Data and analytics are becoming the centerpiece of enterprise strategy, focus and investment. Also, many companies are now realizing that data is becoming a critical corporate asset. As a result, Data Science and Analytics coupled with Artificial Intelligence (AI) and Machine Learning (ML) are fast becoming fundamental for companies to keep pace with the competition and succeed in the marketplace. Therefore, the challenge is to use data science, machine learning, big data, AI, and IoT to make the environment completely digital, business intelligence to gather, compute, and interrogate business data that can be turned out into actionable insights.
At KPG Global Enterprises, our approach and process to Data Science and Analytics is using algorithms, methods, and systems to extract knowledge and insights from structured and unstructured data. Furthermore, we use analytics and machine learning to help users make predictions, enhance optimization, and improve operations and decision making. If you have clear business objectives, we will help you drive business results and make informed decisions for your business. We can develop models and perform analysis of data trends that will let your organization forecast future predictions under numerous possibilities, how, and where to utilize maximum data to provide actionable results. This may include but is not limited to the following:
- Predictive Model and Dashboard Development
- Data Classification & Pattern Detection
- Recommendation engines
- Visibility and Big Data analytics
- Technologies utilized include Python, SAS, R, Tableau, and Qlik
- Biostatistics and Bayesian modeling
Furthermore, for data science to be effective, its full lifecycle not only must support traditional analytics, but it must also work in concert with modern applications. In some instances, we may use automated AI technology with Data Science to increase analytical effectiveness. Our approach means that the data science practice must evolve beyond routine, tedious tasks — as much as 85% of a data scientist’s time is spent cleaning, shaping and moving data from place to place, often to feed machine learning. That leaves only a small percentage of time to find patterns and trends, to build models, to predict and forecast, and to interpret results. Therefore, the use of automation can be utilized to optimize a team’s time and operational effectiveness.
