Leveraging Data to be Competitive\nIt is becoming increasingly important for enterprises to leverage data to be competitive. Yet, there are three challenges related to embracing data utilization that all businesses share:\n\n \tit takes time,\n \tadvanced skills, and\n \texpertise.\n\nTogether, these challenges make it difficult for enterprises to fully leverage their data for business growth.\u00a0 Data analytics is not simply prediction by machine learning. Rather, it is a process involving many different steps, including:\n\n \tdata preparation,\n \tfeature engineering,\n \tmachine learning,\n \tvisualization, and\n \tmodel operationalization.\n\nUntil now, completing this process for just a single project would have taken months. Moreover, a wide variety of highly-skilled personnel are needed for each step \u2013 such as domain experts, data scientists, data engineers, and BI engineers.\u00a0 Additionally, processes and outcomes have tended to be highly dependent on the experience and intuition of each individual.\nFeature Engineering Made Easy\nFor feature engineering in particular, it has long been thought that this step can only be done by experts, as it requires deep domain knowledge.\u00a0 The results derived from machine learning have tended to be \u201cblack-box\u201d, so often these results could not be fully leveraged in businesses.\u00a0 For enterprises to benefit from the full utilization of their data, it is necessary to resolve these challenges and streamline data analysis and application.\n\ndotData\u2019s approach to data science solves these problems through AI and automation. The development of the dotData Platform stemmed from my experience in leading more than 100 data analysis projects at NEC, across a variety of industries.\u00a0 What I found is that, no matter the industry, a common thought process could be applied on how to build the data analytics process.\u00a0 From that experience, I was able to invent automated feature engineering.\u00a0 This was previously the most time-consuming and manual step, requiring high levels of skill and domain knowledge.\n\nThe automation of feature engineering is core to dotData in that we can use AI to design hypotheses for features, and automate analytical processes that are applicable to various industries, businesses, or data.\u00a0 Because we can automatically execute data analysis processes from data preparation through feature engineering and machine learning through to model operationalization, it solves the data analytics challenges related to time and skill sets that have existed until now.\u00a0 For example, a data analytics use case for a customer of a financial business, which previously required two or three months of work by data scientists, can now be done in less than a day, with equal or better accuracy.\nData Project Completions Increase\nAs it becomes possible to complete projects significantly faster, there will be an exponential increase in the number of experiments and the discoveries of new use cases.\u00a0 In addition, our approach provides full transparency and interpretability where the basis for the derived results is apparent.\u00a0 \u00a0Therefore, it can easily be implemented in business operations with high confidence and accountability.\n\nAs data analytics becomes more efficient, enterprises can operationalize it as part of their everyday processes and accelerate their data-driven initiatives.\u00a0 We have made it possible for all businesses to utilize AI and machine learning, and have in fact already achieved major results across a number of industries.\n\nAs data science automation is adopted, processes that once relied on peoples\u2019 experience and intuition will instead be executed efficiently using data.\u00a0 As a result, enterprises of all types will be able to analyze data more efficiently.\u00a0 They can now create better products, services, and generally be more productive while ultimately providing benefit to society as a whole.