Do you have a Successful strategy for AI?
When AI is the inspiration for today’s industry where everything is connected and advanced. Complex algorithms taking place for most complex situations. From start-ups to technological gig to asset concentrated industries everyone is bounded with AI. Industries and their teams working on their own plans and strategies for their AI. At another side of the picture where advancement is the one side and the other is how effectively we can use Artificial Intelligence?
Industries are still struggling to develop a successful Al solution. There are could lots of reasons that why they cannot, maybe due to the incapability of data scientist or may be insufficient knowledge of data literacy and so more. At the bottom of the truth, the common reason which comes out is that of no infrastructure that can implement data science operations and algorithms. And in order to implement, industries have to understand and fulfill the basic requirements as data literacy and data collection. We would need to understand the hierarchy as what is required first at the top level and what after that, so considering the hierarchy AI should be at the top of the pyramid and else other basic requirements to the bottom. So, it becomes clear to go at the top level we have to clear the bottom ones.
Here are few steps industries should consider while implementing AI,
Collection of Data
This is the first and very important point to be considered. What data is being collected and how it should be from sensors, devices, or machines? What and which data to analyze? Is the data accurate that can help us with better results? A data which should be accurate that helps make advance decisions. So, the collection of data should be the first step to get a successful strategy for AI.
After collection of data, we need to see if we have scalable and reliable architecture, ETL process and proper IT infrastructure to handle this. The question is like where do we store data and how does it flow? How easily it can be handled with structured and unstructured data. So for the seamless extract, we need to know it.
Explore and Identification
Industries have an ocean of data and to dive down through it we need experts who can deal with any kind of data and find out the hidden insights. This is where Data Science and Data Scientist comes in the picture. Data science is all about methods, processes and systems that help us to generate required insights. This is the field where statistics and analytics are being seen together. Data scientists are the wizard that refines the data removes the anomaly and make the data useful for further processing.
So after the understanding of Data science and the role of data scientist, experimentation or testing framework must be in place to deploy. This should go until we get a satisfactory result which will help from the classification and optimization of new data accurately.
After these steps, you would come up with a great strategy for AI Implementation which would now be ready for your data.