Four Ways Blockchain is Changing the Analytics Landscape
It seems like it has been a long time since big data was all the hype in tech. Over the past year, it’s blockchain that has been grabbing headline after headline. It does appear fitting since blockchain is proving to be quite the disruptive force. This, however, doesn’t mean that data stakeholders can just tune out all this blockchain frenzy. Data is still the lifeblood of modern tech. Disruptive technologies and data will inevitably have an interplay.
At its core, blockchain offers means to transparently and immutably keep records. This alone has created plenty of applications for the technology. New platforms have also built new capabilities like smart contracts and cross-chain interoperability giving rise to a host of new services that are hastening blockchain adoption. Due to blockchain’s growing popularity, many organizations now feel mounting pressure to adopt the technology.
This puts organizations at a very interesting position. Given the hype that data and analytics have received prior to the blockchain, many companies are in the midst of implementing their own data projects. Blockchains are essentially databases and adopting them would mean changes in how data flows. Companies now have to figure out how all these technologies can co-exist in their specific contexts.
As such, data stakeholders must increase their awareness of blockchain developments and understand how it may apply to them. Here are four ways blockchain is altering data and analytics.
1 – Accessibility of Tools
One of the major barriers to data and analytics has been the high cost of creating data and analytics infrastructure. It’s only until recently that smaller organizations were able to acquire the necessary tools to perform their own analyses through subscription-based cloud analytics and business intelligence services. Now, blockchain-based services aim to expand this accessibility of tools by decentralizing and democratizing the technology.
Endor, for instance, has already been working with large enterprises in various predictive analytics efforts. But now, the company seeks to make their technology more accessible and provide individuals and small organizations access to predictive analytics. Endor has created a blockchain protocol that it considers to be the “Google” for predictive analytics. Ordinary users will be able to simply ask questions in order to get accurate predictions.
Key to the effort is building a sustainable ecosystem and economy. The company plans to bring together users, data providers, and developers and have them nurture a predictive analytics collaborative. By relying on a decentralized approach, the cost of predictive analytics can become more affordable.
2 – Data Monetization
Data is now considered to be the fuel of the information age. This is why many large tech companies have put data at the center of their business strategies. Companies like Google, Facebook, and Amazon all have key revenue streams that rely on collecting user information and using it to power their advertising and retail engines.
However, a growing number of users believe that they should have control over who can access and monetize their personal information. Platforms like Wibson encourage data owners to share their information to data consumers and be incentivized for doing so. Wibson’s marketplace provides the infrastructure for individuals to control and monetize their anonymized private information through a token economy. Blockchain’s transparency even allows data sellers to see how their information is being used.
Such mechanisms open up the game for data consumers. Businesses may not have to go through centralized data owners like Google and Facebook for their online marketing and advertising campaigns anymore. By participating in Wibson, businesses will be able to gain access to validated and accurate information. More importantly, they can also be confident that they are acquiring their data with the other parties’ consent.
3 – Data Exchange
Many large organizations gather information with the intention of selling the data to others. But like in the case of analytics tools, there is often a price premium levied on such data. This costly access denies organizations and research groups the capability to work with the volume and variety of data they may need to innovate. What’s needed is more means for data to be exchanged between organizations.
For example, the job market could benefit from a more open exchange of data. Information about job seekers and job openings reside with several centralized authorities. Job seekers have to tediously update profiles across a variety of job sites to ensure consistency. Otherwise, recruiters may encounter duplicate, irrelevant, or even inaccurate information.
To solve this, data exchange platform Dock enables professionals to readily manage online profiles under one platform. It ensures that applications like professional social networks and job sites only work with validated, timely, and accurate information. Dock can even consolidate reputations, certifications, and experiences gained from various job platforms to help professionals create comprehensive profiles.
Research group Forrester estimates that between 60 to 73 percent of enterprise data goes unused for analytics. Data exchanges could allow developers and research groups to work on previously inaccessible data and even possibly make important discoveries. Services like Dock could help companies and professionals search and connect with each other and find their ideal matches.
4 – Blockchain as Data
Speaking of insights, blockchains are essentially databases which means the data they contain can also be subject to analysis. Details such as transaction data can be readily analyzed which could yield insights such as trading behaviors.
Analysis of blockchain data may also help show the patterns in the way humans transact using transparent and trustless mechanisms. In the case of Endor, its protocol allows for the analysis of blockchain data which could potentially be used to predict crypto prices and detect fraud.
Other distributed ledger projects like IOTA are tapping into new and emerging sources of data like the Internet-of-Things (IoT). The amalgamation of all these activities would generate more data and larger and more varied data sets. These can pose new challenges for data scientists to figure out ways to make sense of such information.
Data and Blockchain Interplay
These developments in blockchain are ushering in changes that impact the data and analytics landscape. Better accessibility means more people and organizations can benefit from insights and analytics. Large organizations may have to find other sources of competitive advantage since access to big data and advanced tools has been democratized.
Blockchain is also affecting the data users and organizations are generating. As more people use blockchain-driven services, analytics must also evolve be able to generate insights from new data. In addition, the increasing advocacy for privacy may also affect the volume of personally identifiable information that can be collected from users.
On the upside, with the emergence of data marketplaces, dark data and lost data – data that are otherwise left idle, unused, or uncollected – may soon be used to generate new and rich insights. Given these developments, data stakeholders may have to truly plan for a future where data and blockchain will go hand-in-hand.