Our Approach to Successful Data Science Projects3.12.2020
There is always the question of how to start. We follow a few simple steps to help our customers apply and gain a competitive advantage when it comes to the implementation of AI projects.
Understand where AI can be efficient for you and what to expect
Defining business needs is the first step when it comes to implementing AI. You need to have a clear view of what you would like to achieve.
We can provide you with a list of use cases and how you can leverage them by applying them to your business environment. Each case has its own benefits but it’s up to you to create a prioritized list and decide where would you like to start.
At the end of this step, you will know exactly what your business needs and what the bases are for defining the scope of your project.
Define the key-value
After we’ve set up a list of pain points, we can start brainstorming the best approach to solving them.
At this point, we will put the solution on ”paper”. Defining the scope of the project is important, it gives you an idea of how much effort will be needed, what are your responsibilities and the key tasks that have to be done, and most importantly, what are your expectations in order for the project to be successful.
The key-value of the project is essential. It will give us an idea of what we would like to achieve and defines potential business and financial needs. When implementing a solution, you must not lose sight of value drivers. Implementation of AI is not always about financial value, it can also improve the value for your customers or improve the performance of your employees.
There might be several different solutions and together we can choose the one that best suits your current environment. Selecting proper technology, preparation of data, availability of resources and timeline should all be a part of an SOW (statement-of-work) document, which is the end result of this step.
Select the technology
AI is expected to be delivered across all business sectors. In this case, you should consider going with a scalable AI platform that enables you to unlock the value of your data.
When selecting proper technology, we will consider several key points that suit your needs and requirements:
- Provide access to scalable computing infrastructure (cloud, hybrid, or on-premises)
- Delivers advance natural language processing (NLP)
- Offers to compute power acceleration
- Furnishes microservices and application programming interface (API) – driven access to algorithm libraries and services
- Supports automation of processes
- Supplies supervised and unsupervised learning
- Facilitates machine learning interpretability
- Enables automatic visualization
The end goal of this step is to choose AI platforms or concepts that provide the infrastructure to support contextual decisions. These contextual decisions power an array of AI-driven smart services required to deliver the next best action across a range of business processes.
Data is everything, but without proper preparation, cleansing and quality will not give you the results you would like to achieve. It doesn’t matter with what type of algorithm you will use to get the result, if you don’t provide them with proper and quality data sets, they won’t work for you.
When it comes to AI projects data preparation is essential, it takes up to 80 percent of all of the work. We use our own methodology and assets that help us speed up this process. We can easily integrate different data sources and automate the loading process. By implementing DQ (data-quality) rules we are able to have full control of data coming into the model and by providing data linage, we can always understand where the results came from. Full documentation build-up just on click can provide your data science team with all the information they need regarding data preparation.
What should be a proper approach
Based on internal capabilities, you need to see what you are capable of on your own and how you can leverage for an external consulting team.
We offer different types of services for clients.
Joint approach together with the your existing or future data science team is one option of collaboration. We will provide you with our senior consultant team to guide you through the whole process of setting up the model. We will also support any future upgrades and improvements to the solution.
Turn-key solutions is also one option. You get an end-to-end solution and we take care of everything for you – from data preparation to choosing the right technology and setting it up for you, building a model and deploying it into production. Further from here is up to you. If you would like us to transfer the knowledge on to you and you can manage it on your own or if you want our team to take care of everything for you.
Training and mentorship of your data science teams or helping you build a data science team is the third option. Our well trained and certified team can provide you with knowledge and training courses for different technologies such as:
- R training
- Phyton data science training
- IBM Watson / SPSS training