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By Matt Savare and Kurt Watkins

  • Unless you have been living under a rock for the last year, you have heard and read about Bitcoin, which is electronic cash supported by a peer-to-peer network of computers linked via the Internet.  Although Bitcoin receives the lion’s share of the press – and is the most valuable cryptocurrency by market capitalization – another such network, Ethereum, is powering a transformation in the way business is conducted.  Ethereum is an open source blockchain platform that utilizes a Turing-complete programming language (i.e., capable of computing anything that is computable given sufficient resources) that enables the creation of decentralized applications and “smart contracts.”  Without being hyperbolic, smart contracts, which automate business processes using blockchain technology, have the potential to disrupt and revolutionize the provision of business services the same way robots did manufacturing.

Rise of the (Decentralized) Machines

  • Today, businesses typically store their information on centralized servers(their own or those of cloud service providers) and share that data with the central servers of third parties, such as banks, vendors, and customers.  Such a centralized-server model has three fundamental weaknesses: (1) the costs and inefficiencies associated with having to reconcile data sets;(2) the vulnerability of the central locations to cyber-attacks; and (3) the necessary provision of sensitive data to third parties who facilitate information sharing.  A new model based on the blockchain has arrived.
  • Blockchain technology obviates the need for centralized servers.  Instead, data is transacted and stored in a decentralized or “distributed” fashion using the combination of four elements: nodes, a shared ledger, a consensus mechanism, and a virtual machine, which together comprise a “blockchain.”
  • Nodes are computing devices connected to the Internet and running the blockchain software. They represent the physical element of a blockchain and store all of its information.  The diffusion of information across the various individual nodes is what makes the blockchain decentralized.
  • The shared ledger is the information stored on each of the nodes. Any transaction, or specified group of transactions, is grouped into a “block” of information, and each block is cryptographically linked (or chained) to the adjacent blocks, hence the name blockchain.  Together, these transactions and the blocks that store and connect them are the shared ledger.  Each node powering the blockchain maintains an identical copy of the shared ledger in its entirety (i.e., each block on the blockchain from the very first block to the very last one).  Because reconciliation of the shared ledger is automatic and simultaneous across the entire blockchain, the shared ledger is a single store of truth in an otherwise trustless, decentralized environment.
  • The consensus mechanism is the algorithm the blockchain uses to automatically guarantee properly functioning nodes and data transactions. There are a variety of consensus algorithms (e.g., proof-of-work and proof-of-stake), but each one ensures the consistency of the data across all the nodes in a blockchain and ensures those nodes are behaving normally.  Should any one node be compromised, the algorithm will recognize it as behaving abnormally and instruct the remaining nodes to take corrective measures.  Further, if an improper data transaction occurs, the additional security provided by the cryptographic links will forbid the impropriety.
  • The virtual machine is what processes the instructions for the blockchain. It is, in essence, the computer that would exist if all the nodes of the blockchain were contained in a single computer.

The first functional platform to realize these advantages for broad application was Ethereum.  Although Ethereum issues tokens (Ether) that can be used for payments, its core value emanates from the ability of its virtual machine to process data transactions and decentralized applications rather than simply exchange tokens, like Bitcoin does.  This enhanced functionality enables Ethereum to power and process smart contracts, presenting businesses with two critical advantages: a massive reduction in overhead and greater reliability than current technology models.

Automatic for the People: The Emergence of Smart Contracts

Although there is no definitive definition of “smart contracts,” they are, at the most fundamental level, programs (or “autonomous agents” as the Ethereum white paper dubs them) that execute instructions exactly as programmed.  After they are developed and deployed onto the blockchain network, smart contracts are self-executing and self-enforcing.For example, suppose a bank has decided to reap the benefits of smart contracts and created a decentralized system on the Ethereum blockchain to handle its accounting.  Now, the bank wishes to issue a mortgage to a borrower.  The parties agree to run their mortgage on the Ethereum network, thereby automating payments without the need to develop or license a costly proprietary system.  The computer-coded terms would be stored not only by the parties mentioned, but also every other node on the blockchain.  This ensures that the smart contract cannot be changed without the consent of the bank and the borrower.  If the borrower does not have enough funds in his or her account, then the bank would be instantly notified and different terms could be enforced in the event of a default.  And, once the borrower has paid back the mortgage, the network would update and automatically remove the debt obligation on the bank’s system.  If properly programmed and linked with an available smart contract processing title documents, the final payment would automatically update the title of the home and remove the bank’s lien on the property.

Other examples abound.  In the world of financial transactions, smart contracts can be programmed to execute various trades (e.g., stocks, bonds, Bitcoin) when certain conditions precedent are satisfied and without having to trust any party to perform the transactions.  A supply chain blockchain can automatically track goods in real time, properly route them to the correct destinations, and remit payments to the appropriate parties along the way.  An artist can track the use of his or her intellectual property in real time as well as manage those digital rights and receive payment without the long line of intermediaries that currently dominate the system (e.g., Apple’s 1/3 cut).  A company can automatically track its shares, update its cap table, and administer shareholder voting.  With smart contracts, these and many more processes that ordinarily require several layers of human oversight can be more easily managed, provide data in real time, reduce overhead and confusion, and create new opportunities.

However, utilizing a distributed network (public or private) requires not only retooling a business’s current environment, but also obtaining buy-in from all of the interested parties.  Because smart contracts process binding transactions, a company’s counsel (in-house and/or external)should be involved with their development and deployment to ensure that the smart contracts reflect the human negations and do not expose the business to otherwise unforeseen legal consequences.  Most critically, there must be meaningful cooperation among the parties that will use the system and the software developers who will help code them.

Building Blocks: Creating and Deploying Smart Contracts

In order to craft and deploy smart contracts that accurately reflect the parties’ understanding, that transform the way transactions are executed, maintained, administered, and enforced,and that reliability replace standard contracts (in whole or in part), lawyers, software developers, and the interested parties to the transaction must collaborate in a number of meaningful ways.

As a preliminary issue, although smart contracts more efficiently govern transactions among the parties, a traditional contract must still govern the blockchain’s terms of use and the legal relationships and obligations among the parties. The traditional contract will set forth the terms of the software license, what law applies in the event of a dispute, where the dispute should be resolved, each party’s contribution to the production of the blockchain, what smart contracts will be deployed, and other standard terms such as representations and warranties, indemnification, and limitation of liability.

The traditional contract will also set forth what kind of platform the parties will run among themselves.  Broadly speaking, the platform will be either a public blockchain, like Ethereum, or a private blockchain, like a Hyperledger project.  The advantage of a public blockchain is that the platform is already built and trusted.  However, it is more difficult to ensure privacy, efficient transactions times, and develop sophisticated tools on such a platform.  The alternative, a private blockchain, requires more upfront investment and buy-in from the participants, as it must be customized.  Either option will fundamentally do the same two things: form a universal consensus on the facts and execute actions based on that consensus.  This first technical decision, as well as all those that flow from it, must anticipate the legal requirements of data storage, privacy, and the transactions to be performed on the blockchain.  The parties’ legal teams are best positioned to aid in this decision.

Once the platform is built, the parties need to develop and deploy their smart contracts.  Much like the creation of other customized digital applications, software developers write the code to perform specific functions and the users of the code (i.e., the various parties to the smart contract) interface with the application by providing correct inputs and access to the appropriate information. During this process, however, the developers need the support of legal specialists, who can accurately convey the legal requirements of the code as well as listen to, and work with, the developers when a straightforward method of coding for the legal parameters cannot be achieved.

To execute or go live with a smart contract, a party must “sign” the contract.  Unlike in the case of traditional contracts, smart contract execution is not achieved through a handwritten or electronic signature.  Rather, every party on the platform has a unique signing tool, its private key.  By running its private key against a smart contract, the smart contract will execute the functions the party requires of it.  This “signing” leaves an indelible imprint stating the time that the party executed the smart contract.

Finally, the terms of the smart contract are broadcast to the entire decentralized system, which automatically updates the shared ledger after reaching consensus that the transaction is valid.  To ensure privacy, the terms can be “hashed,” a cryptographic process whereby the terms are given unique markers that cannot be reverse engineered.

Although smart contracts deployed on the blockchain are relatively new phenomena, they are already profoundly transforming the way we conduct business.  From shipping to banking, logistics management to livestock, the legal profession to insurance, and the entertainment industry to the Internet of Things (IoT), smart contracts are becoming more widely adopted across a range of uses cases for businesses, individuals, and even IoT devices.  Successful creation and deployment of these smart contracts requires smart collaboration among the various stake holders, their developers, and their attorneys.

Matt Savare is a partner and Kurt Watkins is an associate at Lowenstein Sandler LLP, where they practice intellectual property, technology, blockchain, and privacy law. 


What Skills Does a Data Scientist Need?



What Skills Does a Data Scientist Need? 1

In this modern and complicated time of economy, Big data is nothing without the professionals who turn cutting-edge technology into actionable insights. These professionals are called Data Scientists. Modern businesses are awash with data and many organizations are opening up their doors to big data and unlocking its power that increases the value of data scientists. Data is one of the most important features of any organization which helps to make decisions based on facts, stats, and trends.

As the scope of data is growing, data science came up as a multidisciplinary field. Data science is an integral part of understanding the working of many industries, complex or intricate. It helps organizations and brands to understand their customers in a much better, enhanced, and empowered way. Data science can be helpful in finding insights for sectors like travel, healthcare, and education among others. Its importance is increased as it solves complex problems through Big Data. With data science, companies are using data in a comprehensive manner to target an audience by creating better brand connections. Nowadays data science is taking an important and big prime role in the growth process of brands, as it is opening new fields in terms of research and experiments.

Let us know about the much-hyped role of a data scientist, the skills required to become one, and the need to take data science training.

Who is a Data Scientist?

Data Scientists are the individuals who gather and analyze large sets of structured and unstructured data. It combines the roles of computer science, mathematics, and statistics to create actionable plans for companies and other organizations. They gather, analyze, and process the data and then find the filtered results. Their work is to make sense of large, messy, and unstructured data using sources such as social media, smart devices, digital channels, emails, etc.

In other words, data scientists are analytical data experts who solve complex problems through technical skills to explore what problems need to be solved with available data. They are struggling with data all the time and experimenting via complex mathematics and statistical analysis. Usually, data scientists are required to use advanced analytics technologies such as machine learning, advanced computing, and predictive modeling. They use various types of reporting tools and analytical skills to detect problems, patterns, trends, and connections between data sets. Their goal is to provide reliable information about campaigns and consumers that help companies to attract and engage their customers and grow the sales.

A job of a data scientist is also known and advertised as a machine learning architect or data strategy architect. Data scientists generally require enough educational and experiential background of big data platforms, tools including Hadoop, Pig, Hive, Spark, and MapReduce and programming languages such as SQL, Python, Scala, and Pearl; and computing languages like R.

Skills Needed To Become a Data Scientist

To become a data scientist, it is recommended to have a master’s degree. This means a very strong educational background and the deep knowledge is must-required to become a data scientist. You must have a bachelor’s degree in any stream such as computer science, Physical science, social science, statistics, and mathematics or engineering.

The skills required to become a data scientist are categorized into technical and non-technical. Some of them are mentioned below:

Technical Skills

● R Programming

R is specially designed for data science to deal with big data. It is generally preferred for data science to gain in-depth knowledge of analytical tools. Almost 43% of data scientists are using R to solve data problems and statistical issues.

● Python Coding

The most required technical skill to become a data scientist is having the knowledge of the most common coding language that is Python along with C, C++, Java, and Pearl.

● Hadoop Platform

It is the second most important skill to be a data scientist. This platform is heavily used in several cases. Hadoop is used to convey the data quickly to different servers.

● Apache Spark

It is becoming the most popular big data technology in the whole world. Just like Hadoop, it is a big data computation framework, but it is faster.

● SQL Database/Coding

With SQL database and coding, data scientists are able to write and execute complex queries in SQL.

● Data Visualization

A data scientist can visualize the data with data visualization with tools such as ggplot, d3.js and Matplottlib, and Tableau.

● Machine Learning and AI

Machine learning techniques include reinforcement learning, neural networks, adversarial learnings, etc. Along with it, supervised machine learning, decision trees, logistic regression can help you stay ahead from other data scientists.

Non-Technical Skills

There are also some non-technical skills such as Intellectual curiosity, Communication skills, Business acumen, Teamwork, etc. that can make you a successful data scientist.

Ready to Learn Data Science?

Data Science is nowadays a buzzing word in the IT sector. It has become an evolutionary technology that everyone is talking about. Several people want to become data scientists. It is a versatile career that is used in many sectors such as health-care, banking, e-commerce industries, consultancy services, etc. This career is one of the most highly paid careers. Data science careers have been always in high demand so the seekers have numerous opportunities to start or boost their careers.

It is a widely abundant field and has vast career opportunities because there are very few people who have the required certifications and skill-set to become a complete data scientist. You can gain these skills by enrolling in an online data science training program. By learning from industry experts, you will have a strong foundation of data science concepts. You’ll also be able to work on different data science tools and industry projects through a training course. So it’s the right time to get certification and grab the golden opportunities in the Data Science career.

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How to use data to protect and power your business



How to use data to protect and power your business 2

By Dave Parker, Group Head of Data Governance, Arrow Global

Employees need to access data to do their jobs. But as data governance professionals, it’s our job to protect it. Therefore, we must perform a fine balancing act to weigh robust data protection against the productivity of workers who need the data to maintain business-as-usual working processes.

Data grows exponentially, and most organisations will admit that they simply don’t know what data they have, where it is, and the controls that exist around it. This creates 2 challenges:

  1. Burgeoning amounts of unstructured data makes the business increasingly vulnerable from external attackers or internal data breaches.
  2. Because data is the key to understanding a customer’s wants and needs, if the business can’t identify its data and unlock its value, it’s at a competitive disadvantage.

As a European investor and alternative asset manager, here at Arrow Global we take care of £50bn of assets and own a data estate exceeding 160TB. How we manage our data is key to our success. We understand the difficulties involved in opening up environments to allow people to work productively, while at the same time locking them down to protect our organisation.

When it comes to analytics, I believe that Arrow is highly proficient because we employ a talented team of data scientists. But even for us, the sheer volume of raw and processed data, that resides in both our structured systems and unstructured data repositories, has the potential to put our business at risk.

We know there’s always more that can be done to strengthen our security posture and ensure regulatory and contractual compliance, while at the same time using our data to drive the business forward.

Data protection isn’t just about compliance

For many organisations, data protection has centred on demonstrating compliance with the GDPR. At Arrow, our efforts have gone one step further to include our contractual exposure.

Being a more mature data organisation, we had previously tried to develop an application in-house to manage our data estate. However, with 160TB across the company in production data alone, we simply couldn’t achieve the scale we needed to handle the sheer volume of data. Of course, the volume is just the start – once you know what data you have, you then need to be able to categorise the data and put it into a structure, so the business can analyse it for a specific use case.

We knew we needed to go to market to find an industrial-strength data discovery product to replace our in-house application. By aligning our choice of product to our overall IT and change strategy, meant that ultimately, we ended up with a far better outcome than we’d anticipated.

Position data as both a risk and an asset

Data touches every part of an organisation, so when it came to building a business case for buying-in a data discovery software platform, we approached it in a way that would speak to different people at the same time. We did this by posing the question:

“What do we want to do with data in a way that is GDPR-compliant, contractually-compliant and enables us to better service our clients?”

These are the black and white tests of data governance – to recognise the importance of securing and protecting data. They’re applied in a way that enables us to commoditise data and use it to drive the business forward, by forcing us to consider how we would use the data – for example, creating value-based pricing for our clients.

In aligning the business case to initiatives that were already priorities within the boardroom, we knew that we’d gain the attention of the senior leadership team and it would be easier to get the buy-in and budget we needed. And in the end, everyone wins – we get what we need to protect the data, and the business gets to distil the data’s value to better meet our customers’ expectations.

Dave Parker

Dave Parker

Get visibility of data at scale

For us, things got really exciting once we were able to see all of our data at scale. We chose Exonar because it allowed us to discover our data in ways that other products couldn’t. And the interface between the user and Exonar meant that everyone – both technical and non-technical users – could understand the technology and the findings it revealed.

When we saw exactly what data was in the estate, where it was and who had access to it, data security became much easier and the risk of data being compromised was dramatically reduced. We can see exactly where the vulnerabilities are and restructure how our data is stored to strengthen security. Then over time, we can use search, workflow and analysis to optimise the infrastructure and continually identify new areas to improve.

Commercialise the data

From a wider-business perspective, once people can see the data, they can start asking “What if…” to query it and distil its value. But it’s more than just the data itself. It’s not uncommon for data relating to the same thing to exist in unconnected systems across the business. For example, customer interactions and incidents or events.

Exonar is capable of joining the dots in disparate data sets. By stitching these data sets together, we can get a better overall view of our customers and use the outcomes to think of new, different or better ways of serving them through enhancing or adapting our offerings.

Why other financial services businesses should also take a smarter approach to data

  1. By changing the way you approach data, you can use it to protect and power your business and the people you serve.
  2. By positioning data as both a risk and an asset, you elevate its position to give it priority in the boardroom. Ultimately, it’s data that helps the business make informed strategic decisions about how to strengthen its competitive advantage.
  3. By gaining visibility of data at scale, you can see exactly what data you have and where it is. This gives the business confidence about the actions needed to ensure it is secured in both a regulatory and contractually compliant way, and that people are doing the right thing with data at all times.
  4. And joining different data sets provides you with a single view of ‘X’ within your data, no matter where it is. Helping to support your wider-business strategy and priorities, it gives you the information you need to secure a business advantage and generate value.
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How business leaders can find the right balance between human and bot when investing in AI



How business leaders can find the right balance between human and bot when investing in AI 3

By Andrew White is the ANZ Country Manager of business transformation solutions provider, Signavio

The digital world moves quickly. From keeping up with consumer behaviour patterns, to regulation and compliance, the most successful organisations are always on the cutting-edge of technological developments.

However, when it comes to investing in artificial intelligence (AI), a hard and fast strategy does not guarantee a top spot amongst the league of tech greats. Instead, it pays to take a considered approach to balancing reliance on automated processes with a human touch. Why? Because creative and strategic thinkers are the true propellers of innovation; automation is simply the enabler.

The International Monetary Fund (IMF) developed the ‘Routine Task Intensity’ (RTI) index as a measure of which processes are likely to benefit most from automation. According to this metric, jobs requiring analytical, strategic, communicational and technical skills score low on the RTI index, while simple, repetitive tasks scored highly.

The lesson for business leaders here is simple; your digital investments are just as important as your stake in talent. When deciding which processes to automate, start simple, and remember to value the skills and potential of your people.

Keep customer-centricity at your core

Customer-centricity means that every business decision, dollar spent and new hire is centred on one question: how does this benefit my customer? Investments in AI are no different. To be truly successful, they must have a customer-focused outcome.

Where companies get this wrong is by implementing cost-saving measures or ‘copy and paste’ software that fails to improve the customer experience – often having the adverse effect.

Take the virtual chat-bot, for example; if implemented poorly, it can send your customers into a frustrating and seemingly infinite cycle of dead-ends. The modern consumer is far too digitally savvy for this shortcut, and will quickly move onto the next merchant offering a more seamless customer service experience.

To guarantee your investments are delighting rather than infuriating your customers, it helps to take an outside-in perspective of your business processes, aided by Customer Journey Mapping (CJM).

Before you commit to digital investments, CJM can trace and map each customer touchpoint, signalling pain points or conversion rates throughout their journey. These data-driven insights lead you to the areas that would benefit the most from automation, instead of implementing a broad band-aid solution.

Avoid the ‘set and forget’ method 

When investing in enterprise-wide AI, the ‘set and forget’ method rarely works. Real transformation requires an ongoing dedication to refining and improving AI-driven processes, as well as adapting them to the evolving needs of your customers. This is the best way to achieve customer loyalty, by proving that your organisation listens to, and understands its users.

A human perspective is invaluable here, paired with process mining – a method that thrives on finding process inefficiencies – to create a consistent feedback loop of improvement.

During periods of uncertainty, customer loyalty is everything, so aim to protect it at all costs.

The power of your people

The rise of automation can be linked to the corporate world’s obsession with speed and efficiency. However, the psychology behind this goes deeper than being the biggest and fastest producer; it’s also about reallocating resources into attracting and retaining the brilliant minds that drive companies into the future.

When communicating digital change, it’s critical to highlight the valuable impact AI has on augmenting jobs; removing the burden of mundane, repetitive tasks and allowing for more strategic skill-sets to shine through. For lower-skilled workers, invest in upskilling or re-education where possible.

Successfully rolling-out digital transformation plans means that every employee across all tiers of your company understands the value of AI. The starting point here is education to achieve buy-in. Change communications must be accessible, constructive and value-focused, supported by key culture influencers who champion automation within teams.

Enterprise-wide buy-in is an important element of refining and improving digital processes, as cross-functional collaboration can offer valuable insights into common pain points or inefficiencies ripe for automation. Supported by process mining, collaboration provides a holistic view of how each investment will impact other processes. There is no point investing in automation that streamlines one process and makes another more people-centric, so be sure to take a balanced approach to your investments.

Remember, AI is not about creating an army of robot workers; it’s about increasing efficiency and productivity so that an organisation, and its people, can work smarter.

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