We turn your data into insight
bbv's specialists support you along the entire data value chain: they help you develop and implement your specific data strategy, set up tools for data collection and structuring, and select suitable analysis tools and methods.
- Competitive advantages through data: Use your data to better understand your business and customers and gain a competitive edge in the marketplace.
- Strategic decision-making: Our analytics help you better anticipate future developments and make strategically intelligent decisions.
- Optimise business processes: Machines, sensors and people in organisations are generating more and more data. Identify opportunities to improve efficiency in areas such as procurement, production and sales.
- Drive innovation: Use data-driven insights to develop more successful innovations and business models.
- Make complex data actionable: We offer tools that enable you to make effective use of even large and complex amounts of data.
- Make insights visible: Our solutions reveal hidden patterns and correlations in your data to optimise your customer relationships.
- Data strategy development: Our specialists support you in planning and implementing a customised data strategy that meets your business objectives.
- Data collection tool development: We provide solutions for efficiently collecting and structuring your data to provide a solid basis for analysis.
- Selection of analytical tools: We advise on the selection of the optimal analytical tools and methods for your specific requirements.
- AI and machine learning expertise: Our software engineers are up to date in areas such as artificial intelligence, smart bots, machine learning and data mining.
- Dedicated tool development: We quickly develop the tools you need to effectively use and understand your data.
- Technical and business expertise: Our experts not only master the technical aspects, but also understand the business challenges to deliver analysis and reporting with measurable value.
The Data Science Innovation Workshop offers you an ideal format to assess the potential and risks of data and analytics more quickly and effectively. We support you with our process, method, technology and business expertise.
Are you looking to turn your data into insight that will benefit your business?
Then book a free consultation now!
FAQ on 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
- 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»).
- 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:
- 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.
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