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The role of Data Modelling & Prediction for Business Transformation

The role of Data Modelling & Prediction for Business Transformation

IT teams in small and medium-sized companies struggle with budget constraints and a shortage of skilled workers. When the demand for IT services increases, they are heavily overloaded and look for ways to increase efficiency. Additionally, organizations are reaching a point where their data storage and computing are unable to keep up with the growth of data and technological advancements.

 

As data, a critical asset for organizations continues to rise exponentially, business executives around the world are heavily investing in IT automation. Also, the digital transformation is pushing the boundaries, enticing businesses entities to invest in technologies that can predict possible outcomes, and to gain a competitive advantage. One of the emerging and appealing technology that businesses can benefit from in many ways is Predictive analytics. By definition, predictive analytics is a mathematical principle that uses algorithms and artificial intelligence (AI) to derive probabilities from historical and current data. It is currently one of the most important big data trends. The predictive analysis leverages statistical techniques such as predictive data modeling, machine learning, and even artificial intelligence to uncover patterns in big data.  It helps organizations to make data-driven decisions and get useful, business insights that can help them increase company profit.

 

It is a process that uses data mining and probability calculations to predict results. It includes the collection, analysis, and interpretation of data from various operational sources. The method uses structured and unstructured data, for example from internal and external IT systems (big data/data mining). Predictive Analytics collects this information using text mining, among other things, and combines it with elements of simulation processes. Thanks to machine learning, the algorithms automatically draw findings from their own data processing and use this as a basis to automatically develop predictions. The aim is to predict complex economic relationships and future developments based on the analysis of the existing data in order to make better decisions and gain a competitive advantage. Each model consists of a number of predictors, which are variables that can influence future results.

 
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The underlying software has become more accessible and user-friendly over time thanks to user interfaces that are suitable for specific departments. The goal is to identify trends, announce disruptive industry changes, and enable more data-driven decision-making. Such predictions serve to optimize the use of resources, save time and reduce costs. Optimized timelines for the introduction of new products or services can also be created. The models developed in the process are intended to help achieve or support the goals set.

 

Any area in which data is being collected is suitable for predictive analysis as there are many uses for it. These include detecting data misuse, improving cybersecurity, optimizing marketing programs, and improving business processes. Predictive analysis can use adaptive algorithms to examine systems, applications, and network performance by allowing companies to take a more proactive approach to IT operations management. With this technology, IT security experts can identify potential vulnerabilities, determine the likelihood of cyber-attacks and work on improving the company’s security structure.

 

Adapting to advanced analytics will allow your organization to stay on top. Just as technology is constantly innovating, so should companies adapt. Predictive analytics focuses on improving profitability, productivity and reducing costs through process optimization.

Do you have areas of the company in which you want to improve prediction/reporting?  If you answered yes, please contact us directly, our experts will gladly support you.

Smart companies: Tips for a smooth integration of AI

AI (Artificial Intelligence) has a long history of being considered science fiction but opens up enormous potential for companies in terms of productivity, the efficiency of business processes, gain sustainable competitive advantage and customer relationships. Covid-19 pandemic is the proof of accelerated use of AI across multiple industries around the globe.

Smart companies Tips for a smooth integration of AI

According to the latest title “Global Artificial Intelligence Market” published by Facts & Factors, the global Artificial Intelligence market size is expected to reach USD 299.64 Billion by 2026 from USD 29.86 Billion in 2020, at a compound annual growth rate (CAGR) of 35.6% during the forecast period 2021 to 2026.

Most companies believe that AI is certainly one of the foremost technologies of the future even though they still aren’t making the most out of their relationship with AI. Here below are few obstacles to AI adoption and how they can be avoided.

 

The Preparation Phase

For many people, there is still something mystical or threatening about AI. Although intelligent technologies act invisibly in our everyday life, the image of AI often emerges as futuristic, emotionless robots that look amazingly like Arnold Schwarzenegger are going to hunt us down and kill us. But AI is only aimed to develop machines/computers that are capable of doing things normally done by people. The lack of knowledge is one of the main obstacles to AI adoption. The implementation of new technologies should always be seen as a long-term project. As there really isn’t a textbook on how to adopt AI at the enterprise level, people with the right mindset need to be brought into an organization to help facilitate changes and capitalize on opportunities.

In many cases, high costs and a lack of resources are also decisive obstacles. But not every company directly needs its own computing resources or expensive, in-house developed platforms. In many cases, it’s worth taking a look at third-party AI platforms or in the public cloud. They enable the use of powerful and scalable AI solutions without the need for extensive investments of your own. The experience of the major platform providers also helps to implement projects as quickly as possible.

 

Communication is the key

The challenge of scaling AI and automation often does not lie in the technology itself. Rather, the corporate culture is often important in order to implement changes in the work environment. Thus, before the introduction of the AI, timely communication with employees is essential. The benefits of AI must be well elaborated and appropriate training must be planned for all employees. Artificial intelligence requires specialists who are well educated and have to be trained. This is the only way to develop, operate and maintain intelligent systems and to handle advanced troubleshooting and continuous improvement of these solutions. Tasks and responsibilities transformation must also be openly discussed to deal with the fear of losing jobs among employees, as, AI will complement rather than replace employees.

 

Introduction of a clear AI strategy

Small and medium-sized companies, in particular, are often reluctant to implement AI because they lack a clear strategy. In the first step, however, a fully developed strategy is not absolutely necessary: ​​rather it is more important for companies to understand the technology and recognize the possibilities it offers. At this point, experts should be consulted to elaborate on the benefits of AI and how can this actually benefit the company? What are the installation process and its duration? What type of data or tools are needed to work successfully?  What can be done to achieve results? Once all questions are clarified, and a strategy has been worked out the introduction can be prepared.

 

AI technical requirements

Like a human, an AI system also needs time to learn. That is why it takes time for the first successes to be measurable. In order to have a decisive influence on the development of companies, good implementation is requisite. The AI requires various available data that it can analyze. This is the only way to generate data models that can be used as a basis for future predictions or decisions. The implementation effort depends, among other things, on the flexibility of the software that a company uses. Another factor is the specific use cases that should be automated with the help of AI and that must be taken into account as early as the implementation phase. It is possible to start individually with each communication channel, regardless of whether it is email, chat or telephone. Preferably, however, the channels are placed one after the other. As a result, a company does not lose any time, because the advantages of an omnichannel system are that the training results of one channel flow into the learning process of the other channels. Depending on the use case, the AI ​​applies different algorithms and develops certain models. The learning is based on the trial-and-error method until it has developed the right model.

 

The AI ​​promises long-term optimization in terms of profitability and efficiency of in-house processes. With the ability of self-learning, algorithms can be used to improve existing processes and products as well as develop new business models. This means that AI has the potential to change entire industries and value chains over the long term. Artificial intelligence also opens up cross-sector value creation opportunities and growth potential for small and medium-sized companies. To gain all benefits related to AI, the first step is the will to deal with the topic of AI and ultimately its implementation. Therefore a well-developed strategy is required.

 

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Software Development: Why is Software Quality Control + Testing So Important?

Software development is constantly changing and becoming more and more complex. In order to keep up with this development, the ever-changing and innovative business landscape, test tools, and security requirements were further developed. Development teams are under increasing pressure not only to create quality software on a tight schedule but also to ensure that the software complies with both internal and external standards such as GDPR.

 

This also means that, as far as we know today, there are no error-free applications. Problems can arise in software development that has consequences: There is a risk that the originally planned development costs will be exceeded, leading to a delay in the development period. There is also the risk that the end product will have qualitative defects.

 

This makes quality control so important. Testing software is, without question, very important for a high-quality end product. In order to make sure the released software is safe and functions as expected, the concept of software quality was introduced. Software Quality Testing is an honest review and evaluation of software regarding its quality and compliance with the organizations/client specified requirements, expectations, and market standards. The goal is to identify and fix errors at an early stage. Software testing is not only an important part of quality assurance but also an integral part of the software development process.

In this article, we’re going to look at the major challenges encountered during software developments and how can we resolve them to deliver a quality project.

 

  • Programming standards

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Nobody wants to take the time to plan but start programming right away. It is best for the programmers to sit down, discuss important points, such as choosing the right technology, interpret customer’s needs to properly defining the purpose of the software or even write the first few lines of code together. Because if every developer interprets programming standards in his own way, there can be significant discrepancies in the end results, which means that extensive reworking is necessary. It is advisable to provide internal manuals with the programming standards and, above all, to ensure that these are actually used by the project team. This is how each party, programmer, client, service provider, etc. can save time and be productive right from the start.

 

  • Ensure clear processes

Due to a lack of planning, the exact requirements for a project are also missing. Non-transparent and inaccurate description of the processes in software development can quickly lead to uncertainty among those involved. For example, in many projects, the requirements are only formulated in writing and are not communicated correctly. This is mostly due to the lack of communication between project leaders, team leads, and managers and between the actual project formulator and the one who programs the whole thing. This makes it extremely difficult for the developer to implement the right things.

Misunderstandings can result in huge problems as a result of which errors creep in, which can only be eliminated again with increased effort and the project can be unnecessarily long. It is recommended to enable direct communication between the project formulator and the programmer.

 

  • Set priorities

It is noticeable again and again that no priorities are set with regard to the planned features, which usually means that the features are completed in the wrong order and or errors are often made, especially when it comes to the selections of technology for development. New, exciting technologies are often used but the problem arises when the manufacturer of the new technology decides on an update, then you may have to rewrite the entire code. A thing that is very time-consuming and not really practical. Additionally, only prioritized tasks should remain in the project scope.

It is crucial, but it can be a challenge to prioritize several dozens of items efficiently. A dedicated team should therefore decide in advance which work step is to be tackled first with already tried and tested technologies. It is advisable to make the decision internally and to leave the client outside. Because clients often lack an overview of which feature is particularly important for the success of the project. With prioritization, you can also keep the track of the expenses and effort do your team spends on the project activities.

 

  • Project’s delivery time

There is hardly a single project in which the timeframe of software development is respected because the time required is incorrectly estimated at the beginning of the project. Due to the new requirements made by the client during the project, but also to the fact that a developer cannot accurately assess larger projects can cause delays in project delivery.

Delivering software on time is never easy when the scope and timelines in question are significant.  To deliver software projects on time, it’s recommended to break down the assignment into smaller modules of a few days. This makes it possible to give a more precise expectation of what will be delivered and when it will be delivered.

 

  • Professional qualifications of the employees

For every project, it should be ensured that all employees involved are adequately qualified. This is especially true for very complex software projects in which many developers are involved. It is also important that the individual employees have a similar technical level in order to avoid communication problems with their team members.

Additionally, it should be avoided that the developers assigned for a project leave the company while the project is running and are replaced by other/new employees. Because if an employee leaves the company during development or changes to another project, it is extremely difficult to transfer the knowledge of a departing employee to the new developer.

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