In increasingly data-driven business environments, technical analysis plays a central role in decision-making and process optimization. Companies that analyze data effectively can make more informed decisions, identify problems faster and take advantage of opportunities sooner. But what makes a successful technical analysis and how can companies ensure they are making the most of their data? This blog post looks at how companies can increase their efficiency and innovation through structured technical analysis. 

What is technical analysis?

Technical analysis is the process of systematically examining data to identify patterns, trends and anomalies that lead to informed business decisions. In contrast to process analysis, which focuses on optimizing workflows, technical analysis focuses on the accurate evaluation of company data, often using modern technologies such as Business Intelligence (BI) systems, Big Data and Machine Learning. 

Three goals of technical analysis: 

  • Problem identification: Technical analysis helps to quickly identify inefficient processes, bottlenecks and faulty workflows. 
  • Trend prediction: By analyzing historical data, companies can predict future trends and adapt their strategies accordingly. 
  • Performance optimization: Data-based insights enable companies to improve their performance on multiple levels, from production to customer service. 

Why is successful technical analysis crucial? 

Decisions based on intuition or incomplete information are often no longer sufficient in the complex business world. However, successful technical analysis enables companies to make data-driven decisions based on facts rather than assumptions. This is crucial in order to remain competitive, work efficiently and be innovative in the long term. 

Competitive advantage through data: Companies that use data strategically can respond faster to change, better understand their customers and proactively adapt to market trends. This gives them a clear advantage over competitors who only use data reactively. 

Informed decision-making: Technical analyses provide insights that go beyond mere key figures. They provide deeper analysis that shows why certain trends are occurring and what actions should be taken to maximize positive results or minimize negative effects. 

Process monitoring and adjustment: Ongoing monitoring of processes using data makes it possible to identify deviations at an early stage and initiate corrective measures in good time. This prevents unnecessary costs and promotes continuous improvement.

Successful implementation of technical analysis in the company 

Successful technical analysis requires a strategic approach. It is not enough to simply collect data – it must be analyzed in a targeted manner and applied to relevant questions and problems. Here are the key steps to successfully implementing technical analysis in your organization: 

  1. Define clear objectives: Before you start technical analysis, you should define exactly what you want to achieve. These can be goals such as reducing costs, optimizing processes or improving customer satisfaction. A clear goal helps to focus the analysis on relevant data and draw the right conclusions. 

  2. Selection of the right data sources: Successful analyses are based on high-quality and relevant data. It is crucial to select data sources carefully and ensure that they are reliable and complete. This can include customer data, production data, financial data or even external market data.

  3. Data preparation: Data must be cleaned and prepared before it can be analyzed. Incomplete, incorrect or inconsistent data often leads to incorrect conclusions. The process of data preparation in this context includes cleaning, standardizing and consolidating the data so that it is suitable for analysis.

  4. Selection of analysis tools: Depending on the company’s requirements, suitable analysis tools and methods should be selected. Simple BI tools for descriptive analyses can be used as well as advanced algorithms for predictive or prescriptive analyses. Machine learning, AI and big data tools offer increasingly powerful options for analyzing large volumes of data quickly and precisely.

  5. Interpretation of the results: One of the biggest challenges in technical analysis is interpreting the results correctly. Here it is important that not only statistical figures are evaluated, but that the results are placed in the context of the corporate strategy and operational processes. Cooperation between data specialists and operational managers is crucial here.

  6. Derive recommendations for action: Data analyses are only successful if they lead to concrete measures. Once the analysis has been completed, clear recommendations for action should be formulated. These can include short-term measures to rectify problems or long-term strategies to improve company performance.

  7. Continuous monitoring and adaptation: Technical analyses should not be seen as a one-off project. You need to perform them continuously to monitor changes in the data and associated processes. Monitoring makes it possible to react to new developments and make necessary adjustments at any time.

Challenges in technical analysis and how to overcome them 

There are also challenges that companies have to overcome when it comes to technical analysis. Here are some of the most common obstacles and possible solutions: 

1. Data quality and data integration 

The challenge: 

Incomplete or inaccurate data can distort the results of analysis and lead to incorrect decisions. Data is often spread across different systems and integrating these data sources can be difficult. 

The solution: 

Data quality must be ensured from the outset. Companies should invest in data management tools and establish clear guidelines for data collection and storage. Data integration tools, as discussed in previous posts on data integration and process optimization, can help bring together different data sources and create a consistent database. 

2. Lack of analytical capabilities

The challenge:

Data alone is worthless if it cannot be properly analyzed. However, many companies do not have the internal resources or expertise to perform complex data analysis. 

The solution: 

Training and education are crucial to strengthen employees’ analytical skills. Alternatively, companies can bring in external data analysts or consultants to expand analytical capabilities. Building a data science team can be a long-term solution to ensure that the company retains full control over its data analysis. 

3. Interpretation and implementation of the analysis results 

The challenge: 

Even when data analysis has been successfully carried out, many companies find it difficult to translate the results into concrete action plans. This can be due to a lack of understanding of the practical implications of the data. 

The solution: 

Collaboration between technical analysts and executives is critical. It is important that the results of the analyses are clearly communicated and that a joint effort is made to work out how these findings can be incorporated into the corporate strategy. Workshops and joint meetings between the different departments should then help to promote understanding and develop a unified vision. 

4. Technological complexity 

The challenge: 

As the amount and variety of data available increases, it becomes more difficult to implement the right technologies and tools. Many organizations are overwhelmed by the choice of analytics platforms and don’t know which solution best fits their needs. 

The solution: 

Companies should focus on scalable and user-friendly tools that meet their current and future needs. Here, it can make sense to start with smaller, easily accessible tools and supplement these with more comprehensive solutions as required. It is important to consider the technological complexity in relation to the actual benefits and to ensure that the tools used can be understood and used effectively by the users. 

Summary of the most important points: 

  • Technical analysis is crucial for data-based decision making and increasing efficiency within the company. 
  • Successful analysis is based on high quality data and a systematic approach. 
  • The biggest challenges lie in data quality, lack of analytical skills and the correct interpretation of results.  

Are you ready to improve your business processes through sound technical analysis? We help you make the best use of your data and make informed decisions. 

Tags:
#Data integration #Digitalization #Innovation #Tools

About the author

Mathias Herrmann

CEO at ALLEHERZEN

Mathias Herrmann is an internet entrepreneur going back to the Internet’s early days with a deep interest in digital and future technologies. For over 20 years, he has been helping companies make the most of their data by forging innovative solutions – without forgetting the people behind the data.

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