Artificial intelligence has become a crucial part of our lives. The increased dependence on AI-oriented technology is evidence of the inevitable bright future in it. Experts say that the AI industry will provide opportunities to the business industry- together they will make a strong and unbeatable market.
While most of us are thinking if this is possible or not, some people have started building their ways to capture the market with the help of AI-generated apps, websites, and robotics. Artificial Intelligence is based upon the knowledge of technology; if you lack it then you might lose this opportunity.
But there is another way through which you can successfully build whatever the kind of product of desire; a team of expertise. Yes, it is possible to develop your desired product if you have sound knowledge of technology or not. But this step is not that easy. To get the work done with the help of a team, you need to be a good leader as well as a coordinator. And with the time you will grace your game.
Now the question arises, which team will suit the best for such a market?
Stop here and check out the name of posts that should be a part of your team:
1. Data Analyst:
The work responsibility of Data Analyst is to organize the huge amount of complex data into AI power. There are subcategories to this post; the person could be good at converting the data from SalesForce, Firebase or SAP.
The work starts from looking into digital and geographic locations- like web addresses, physical store or warehouse information. This will be followed by keeping track of customer visits at the store, the amount of time spent there and the name of the device they used to visit, the kind of products and service they looked for and finally, what they purchased.
Data Analyst is responsible to develop the initial stage. Mostly the work runs around locating and cleansing of data so that it provides transparent and easy access.
Note: Data Analysts do not blend data; they are only supposed to organize data.
2. Data Engineer:
Once the Data Analyst is done organizing the data, Data Engineer comes in play.
Data Engineer is liable to blend the data in a way it becomes a cohesive whole. In simple words; data engineer moves data in such a way that it turns out to be effective and less time-consuming.
In this process, one place will store structured data (mostly the data which comes from database) and second place will hold the unstructured data like images, videos, and audio files form call centers or PDFs of invoices.
The job authority revolves around keeping and fetching data in the right way. At this stage the effectiveness of your team is determined; the way data engineer optimizes data flow tools like Apache Beam, the open-source programming model for creating the data (processing pipelines, including ETL, batch and stream processing.)
Now your data begins to flow and here is the calling for the next role.
3. Data Visualizer:
Data Visualizer creates interactive online visualizations. They will define the data in business terms and summon elegance from large-scale statistics. Data visualizers use tools like Tableau, Looker, ClickView, and Observable for making statistical graphics, plots, information graphics, color schemes, and crisp fonts.
This work is done to know what is hot today? For better understanding, they categorize customers in lifetime value, location and time. Do they also name the pages and products in a way that pushes people to think about what is it about? Let me check it out.
Data Visualizer is responsible for generating a response from the targeted audience. But this doesn’t mean they can do it in one GO or 100 GOs. So while you think about how to convert it into business, here comes the next role.
4. Data Scientist:
Here come the geeks who derive mathematical conclusions from the structured and unstructured data.
Data Scientists help to know about the customer’s buying journey. There work is to find out the customer’s next step at each stage. They use tools like R, Python, and MATLAB. The mathematical data-driven from these tools is clear information.
At this level of the hierarchy the estimated profits, customers’ needs and the answer to what is the right content are discovered.
5. Machine Learning Engineer:
With skills in multiple disciplines like data science, distributed systems, differential equations, computational neuroscience, and linear algebra; they develop tools of prediction.
The work done by Machine Learning Engineers is dependent upon the data and stats provided by the data scientists. The predictive power of these engineers can be used in other places of business.