Your enterprise requires flawless data analysis to ensure that you make the best possible decisions. The analysis possibilities of data science are also reflected in the data science maturity model:
- Descriptive Analytics: Description and measurement of events.
- Diagnostic Analytics: Description of contexts through statistical procedures. "Why something happens."
- Predictive Analytics: Prediction of future events or actions.
- Prescriptive Analytics: Recommendation and automation of actions based on predictive analytics.
Stages of Data Science Development
Descriptive and diagnostic analytics are always used with innovative approaches to data and business understanding in our projects. Our data science team is adept at the most advanced methods to analyze and predict future behavior. . This includes problem areas and procedures such as the following:
- Predictive Analytics: Existent data are used here to forecast future events or values from complex data structures. Dieses Wissen hilft dem Management, bessere Entscheidungen zu treffen.
- Regression: The eternal classic. A parametric model is specified here, and the parameters are assessed using algorithms. The advantage of these models is that they can be well interpreted and are very familiar. Generated here in addition to good forecasts are valuable insights which help make informed decisions.
- Machine Learning: These method classes contain, in particular, black-box procedures such as random forests, gradient boosted trees, support vector machines, neural networks etc. The models from these processes are difficult or even impossible to interpret, and used especially in the case of complex data structures if no model assumption can be made in advance. Emphasis is often placed here on the best possible prognosis, the model's interpretability being secondary.
- Neural networks and deep learning: As one of the areas of machine learning, deep learning is dedicated to certain issues which are very difficult to explore using other methods, in particular, when it comes to image and audio processing. However, these method classes are also used in other complex data situations involving, for example, web log data or complex time series problems.
- Natural Language Processing: Learn more about your customers, thanks to an intelligent combination of spoken or written language. Modern techniques like Word2Vec, latent Dirichlet allocation (LDA), optical character recognition (OCR) and AI neural networks can be used to obtain and operationalize valuable information from language. Such models are often found in applications such as chatbots.
Deploying these techniques pays off with a big knowledge headstart for your enterprise, thus bringing new opportunities in the form of potentially higher revenues. Our consultants are unquestionable experts in all common data technologies and solutions like Hadoop, SAP HANA, Apache Spark, Teradata, Python, R and Scala.
How is data-driven science revolutionizing the world of enterprises
Big data, cloud technologies, data science and artificial intelligence are revolutionizing the world. The work of data scientists has already resulted in changes and progress in many fields. Though no one knows what will happen in the future, precise forecasts on the basis of existent data are possible thanks to methods such as predictive analytics, machine learning and deep learning.
By deploying machine and deep learning techniques, your enterprise can handle the growing pressure from competitors and stay abreast of ever-increasing demands by customers. Moreover, data science enables you to gain competitive advantages and meet the challenges of digitization.
The success of digitization at a company depends on the use cases. Data science or AI cannot turn a poor use case into a good one. Two essential approaches can ("können") be identified for use cases:
- Automation and optimization: A familiar use case is automated and optimized through new methods and resultant, newly available data. For example, one converts their selection logic for operation with neural networks and uses weblog data in non-aggregated form for this purpose.
- New business models: New use cases are only made possible on the basis of new methods and technologies. As a result of predictive maintenance for machine data, for example, goods are no longer purchased but leased, and the maintenance and insurance businesses become new drivers for manufacturing companies which previously sold goods and often outsourced maintenance to external parties.
An important factor in all use cases is that highly complex data structures can now be deployed. This offers enterprises an inconceivably wide range of possibilities which should be exploited. Technical and algorithmic borders have shifted extremely in the last 3 years. Modern data science and AI can help you utilize new free spaces for your enterprise.