Bridging Technology

and Business

Data Enhancement


Big data is a broad term which covers several aspects of data, specifically volume, velocity, variety and veracity of data. Several years ago business intelligence (BI) applications were introduced. BI uses descriptive statistics, whereas big data uses inductive statistics and concepts from nonlinear system identification to identify valuable decisions.

The first step of data exploration is data cleaning and aggregation. We will bring our tools and our data to combine it with your data. This process ensures that your data is reliable and can be processed and stored by various applications.

Predictive Modeling


Based on reliable data we benchmark up to 10 different prediction algorithms according to the properties of your data set. After the first benchmark test, we will adopt the best algorithm to meet your specific needs.

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Interactive Testing


Finally, you will receive a web interface to test our algorithms. Big data applications are not only about data, humans need to feel comfortable using them and also feel confident about the results.

Typically our clients increase their acceptance rates dramatically. We provide a web interface for any algorithm in order to test the results and increase the acceptance of your workforce.

User Trainings


Knowledge sharing can provide a competitive advantage. In the past, the speed of innovation and the variety of technical solutions has increased exponentially.

We provide training for users with various backgrounds. We offer big data programming lessons in R and Python, and we provide real data for training purposes. Furthermore, we teach the fundamentals of statistics and machine learning in spreadsheet programs. Please contact us so we can send you an overview of our training agendas.

code.py
				  
import spacy.en
from spacy.symbols import VERB, nsubj, dobj

def find_acquisitions(nlp, text, buy_words):
  doc = nlp(text)
  for ent in doc.ents:
	ent.merge(ent.root.tag_, ent.text, ent.label_)
  buy_words = set(nlp.vocab.strings[w] for w in buy_words)
  for t in doc:
	if t.pos == VERB and t.lemma in buy_words:
		buyer = [w for w in t.lefts if w.dep == nsubj]
		bought = [w for w in t.rights if w.dep == dobj]
		if buyer and bought:
			yield t, buyer[0], bought[0]

Custom Algorithms


Our tools are made for everyday use. We enable users from a variety of backgrounds to test and use our results through an easy to use web interface. We also provided spreadsheet formulas to use predictive services based on our algorithms.

Pentesting


An increasing number of smaller companies are also starting to realise that their development cycle lacks security tests, and are introducing them in order to check their IT infrastructure.

Pen testing is a vital part of our development process and documentation. Any IT product for which security is relevant can be tested. A typical example is web applications like online shops that are provided over the internet to a large user base.

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About


To help our clients we serve the analytical needs of business operations and IT leaders across finance, supply chain, human resources and marketing. Helm & Nagel provides insightful and meaningful analytical coverage of best business practices and innovations that affect successful business outcomes, such as the digital transformation of operations, cloud-based business analytics, and social collaboration.

Helm & Nagel applies customer adopted methodology to evaluate the performance of service and technology in terms of innovating and executing against those business outcomes. We deliver insights from data revealed by custom made algorithms, reducing marginal costs and cumbersome decision processes.

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