Data & Analytics

While data analytics focuses on interpreting existing data, data science goes a step further and develops algorithms to identify patterns in complex data. Data science often integrates machine learning and artificial intelligence to create predictive models. Some of the key differences are

Data Science:

• Interdisciplinary: Combines statistics, computer science and business analytics.
• Predictive and prescriptive: Focus on predicting future events and providing recommendations for action.
• Complex data: Often works with large, unstructured data sets («Big Data»).

Daten Analytics:

• Focused: Focuses on the analysis of existing data.
• Descriptive: Attempts to explain the past and present.
• Structured data: Usually deals with well-organised, structured data.

Data science is broader and more complex, while data analytics is more specific and focused. Both aim to gain insights from data, but use different methods and technologies.

The data strategy is a comprehensive plan that defines the goals, processes and resources an organisation needs to maximise the value of its data. It guides the collection, processing, analysis and use of data to support business decisions, optimise processes and identify new opportunities.

The key components of a data strategy are:

• Goals and vision: Defines the long-term and short-term goals to be achieved through the use of data, such as increasing customer satisfaction or optimising the supply chain.
• Data sources and quality: Identifies relevant data sources and defines data quality standards, including data cleansing and preparation.
• Technology and tools: Selects the appropriate technologies and tools for storing, processing and analysing data, such as databases, data warehousing solutions and analytical software.
• Governance and compliance: Defines policies and procedures for the secure and compliant handling of data, including privacy and data security.
• Analytical models: Develops and implements analytical models and algorithms tailored to specific business problems and objectives.
• Team and competencies: Defines roles and responsibilities within the team and ensures that the skills required to implement the data strategy are in place.
• Measurement and evaluation: Defines KPI’s (Key Performance Indicators) and other metrics to measure the success of data initiatives and adjust the strategy as needed.

Big data and traditional data analysis differ in some key aspects:

Big Data:

• Volume: Big data refers to extremely large amounts of data, often measured in petabytes or exabytes.
• Variety: Big data can include structured, semi-structured and unstructured data from a variety of sources.
• Speed: Big data often requires real-time or near real-time processing.
• Complexity: Due to its size and variety, big data requires specialised tools and technologies for storage, processing and analysis.

Traditional data analysis:

• Scalability: Traditional data analysis is typically designed for smaller, structured data volumes.
• Simplicity: Data is often well organised and requires less complex analysis tools.
• Timeframe: Traditional data analysis is less focused on real-time processing and can focus on historical data.
• Tools: Uses off-the-shelf databases and analysis software that is less focused on processing large and complex data sets.

In summary, big data is geared towards processing large, diverse and rapidly changing volumes of data, whereas traditional data analytics tends to focus on smaller, structured data and is less reliant on real-time processing.

Data mining is the process of discovering patterns, correlations or anomalies in large amounts of data using algorithms and statistical methods. The goal is to gain new insights from existing data or to better understand existing data structures. Data mining is often seen as a step in the broader process of data analysis or in the context of data science.

Some key aspects of data mining are:

• Pattern recognition: Data mining helps to identify recurring patterns or trends in data.
• Classification: It can be used to classify data into different categories or groups based on certain characteristics.
• Association rules: Data mining can reveal relationships between different data points or variables.
• Anomaly detection: It can also be used to identify unusual or anomalous patterns that may indicate errors or fraud.
• Prediction: Some data mining techniques can be used to predict future events based on historical data.

Data mining is used in many fields such as marketing, healthcare, finance and even science to analyse complex data sets and gain valuable insights.