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Decision Intelligence (DI) is a field that combines analytics artificial, intelligence and automation to improve decision making across organization. This aims to take decision process more effective by providing data integrity, AI & machine learning models, focusing human understanding, optimization to the cause of action. This is used in many industries such as manufacturing, public sector, retail and etc. Descriptive analytics focus on what has happened in the past. • We can find the trend such as peak months of the last years. Eg: November and December sales were x% higher than in other months. This helps to identify the higher sales in the w inter months due to the holiday season but it doesn’t predict what will happen next. Diagnostic analytics focus on past data and helps to understand why something happens. • Company can go deeper into why those trends occurred. Eg: there was a sales peak in last year December due to increase of marketing campaigns and promotions. This helps the company to understand the reasons behind the successful sales but it doesn’t give insight about the future. Predictive analytics forecast future outcomes by using historical data. · Company can forecast what will happen in upcoming months Eg: Based on the historical trends the company predict that the sales will increase by X% in December this year For the decision intelligence this will provide the deeper insight since it helps company to forecast future outcomes and plan accordingly. Prescriptive analytics providing the recommendations based on the predictive data. · Company can decide the best action for future. Eg: To achieve predicted X% of sales increase we recommend ramping up inventory for the most popular items start campaign in end of October increase staff at stoles and etc. This doesn’t predict what will happen but provide the best recommendations based on the prediction. When comparing the above types of analytics 'predictive analytics' contributes the most to the decision intelligence since it provides what will happen in future (eg: it will provide the forecast of future sales). Therefore, this helps the company to data driven decisions such as budgeting planning and etc.