The ever larger and more varied data sets generated from cognitive and IoT (Internet of Things) technologies have become a big challenge for businesses to find the value of that data without a data scientist. The failure of applying data science led to a competitive disadvantage. According to the Forbes report, Data scientist is one of the highest demand jobs today. In Order to get actionable business insights from their data, and not fall behind in the competition, companies should find new tools, staffing models, and training strategies.
Automation and employee training are giving organizations access to data science without having to wage war for talent. According to the Gartner report, more than 40% of data science tasks would be automated by 2020. Thus, the companies, who adopted data science automation tools early can get benefits in terms of cost and time savings along with revenue gains.
There are various tools available in the market to simplify the application of data science strategies. Meanwhile, The demand for low-code development platforms is rising significantly, and multiple training courses and boot camps have already started to help professionals from diverse backgrounds acquire relevant data science skills with basic coding and mathematics.
Though the demand for data scientist will be in the top, the below five factors are making data science crucial capabilities accessible to more professionals and potentially mitigate the skill shortage.
Automated machine learning
According to estimates, data scientists spend 80% of their time on tedious and repetitive tasks. Such as, data preparation, algorithm selection and evaluation, and feature engineering and selection – this kind of functions will be partially or fully automated. Many technology vendors have launched numerous tools and techniques in the market to automate such tasks.
The automation makes data scientists work more effective and more productive. Hence, businesses will be able to utilize data science automation dynamically to empower and leverage excessive skills.
App development without coding
Low-code or No- code development platforms offers user-friendly structures like drag-and-drop modules, graphical user interfaces to help both IT and non-technical staff boost build AI apps and tools development for their business. For instance, by utilizing a no-code platform, salespeople will be able to develop a machine learning based tools themselves to give product suggestions to clients based on strategically pitch opportunities.
Thus, The demand of these Low-code or no-code platforms is proliferating than the traditional method. Many organizations have already acquired and getting benefits from these tools amid a talent shortage for data scientist and developers.
Pre-trained AI models
Data scientists core activity is developing and training machine learning modules. Now, various software vendors have introduced pre-trained AI models, offering viable machine learning skills and transforming into products. These solutions can reduce the exertion and save the time to required for training. These solutions can reduce the effort and save the time to need for training, or even can start delivering clear insights directly.
Mostly pre-trained models are accessible for use cases related to Audio, video, image, or text analysis like automated equipment inspection, sales opportunity workflow automation, customer service, sentiment analysis, customer service, and interactive advertising. We can expect more pre-trained AI models will be available in the coming days.
Self-service data analytics
Some useful tools available in the market for business and non-IT users, these tools enable them to get data-based insights without the need for data scientists and analytics experts. These tools provide incredible features and aid the process of developing and deploying machine learning models.
The features such as NLP natural language search and query, visual data discovery, and NLG (Natural Language Generation) help users automatically find, visualize, and analyze data findings like exceptions, correlations, links, clusters, and predictions. Hence, business users are capable of performing complex data analysis and getting valuable insights without the need of data scientist.
Accelerated learning
AI and Data science-related training courses and boot camps are growing rapidly. These training platforms train professionals with basic coding and mathematics to bring basic data science skills in a short period of time. These courses are aimed to enable professionals to perform basic data science skills.
Organizations should take a multi-pronged approach to data science tasks as a blend of skills and talent needed to achieve success when applying data science. Some companies rely on data scientist but fail to reckon with the significance of functional and business skills to the success of a project. Success depends on multi-technology talent; it requires the perfect blend of expertise and skills.
Finally, the democratization of data science will enable effective collaboration between data science experts and business in building data centred solutions. Some organizations have already started effectively expanding their data science efforts by providing data science automation tools to a combine of professionals including business users, data engineers, data scientists, and statisticians.
