The hype around the Internet of Things (IoT) doesn’t exactly surprise anyone. Today’s business operations are fully reliant on technology and with evermore affordable sensors. They are better equipped to utilise these to record anything and everything that is happening in their premises. The result is an impressive pile of raw data which, using sophisticated analytics, can catalyse improvements throughout the company.
Whether IoT is enabling manufacturers to optimise factory operations or helping retailers improve the customer experience, the enthusiasm around IoT has been fuelled by the wild success of some early adopters such as Amazon, whose stock price recently hit the $1,000 mark and whose success can largely be attributed to its cloud computing business, Amazon Web Services.
Additionally, the high-level IoT concepts that have graced news headlines recently are further driving excitement. Apple, Tesla and traditional car companies like Ford are building self-driving cars or trucks that rely on IoT, while Amazon is extending its AI assistant Alexa to tablets and portable speakers.
>See also: The death of the IoT
Indeed, most of this hype is being driven by the investments of industry heavyweights — the Amazons, Googles, Fords and eBays of the world. But what about smaller businesses? Enthusiasm may be high, but you don’t often read about many small or midsize businesses taking full advantage of the wide range of data available to them to fuel IoT projects.
If you could put numbers to the IoT success seen by small businesses, it’s likely that the facts wouldn’t match the fervour.
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The key issue for many businesses wanting to utilise IoT is that the definition of their data and analytics strategy is vague to begin with. Some enterprises will say they have a reporting and analytics strategy, when all they actually have in place is a data visualisation solution like Tableau – or worse, their strategy constitutes some sort of basic spreadsheet reporting. In these cases, their IoT initiatives are doomed from the start.
Even when businesses do have proper strategies, most lack the right personnel – data scientists – to successfully manage the process. Data scientists play a vital role in data and analytics strategies as individuals who can direct, interpret and validate the output.
New tools and technology can help businesses process data, but the personnel are the brains behind the scenes and they aren’t easily replaced. That’s why data scientists now command hefty salaries that many small and medium-sized businesses can’t support.
And it doesn’t exactly help that designing and executing on a comprehensive data and analytics strategy is easier said than done. You need to be able to manage data effectively, from collection and access to cleansing and preparation. Then you must be able to determine which analytical model will yield the best predictions, and which requires data scientist expertise to train, test, evaluate and score the models.
Once the data and model is set, you need to figure out how to operationalise your analytics and use them in production. Finally, the process itself must always be under review in the face of changing business conditions.
These ingredients for successful IoT implementation cost lots of money and resources, which is why the real promise of IoT has been limited to industry goliaths thus far. Making information free and accessible to the end user, or democratising data, may be the key to solving this problem.
Of course, every business will face its own unique challenges when designing a data and analytics strategy. However, the affordability of specialised resource is a problem that persists across most small and midsize businesses, and the answer is not as simple as hiring a large team of data scientists – that’s simply not feasible.
While analytics-as-a-service can provide support for self-contained use cases like text-to-voice, outsourcing the entire process around strategic decisions is a no-go for most organisations as well. Even the influx of new tools that make data scientists more effective do not have much impact on the democratisation of analytics for smaller enterprises.
One possible answer is automation. To credibly make advancements in this area, the analytics process must be made capable of self-learning – and not just applying learning to the output of the analytics.
This next- generation approach will apply meta-learning principles to machine-learning, where learnings from one machine or entity are automatically applied to other machines or entities.
As a result, meta-learning considerably lowers the cost of running machine-learning experiments, making it more accessible to smaller organisations.
The shortage of both data and analytical skills means that many businesses are currently not fully benefitting from IoT. Democratising analytics has the potential to change this.
By automating the data science lifecycle, it will greatly reduce the need for a costly and sparse resource only available to the biggest players in the industry.
Sourced from Mark Armstrong, VP and MD, Progress Software