Drivers of the digital era: Stay ahead of the competition
The quest for digitisation may be deemed elusive by some, however in switching from postal mail to fax and eventually to email, from paper-ledger to spreadsheet or even to fully fledged process automation, the road to digitisation has already been embarked upon by organisations. Staying ahead of the competition, however, is not about the technology itself, but a matter of strategy.
Strategy is the art of making use of time and space. I am less concerned about the latter than the former. Space we can recover, lost time never.” (Napoleon Bonaparte)
The strategy resorted to by US clearing houses over the past century places the “use of time and space” conundrum into perspective. In an age were mail is transmitted in milliseconds, a two-day settlement (T+2) delay for securities comes as a surprise when considering the possibility of live trading and automatic (T+0) clearing houses. Interestingly, the move from T+3 to T+2 in Europe, Australia, Hong Kong, Canada and the US over the past decade is reminiscent of trading on the New York Stock Exchange (NYSE) in the 1930s. Before 1933, next day settlement (T+1) was standard on the NYSE. Going slower was all a matter of strategy. As trades increased, and back-office operations struggled to cope, recruiting more people would have entailed more costs that would, in turn, have been recharged to investors. The solution was to slow down the settlement process, reaching T+5 in 1968. Today technology is a core component of settlements, and even though real-time settlement is possible, the strategy set by settlement houses and exchanges has settled for T+2, allowing a netting-out window for brokers to conserve on transaction costs.
The digital era is driven by the ability to innovate. Jumping onto a trend bandwagon just for the sake of it is unlikely to place an organisation in a position to experience the digital era at its full potential.
An organisation’s strategy should ensure a competitive edge in its identified marketplaces through information dominance, that is, employing superior automated data collection, analysis and processing techniques, enabling real-time, contextually relevant insights which can support data-driven decision making based on full situational awareness.
Process efficiency should be at the core of any strategy, and this is achievable through a bird’s eye view of one’s organisation such that synergies can be identified and exploited. Many businesses hold and view their available data in isolation, without reaping the benefits of what each data set represents in the context of another. Going forward, successful enterprises will thus focus on the big picture and plan for the adoption of interrelated technologies. Interconnecting brokers for automated netting-off of balances across NYSE transactions could lead to T+0 settlement. Similarly, organisations are to interconnect their static repositories of data as well as their software applications into a structured solution.
Digital democratisation via distributed ledger technology, robotics and artificial intelligence is transforming entire industries. Future technology adoption strategies are to be set now, tapping into the right skill set and employing technology to implement efficient processes which enable sustainable growth.
It is time to get on board and embark on the path towards the innovation horizon from manual to cognitive enterprise. Cognitive enterprise would entail the use of technologies that are adaptive and interactive, capable of understanding context and learning from their environment, freeing up human resources to perform high-value activities, automating the manual execution of high-volume repetitive and routine tasks, and curbing human error.
Those in the lead can collect and analyse data that competitors following suit will lack and possibly achieve at a later point in time. Resorting to the tools in the arsenal that drive digital transformation is therefore vital. Some technologies relevant to this are:
- Robotic Process Automation (RPA): A machine or software that manages, acts on or processes high-volume, repeatable tasks that previously required a human to perform.
- Internet of Things (IoT): Interactive software components that have their own function, or interface to interconnect devices to other enterprise systems for the creation of business orchestration across complex data, technology, processes and functions.
- Data Analytics: Data modelling focused on specific business problems or outcomes to identify patterns and anomalies. They provide actionable insight. Models improve over time based on feedback from actual results of prediction.
- Natural language processing (NLP): The advanced ability of a computer or program to understand human speech (speech recognition) or written text (unstructured text) and derive intelligence, take action or present results that normally require manual interpretation.
- Blockchain: A ledger where activities are recorded in ’blocks’ as a chronological ‘chain’ providing an immutable audit trail. A public type blockchain replaces central authority with a decentralised consensus-driven approval process. Blockchain offers trust, transparency and a consistent approach to supply chain integration, which constitutes a significant advantage for risk management.
- Chatbots: A computer program that conducts a conversation via auditory or text methods, designed to simulate human conversation and are typically used in dialogue systems, such as customer service or information acquisition.
- Artificial intelligence (AI): Systems and software able to perform tasks that typically require human intelligence, such as reconciliation, investigation, validation and repair within complex and multi-input processes.
- Augmented reality (AR): Interactive visualisation that superimposes computer-generated information into real life using glasses or projection to assist in real time activities, layering data sets into processing.
Cognitive enterprise would identify the resources available and adopt the best-suited technologies to implement its strategy. AI is likely to build upon RPA, data analytics, NLP and other technologies. Long term, the digital transformation will impact enterprise through data, cost efficiency and risk management.
This is exemplified in the AI processes built into today’s mobile phones. Data collected via AI is analysed, and improvements are pushed onto the phones via automated (RPA) updates, a three-way technology collaborative approach that enables such enterprise to stay ahead of the competition. Similarly, in a compliance environment, predictive modelling tools used in data analytics could monitor data collected via RPA and chatbots, possibly applying natural language processing techniques to decode the information. The process could be run entirely off an AI solution.
It all lies on the horizon. There is an opportunity cost to miss out on taking stock of the current state, setting a strategy, and implementing it at a manageable pace commensurate with available resources.
The strategy is to be set on precise data. It is likely that most businesses are not starting from scratch. Data collection and retention policies are typically in place, together with solid reporting foundations that are still maturing, possibly hosted on a scalable data centre with reliable cyber defence mechanisms. The data may, however, need to be restructured, possibly introducing automation solutions in operations coupled with machine learning data analytics.
Just like the shift from manual ledgers (analogue data) to bookkeeping software solutions (static digital data) facilitated the cross-mapping and reconciliation of various accounting entries, the technology arsenal is to be implemented as part of a cohesive holistic strategy, enabling precise cognitive data for the furtherance of the entrepreneurial spirit into a digital era based on live data analysis; a far cry from ex post facto management accounts or annual financial statements compiled after month- or year-end.
Staying sharp in the digital era is crucial. This is the information age; be it precise business data analytics or continued personal education, it is a quest for knowledge to maximise one’s potential in the digital era and stay ahead of the competition.