Data Analytics for Corporate Success

1. What is Data Analytics?
2. The greatest strengths of Data Analytics
3. Challenges in Data Analytics
4. Data Analytics vs. Business Analytics 
5. Data acquisition - Opportunities and Outlook
6. The world of data interpretation
7. Understanding scales
8. How to interpret data?
9. Qualitative vs. Quantitative Data Evaluation
10. Common challenges in data analysis and interpretation
11. Formulate the right questions for your data interpretation
12. Visualize your data
13. Conclusion

The world of data analysis has undergone dramatic changes in recent years. While mathematical and statistical analysis methods have long existed, today we are experiencing an unprecedented flood of usable information. According to a forecast by Statista by 2027, the volume of generated/replicated digital data is expected to rise to 284.3 Zettabytes (billion terabytes). In 2020, the volume was 64.2 Zettabytes - this corresponds to a growth of over 400% in 7 years.

In the context of IT support and methodical analysis of raw data from various sources, we now use specific algorithmic processes. The main aim is to discover patterns and correlations in the information using data mining. These insights enable companies to make informed decisions that are no longer based on assumptions, but on verifiable knowledge. Data analytics also plays a crucial role in verifying or refuting theories and models.

Data analytics is now a critical cornerstone for business success. Companies that recognize this fact and invest in data analytics are better positioned to thrive and grow in a rapidly changing economic world. It is therefore crucial to keep reminding ourselves of the added value that data analysis can offer businesses.

What is Data Analytics?

The discipline of data analytics deals with extracting valuable insights from data and encompasses all processes, tools, and methods applied for this purpose. This includes data acquisition, data organization, and data maintenance. The primary purpose of data analytics is to recognize trends and tackle challenges using technology and statistical evaluations. Data analysis draws influences from various other science fields, including computer science, mathematics, and statistics.

The aim is to examine data in order to describe performance, make predictions, and ultimately achieve improvements.

The greatest strengths of Data Analytics

1. Information acquisition: In an era where information is worth gold, data analytics enables companies to draw valuable insights from their own data and external sources. These insights are crucial for understanding customer needs, identifying trends, and making strategic decisions.

2. Competitive advantage: Companies that effectively use data can gain a significant competitive advantage. They are able to react faster to changes in the market, offer customer-oriented solutions, and optimize their business models.

3. Personalization: Data analysis allows companies to offer personalized products and services. By understanding the behavior and preferences of their customers, they can create tailored experiences that strengthen customer loyalty.

4. Efficiency increase: Data analysis helps companies optimize internal processes and use resources more efficiently. This leads to cost savings and an increase in operational efficiency.

5. Risk management: Data Analytics enables companies to identify risks and respond to them early. This is particularly important in industries where compliance and safety are of high importance.

6. Promotion of innovation: Data analysis drives innovation by assisting companies in developing new ideas and products. It fosters the discovery of market gaps and new business opportunities.

7. Evidence-based decisions: Data Analytics provides the basis for evidence-based decisions. Companies can base decisions on facts and figures, rather than gut instinct or assumptions.

8. Real-time change: In a world where markets and customer needs are constantly changing, it is crucial to be able to react in real time to developments. Data Analytics enables companies to monitor change and react quickly to it.

9. Future-proofing: Companies that integrate Data Analytics into their business strategy are better prepared to meet the demands of an increasingly digitalized world. They can predict trends and adapt to changing circumstances.

10. Customer orientation & Customer Intelligence: In the age of customers, companies that understand their customers and their needs best are most successful. Data analytics allows for a deeper customer orientation, providing insights into customer behavior, preferences, and feedback. This customer intelligence empowers companies to offer customized solutions and increase customer satisfaction. Customer-oriented companies are better positioned to build long-term customer relationships and maximize customer value.

Challenges in Data Analytics

While data analytics offers numerous benefits for companies, it also poses some challenges:

1. Data protection and compliance: Companies must ensure that they collect, store, and process data in accordance with applicable data protection laws. Data protection breaches can have significant legal and financial consequences.

2. Data quality: Data must be of high quality in order to conduct meaningful analyses. Poor data quality can lead to incorrect conclusions.

3. Data integration: Companies often have data in different formats and from different sources. Integrating these data into a uniform format for analysis can be complex.

4. Lack of resources: Hiring and training professionals for Data Analytics can be costly. Smaller companies may struggle to provide the necessary resources.

5. Complex analyses: Advanced analyses, such as machine learning and artificial intelligence, require specialized knowledge and skills. Recruiting or training data science experts can be challenging.

6. Cultural change: A cultural shift towards data-driven decisions can be a challenge in companies. Employees must accept the importance of data and integrate it into their daily routines.

Overcoming these challenges requires a clear strategy and investment in technology and talent. Companies that successfully overcome these obstacles can benefit from the diverse advantages of data analysis and strengthen their competitive position.

Data Analytics vs. Business Analytics 

Business Analytics is a specialized component of Data Analytics, which focuses on the analysis of business data to improve decision making, optimize operational processes, and drive the growth of businesses. Business Analytics combines statistical analyses, data management, and business intelligence, to provide relevant insights for business action capability.

Business Analytics represents a solution based on analysis models and simulations. It enables organizations to create scenarios to understand reality and predict future developments. With the help of Business Analytics, organizations are able to make informed decisions and optimize their business operations.

The distinction between Business Analytics and Data Analytics, is relevant because they target different application areas and goals. While both disciplines deal with data to gain insights, their focuses and approaches vary. Here is a short explanation of the relevance of the distinction:

Application area: Business Analytics focuses specifically on the application of data analysis to tackle business challenges, support decision-making, and optimize business processes. Data Analytics, on the other hand, is a broader field that focuses on the general analysis of data in various contexts, not just business applications.

Goal setting: Business Analytics aims to deliver actionable insights that help improve the efficiency and effectiveness of businesses. Data Analytics can have many goals, including data exploration, identifying patterns and trends, problem-solving, and information acquisition.

Methods and techniques: Business Analytics often uses specialized tools and techniques that are geared towards business applications, such as data mining, statistical analyses, and business intelligence. Data Analytics can encompass a wide range of techniques and tools for understanding and interpreting data.

Impacts: Business Analytics can have direct impacts on business success by optimizing decisions, enabling efficiency gains, and creating competitive advantages. Data Analytics can be used in various contexts, from scientific research to social analysis, without necessarily being aligned with business goals.

Overall, the distinction between Business Analytics and Data Analytics is relevant as it helps organizations and businesses to choose the right approaches and tools to achieve their specific goals. Depending on the requirements and goals of an organization, one discipline may be of greater importance.

Data acquisition - Opportunities and Outlook

To benefit optimally from analytics, access to as many relevant pieces of information as possible is crucial. This access has greatly expanded in recent years thanks to fundamental improvement in data acquisition in three critical areas:

1. Processing speed: Significantly higher processing speeds, as made possible by in-memory computing optimized by SAP, for example, allow immediate access to huge volumes of data. In this approach, the working memory primarily serves as data storage, enabling the processing and evaluation of information from operational systems such as ERP in real time.

2. Specific evaluation methods: Advances in specific evaluation methods allow the processing of types of data that were previously hardly or not available at all. For example, using semantic methods not only numerical data but also texts such as scientific articles can be interpreted. In addition, artificial intelligence broadens the horizon by making video images and sensor measurements in production facilities usable as important data sources.

3. External data sources: The number of external data sources has increased dramatically. These include market research results, publicly available information about competitors, and data from social networks. Added to this are further external inputs such as weather forecasts, traffic information, and geo-data.

These developments not only expand the amount of available data, but also offer new opportunities for data analytics and business analysis. The outlook for data acquisition shows that businesses are able to use data in unprecedented ways to make informed decisions and promote future growth.

The world of data interpretation

Data interpretation refers to the process where various analysis methods are used to critically examine data and draw relevant conclusions. This step is crucial for Data Analytics as it helps businesses organize, refine, and aggregate information to answer important questions.

The importance of data interpretation is evident, and therefore requires a precise approach. Often data comes from diverse sources and is introduced into the analysis process in different structures. Data interpretation tends to be very subjective, as it can vary from business to business, depending on the type of data to be analyzed. Various procedures exist that can be applied depending on the type of data, with the most common dividing into "quantitative analysis" and "qualitative analysis".

Understanding scales

Before beginning an in-depth examination of data analysis, it's crucial to note that the visual representation of the data results is irrelevant as long as a well-founded decision about the scales has not been made. The selection of the scales has long-lasting effects on the effectiveness of data interpretation. There are different types of scales, including:

1. Nominal scale: This includes non-numerical categories that cannot be placed in a quantitative order. The categories are exclusive and comprehensive. In a favorite color survey, for instance, the colors themselves (e.g., Red, Blue, Green) are on a nominal scale as they do not have a quantitative order and are exclusive. It makes no sense to say that "Red" is greater or less than "Blue".

2. Ordinal scale: This includes exclusive categories that have a logical order but are not quantitatively evaluable. Examples include quality ratings or consent ratings (e.g., good, very good, moderate or agree, strongly agree, disagree). In a customer satisfaction survey, the answer options "very dissatisfied", "dissatisfied", "neutral", "satisfied" and "very satisfied" could be on an ordinal scale. Although they have a logical order, the gaps between the categories are not even, and we cannot quantitatively say how much more satisfied "very satisfied" is compared to "satisfied".

3. Interval: This is a measurement scale where data are grouped into categories that have an ordered and regular distance division. However, there is always an arbitrary zero point. A temperature scale like Celsius or Fahrenheit is an example of an interval scale. The intervals between temperature values are even, but there is no true zero point, as temperatures below the zero point (in Celsius or Fahrenheit) still exist. A temperature of 20 °C is not "twice as hot" as 10 °C. In other words, temperature changes referred to in Celsius or Fahrenheit are not proportional measures of actual heat intensity. Therefore, we cannot say that 20 °C is "twice as hot" as 10 °C, as this does not reflect the underlying physical reality.

4. Ratio: This scale includes features from all three mentioned categories. Weight in kilograms is a good example of a ratio scale, as it includes all features of the nominal, ordinal, and interval scales. There is a clear order of values, the intervals between the values are even, and there is a true zero point (0 kg) indicating that no weight is present. A weight of 40 kg is twice as heavy as 20 kg.

How to interpret data?

When asking the question, "How to interpret data?" The distinction between correlation, causality, and coincidences plays a crucial role, as well as identifying biases and other influencing factors that might have led to a particular result. There exist various approaches to data interpretation that can be applied.

The interpretation of data aims to help individuals understand data that have been collected, analyzed, and presented. Applying a fundamental method or unified approach to data interpretation provides analysis teams with clear structure and common ground. If different departments pursue different approaches to interpreting the same data, even though they pursue the same goals, it can lead to goal conflicts. Different methods can lead to redundancy, inconsistent solutions, wasted time, and resources. The following sheds more light on the two main methods of data interpretation – qualitative and quantitative analysis.

Qualitative vs. Quantitative Data Evaluation

The qualitative data analysis can be summarized in one word: categorical. With qualitative analysis, the data is not described by numerical values or patterns, but by using descriptive context (i.e., text). Typically, narrative data is collected from person to person through an array of techniques. These techniques include:

1. Observations: Recording behavioral patterns occurring within an observation group. These patterns can be the time spent on an activity, the type of activity, and the mode of communication.

2. Focus groups: Grouping people and asking them relevant questions to spur a communal discussion about a research topic.

3. Secondary research: Similar to observing behavioral patterns, various types of documentation resources can be coded and divided according to the kind of material they contain.

4. Interviews: one of the best data collection methods for narrative data. The respondents' answers can be grouped by themes, topics, or categories. The interview approach allows for a high degree of focused data segmentation.

A significant difference between qualitative and quantitative analysis is noticeably in the interpretation phase. Qualitative data are highly interpretable and need to be "coded" to facilitate the grouping and labeling of data into identifiable themes. As the data collection techniques from person to person often lead to disputes over the correct analysis, qualitative data analysis is often summed up in three basic principles: perceiving things, collecting things, reflecting on things.

When it comes to interpreting quantitative data in a word, that word would be "numeric". There are few certainties when it comes to data analysis, but you can be sure that it's not quantitative evaluations if it doesn't contain numbers. Quantitative analysis refers to a set of procedures for analyzing numerical data. It mostly involves the use of statistical models like mean, standard deviation, and median. 

1. Mean: The mean provides a numerical average for a set of responses. If it's a dataset (or multiple datasets), the mean is a central value of a certain group of numbers. It's the sum of the values divided by the number of values within the data set. 

2. Standard deviation: This is another statistical term often seen in quantitative analysis. The standard deviation informs about the distribution of the responses around the mean. It describes the degree of consistency within the responses; along with the mean it informs about the data sets.

3. Frequency distribution: Describes the measure for the frequency of a response occurring in a dataset. In a survey, the frequency distribution for instance could determine the frequency of occurrence of a particular response on an ordinal scale (e.g., agree, strongly agree, disagree, etc.). Frequency distribution helps a lot when determining the degree of agreement between data points.

The measurement of quantitative data is typically realized by a visual representation of correlation tests between two or more significant variables. Different procedures can be used together or separately and comparisons can be made, ultimately to arrive at a conclusion. Other characteristic interpretation procedures for quantitative data are:

1. Regression analysis: Essentially, regression analysis uses historical data to understand the relationship between a dependent variable and one or more independent variables. If you know which variables are related and how they behaved in the past, you can predict possible outcomes and make better decisions for the future. For example, if you want to forecast your sales for the next month, you can use a regression analysis to understand which factors will affect the sales, such as product sales, the launch of a new campaign, and much more.

2. Cohort analysis: With this method, groups of users are identified who share common characteristics during a certain period. In a business scenario, cohort analysis is usually used to understand various customer behaviors. A cohort could, for example, be all users who signed up for a free trial on a particular day. It analyzes how these users behave, what actions they take, and how their behavior differs from that of other user groups.

3. Predictive analysis (Predictive Analysis): As the name suggests, the predictive analysis method aims to predict future developments through analyzing historical and current data. With the help of technologies like artificial intelligence and machine learning, businesses can recognize trends or potential problems and plan informed strategies in advance using predictive analysis.

4. Prescriptive analysis: In prescriptive analysis, which is also based on predictions, techniques like graph analysis, complex event processing, and neural networks are used to determine the impacts of future decisions and adjust them before they are actually made. This helps businesses in developing responsive, practical business strategies.

5. Conjoint analysis: The conjoint approach is usually applied in survey analysis, to analyze how individuals evaluate various attributes of a product or service. This helps businesses in setting prices, product features, packaging, and many other properties. A common application is menu-based conjoint analysis, where individuals are given a "menu" of options from which they can assemble their ideal concept or product. In this way, analysts can understand which attributes they would prefer others to have and draw conclusions from them.

6. Cluster analysis: Last but not least, cluster analysis is a method for grouping objects into categories. As there is no target variable in the cluster analysis, it is a useful method to find hidden trends and patterns in the data. In a business context, clustering is used for target audience segmentation, to create targeted experiences.

Common challenges in data analysis and interpretation

It's widely spread to hear in the era of digital data that "Big Data" could mean "Big Problems". Although this claim doesn't fully apply, it's certainly true that certain challenges and "pitfalls" exist when it comes to the interpretation of data, especially when it comes to the speed of thinking. The following explains some of the most common risks in misinterpreting data and how they can be avoided:

1. Confusion of correlation with causality: A common mistake in data interpretation is mistakenly interpreting the correlation between two events as causality. This means assuming that if two events occur together, one has caused the other. However, this is not necessarily the case, as correlations can occur without a direct cause-and-effect relationship. Suppose you believe that higher sales are achieved due to a larger number of social media followers. There could be a significant correlation between these two factors, especially in today's world of multi-channel purchase processes. However, this does not necessarily mean that an increase in followers directly leads to higher revenues. There could be other common causes or indirect effects or externalities. Therefore, try to isolate the variable that you believe is the cause of the phenomenon.

2. Confirmation Bias: Another problem occurs when you already have a hypothesis or theory in mind and only look for data patterns that support this theory, while at the same time ignoring patterns that contradict it. For example, your supervisor asks you to analyze the effectiveness of a recent cross-platform social media marketing campaign. While you're evaluating data from the campaign, you're only focusing on the social media posts that you think were successful, and neglect the ones that weren't. This is a classic example of confirmation bias or confirmation error. To combat this issue, it can be helpful to conduct the data analysis with a team of individuals who have no fixed opinions. In general, it's always better to try and disprove hypotheses rather than confirm them.

3. Irrelevant data: In today's digital era where large amounts of data are analyzed in real time, it's easy to focus on data that's irrelevant to the problem to be solved. If you're trying to assess the success of an email marketing campaign for lead generation and notice that the number of website visits directly attributable to the campaign has increased, but the number of monthly newsletter subscribers has not, it could be tempting to tag the campaign as successful, even though it actually didn't generate any leads. Therefore, clearly define in advance the variables and KPIs to be analyzed that are important for your specific problem. Focus on the data that answer your specific question and solve your problem.

4. Sample size: Another common problem in data analysis is using a sample size that's too small. The accuracy and reliability of results often depend on the sample size. Suppose you ask 30 people a question, 29 of which answer with "yes", that's 95%. Now imagine you ask the same question to 1000 people, 950 of which answer with "yes", which is also 95%. Even though the percentages are the same, the results from the small sample size of 30 people are less meaningful and can lead to incorrect conclusions.  To determine the right sample size for meaningful results, a margin of error and a confidence level should be set. This enables the calculation of an adequate sample size that delivers representative results.

The correct interpretation of data is of great importance to draw informed conclusions and make well-informed decisions. As already explained in this article, the interpretation of data is both an artistic and scientific task. To help you on this journey, we present some relevant techniques, methods, and strategies for data interpretation that can assist you in your data management process.

Starting, it's crucial to establish the purpose of your data analysis and choose the appropriate methods. Clearly distinguish between qualitative analysis (observing, documenting, interviewing, gathering, and reflecting) and quantitative analysis (conducting an investigation with extensive numerical data to be analyzed with various statistical approaches).

Formulate the right questions for your data interpretation

A key technique in data evaluation is the clear definition of the starting point of your work. You can achieve that by answering yourself basic questions that serve as a valuable guide. For example: What goals do I pursue with my analysis? What method of data interpretation will I use? Who will access these data in the future? And especially, what general question am I trying to answer?

Once this information has been established, you can start collecting data. The methods for data collection, as discussed earlier, depend on the type of analysis you are performing (qualitative or quantitative). Once all necessary information is in place, you can start the interpretation. However, it is advisable to first visualize your data.

Visualize your data

Data visualizations like business graphics, diagrams, and tables play a crucial role for successful data interpretation. Visualizing data through interactive diagrams and graphics significantly simplifies the understanding and accessibility to information. However, there are different types of visualizations, and not every one is suitable for every analysis purpose. Selecting the wrong diagram type can lead to a misinterpretation of your data. Therefore, it's of great importance to choose the appropriate visualization.

Considering the growing importance of data visualizations for business success, many tools have established themselves to assist users in visually preparing their data. A popular instrument is so-called Self-Service Business Intelligence Tools (SSBI-Tools).

Self-Service Tools for Business Intelligence (BI) are software applications that allow users to analyze data from various sources and create reports or visualizations, with little or no IT department support. Here are two examples of the most well-known Self-Service BI-Tools:

1. Tableau: Tableau is a popular self-service BI tool that allows users to import data from a variety of sources, visualize it, and create interactive dashboards. It offers a user-friendly drag-and-drop interface and is well suited for creating visualizations.

2. Microsoft Power BI: Microsoft Power BI is a powerful self-service BI tool. It allows users to import data from a variety of sources, create models, and make appealing reports and dashboards. Power BI also offers tight integration with other Microsoft products.

These self-service BI tools enable users without extensive technical knowledge to perform pragmatic data analyses and at the same time gain meaningful insights into the data. 

The visualization of data plays a critical role within Data Analytics for several reasons:

1. Data understanding: The visual representation of data makes them easier to understand. When data is represented in tables or lists, it can be difficult to recognize patterns or correlations. Charts, graphics, and visualizations allow users to interpret the data more quickly and easily.

2. Quick insights: Visual representations allow data to be monitored in real time and quick insights to be gained. Dashboards and real-time visualizations help to immediately recognize developments and trends, which is crucial for quick decision-making.

3. Communication: Visualizations are an effective method of communicating complex data and analyses to other people. In businesses, it often is necessary to share data and insights with decision-makers or colleagues, who aren't data experts. Visual representations are more accessible to this group.

4. Discovery of patterns and trends: Visualization allows users to recognize patterns, trends, and outliers more easily. This allows them to identify information hidden in the data that could be critical for decision-making.

5. Interaction and exploration: Interactive visualizations allow users to explore and analyze data at various levels and angles. Users can often zoom in on visualizations, filter them, and view details to gain deeper insights.

6. Validation and testing of hypotheses: Visualizations help verify hypotheses. Users can check their assumptions by graphically representing data and validating or refuting hypotheses.

7. Effective presentation: When presenting the results of analysis, be it in internal meetings or customer presentations, visual representations are often more persuasive. They help clarify the results achieved and the recommendations.

8. Early warnings and monitoring: Visualizations can serve as early warning systems to detect irregularities or problems in real-time. This is particularly relevant for areas like operational business and quality management.

Overall, data visualizations help bring data to life and facilitate the analysis process. They enable users to fully reap the benefits of Data Analytics, as not only they are looking at data but also understanding and responding to it. Therefore, visualizations are an integral part of any Data Analytics strategy.

Conclusion

1. Data is invaluable: In the digital era, data has become one of the most valuable resources for businesses. The ability to collect, analyze, and interpret this data can provide a competitive advantage.
2. Business Analytics: Business Analytics focuses on the analysis of business data to facilitate decision-making, process optimization, and business growth. It applies advanced techniques such as data mining.
3. Data visualization: Data visualization is crucial to make data understandable, gain quick insights, and recognize complex patterns. Different types of visualizations are suitable for different analysis purposes.
4. Self-Service Business Intelligence Tools: Self-Service BI-Tools enable users to analyze data themselves and create visualizations without relying on data analytic experts.
5. Responsible handling of data: Handling data requires responsibility and ethics. Data protection and security are critical aspects to consider in data analysis and visualization.
6. Critical data interpretation: When interpreting data, it's important to ask critical questions and not be guided by hasty conclusions. The distinction between qualitative and quantitative analysis is a key aspect.
7. Proactive action: Data analysis should not only be retrospective. It also allows the proactive identification of trends and patterns to predict future developments and better prepare for them.

In conclusion, data analysis and visualization form the foundation for data-driven decision making and business optimization. Businesses that invest in these skills and practice ethical data use have the opportunity to succeed in today's data-centric world.