In the last few years, analytics has evolved significantly. We’ve moved from the adoption of big data technology that was all about Hadoop and Spark, to an increased focus on machine learning algorithms, deep learning and artificial intelligence (AI).
In the last year alone, I’ve noted a renewed hype around machine learning, which together with the Graphics Processing Unit (GPU) evolution is bringing innovation to the marketplace. In general, this ‘new wave’ of machine learning and AI is being adopted much faster than the old wave of Hadoop and Spark. To some, Hadoop and Spark are already becoming legacy.
In this timeframe, I’ve had the pleasure to work with clients across the globe, which has allowed me to see how innovation progresses differently in different places. For a number of years now, companies have been combining software engineering with very advanced machine learning capabilities and applying analytics operations frameworks. It’s quickly becoming widely accepted that advanced machine learning should be part of any data science practice to help customers solve business problems through things like advanced deep learning techniques.
In the fast moving space, North America, the UK and Germany, and the Nordics are the leaders in innovation currently, with the rest of Europe slightly lagging behind. Looking at Asia, it’s fascinating to see that China and Japan are focusing on AI – they lead the results when you Google “countries adopting machine learning.”
From an industry-specific perspective, adoption does vary from sector to sector, but one thing remains the same – when we speak to market leaders and senior management, such as CTOs and chief analytics officers, they all identify AI as a top agenda item. I’d say that by 2020, every company will be seeing AI as a top 5 issue for their business strategy, whatever the sector.
In January this year, for example, new research came out that shows how deep learning is being used to detect the earliest stages of skin cancer, matching human doctor capability. This is an example of just how much investment healthcare is putting into predictive analytics. In the fintech space, there’s a buzz around AI being used to fight fraud and improve cyber security.
But it’s not just these sectors. Although adoption of analytics may be a little slower with Telcos and manufacturers, it won’t be long before machine learning leads their agenda too. As early as 2025 we’ll see the new wave of technology break down the existing data silos, and companies will achieve the results that they expect and need.
The last three years have shown that the question is no longer whether data analytics will change businesses, but to what extent, and how quickly that change will happen. All organizations now have an analytics department, even if they call it something different like “data mining” or “statistical analysis.”
Recently, I’ve seen companies embracing the combined approach – blending data science with engineering. This generates value by uniting a company’s capabilities, which has until recently resided in silos.
Adaptable organizations are going to ride faster than their rivals on the AI and machine learning wave. Combining skills from a variety of departments will serve the wider company goals, and it’s exciting to think what the next three years hold for the wider analytics space.
For more on how leading business are using and adoption AI today, take our look at our comprehensive State of AI for Enterprises report.
Eliano Marques, Head of Data Science International at Think Big Analytics. Eliano has successfully lead teams and projects to develop and implement analytics platforms, predictive models, analytics operating models and has supported many businesses making better decisions through the use of data.
Recently, Eliano has been focused in developing analytics solutions for customers around Predictive Asset Maintenance, Customer Path Analytics, Customer Experience Analytics with a focus in Utilities, Telcos and Manufacturing.
Eliano holds a degree in Economics, a MSc in Applied Econometrics and Forecasting and several certifications in Machine Learning and Data Mining.
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