Advances in Blockchain Analytics, Through more Investment & Collaboration

Since our previous post, raising questions for Blockchain to answer, I have seen more news on Blockchain Analytics.

It seems many individuals and firms are working in this space. To bring together the power of analytics (sometimes plus AI), to help both analyse blockchain data and enable new types of analytics.

We raised, in that previous post, the unanswered questions on whether blockchain could help overcome current data challenges & how data held in blocks in chains could be analysed.

In this short follow-up post, I’ll share some progress I’ve made in seeing answers to some of those questions. This includes news items & a video, together with an encouraging increase in collaboration across these different technology specialisms.

Investment in cracking Blockchain Analytics

There have been several stories in UK & US news about investments in blockchain start-ups & more established firms. Indeed the US government seems particularly committed to investing in this technology development.

One story that caught my eye, as it offered promise for more out of the box solutions to blockchain data analytics, is this news item on the acquisition of Skry (formerly CoinAnalytics) by Bloq. If they are able to realise the potential outlined in this article, it could make for some breakthroughs of real interest to Analytics leaders: bloq-acquires-skry-supercharges-blockchain-analytics-ai-and-machine-learning

Visualising the Blockchain

Another challenge is understanding the data being analysed & how to proxy traditional Exploratory Data Analysis (EDA) in these more distributed data structures.

One key may be the use of powerful data visualisation. Increasingly a key skill for all analysts, the power of some data visualisation installations has enabled their use as real-time monitoring tools in their own right.

This impressive video, from the Data Science Institute at Imperial College, really brings to life how their visualisations enable analysis of Bitcoin transactions. Well worth watching & considering how such a visual approach to monitoring blockchain data (public or private) could work for you. I could see a number of different use cases, where this acts as a form of EDA to prompt further analysis:

When forces collide IoT, BlockChain & Data Science

Although, as I raised at #CityChain17 event, there is a risk that blockchain use cases are currently being considered in isolation to Analytics/Data Science work there are also signs this is changing. Both research work, as above, and developments by vendors are beginning to (rightly in my view) highlight the opportunity for collaborating disciplines.

Treated in isolation, Blockchain, IoT, Machine Learning, Big Data etc, simply add to the apparent tsunami of new technologies & buzz words for leaders to absorb.

However, the greatest benefits for businesses will surely come by identifying how these different approaches to data problems/opportunities could collaborate to offer more complete solutions.

Further progress in such a convergence of technologies is demonstrated in the plans of IBM. In this post from CoinDesk blog, we get a peek inside how IBM is seeking to merge use of Artificial Intelligence (from Watson) with their blockchain frameworks, as part of their offering for analysing IoT data: ibm-watson-artificial-intelligence-blockchain

Have you found answers? Are you considering Blockchain Analytics?

I hope that helped provide some updates on the questions posed in our previous post.

None is a complete solution & there does seem to be more focus on analysing data in blockchains, rather than using blockchain technology to deliver better data stores. However, it is encouraging progress nonetheless.

What about you? Have you found more of the answers Analytics leaders need in this space? Have you been convinced to explore blockchain technology for your business challenges?

Please do share your thoughts in comments below. There is much for us all to still learn as this technology approaches mainstream adoption.