Leaders across multiple industries struggle with getting value from data science 2020 projects. Gartner Group research says 85% of big data projects fail. A 2019 survey by Dimensional Research said 96% of companies struggle with artificial intelligence and machine learning. There seems to be a fundamental disconnect between data science teams and business users. What are the trends in data science 2020 that could change this lack of success?
Automation
The increasing automation of data science takes a significant amount of headaches out of the process. Automation can leverage artificial intelligence and machine learning to analyze vast amounts of data, create hypotheses, track data patterns and train hundreds of machine learning models. What this allows data scientists to do is test significantly more use cases and shorten the time needed to identify the most impactful ones. This sort of automated experimentation with data gives the data scientist freedom to build more business models accelerating the use of data science in business applications.
Data Privacy and Security
IBM has reported that there is a 25% probability an organization will have a material data breach in the next year. To be effective, data science must be able to work with confidential data yet maintain privacy and security of those data sets. Sharing data is a key component to getting the most out of the data that organizations collect. Companies are using a variety of methodologies to keep their customer data from potentially compromising positions. These methodologies include differential privacy which is accessing data at a high level for analysis but not allowing access directly to the data at the individual level, homomorphic encryption which allows the data scientist to compute on the data without decrypting it and secure multiparty computation which allows multiple parties to compute on a function while keeping the inputs private. All methods have their pros and cons but ensuring data privacy while allowing for innovation from information sharing is critical for data science success.
Cloud Computing
Super-sized data in the cloud offers anyone anywhere the access to practically limitless processing power on limitless data. Data Scientists can use a variety of platforms and programming languages for free as well as for a fee that they wouldn’t always have had access to in the past. Data Scientists have to be comfortable using MapReduce tools like Hadoop to store data and retrieval tools like Pig and Hive to work effectively in cloud computing. With the amount and growth of data, understanding the use of the cloud is another critical trend in data science 2020.
Natural Language Processing (NLP)
Do you have an Echo or Alexa in your home? Do you interact with Siri on your phone? Then you are already utilizing natural language processing in your day-to-day life. Email assistants like auto correct, grammar and spell check, even the spam filter on your email are all examples of NLP at work in your life. NLP is driving ecommerce with chat bots on websites to assist you in shopping online and even providing better search results even when you spell things incorrectly or aren’t even sure how to ask for what you want. Gartner Research predicts 85% of customer interactions in ecommerce will be managed without humans by 2020. Google Translate is used by 500 million people every day to understand each other in more than 100 world languages. Extracting and summarizing information in the medical field is happening faster than medical professionals can absorb it.
