Tell us about your role at Data Practitioners and the team/technology you handle.
Our work at Data Practitioners focuses on the use of data science and AI to deliver predictions and recommendations to our clients. My role as Senior AI Advisor lies with the implementation of the AI solution itself and ensuring that the platform is equipped to evolve in line with key trends in the Sales and Marketing landscape. It’s important that we take AI in the right direction as business demand and interest around its real-world capabilities increase.
What is the state of AI and Data Science in Marketing and Sales Technology? How do you enable technologies to benefit from your advanced expertise in AI/ML?
Undoubtedly, AI remains a very new technology and lots of firms are still getting to grips with how it might benefit them in the future. Clearly, most companies, especially in the Marketing and Advertising industry, have already understood how important AI will be, but it will take time to learn how it works and more importantly, how to leverage the technology to extract the most value, which will grow the business.
Tell us more about your work in the field of Data Unification, Management and Activation. How much of it is managed by AI/Machine-Level algorithms?
The platform is an AI solution, which employs state-of-the-art Machine Learning algorithms to reveal crucial insights about the customer, by gathering, processing and unifying clusters of data to form one single dataset. We also like to run surveys to build on the data that a company will already possess. This allows us to make predictions with algorithmic modeling about how clients can best reach and engage with their customers. This data evolves and changes in real-time.
My experience has been mostly working with global banks and the wider financial sector, but data practitioners work with a huge range of companies across marketing, FMCG and luxury sectors.
Tell us about the product roadmap you have designed for Data Practitioners. Which technology providers could better leverage your products?
Our vision is to help our clients embark on a transformation, with the end goal to become AI-driven companies. We have designed an AI solution, which processes and analyzes data in a matter of hours, leaps ahead from even a few years ago. By nature, it is easy to shape in order to produce specific insights, as it can easily be molded to meet the demands of the client. It’s important to remember that rules must be created in order to make the algorithm work in the way you need it to — without rules, algorithms will create their own!
Changes in the business landscape will ultimately dictate how the use of AI is deployed, depending on the needs of our clients and how they wish to best reach their customers. As technology providers ourselves, we want to ensure that our solution helps our customers to drive the sales of their own products and services.
How do you work with AI/Machine Learning in creating a unique “single-view” definition of customer and customer behavior?
The platform analyzes clusters of data to group people together. This will help businesses determine the segments of their customer base that will provide the best opportunities to create value and engage with new demographics. This, then, produces a single view of the target customer and allows businesses to identify potential drivers of sales.
However, the platform sits separately from customer behavior. Our behavioral science team takes the next step and looks critically at the data to identify patterns to drive the creation of the right channels and messages to best engage with different clusters and groups of people. This helps to shape a comprehensive marketing and advertising strategy.
What are the opportunities and risks you foresee in the way Big Data is shaping business analytics for 2020-2025? How do you prepare for these disruptions?
Big Data is a fantastic tool for B2C and B2B businesses, and it is already significantly improving business operations. It helps them to understand their customers on a deeper level, allowing them to deliver a better service. As more companies develop increasingly sophisticated methods of handling and using data, businesses will need to remain agile to be able to adapt to changes in the market as it evolves.
How we store and utilize data is incredibly important today, as companies continue to hit the headlines with costly data breaches and failures to enforce adequate data privacy measures. Since GDPR (General Data Protection Regulation) came into force in the EU in May 2018, it’s never been so crucial to ensure that companies are fully compliant with the regulation, by managing their data securely to protect their customers.
Undoubtedly, businesses will fall under increasing scrutiny from legislators so the potential impacts on business and reputation cannot be underestimated.
Tell us about your predictions for the AI and Big Data landscape for the next two years. Which start-up ideas and companies are you keenly following in this regard?
I’m incredibly excited to see the impact that AI will have on the wider business landscape in the coming years, and as a scientist, I want to continue to contribute to this growth. There is a tendency in the technology industry to use terms such as ‘AI’ and ‘Big Data’ as buzzwords rather than in a real-world, actionable sense, and we’re seeing the hype grow and increased competition in the market.
I tend to be skeptical about firms promising new technologies unless it’s clear that there is a team of expertise behind it. Recent research by MWC Ventures found that as many as two-fifths of European artificial intelligence startups do not currently use AI in their products, which shows that companies want to play a part in the industry, without necessarily having the right tools and skills to implement the technology.
This highlights that in order for AI to truly advance, businesses need to demonstrate how exactly the technology will be delivered to the customer in order to gauge how successful an idea will turn out to be. Businesses must also think about the long-term viability of the product in this regard, because it needs to be useful in the real-world to go beyond the initial excitement around it.
Thank you for answering all our questions!
Dimitris Vlitas is a visiting Professor at the University of Toronto, working on Mathematics and Machine Learning. He is also senior Artificial Intelligence consultant at the National Bank of Greece and at Data Practitioners Limited.
DPL is a data-technology start-up, bringing together data science, behavioral science and advanced technology to provide end-to-end customer insights and engagement. Our product is unique in the way that it combines the latest data analytics and data science technology, with progressively automated behavioral science in an easy-to-use, cost-effective SaaS platform. DPL owns all Intellectual Property. Our product offers continuous comprehensive analytics to clients who have not yet benefited from effective use of their data.