Predictive analytics can often be seen (or sold) as a magic wand that will predict the future, and with the market potentially set to reach up to $63.3 Bn globally by 2032, it’s becoming increasingly difficult to know what’s actually worth the investment.
Now, skepticism is definitely not new when it comes to technology and data, and some of it is more than justified. However, there’s somewhat of a reputation of smaller businesses in particular being less tech savvy and therefore more reluctant to adopt new tools and ‘moving with the times. Realistically, this reluctance is often underpinned by smaller margins and fewer resources, financial or otherwise), not a lack of knowledge or ambition.
The majority of predictive AI models and projects, for want of a better word, fail. International organisations with a workforce of thousands and a turnover of billions have the luxury to test, fail and learn without putting the entire business at risk. Small businesses simply cannot risk their time, money and customer satisfaction only to realise their intuition is still more accurate than this new tool management has forced on them.
The barriers to AI adoption among SMEs are by far insurmountable, but in order to understand how to overcome them, let’s first look at why so many predictive analytics projects don’t succeed…
Unsurprisingly, when it comes to predictive data analytics, the data itself is pretty critical. It’s typically understood that the more data the better.
In order for models to make reliable estimates on what will happen, they first need to know what has happened in the past, why it has happened, and the role different factors play in those outcomes. To do this, and to do this as accurately as possible, it needs the right data. Not simply, lots of data, but the right data.
Yes, traditionally the best models required quantity and structure but we’re not in a position where predictive accuracy doesn’t have to be sacrificed when the data is small, full of gaps, and incomplete. But again, it comes down to having the right data for the model.
This could mean filling in gaps using external data sources or shifting entirely to a different type of machine learning algorithm. Not every model or technology is suited to everyone’s data, find (or build!) the right one for you.
At the end of the day, it all comes down to results. Is the technology producing accurate (or accurate enough) predictions and insights that either increase revenue or efficiency?
Large companies will likely give it months until reviewing the performance of a new tool or technology. After all, the process to implement takes a long time when so many decision makers are involved.
Small business owners or managers on the other hand have to move quickly. Not just with setup, but if they don’t see a material difference within a few weeks, they might decide to cut their losses and stop.
In these situations, models/solutions need to ensure they can provide speed across every stage – implementation, scaling, insight generation, etc. – in order to demonstrate value as soon as possible.
Sadly, there is no magic wand (yet!) that is 100% accurate and gives you everything you need with a single click. That’s why it’s so important to set clear, measurable, and realistic objectives… as you would with any project or campaign.
And part of this is understanding the potential limitations of the technology/models used. Whether building your own tool or using a third party solution, acknowledging that not even the most accurate and complex model can predict the future 100% of the time.
Blindly following whatever numbers come out may be tempting, but could have disastrous consequences. The value in having a data partner like TUBR is not simply in the predictions, but the ability to help you understand the different outputs within the context of your business, giving you the best chance of success.
Learn more about TUBR’s predictive technology here.
Generative AI has been the ‘hot trend’ of the year, with 87% of companies surveyed by Bain & Company reporting that they are developing generative AI applications. Generative AI refers to a class of artificial intelligence systems designed to create new content—such as text, images, music, or code—by learning patterns from existing data. Common examples of generative AI include ChatGPT and even certain TikTok filters and effects.
Unlike traditional AI, which typically performs classification, prediction, or decision-making tasks, generative AI focuses on producing outputs that are similar to, but distinct from, the data on which it has been trained.
With the growing pressure on organisations to adopt and develop generative AI technology, some less-than-impressive creations have inevitably made their way into the public eye. Below are a few standout examples from recent years:
New Zealand supermarket Pak ‘n’ Save launched Savey Meal-bot, an AI tool designed to create recipes based on a user’s shopping list. The goal was to help customers reduce food waste and find creative uses for leftovers—though “creative” might be an understatement. Outputs included dishes like “Oreo vegetable stir-fry,” “bleach-infused rice surprise,” and an “aromatic water mix” that was, in fact, chlorine gas.
Two lawyers found themselves in hot water after using ChatGPT to draft a legal brief for a personal injury claim. Unfortunately for them, many of the citations the AI provided were either incorrect or entirely fabricated—something that only came to light after the brief had been submitted to a judge.
AI’s struggles with generating realistic images of people—particularly hands—are an ongoing challenge. Some argue this is due to limited data; photos tend to include fewer clear images of hands compared to faces, and those that do are highly nuanced. As a result, AI models often lack the data needed to accurately learn how to render them.
In February 2024, an unlicensed two-day “Willy Wonka Experience” was held in Glasgow, with tickets costing £35 each. Some families even drove hours to attend this supposed “celebration of chocolate.” Unfortunately, not only were the images used on the event’s website clearly AI-generated, but the event itself was such a disappointment that the police were called. The experience was ultimately cancelled after half a day, with full refunds promised. This serves as a reminder not just about the risks of false advertising with AI, but also about the importance of delivering a product that truly delights your customers.
While we’ve seen a fair share of generative AI ‘fails’ spread across the internet this year, for every public misstep, there are many more technologies that don’t even make it past the experimental stage. As with any scientific exploration, failure is rarely a true failure—each iteration brings valuable insights and sometimes leads to solutions you weren’t even looking for (Penicillin, anyone?).
It’s also important to remember that there are no magic bullets when it comes to AI. Few, if any, artificial intelligence systems succeed without human intervention or oversight, and not all AI will be relevant to your business. Just because you can use a technology doesn’t mean you should.
That said, this isn’t a cautionary tale. Technological advancements have created incredible opportunities for SMEs that were once only available to large enterprises. This is why finding the right technology partners is so crucial—they can help you understand the opportunities and limitations of different technologies and determine what is best for your business’s specific needs.
Learn more about how TUBR can work with your team to help grow your business here.
Spatial Time Series is an interesting and curious concept. As discussed in our earlier blog all about Spatial Time Series, it’s all about feeding a Machine Learning algorithm with data points collected over a very specific interval of time and defined by their spatial position.
Though there are a lot of applications for this in the business world, the real power of Spatial Time-Series lies in its capabilities of bringing real change in the fight against climate change.
Exploiting the Machine Learning model to understand what changes will take place due to global warming can help policymakers to delineate a set of proactive measures to avoid natural disasters – rather than taking on reactive acts when the earth shakes or the tsunami hits.
According to this research paper, most of the data around biological effects of climate change are collected seasonally which lacks the spatial and the temporal changes that take place in a microenvironment.
Well, Spatial Time-Series is here to provide exactly that.
Spatial Time-Series is becoming increasingly important in understanding global warming, predicting future temperatures and helping with environmental decision making.
A group of researchers from Ghent University and Chinese Academy of Agricultural Sciences have published an interesting article in the Advances in Atmospheric Sciences journal.
Their research focused on implementing a time series modelling to examine one of the most important environmental variables, the monthly records of absolute surface temperature.
This demonstrated that the surface temperature is going to rise and that their model can be used along with other environmental models to implement short-term environmental decision-making.
This model has the capabilities of improving communication between policy makers, environmentalists and researchers to bring active solutions on the table in terms of land utilisation, rising sea levels and natural disasters.
Unfortunately, examples of extreme weather are becoming more and more frequent.
Just three months ago, India and Bangladesh were hit by heavy rains that caused the water levels of rivers to rise and flood all cities. It goes without saying that people were harmed and buildings were damaged.
Another example is brought by the extreme heatwave that Europe has experienced this past summer. This caused wildfires as well as extreme droughts.
In this context, Machine Learning and Spatial Time-Series can be the solution policy makers have been looking for to fight climate change and, most importantly, come up with preventive measures to fight natural disasters.
For example, one of the main benefits of using Spatial Time-Series is its ability to predict future outcomes. By analysing labelled data and identifying patterns, the most at-risk areas of future natural disasters will be predicted and policy makers will be able to implement proactive strategies to minimise the effects.
The goal of many companies is to reach “Net Zero” which means that they are trying to reduce the carbon emission that is generated by their operations.
According to the EPA, the total emissions of CO2 were around 5,981 Million Metric Tons of CO2 equivalent just in the USA. In this figure, the transportation industry contributed by 27% and is the largest source of greenhouse emissions.
The greenhouse gas in the transportation industry comes from the use of fossil fuels to operate vehicles.
Spatial Time-Series is a powerful tool to use to reduce the carbon emissions caused by transport and traffic. For example, if councils performed an analysis of the hourly or daily traffic volume data, they could understand the number of cars going from A to B and implement innovative strategies to reduce the traffic with effective urban planning that incentives the use of alternative means of travel.
According to the IPCC, creating new buildings and maintaining them accounts for one fifth of the world’s emissions.
Not a great figure that puts managing infrastructure and offsetting carbon emissions at the top of the areas to tackle in the fight against climate change.
Spatial Time Series can help in managing and reducing the environmental impact of buildings, especially in cities.
For example, councils could use Spatial Time Series to understand which areas in the city are the most carbon intensive because of the heating and ventilation of their buildings. This could help policy makers to promote alternative energy to heat or to use electricity.
To wrap up, climate change is a phenomenon that we are all dealing with. It is still not a reality that we have to accept and there are steps that can be taken to reduce or to mitigate its effect. The solution lies in new technologies and the power of using untapped data.
Spatial Time Series is a powerful tool which has the ability of helping save cities and people by predicting and helping decision makers in their jobs.
To outsource or not to outsource, that is the question.
Well, according to 70% of B2B decision-makers, delegating certain tasks and operations to an external company is the most effective way to ensure time-saving and cost-saving.
There are numerous challenges that businesses are facing everyday. These challenges are not only caused by big historical events such as Covid-19, but are also caused by shifts on a smaller scale, like higher fuel prices or national strikes.
There’s only so much a company can do. You can be a great logistics provider and you want to optimise your performances, but do you really think that creating an in-house machine learning algorithm is the best way to approach this or would you rather put the experts in charge?
Just some food for thought!
Adding machine learning solutions to your operations needs the expertise of highly trained technical staff. This is a rule set in stone. No amount of Coursera courses or self-teaching on YouTube will give you what you are looking for.
You could hire data scientists and build an in-house model, but have you conducted a research on Glassdoor to see how much is the average salary of a data scientist or machine learning developer?
In this case it is not about soft skills, it is all about technical hard skills that are developed through years of studying and training. The issue that you might be facing might be too complex for your staff to deal with.
This is why working with a predictive analytics platform that turns small data into actionable insights is the best decision for you. We have a great team of machine learning developers who are eager to get their hands on your data to help you meet your targets from the get go, without any training required, only your spreadsheets.
Big data has been a buzzword for quite some time now. The new buzzword is small data.
It means that the enormous amount of data that is around us is not of concern, your business data is. Outsourcing to TUBR means that you can execute highly targeted projects without impacting your core business.
Do you want to improve your customer service experience without consistent data, but it is peak season for Christmas deliveries and you do not know where to start?
TUBR is what you’ve been looking for. We have adopted a physics approach to understand how data is connected, validating gaps and recreate a system into a model in which external factors support the prediction.
This means that we find relationships between the data and actively fill the gap to provide you with predictions that are reflected in the external environment.
Multitasking is only enjoyable when your tasks are simultaneously eating and watching Netflix, not when improving your customer service experience while delivering Christmas presents.
By outsourcing your machine learning processes, costs will be reduced, profits will increase and your accountant will be happy.
Why?
Well…
These are just two prime examples of how much you will save by outsourcing to a company who is going to provide you with all the predictions you need, without going bankrupt.
There’s nothing more unifying all businesses than the fear of not complying to GDPR regulations.
A skilled and highly trained machine learning workforce understands the need of handling, storing and systematically managing small data on various platforms.
Outsourcing to a predictive analytics platform such as TUBR reassures you that the company’s sensitive information is kept secure and far from malicious eyes.
Ultimately, there’s many more reasons that go above and beyond the basics we’ve outlined here. TUBR is the company you want to work with if you want to reduce waste and optimise your assets efficiently and without breaking the bank.
Why don’t you give us a call?