What Do You Know About Data Lakes?
What do you know about data lakes? Due to its fast-growing popularity, massive repositories of all sorts of data have developed–structured, unstructured, numerical, and textual.
Data lakes have the five Vs: volume, variety, velocity, veracity, and value. Many companies think that if they collect all of a corporation’s data together and apply analytics through Machine Learning (ML) or Artificial Intelligence (AI), the fifth V will certainly appear. However, how do they find the right approach to extract this value from the data and make it an actionable asset?
The main mistake is using the wrong approach. They want to find answers in the places they expect to see them, using new technologies to solve old problems. But in most cases, finding the answers requires a more thoughtful, tempered approach.
The Scientific Method from Corporate Data Silos to Data Lakes
Today, we have chemical and biological data thanks to scientists who collect and find it. This expands our knowledge of various diseases and provides us with the opportunity to create special medicines.
People are always searching for new technologies to make this process faster. ML algorithms and AI technology have been used in the life sciences and healthcare industries for quite some time now, from using special equipment with patient data, new AI robots, and programs which can help to discover new diseases to the application of more complex natural language processing to better define clinical trial cohorts.
Data is a serious problem as it has significant potential for changing and even transforming our industry. But, if data is combined, is it possible to use AI technology to troll through all the data?
Data Lakes: The Role of the Data Scientist
Data scientists are responsible for the way people apply new technologies. The industry is at a tipping point now since people are striving to become more digital. In other words, this is a shift from the legacy ‘experiment first’ culture with special data to disprove a hypothesis to a ‘data first’ culture, where data scientists are the main heroes, and they need to find and extract insights from data that had been long forgotten.
That’s why many companies try to hire data scientists as quickly as possible. They can drive the transformation of life sciences using analytics and ML techniques.
The life sciences industry has to become a comfortable place, especially with NI investments. However, AI will try to replace NI resources since the industry will get the value of the data and start to trust the intelligence (natural or artificial). Of course, if you want the benefits of these technologies, you need to have the right specialists to help you.
People believe that AI will have a leading role in life sciences and healthcare industries, but we should resist the urge to apply AI to all existing databases and data lakes because it can’t solve every current problems. That’s why companies need to pay great attention to data quality, metadata, interoperability, and domain applicability if they want to get value in the form of data.