By Gary Richardson, Managing Director, Data and Emerging Technology, 6point6
With another year under our belts, and an even heftier investment figure, companies who’ve poured millions into digital transformation projects will once again feel saddled with disappointment that the hype of shiny new technologies falls short of their grandeur promises.
With many executives having expressed concern over ‘game-changing’ technologies, many of the failures have become a crucial lesson in sustainable implementation. With this fresh in the minds of companies heading into 2020, we will begin to see a widespread return to getting the fundamental basics right to ensure the digitalisation of business can operate at scale without compromising stability, and quality.
Enter DataOps – a set of agile tactics designed to support and streamline AI, data analytics and machine learning initiatives. To make more intelligent and strategic use of data, actionable insights across the entire business pipeline must be consistently realized to paint an accurate picture of execution, and adapted accordingly. In 2020, DataOps will bring much needed respite to businesses where disillusionment runs rife. Here, we breakdown why and how DataOps will pave the way for success.
Dirty data is no longer an option
Data’s status in financial services has grown more prominent as emerging technology initiatives move from proof of concept to live in real-time. The hygiene and care of data up and downstream is becoming a normal course of business. To do it right, data quality and governance must be front and centre to ensure they are consistently aligned with business objectives. The alternative – poor and insufficient data – can leave digital transformation projects dead in the water.
Not only can dirty data hamper the progress of digital projects, it can even wreak havoc on regulatory requirements and create sub-optimal business strategies. That is why data hygiene is of paramount importance. While there are various platforms available to carry out large scale data cleaning, the fatal mistake companies are learning to avoid is the failure to implement a data culture. As with any advanced tool, its users’ understanding of how to apply it to business objectives will determine its success or downfall.
Introducing a DataOps function not only helps firms to mature their data-cleansing processes, meaning all data produced can be analysed and applied elsewhere in the business pipeline. It allows people across large enterprises to truly access the potential of data.
A data culture is the key to the kingdom
DataOps is designed to greaten the understanding of the data journey across enterprise. The aim is to ensure everyone, from data scientists to business operations, can extract and master the information for the business’ advantage.
As more processes go digital, a dynamic cross-team approach to analytics will allow firms to generate a better picture of data assets. A DataOps function will act like a steward, providing the guidelines for cross-functional teams to focus on short and long term strategy and data literacy; ultimately enabling data to flow seamlessly across a business. This creates a culture whereby everyone is working towards a common goal closely aligned to the objectives of each pipeline.
The true value of any digital transformation project can only be unlocked when the data is refined throughout the organisation. If the data is mishandled or misunderstood by any team member at any touchpoint, then the most meaningful insights can be lost.
Without strategic deployment of a DataOps culture, silos and isolated pockets of disconnected information will continue to disrupt productivity and growth. Businesses looking to establish themselves as data-driven who ignore the crucial ingredients of DataOps will fail to access the benefits of a more complete, holistic and accurate view of data.
Once a company embraces the principles of DataOps, the road to more robust operational resilience is clearer. Maintaining the security of data systems has never been more important and it deserves its place as a priority.
DataOps: a proactive approach to operational resilience
Companies have learnt that the pursuit of short-term financial goals combined with rushed ‘lift and ship’ data platforms ultimately mean projects become obsolete before completion. DataOps provides a framework where resilience is met through a combination of strategic planning and proactive measures put in place to tackle challenges before they’ve occurred. As we’ve learned the hard way, it is much more cost-effective to solve an issue at the design stage of the life cycle rather than later down the line.
DataOps encourages firms to bring each team together to leverage data to assess the full lifecycle. From design, build to system maintenance, DataOps embeds the connective tissue throughout to ensure that the deployed capability is and continues to be resilient while avoiding any critical platform failures. This can and should be applied to any digital transformation project. Ongoing monitoring and optimisation leads to projects that will stand the test of time in an ever-changing data landscape.
Long may DataOps reign
The time for false promises and fairytales is over. It is clearer than ever that a DataOps strategy is required to enhance operational resilience and give businesses the edge to compete in today’s data-driven environment. What will really demonstrate DataOps’ ability to stand the test of time is the way in which it can be successfully applied to culture. Investment in technology is no longer enough, companies must make a valuable commitment to address the human side of data to really seize its benefits.
RISK VS REWARD: IS AI TAKING OVER?
Xavier Fernandes, Analytics Director at Metapraxis
A study by Oxford University academics into “The Future of Employment” in 2013 prompted apocalyptic headlines which stated that in the future 40% of jobs will be automated thanks to advancing technology.
The researchers subsequently claimed that the truth was in fact a little more prosaic; rather than facing complete automation, the research found that 40% of jobs faced some aspect of automation in their activity. So with new ‘AI processes a likely reality for almost half us, what does that mean for our current roles and should we be worried?
The fourth revolution?
The first industrial revolution saw machines replacing muscle, both human and animal. The second and third saw electrical power, mass production and computerisation revolutionise the job market. Now, with daily headlines of AI as an employment superpower, there is some concern that AI is bringing a fourth revolution, and with it, unknown circumstances.
This ‘fourth industrial revolution’ is defined by replacing brain power with machines. Our thinking capacity is what inherently sets us apart from other species, so it’s not surprising that any encroachment on it triggers some existential angst.
Evolve to reap the rewards
While many businesses still don’t fully understand the capabilities of AI, those who fear its development are, instead of embracing it, missing all the benefits that it can bring to the workplace. Businesses that utilise AI appropriately are seeing vast improvements across their entire value chain; better customer experience, reduced costs, and more insightful analysis to support management decisions.
AI is particularly useful for supporting tasks with repetitive activity, for example, performing financial checks and assessing large sets of data within financial services firms. AI performs particularly well within this context, spotting outliers before a human expert would notice them, allowing impending problems to be flagged and avoiding costly mistakes.
There is also an increasing focus on maximising customer lifetime value through the use of AI. Being able to predict existing customers’ needs as well as track trends in their financial circumstances is supercharging the old cross-selling approach with testable, predictable outcomes.
With potential benefits like these on offer, management teams of innovative financial services are increasingly relying on AI to help them with some of the heavy-lifting of analysis. Using advanced data capabilities and learned behaviours, AI analyses market trends to provide predictions of future performance. This insight is invaluable and allows management teams to change direction and correct any problems accordingly. This offers a huge advantage over those that have not adopted such tools.
Supporting the workplace
Algorithms and AI are typically ‘smart’ at doing one, tightly-constrained task, but they can be less helpful with many of the activities that humans find straightforward. In most white-collar jobs, automation tends to replace certain tasks in the job, rather than the role in its entirety, as the need for human intelligence is still highly necessary. In particular, we still need human input to first challenge, and then synthesise, this information before taking action. Employees should therefore work with the business to proactively identify what areas of their role could be automated, so that they can focus on the areas that add real value to the business’ commercial goals.
Challenging AI is certainly still important. We know that algorithms can be much better than humans on certain, bounded tasks. However, many algorithms rely on existing data sets to build their understanding. As a result, when a business unit has ‘symptoms’ that fall outside of that body of knowledge, the algorithm may suggest the wrong course of action with costly results.
Indeed, even with plenty of data, algorithms will reflect any biases the data set contains. We’re seeing this with some legal sentencing algorithms where there is evidence that they are treating disadvantaged people more harshly. Getting the answers to why and how far we should trust our algorithms should therefore become an everyday part of any job affected by AI.
Rather than depending entirely on AI for all decisions, workers should be taking all these new, AI-generated insights and using them to complement the human decision-making process. No manager of a complex business ever has enough time to sieve through all the analysis available, but with AI driven algorithms able to flag up any issues and indicate where action needs to be taken, we may find that we have some AI ’colleagues’ who will cover our backs and suggest innovative options. Yes, there will be times when the algorithms get it wrong, but as long as we’re watching out for those, the future is bright.
HOW TECHNOLOGY IS FUTUREPROOFING STOCK MARKET TRADING
Tony Shaw, Executive Director, London Office and Head Sales UK & Ireland at the Swiss Stock Exchange
Markets are shifting, there’s no doubt. Amid all the disruption and volatility from the past year, the Swiss Stock Exchange asked traders about what they expected in 2020 and beyond in our industry survey. The findings point to a rise in digital to help traders content with external forces.
First and foremost, traders are enthusiastic about what digital assets can offer.
Two thirds of traders polled said they’d had a marked rise in interest from their clients for digital assets and crypto-products. Given the interest, traders are increasingly bullish about the potential of these products – so much so that 80% have predicted an increase in overall demand in the long term. Market users believe these assets will help generate cost synergies and streamlining trading and settlement processes by creating efficiencies and ultimately reducing costs.
Our 2019 results reflect what traders have told us when it comes to digital assets and products. Last year, we saw significantly higher trading volumes from products with crypto currencies as underlyings. Overall volumes grew by +8.5% over 2018, but the increase in crypto products alone was +17%, reaching CHF 518.2 million ($534.54 m). There was a year-on-year increase in the number of transactions, as well (+21%): 19,636 trades in total.
The potential digital assets hold is clear – evidenced by the building of the SIX Digital Exchange (SDX), a fully integrated issuance, trading, settlement and custody infrastructure for digital assets.
According to traders, artificial intelligence (AI) is expected to bring further benefits to market operations.
Two thirds of our survey respondents anticipate AI will create more opportunities for the traditional equities business, while a similar number expect it to reduce the cost of trading. Innovation in AI is already – and will continue to be – a key driver in making our industry more effective at withstanding future risks and challenges both within and beyond the market itself.
In Europe, there is growing momentum behind calls for shorter trading hours – this trend was reflected in our survey as well.
Industry groups such as the Investment Association are advocating for stock market trading hours to be cut from 8.5 to 6.5 hours to open the industry to working parents and women who cannot commit to such long workdays. We found traders were largely supportive of this, with many saying that it could even facilitate operational benefits. The roll of AI is clear here in improving efficiency while minimising time wastage: 36% of traders said the introduction of shorter trading hours would prompt greater market liquidity.
Beyond the market itself, geopolitics continue to shape wider market sentiment.
It comes as no surprise that four fifths of traders said their strategies have been – to some extent – influenced by Donald Trump’s tweets. Interestingly, only 39% of those polled viewed Brexit as an influencing factor in trading activity, while three quarters believe the US election will drive trading activity in 2020 and 65% acknowledged trade wars would also have an impact.
More broadly, traders are split on the state of the global economy – 58% are bracing for a global recession while 42% predict stable macro-economic conditions over the next three years. What seems clear is that whatever happens in the wider economy, traders are making headway with new technologies that can improve their strategy, efficiency, and overall market health.
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