Breaking Code: How Programmers and AI are Shaping the Internet of Tomorrow

Eoin Pierce our consultant managing the role
Posting date: 9/13/2018 9:10 AM
Data. It’s what we do. But, before the data is read and analysed, before the engineers lay the foundation of infrastructure, it is the programmers who create the code – the building blocks upon which our tomorrow is built. And once a year, we celebrate the wizards behind the curtain. 

In a nod to 8-bit systems, on the 256th day of the year, we celebrate Programmers’ Day. Innovators from around the world gather to share knowledge with leading experts from a variety of disciplines, such as privacy and trust, artificial intelligence, and discovery and identification. Together they will discuss the internet of tomorrow. 

The Next Generation of Internet


At the Next Generation Internet (NGI), users are empowered to make choices in the control and use of their data. Each field from artificial intelligent agents to distributed ledger technologies support highly secure, transparent, and resilient internet infrastructures.

A variety of businesses are able to decide how best to evaluate their data through the use of social models, high accessibility, and language transparency. Seamless interaction of an individual’s environment regardless of age or physical condition will drive the next generation of the internet. But, like all things which progress, practically at the speed of light, there is an element of ‘buyer beware’, or in this case, from ‘coder to user beware’.

Caveat Emptor or rather, Caveat Coder


The understanding, creation, and use of algorithms has revolutionised technology in ways we couldn’t possibly have imagined a few decades ago. Digital and Quantitative Analysts aim to, with enough data, be able to predict some action or outcome. However, as algorithms learn, there can be severe consequences of unpredictable code

We create technology to improve our quality of life and to make our tasks more efficient. Through our efforts, we’ve made great strides in medicine, transportation, the sciences, and communication. But, what happens when the algorithms on which the technology is run surpasses the human at the helm? What happens when it builds upon itself faster than we can teach it? Or predict the infinite variable outcomes? Predictive analytics can become useless, or worse dangerous. 

Balance is Key


Electro-mechanical systems we could test and verify before implementation are a thing of the past, and the role of Machine Learning takes front and centre. Unfortunately, without the ability to test algorithms exhaustively, we must walk a tightrope of test and hope.

Faith in systems is a fine balance of Machine Learning and the idea that it is possible to update or rewrite a host of programs, essentially ‘teaching’ the machine how to correct itself. But, who is ultimately responsible? These, and other questions, may balance out in the long run, but until then, basic laws regarding intention or negligence will need to be rethought.

Searching for a solution 


In every evolution there are growing pains. But, there are also solutions. In the world of tech, it’s important to put the health of society first and profit second, a fine balancing act in itself.

Though solutions remain elusive, there are precautions technology companies can employ. One such precaution is to make tech companies responsible for the actions of their products, whether it is lines of rogue code or keeping a close eye on avoiding the tangled mass of ‘spaghetti’ code which can endanger us or our environment.

Want to weigh in on the debate and learn how you can help shape the internet of tomorrow? If you’re interested in Big Data and Analytics, we may have a role for you.

Check out our current vacancies. To learn more, contact our UK team at +44 20 8408 6070 or email us at info@harnham.com.

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With over 10 years experience working solely in the Data & Analytics sector our consultants are able to offer detailed insights into the industry.

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Using Data Ethically To Guide Digital Transformation

Over the past few years, the uptick in the number of companies putting more budget behind digital transformation has been significant. However, since the start of 2020 and the outbreak of the coronavirus pandemic, this number has accelerated on an unprecedented scale. Companies have been forced to re-evaluate  their systems and services to make them more efficient, effective and financially viable in order to stay competitive in this time of crisis. These changes help to support internal operational agility and learn about customers' needs and wants to create a much more personalised customer experience.  However, despite the vast amount of good these systems can do for companies' offerings, a lot of them, such as AI and machine learning, are inherently data driven. Therefore, these systems run a high risk of breaching ethical conducts, such as privacy and security leaks or serious issues with bias, if not created, developed and managed properly.  So, what can businesses do to ensure their digital transformation efforts are implemented in the most ethical way possible? Implement ways to reduce bias From Twitter opting to show a white person in a photo instead of a black person, soap dispensers not recognising black hands and women being perpetually rejected for financial loans; digital transformation tools, such as AI, have proven over the years to be inherently biased.  Of course, a computer cannot be decisive about gender or race, this problem of inequality from computer algorithms stems from the humans behind the screen. Despite the advancements made with Diversity and Inclusion efforts across all industries, Data & Analytics is still a predominantly white and male industry. Only 22 per cent of AI specialists are women, and an even lower number represent the BAME communities. Within Google, the world’s largest technology organisation, only 2.5 per cent of its employees are black, and a similar story can be seen at Facebook and Microsoft, where only 4 per cent of employees are black.  So, where our systems are being run by a group of people who are not representative of our diverse society, it should come as no surprise that our machines and algorithms are not representative either.  For businesses looking to implement AI and machine learning into their digital transformation moving forward, it is important you do so in a way that is truly reflective of a fair society. This can be achieved by encouraging a more diverse hiring process when looking for developers of AI systems, implementing fairness tests and always keeping your end user in mind, considering how the workings of your system may affect them.  Transparency Capturing Data is crucial for businesses when they are looking to implement or update digital transformation tools. Not only can this data show them the best ways to service customers’ needs and wants, but it can also show them where there are potential holes and issues in their current business models.  However, due to many mismanagements in past cases, such as Cambridge Analytica, customers have become increasingly worried about sharing their data with businesses in fear of personal data, such as credit card details or home addresses, being leaked. In 2018, Europe devised a new law known as the General Data Protection Regulation, or GDPR, to help minimise the risk of data breaches. Nevertheless, this still hasn’t stopped all businesses from collecting or sharing data illegally, which in turn, has damaged the trustworthiness of even the most law-abiding businesses who need to collect relevant consumer data.  Transparency is key to successful data collection for digital transformation. Your priority should be to always think about the end user and the impact poorly managed data may have on them. Explain methods for data collection clearly, ensure you can provide a clear end-to-end map of how their data is being used and always follow the law in order to keep your consumers, current and potential, safe from harm.  Make sure there is a process for accountability  Digital tools are usually brought in to replace a human being with qualifications and a wealth of experience. If this human being were to make a mistake in their line of work, then they would be held accountable and appropriate action would be taken. This process would then restore trust between business and consumer and things would carry on as usual.  But what happens if a machine makes an error, who is accountable?  Unfortunately, it has been the case that businesses choose to implement digital transformation tools in order to avoid corporate responsibility. This attitude will only cause, potentially lethal, harm to a business's reputation.  If you choose to implement digital tools, ensure you have a valid process for accountability which creates trust between yourself and your consumers and is representative of and fair to every group in society you’re potentially addressing.  Businesses must be aware of the potential ethical risks that come with badly managed digital transformation and the effects this may have on their brands reputation. Before implementing any technology, ensure you can, and will, do so in a transparent, trustworthy, fair, representative and law-abiding way.  If you’re in the world of Data & Analytics and looking to take a step up or find the next member of your team, we can help. Take a look at our latest opportunities or get in touch with one of our expert consultants to find out more.

It Takes Two: Data Architect Meets Data Engineer

Information. Data. The lifeblood of business. Information and data are used interchangeably, gathered, collected, and analysed to create actionable insights for informed business decisions. So, what does that mean exactly? And to that end, how do we know what information or data we need to make those decisions? Enter the Data Architect. The Role of a Data Architect Just like you might hire an architect to sketch out your dreamhouse, the Data Architect is a Data Visionary. They see the full picture and can craft the design and framework creating the blueprint for the Data Engineer, who will ultimately build the digital framework. Data Architects are the puzzle solvers who can take a jumble of puzzle pieces, in this case massive amounts of data, and put everything in order. It’s their job to figure out what’s important and what isn’t based on an organisation's business objectives. Skills for a Data Architect might include: Computer Science degree, or some variation thereof.Plenty of experience working with systems and application development.Extensive knowledge and able to deep dive into Information ManagementIf you’re just starting your Data Architect path, be prepared for years of building your experience in data design, data storage, and Data Management. The Role of a Data Engineer The Data Engineer builds the vision and brings it to life. But they don’t work in a vacuum and are integral to the Data Team working nearly in tandem with the Data Architect. These engineers are building the infrastructure – the pipelines and data lakes. Once exclusive to the software-engineering field, the data engineer’s role has evolved exponentially as data-focused software became an industry standard. Important skills for a Data Engineer might include. Strong developer skills.Understand a host of technologies such as Python, R, Hadoop, and moreCraft projects to show what you can do, not just talk about what you can do – your education isn’t much of a factor when it comes to data engineering. On the job training does it best.Social and communication skills are critical as you map initial designs, and a love of learning keeps everything humming along, even as technology libraries shift, and you have to learn something new. The Major Differences between the Data Architect and Data Engineer RolesAs intertwined as these two roles might seem, there are some crucial differences. Data Architect Crafts concept and visualises frameworkLeads the Data Science teams Data Engineer Builds and maintains the frameworkProvides supporting framework With a focus on Database Management technologies, it can seem as though Data Architect and Data Engineer are interchangeable. And at one time, Data Architects did also take on the Data Engineering role. But the knowledge each has is used differently.  Whether you’re looking to enter the field of Data Engineering, want to move up or over with your years of experience to Data Architect, or are just starting out. Harnham may have a role for you. Check out our current opportunities or get in touch with one of our expert consultants to learn more.  

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