Data strategy
Want to get maximum value from your data and make better decisions with better information? Find out on this page:
- How your company benefits from a clear data strategy
- What a functioning data strategy could look like in reality (including practical examples)
- The mistakes you should definitely avoid when developing a data strategy
- Plus: 3 absolute pro tips to help you set up a brilliant data strategy yourself.
What is a data strategy?
The most successful companies have mastered one thing: the strategic use of their data. Whether in e-commerce or the financial sector, those who use their data correctly make better decisions, save resources and recognize opportunities before the competition does. Without a clear strategy, on the other hand, your processes are characterized by waste and unused opportunities. A data strategy defines how a company wants to use its data to achieve business goals. It defines how data is transferred, collected, processed, managed, analyzed and protected so that it supports both day-to-day decision-making and long-term strategies.
Why a data strategy?
A data strategy (also known as a data concept) provides you with answers to questions such as:
- Where do I store my data?
- Which software do I use for this? (Data warehouse, data integration software, databases, data governance tools, etc.)
- How do I process the data?
- How do I get the data in real time?
- How do I keep the quality of the data high?
- How do I regulate who has access to which data?
The focus here is on organizing and linking data processing in such a way that it is always available in high quality and for all relevant areas of the company. This enables you to make well-founded decisions and recognize strategic opportunities more quickly. A data strategy comprises sets of rules and processes that standardize the handling of data - from collection and storage to analysis and use. It serves as a guide for you and your teams to ensure that data is not only collected, but can also be used in a targeted and profitable way.
Our technologies
nedyx®
nedyx® is a low-code platform that enables business apps without programming knowledge. With a drag-and-drop interface and Office-like operation, it offers individual dashboards, chatbots and cloud integration. nedyx® supports flexible planning, operational control and adapts to different business requirements.
Microsoft Azure
Microsoft Azure is a flexible cloud platform that helps companies drive their digital transformation. With scalable solutions for data management, artificial intelligence and analytics, Azure provides the foundation for efficient processes and innovation. From infrastructure to advanced applications, Azure optimizes the IT landscape and increases agility.
Microsoft SSIS
Microsoft SSIS is the versatile ETL tool for the integration and transformation of data. With its powerful functions, it enables efficient data flows, automation and the connection of a wide variety of data sources - the ideal solution for smooth data processing and provision.
Microsoft Fabric
Microsoft Fabric is an integrated data platform that supports companies in analyzing and using their data. It enables centralized data management, fast analysis and informed decision-making. With functions for real-time analysis and AI-supported insights, Fabric optimizes the data strategy and increases efficiency.
Why a solid data strategy can be crucial to the success of your business
A clear data strategy turns your data into a strategic success factor. It helps you to transform abstract data into clear, tangible statements - and to derive a path from the actual state to the target state. Without a data strategy, the potential of a lot of information remains untapped - which robs you of the opportunity to optimize inefficient processes, eliminate unnecessary cost drivers and identify market opportunities at an early stage. And yes, dealing with big data can be challenging, especially in companies with complex structures. In our customer projects, however, we repeatedly find that there is hardly a problem that cannot be solved with a targeted data strategy:
Data chaos and inconsistencies
Without defined standards, inconsistent data formats and structures can quickly arise, requiring manual cleansing and a great deal of administrative effort. This makes it difficult to analyze the data in a meaningful way and base decisions on it. In addition, the risk of dangerous data leaks is significantly higher.
Complexity of the data landscape
You probably know the situation: In your company, you have to process data from a variety of sources with different quality standards, formats and security and compliance requirements. Managing this complexity is almost impossible without a clear strategy and in most cases leads to inefficient workflows, unreliable results, a lack of data integrity and a high level of cleansing effort.
Real-time analytics
In a perfect world, every company would like to process all its data in real time in order to be able to react even faster to changes. However, this places high demands on the infrastructure, requires extremely powerful systems and an efficient data flow. A well thought-out data strategy will show you how to overcome this challenge.
Security and data protection requirements
Data often contains sensitive information, the protection of which is essential for legal and reputational reasons. For this reason, a data strategy is also essential in terms of data security and data protection, as it defines clear security guidelines. These ensure that data protection regulations are adhered to and that the trust of customers and partners is maintained.
In short: with a targeted data strategy, companies regain control of their data, improve data quality and create a basis for well-founded decisions and future growth.
In which areas does it make sense to work with a data strategy?
Business Intelligence (BI)
With a centralized data strategy, you are able to consistently bring together information from different departments. This gives you the opportunity to carry out well-founded analyses, recognize patterns and make reliable, data-based decisions.
Customer Relationship Management (CRM)
In customer management, a clear data strategy enables you to create a holistic picture of your customers. By bringing together relevant customer data from various sources and processing it consistently, you can provide your customers with targeted, personalized offers, which has a positive impact on sales, customer lifetime value and customer satisfaction.
Supply Chain Management (SCM)
With a data strategy, you are able to optimally monitor the entire value chain and optimize it as required. The result: thanks to harmonized data from production, warehouse and logistics, you can identify bottlenecks at an early stage, which makes planning easier.
Marketing and sales
A data strategy also helps marketing and sales departments to work in a more targeted and efficient manner. By analyzing central customer and market data, you gain deeper insights into customer needs and market trends - which enables targeted campaign planning and has a positive effect on your conversion rates.
Risk management and compliance
A well thought-out data strategy creates a wonderful basis for unconditional adherence to compliance requirements and for risk assessment, because: Only if there is a clear plan for the collection and processing of relevant data can emerging risks be identified promptly and the right measures taken in good time - which consistently protects your company from legal and financial risks in the long term.
How do you develop a data strategy?
In practice, the development of a data strategy begins with a clear definition of objectives and the selection of relevant data sources. Processes for data collection, storage and processing are then established to ensure that the data is consistent, accurate and secure. As a rule, this includes
- Data integration: Different data sources are linked together to obtain a holistic view of the relevant information.
- Data quality: When saving data, we ensure that the information collected is error-free, complete and up-to-date.
- Data analysis: The collected data is analyzed in order to gain valuable insights that enable well-founded decisions.
- Data protection and security: The security and protection of data is guaranteed by measures such as encryption or access controls with legal requirements.
- Automation: Recurring tasks, such as data updates, are automated to increase efficiency and scalability.
But what could this look like in practice? Here are 3 data strategy examples to illustrate this:
Data strategy example #1: e-commerce
An online store implements a data strategy to better understand customer behavior. Data from web analytics, CRM systems and social media channels are combined to create personalized product recommendations. By analyzing buying patterns, marketing campaigns can be targeted at specific customer groups, leading to higher conversion rates and improved customer loyalty.
Data strategy example #2: Healthcare
A hospital implements a data strategy to optimize patient treatments. Data from electronic medical records, lab reports and medical devices are integrated. With the help of predictive analytics, doctors can make informed decisions by using early warning systems for potential health risks based on historical data. This leads to better care and reduced costs.
Data strategy example #3: Financial sector
A bank uses a comprehensive data strategy for data preparation to improve risk management. By integrating transaction data, credit scoring and market analysis, potential payment defaults can be identified at an early stage. Machine learning models are used to automate credit decisions in order to react faster and more accurately to market changes and improve customer service.
Are you wondering what a possible solution could look like in your area? Then book your appointment now for a free data strategy consultation.
Excursus: What exactly does a data strategy regulate?
Think of your data strategy as a comprehensive guide to a giant jigsaw puzzle. It shows you exactly how to collect, organize, store and use the pieces - i.e. the data - correctly so that the picture makes sense at the end. It ensures that you always know
- where to find which puzzle pieces
- how to put the pieces of the puzzle together
- and what you can do to ensure that the pieces of the puzzle are not lost
The data strategy also makes it clear who in the company is responsible for the quality of the pieces so that no broken or incorrectly cut pieces end up in the puzzle.
It defines how you handle the data step by step - from collection to evaluation to the development of measures - and helps to ensure that everything runs in accordance with the rules so that, for example, no personal data is used without permission.
Plus: A data strategy ensures that your company always stays on the right track, even in the event of changes such as new technologies, adapted legislation or higher data volumes.
In short, a well-thought-out data strategy helps you to handle your data securely so that you can make the most of it and achieve your business goals.
5 advantages of a solid data strategy
- Consistent database: A functioning data strategy provides you with a reliable and consistent database that enables you to make well-founded decisions. You avoid errors caused by incomplete or inconsistent data and benefit from a clear overview.
- Optimized efficiency: A data strategy creates the basis for automated data collection and analysis, which saves you many hours of time and reduces manual effort. This allows you to use 100% of your resources where they are needed.
- Improved decision-making:A well-structured data strategy gives you deeper insights into business processes, customers and markets. This information helps you to plan ahead and make well-founded, strategic decisions.
- Higher data quality: By clearly defining processes and responsibilities in the data strategy, the quality of your data is continuously monitored and improved. This minimizes data errors and inconsistencies.
- Scalability and flexibility: A well thought-out data strategy also includes future growth and possible scaling challenges. It shows how you can easily adapt your system to new requirements and growing data volumes.
3 mistakes you should avoid when developing a data strategy
- Not defining clear goals: Make sure you know exactly what you want to achieve with your data strategy. A vague objective makes it difficult to identify suitable measures, hinders measurability and leads to inefficient use of resources. Example: A company develops a data strategy with an unclear objective. As a result, difficulties arise in identifying suitable data sources.
- Ignore data quality: Poor quality data can boycott an otherwise sound data strategy because it makes necessary data consolidation difficult. You should therefore make sure that your data is correct, complete and up-to-date before incorporating it into your strategy. Example: An online store integrates customer data from different channels without first checking it for errors or inconsistencies. This leads to outdated customer data such as incorrect addresses or duplicate entries being transferred into the system, resulting in incorrect deliveries and poor customer experiences.
- Disregard future challenges: Your data strategy should not only cover current requirements, but also take into account future growth and adaptation needs. Conversely, a strategy that is not scalable will quickly reach its performance limits. Example: A company implements a data strategy that only covers the currently relevant data sources without taking into account that new data sources will be added in the future (e.g. IoT devices or social media data). As the company grows, the original strategy may struggle to keep up with the increasing volume and variety of data.
3 tips for implementing a data strategy
- Rely on data-driven decision-making - not just data-driven collection: A successful data strategy goes beyond simply collecting data. It is crucial that you ask the right questions before you collect data. Ask yourself: What data will help us make better decisions? Avoid collecting 'junk data' that adds no value to you. Focus and clear objectives are the key to efficient implementation.
- Use automation as an accelerator: Most data strategies fail due to the high effort required to do so. That's why you should integrate automation tools early on in the process to speed up recurring tasks such as data cleansing and aggregation. An automated process ensures that your data strategy runs continuously without the need for manual intervention. This not only saves time, but also minimizes sources of error and ensures consistent data quality.
- Build a data strategy iteratively and flexibly: Instead of following a rigid, sprawling plan (also known as the "big bang approach"), develop your data strategy in iterative steps. Start with small, concrete goals and gradually expand the strategy. This allows you to react quickly to changes and continuously adapt your strategy to new requirements and technologies. A flexible model helps you to keep pace with the flood of data and constantly changing market conditions.
Do you want to turn your data into cash? A data strategy shows you how
Whether you want to increase efficiency in production, strengthen customer relationships or better manage your risks - a well thought-out data strategy makes it all possible. We would be happy to help you take the first step.
Book your free consultation now and we will show you what a data strategy for your company could look like.
Arrange a meeting
Select the desired date and arrange a non-binding and free initial consultation with Dieter Höfer (Partner & Sales Director). We will be happy to answer your questions and discuss solutions for your individual requirements.