Transform Data into Value
Data volumes in companies are constantly growing, markets are becoming increasingly global and unpredictable, business models are constantly changing. Business-critical information and reports must be available at the right time and in the right form in order to guarantee fast and well-founded decision-making.
What types of analytics are there?
Until now descriptive analytics have concentrated on looking at the past. With predictive and prescriptive analytics, however, methods and tools are available to look towards the future and make predictions that involve uncertainties. In all types of analytics, the focus is on identifying data patterns in order to generate benefits for companies. Analytics often includes the areas of business intelligence, reporting, big data, AI, machine learning, data science and others.
Traditionally, analytics support operational processes in businesses in order to give an overview of the state of the company. A distinction is often made between operational and strategic reporting, depending on how the data will be used. In addition, also with regard to the intelligent enterprise this includes static and mathematical models in order to be able to evaluate the already existing data. The approaches of AI, machine learning, and data science have emerged from this.
In order to be able to deal with the ever-increasing amounts of data as well as to be able to analyze unstructured data, various approaches are pursued under the term big data. The focus here is on linking structured data from operational systems with unstructured data from, for example, tweets from Twitter. This is designed to create additional benefits.
From looking into the past to forecasting the future
Creating added value with an analytics strategy
All forms of analytics process data that already exist in the company or are available from external data sources in order to provide information that is essential for managing the company. It is therefore important for companies to address the role of analytics and to develop an analytics strategy that takes a holistic view of the issues and allows them to ensure maximum value for the company – from looking back at the past to forecasting the future.
Turn data into information
Analytics was and continues to be a support function for the operational processes of a company. The switch to new ERP systems is increasing the need for information in order to manage companies effectively and efficiently. Analytics provides the necessary tools to generate information from data. For this purpose, not only the structured and usually easily available data are used, but also the unstructured, less accessible data. By combining the two types of data, companies gain insights that can generate a decisive competitive advantage.
Making unstructured data usable
The challenge of combining data from a wide variety of sources and making it analyzable is a major challenge in the field of analytics. Understanding the data and the possible links between data from different data sources is usually the crux of analytics projects. But only this combination of structured and unstructured data allows a clear view of the future development of companies in uncertain times. This requires a knowledge base that is usually not yet present in companies and needs to be developed first.
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Artificial intelligence (AI) is a major driver of digitalization. As part of the value chain, AI enables data-based decisions to be made.
Utilize the full transparency provided through process mining to get data driven insights in your finance, procurement, order to cash, production, logistics, supply chain or IT processes.
With their many years of experience, Camelot’s analytics experts accompany you from the analytics strategy to the implementation of analytics solutions. Our consultants are available to assist you with the implementation of big data and data warehouse initiatives as well as front end optimization. We also develop packaged solutions based on our project experience which enable you to start projects quickly.