Existing technologies are undergoing radical digital transformation, where they are able to learn everything about users behavioural and consumption trends, becoming better able to cater to their needs and preferences before users explicitly express them. This will undoubtedly work to revolutionise many aspects of daily life.
Many modern examples work as evidence to this claim. Large B2C organisations such as Facebook, Sephora and Uber already utilise voice-powered assistants and chat-bots which are capable of engaging with consumers and predicting their needs.
Trends in preferences and product suggestions can be established in real-time by recognising returning customers from their previous purchase history and understanding voice commands and new requests.
>See also: Digital transformation for the UK economy
Now, consumers are able to enjoy their shopping, ordering and scheduling experiences with more ease thanks to enhanced predictive analytics and machine learning that comes with modern technology.
This is exciting but nothing too new. Predictive analytics and machine learning have long been discussed in fields like marketing and advertising where analysing a constant stream of data and automating responses on the right platform, at the right time, has been used to target and engage with consumers. The interesting advancements now are centred on this technology within the industrial space.
Put your best cognitive foot forward
A number of organisations are starting to introduce cognitive applications into their daily operations, but these tend to be the reserve of those with the available resources to scale their solutions and tackle platform and data integration challenges. Examples of these include GE and Siemens.
However, the great potential for both B2B and B2C cognitive application is quickly becoming more accessible and will soon be accessible to the many, not just the few. Take, for example, manufacturers who supply washing machines to huge hotel chains.
The software in washing machines has been available for years and has been used to gather data that could be used for predictive analytics – to tell when individual machines may break down and/or need repair.
Now that the IoT is a reality, manufacturers can use this information to offer timely and relevant additional customer services based on sophisticated software that can truly interrogate, interpret and use data.
For the customer, diagnosing a fault in a device or appliance before it happens can be cost and time effective. This means that predictive maintenance and condition-based monitoring services can prove critical for the organisation’s business model, especially when it comes to eliminating costly machine breakdowns and unplanned system downtime. In addition, addressing a potential issue with a product before it becomes a problem can do wonders for a business’ brand equity.
Companies can often be overwhelmed by the sheer volume of data which limits their capabilities to process and effectively use it. As data continues to multiply due to modern infrastructure, the ability to handle, integrate, model and construct data becomes increasingly difficult, especially for organisations that do not typically sit within the ‘technology giant’ bucket. This is because they are still struggling to unite data collected from software sensors with data from systems of record and layer predictive analytics and machine learning on top.
What’s more, the lack of suitable interpretation of data can severely hinder the ability to obtain precise answers under particular conditions or scenarios. Large corporations are often able to hire data analysts for this job, but SMEs and smaller companies who lack the funds to do the same are disadvantaged.
Always think cognitive first
In order to conquer these challenges, businesses must look towards implementing powerful development platforms which are integrated with the machine learning and predictive analytics of AI technologies.
Moreover, organisations must ensure these technology platforms will target both current and future needs, as well as integrating modular based AI technologies that enable advanced machine learning to be successful in the long term.
This means data gathered from traditional analytics software would be used repeatedly, particularly for predicting future contexts and not only for single occurrences. Machine learning must also be able to manage hundreds of different data variables as well as model and structure data trains so that they can provide precise insights across multiple different user case scenarios.
From an application development perspective, businesses will also need to get to grips with speech patterns, semantics and contextualised development. Up until now, when building traditional systems, they used to begin with process and logic, writing the code and ultimately inserting the data. A cognitive-first application development strategy, however, follows a different path. Developers need to lead with the data, being able to gather all of the information in the moment, and let it drive them to the logic.
Start building the future now
Cognitive application development, predictive analytics and machine learning build on top of layers of existing technologies and applications to make them more powerful. They emulate the evolution of the human brain with its different interconnections and complexities. Training your technology to learn now will soon allow it to teach itself to finish the job.
Sourced by Mark Armstrong, vice president and managing director international (EMEA & APJ) at Progress
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