Leverage the power of data with smart algorithms, to optimize costs, improve efficiency, generate new streams of revenue and manage risk and fraud.

What are smart algorithms?

An algorithm is a sequence of mathematical steps that produce a result. They are used for exploring data to discover insights and patterns, for making models to generate predictions, recommendations, and even automated reasoning and decision making. Algorithms make use of advanced techniques such as big data, artificial intelligence, machine learning and robotic process automation.

Data → Information → Knowledge → Intelligence → Action

For a human, it is quite challenging to encode all of the rules and criteria leading to recognize a dog in a picture. Furthermore, multiple humans would probably come up with different criteria and rules, and it would be hard to find a general pattern. For algorithms however, these tasks become more and more easy to solve!

While machines can imitate and reproduce human behavior, we believe that machines cannot reproduce human thinking. Rather than trying to automate actual human expertise, with algorithms we should improve human work performance.

“Data scientists need to verify whether their findings make sense. Machine learning isn’t replacing people, it is helping them” - Douglas Merrill, ZestFinance Chief Executive

Advantages of Smart Algorithms

Faster decision making - By using AI alongside other programmed technologies, we can develop valuable insights and people can confidently make business decisions much faster.

Reduce human bias and error - With algorithms, errors are reduced and there is a much higher accuracy and precision of task execution.

Help reduce repetitive jobs - AI algorithms are great for those tasks that humans like to do least of all, for example manually checking documents for errors. With artificial intelligence, we can automate these everyday tasks and free people up to devote time on more productive tasks.

Creating Smart Algorithms

Think Big - The domain of applications is enormous and furthermore multiple algorithms can serve to achieve one goal! Some algorithms can be more efficient than others. dataroots has a lot of experience about this and can advise you which best suits your needs!

No data, no machine learning - Without data (or sufficient quantity and quality thereof), a smart algorithm cannot learn the inherent patterns. You need data for the algorithm to learn and compute (cor)relations that generalize and tackle your problem. AI algorithms can determine patterns, learn and take decisions only if the data you use is representative of your problem. It unlocks possibilities to explore broader areas, in a faster way.

Supervised, unsupervised or reinforcement learning? - Whilst the area of application of machine learning is almost infinite, your challenges can often be reduced to 3 main classes of models: supervised, unsupervised and reinforcement learning.

• If you have historical data with information that you would like to forecast, like a tag, a name, a type, or a number; then you can use a supervised model. It will use the labels and find relationships in the data.
• If your data comes without label, and you would like to discover patterns and insights in your data, you can use an unsupervised model to discover structural data connections.
• Finally, if you would like to find the best feasible solution to a complex problem, you can use reinforcement learning and optimization methods. You need to determine an objective to optimize (e.g. profit) and let the algorithms explore data and propose actions to optimize your goal.

Data types - Nowadays, we are able to process, understand and extract information from almost any type of data, such as text, images, videos, music, social media profile, conversation, geolocation, and much more!

Smart Algorithms solve real-life challenges

How can dataroots help you?

Dataroots has a large body of expertise on the state of the art of machine learning, artificial intelligence, data science and everything that comes with this. We can guide and support you through the entire AI-journey; from defining your business challenge, determining and developing the best suited solution for you to integrating in onto your system. This allows us to create the best solution for each and every problem and face the most complex challenges equipped with the required knowledge.

Through the use of the correct algorithm for the situation, we solve difficult challenges and achieve striking results. In doing so, we make sure to always use a pragmatic approach, by not overcomplicating our solutions and keeping in mind the optimal trade-off between the ability to understand the solution and the effectiveness of the solution.

We generally work with the following phases

• AI strategy workshop - We organize a workshop to understand your business question and identify the possibility to apply AI. The first phase is to get to know your needs, your business and where an AI project could bring most value. Together, we define a scope and determine high level next steps. Read more ≫
• Pilot project - We create an initial project implementation plan and actual solution to determine feasibility and performance. During this stage, we explore your data in depth, and create and test a concrete modelling approach, while iteratively gathering your user input and feedback. At the end of this phase, we deliver recommendations on feasibility and next steps.
• Co-creation - In this phase, we extend the project to build and deploy AI-solutions and products. We also integrate the solution into your system. We deliver quality AI solutions generating real value!

Close collaboration

We are not developing an AI solution for you, we are developing an AI solution with you! During every step of our phased approach, we work together with you in close collaboration, providing detailed information on our work, as well as gathering your input and feedback.