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The Future of Controlling
What do I do with artificial intelligence, machine learning, data science, and progress through digitization as a controller? – More than you think!
Companies worldwide are increasingly feeling the need to integrate new, data-based technologies to remain competitive. The use of these technologies implies far-reaching changes in the company’s internal handling of data, affecting control. You can check ProjectPro Machine Learning Projects to learn what kind of machine learning project is used by some biggest companies.
Do not be afraid of these changes, but seize the opportunity and make yourself indispensable for the upcoming transformation. Innovations in the use of data are difficult to implement without support from the specialist area. It is not uncommon for projects to fail due to a lack of a common basis for communication.
If not you as a controller, who is better suited to act as an interface between the department and data science? Their technical expertise is more in demand than ever because they are familiar with business practice and company data.
Actively Helping To Shape Progress
Prepare yourself in good time for future requirements and actively shape your company’s future! A first step in the right direction is to get a realistic picture of the job of a data scientist.
Build Up Knowledge – Assess Benefits
Brush up on your basic statistical knowledge from your school and university days! You can use various options for this:
Print And Online Media To Build Up Basic Knowledge
Numerous print and online media entertainingly convey the basics and largely do without mathematical jargon and complicated formulas. Familiarize yourself with how basic statistical techniques work. So you can have a say when it comes to correlations, regressions, classifications, and clustering methods.
Once you have established a basic understanding, you will soon understand machine learning, neural networks, and artificial intelligence (AI) principles. You will find that this is not rocket science or sheer magic.
Online Courses For Deeper Insights Into Practice
To delve deeper into practice, the Internet has a variety of free or inexpensive online courses available. These offer an easy introduction to coding with Python or R and other data science applications.
You do not have to complete retraining to become a data scientist, and a rough understanding of the instruments and the possibilities is sufficient. In this way, you reduce reservations and better assess the added value of data science.
Promotion By The Employer
Coping with such a build-up of knowledge in addition to professional and private obligations is undoubtedly a challenge. Here, the employer must be made aware of further training measures. Actively claim your funding. Do not wait until the topic has taken you by surprise and suddenly you are confronted with data scientists as work colleagues.
If this is already the case, treat them with suspicion and interest. They can learn a lot from each other and benefit from them. If your employer offers further training on its initiative, you should take advantage of them. In this way, you do not get sidelined with new developments in the company.
How AI and Machine Learning Can Be Used In Controlling
As soon as you have recognized the potential of data science, you can actively help shape innovations and act as the linchpin for new projects. Machine learning and deep learning in controlling make everyday work easier and relieve you of annoying repetitive tasks.
Time-consuming activities that follow fixed procedures and rules and require a great deal of attention can often be automated relatively easily. Machine learning and AI have proven themselves many times in the finance and accounting departments and the creation of reports and dashboards.
As a controller, you do not have to fear a loss of importance in your job. As an expert, you have an exclusive understanding of the business processes based on the numbers. Combined with your acquired basic understanding of data science, you make yourself indispensable for your company. Only you can deliver solutions where algorithms fail.
In the meantime, you can concentrate on your core task as a controller and provide important impulses for planning and controlling company processes. In this way, you can locate the control part more strongly in control.
It is all the more important to drive change in your own company in these dynamic times. Therefore, the focus of our online conference Digital Finance & Controlling this year is on the successful digitization of the finance sector.
Get to know the DNA of a digital finance area and find out which software can support you in your processes. The event is now available on-demand.
Although algorithms are superior to people when it comes to the systematic processing of large amounts of data, they can only produce meaningful results based on fixed rules and unambiguous data. They are good at recognizing patterns of relationships and deriving rules from them but fail in unforeseen events that do not follow any structure.
The correct classification of such events and the corresponding reaction can only be mastered by actual intelligence. This is where you come into play as a “human in the loop.” Only you have a feel for when algorithms are wrong.
With your knowledge of the limits of technology, you protect your company from consequential decisions made due to blind trust in algorithms. Here, too, control by capable controllers is required.
How Does Machine Learning Work?
Gain a realistic idea of machine learning and its possibilities! Free yourself from exaggerated expectations and gloomy future scenarios from science fiction!
Machine learning is currently the most prominent aspect of the sub-area of computer science dedicated to imitating human behavior: artificial intelligence.
The initial attempt to achieve the set goal by programming complex rules soon reached its limits, as social behavior can only be mapped to a limited extent by static rules. Machine learning takes an innovative way to solve this problem.
With the help of special algorithms, this approach automatically derives rules from data for which results are already available. These rules can, in turn, be used to forecast potential results for data for which they are not yet available (predictive analytics).
Machine learning can therefore be understood as the automated programming of software solutions for data processing:
Also Read: What are Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL)?
What Is Deep Learning?
Deep learning works on the same principle as machine learning, with the difference that data is processed with so-called artificial neural networks. These neural networks extract and compress data into a form that makes it easier and faster for computers to access the information it contains.
The use of neural networks has proven itself in the processing of audiovisual data (speech, image, document, and video recognition) but is not limited to these types of data.
The idea for artificial neural networks for information processing was formulated as early as the late 1940s. Still, it has only been relatively recently that technological progress and the lower prices for high-performance computer processors have made it possible to use this technology cost-effectively.
Neural networks consist of layers of simple, functional units, so-called perceptrons, which receive signals and send out signals when threshold values are exceeded.
To use a neural network to be referred to with the media-relevant term deep learning, there must be at least one additional layer (hidden layer) between an input and an output layer.
