industry is being disrupted beyond belief right now. The funny thing is, I like it”, said Jamie Burke during yesterday’s Ethereum Munich meetup “The State of the ICO”. Jamie is betting his Outlier Venture’s fund on the idea to launch a handful, large ICOs to invest in communities and therefore in economies, rather than in startups.
Jamie’s fireside chat (no, there was no fire but it was hot as hell) with Datarella’s founders Michael Reuter and Joerg Blumtritt was a fascinating tour de force towards a potential next level of venture investing in general, and a new breed of investors focusing on communitarian, anti-fragile investments rather than amassing a portfolio of companies of which 90% will fail.
Before, lawyers Dr. Nina-Luisa Siedler of DWF and Dr. Markus Kaulartz of CMS inspired the audience with their highly informative and at the same time very sympathetic presentation on the legal aspects of ICOs. Both being long-time experts in the field of blockchain, managed to entertain everybody although their messages were far from being easy-going. Especially their slide “Consequences in case of incompliance” filled the room with enthusiasm. Their complete slidedeck “Legal Aspects of ICOs” can be downloaded here.
Again, the Ethereum meetup was a great success: everybody learned a lot, and from what we overheard on the floor, some of the individual conversations until late at night resulted in new ideas for …. future ICOs.
We always take a little time in the very first days of a year to define Datarella’s main goals for this year. This time it was a pleasant task since 2015 went very well for Datarella: we achieved most of our goals and we could start without any legacy issues.
So, what to expect from Datarella in 2016? Beside our growing consulting business with fascinating projects and clients, we will focus on our product Data Trust and our project Data Coach. Due to our tight schedule in 2015, we haven’t published much about Data Trust and Data Coach, yet. I’d like to give a brief overview on both in this post.
Generally speaking, Data Trust is a secure data market model for Big Data projects. Sharing data between businesses makes much sense: Both, data processing and analytics scale with the data, and development, quality assurance, as well as support become very efficient. The problem: Many businesses are hesitant to share their data with partners for security reasons, to maintain their competitive advantage, and also obligatory compliance aspects regarding data protection.
Data Trust solves this deadlock: With it we provide a secure sharing solution for corporations. Datarella organizes each client’s original data in separate data buckets.
Data Trust enables businesses to put their data to work together with the data of their business partners with guaranteed data security and control. Without giving away their data, they can now profit from analytics, results, and predictions that are based on the joint data within their network of partners. Thus, Data Trust is a market model – it provides each participant of a market with unparalleled insights into the market.
The Datarella Prediction Engine runs on top of the separated data buckets. The Datarella Prediction Engine has been designed for gathering precise statements regarding future business success in the areas of media & advertising, eCommerce, finance, mobility and health. Together with the Datarella Prediction Engine, Data Triust provides an absolutely trustful environment for clients to manage and analyze their company’s data.
Of course, the Datarella Data Trust can be audited.
Whereas Data Trust already is a product and is already creating value for our clients, Data Coach is still in an experimental phase. The user interface of Data Coach is an app that provides the user with body activity and environmental data. The user shares this data with a closed professional graph and receives actionable insights into her health condition, behavior, training, etc. as feedback. Based in this feedback the user can react by changing her behavior.
The core of Data Coach is a blockchain environment that provides three essential elements of a professional network:
- Data Security
- Data Provenance
- Peer-to-Peer Architecture
Cryptographic hash functions and completely historicized data chains make data sharing absolutely secure. The user completely owns her data. And she always knows her data’s whereabouts and defines whi can use it, how and when.
An essential part of our Data Coach project is our partner Ethereum, that provides a decentralized blockchain platform we build Data Coach on.
We are running very early tests of Data Coach in the area of sports and entertainment. We are active,y looking for partners to establish a pilot project in the health sector. So, if you think Data Coach could add value to your business and customers or patients, please don’t hesitate to contact me.
A lot of entrepreneurs hire fast and fire slow. In particular when new coders are required to develop a software. A bias towards speed and quick growth drives many leaders to be quick to hire new personnel and strategic cooperation partners. Hiring fast is absolutely fine as long as everything works out well. The problem is, many people do not react quick enough when they figure out problems and significant quality issues with the people they cooperate with.
Datarella decided to hire a German-based software development team for the programming of the prototype app including a backend system. The team had good references and offered us their service at an attractive price. The introductory meeting was very promising. It was clear to hire them quickly.
Over the course of time, we figured out that the development team lost one of their key persons, who supported us on our project. As a result, they stopped hitting the pre-defined milestones and started to deliver poor quality. Firstly, we asked them to take care of the problems they obviously had within their team. Secondly, we put them under pressure to deliver in time. However, our development partner was not able to improve his performance. As a consequence, we decided to quit our agreement and to hire a new development team. Probably we should have done so much earlier…
If you figure out any problems with your staff or partners, react quickly and if worst comes to worst you need to fire fast.