Addepto Guide - ML & BI Solutions

YOUR GUIDE ML THROUGH ML & BI BI SOLUTIONS ADDEPTO WHITE PAPER 2021 WE KNOW HOW TO TURN YOUR DATA INTO ADDITIONAL REVENUES

OUR The purpose of this white paper is to PURPOSE help you plan your Business Intelligence and Artificial Intelligence projects without making common mistakes. We will show you how to avoid a significant extension of project costs and time. Our tips will help you ensure success in your future analytical project. IMPORTANT Pay particular attention to the points described in this document. Otherwise, your BI or AI solution will not perform optimally or give incorrect results. A professional approach to project planning and management will certainly help you to deal with problems along the way and successfully complete the project. Understanding possible problems is the first step to eliminating the bad consequences in the future. IF YOU NEED PERSONAL CONSULTING LET'S TALK! E-MAIL: [email protected]

BI AND DATA WAREHOUSING BUSINESS INTELLIGENCE (BI) Business Intelligence is a technology that enables companies to leverage their data, gain business insights, and make data-driven decisions. BI tools help organizations process big data and create visualizations, reports, dashboards and perform predictive analytics. DATA WAREHOUSE Data warehouse is a widely used system based on Business Intelligence technology for data analysis and reporting. Data warehouse enables the integration and storage of data from various sources. It can store both historical data and real-time data sources, ensuring the most up-to-date information for reporting purposes. Now, there are a few things you need to know before applying data warehousing in your business.

BI AND DATA WAREHOUSING KEY POINTS 1. Selecting the right front-end BI tool is very important at the beginning of the project. It is crucial to understand your audience and the final product. It affects your future costs of solution implementation. 2. Don't immediately deploy a complex system like Enterprise Data Warehouse. Release your data warehouse step by step and verify data quality and business logic. Failure to do so may result in startup failure. 3. Be sure to pay attention to scalability and automation first. Use continuous integration tools to automate your BI and ETL pipelines. Without automation, your solution is not scalable and cannot be deployed in various environments and the system cannot be scaled up.

BI AND DATA WAREHOUSING KEY POINTS 4. The data model should always be oriented towards ad-hoc and OLAP analysis. Use a star schema or snowflake to present your data in a new layer. Data should be denormalized to improve query performance and self-service analytics. 5. Don't forget about machine learning as part of a modern data warehouse. Use your DW as a data source for predictive models to optimize data preparation and data quality.

MACHINE LEARNING Machine learning (ML) enables computers to “think” and learn alike humans, basing their conclusions and future predictions on analysis of historical data and real-time data. It is a rapidly developing technology that impacts almost every aspect of a business. WHICH INDUSTRIES CAN BENEFIT FROM MACHINE LEARNING? Different machine learning solutions can be applied to businesses operating in various industries. Among the most popular ones are: Manufacturing Healthcare Gaming Sales Marketing Finance Economics Logistics and Supply Chain FinTech Energy Ecommerce Now, let’s take a look at the crucial information you need to know before implementing machine learning in your company.

MACHINE LEARNING KEY POINTS 1. Get to know the business in-depth and combine it with statistical knowledge. In the broad field of machine learning, you need to know exactly which algorithms or methods can be applied to a specific problem and what data you will need for it. 2. Plan solutions that best fit your business requirements, not just your data. Machine Learning supports business processes and each model should be as efficient as possible, which means that each solution should exactly meet the business requirements. 3. Transform your data appropriately. Machine learning models need data in the right format, which means you need to properly transpose, convert, and manipulate the data so that it can be used in the model.

MACHINE LEARNING KEY POINTS 4. When designing DWH dimension and fact tables, you should take into account that some of them will also be used for machine learning processes. 5. You also need to dive deep into the attribute selection process to select the correct and most important functions in the right format and structure in the ML process. Otherwise, machine learning models may be based on irrelevant parameters and the forecasts/recommendations will be incorrect. 6. The modeling process should also be properly planned so that the model adapts to the changing data structure and is scalable for each client. 7. Don't overdo machine learning models. A real-world cross-sectional model will produce worse and unstable results, which can have a big impact on your business 8. Correctly plan integration in the system architecture. In addition, before the project, you need to properly plan how the ML will be integrated into the system architecture and at what time the entire flow will run. The entire development and data engineering process will depend on this.

ABOUT US We are one of the Top Machine Learning Agencies Operating Worldwide. We develop Machine Learning, Big Data, and Business Intelligence solutions for enterprises and high-tech companies around the world. Every day our machine learning consulting company works on new solutions that solve our client’s problems and adapt to future changes. Our goal is to minimize the need for human engagement and automate time- consuming processes using machine learning and AI. CONTACT US If you are looking for more details, or you would like to ask us some questions, do not hesitate to contact us anytime. [email protected] Visit our website: addepto.com Find us on social medias: IF YOU NEED PERSONAL CONSULTING LET'S TALK!