Distributed ledger technology (DLT) stands to benefit industries such as financial services with transparency and censorship resistance. DLT systems need to be scalable to handle mass user adoption. Mass user adoption is required to demonstrate the true value of DLT. This dissertation first analyses scalability in ethereum and EOS. Currently, ethereum 1.0 uses proof of work (PoW) and handles only 14 transactions per second (tps) compared to Visa's peak 47 000 tps. Ethereum 2.0, known as Serenity, introduces sharding, proof of stake (Casper), plasma and state channels in and effort to scale the system. EOS uses a delegated proof of stake (DPoS) protocol, where 21 super-nodes, termed ‘block producers' (BPs), facilitate consensus, bringing about significant scalability improvements (4000 tps). The trade-off is decentralisation. EOS is not sufficiently decentralised because the BPs yield significant power, but are not diverse. This dissertation conducts an empirical analysis using unsupervised machine learning to show that there is a high probability collusion is occurring between certain BPs. It then suggests possible protocol alterations such as inverse vote weighting that could curb adverse voting behaviour in DPoS. It further analyses whether universities are suitable BP's before mapping out required steps for universities to become block producers (leading to improved decentralisation in EOS)