Using AI to make fast, data-driven decisions intelligently

Data analytics is the process of analyzing raw data to find trends and to answer questions. With the help of analytics, companies can know where they are, where they’ve been, and where they’re headed. With new machine learning techniques, access to massive data sets, and cheap computing power, data scientists can take a deep dive into the data and find insights that are worth a treasure.


The first step in the analytics process is usually descriptive analytics where data scientist looks to answer the question “What happened?”. This normally entails developing and measuring key performance indicators such as ROI and they vary across industries. Descriptive analytics do not provide forecasts or drives decisions, rather it focuses on summarizing the data in a meaningful and easy to understand way.


Diagnostic analytics help answer questions regarding why things happen. They are based on descriptive analytics and try to find out the cause. The KPIs are analyzed to find out why they are what they are and to check if anything is skewing the results. Some common steps in the diagnostics process include identifying anomalies, finding data related to the anomalies, and using statistical methods to help understand those anomalies and the impact they have.


The next step in the analytics process is predictive analytics where various tools and techniques are used to extract insight, discover trends, and make predictions from data. These techniques include traditional statistics as well as AI methods such as machine learning and deep learning. Some other types of AI techniques include computer vision solutions, natural language processing, sentiment analysis, chatbots, etc. and this gives data scientists new, innovative ways to gather information and extract meaning. Predictive analytics aims to answer the question “What if?”.


The last step of the analytics process is prescriptive analytics and it helps companies understand how to move forward. Using the insight from predictive analytics, managers can make fast, intelligent, data-driven decisions. When faced with uncertainty, the combination of the insights gathered from the different types of analytics can help you lead your company on the path to success.


In conclusion, data analytics plays an important role in success in today’s highly competitive and ever-changing business environment. Through various types of analytics, companies can learn so much about themselves and can use that information to get ahead of the competition.


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