Large data explosions cause headaches for organizations: How can we exploit it and get invaluable business insights?! Everything goes down to the learning machine and data science. Data, oils that are sought Cogs from modern machines. But, there is a problem. The organization struggles to get business insights from this new strength.
N short supply.
On the market, many customers are trying to build a very large data science team. Some, trying to employ hundreds to handle data explosions; With the source starting from customer inputs to IoT devices – this will be the main channel. But it’s not easy, there is a great shortage of data scientists.
There are, as created by Gartner, citizen data scientists – someone who creates or produces a model that uses advanced diagnostic analysis or predictive and prescriptive ability, but whose main work function is outside the fields of statistics and analytics – but they provide a complementary role for data scientists expert. They don’t replace experts, because they don’t have the specific sophisticated data science expertise to do so.
Even with this, many companies really struggle to establish citizen science teams, especially the data scientist team.
Data science is described as a multi-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insight from data in various forms, both structured and unstructured, similar to data mining.
Naturally, it has many different components. One of them is machine learning, namely “the most pleasant part of data science”, according to Ryohei Fujimaki, CEO and founder in DotData.
Real pain produces faces on the side of the data – build data sets so that they are careful for data science to apply. Very complex data, and when collected in the company it is not saved for data learning and data science. It is saved for business purposes; In the graph, for example.
Businesses must change this business data into machine learning format, called “learning features,” Fujimaki said. “And basically we have to apply a lot of domain knowledge to run data.”
So, in this climate, where talents are in short supply, but the data continues to flow, it is necessary to automate the process of end-to-end data science; Including data in pipe features.
Get insight and driving action
Machine learning can estimate, predict and identify new customers, and in financial services, for example, which has the most risks. This prediction * encourages automation of business processes. The core business is integrated with a business system and triggers several business actions automatically. In this way, there are many areas to make business far more efficient.
Another very important result of the learning process and science data is business insight. Very complex data – and industry experts have the knowledge and intuition of the domain – but there is a lot of knowledge hidden behind a large number of data entering the company. Machine learning or data science course processes can usually reveal something unknown or invisible or unexpected, even for an expert.
Example of dotdata.
DotData works with banking customers who apply their platform to predict who new customers will be interested in the product type mortgage loan. They first thought that this product would appeal to younger people. But, what they find is that the very different types of customers are interested in the age of a little more senior. Apparently, that this customer demographic bought this product more than a young demographic prediction.
This type of new business insight means customers can build and design a new promotion campaign to this customer segment; Or they can design new products based on this type of business insight.
Automating science data and machine learning processes produce new business insights from data.
Own data scientist … not good enough
What type of skill set is needed by business to enable data science to extract meaningful business results? The first thing is mathematical or statistical knowledge, but at the same time this business must download very large complex data, large scale, – they need data engineering for this.
“Also, use the same data in solving different business problems, requiring different domain expertise,” Fujimaki said.