7 Steps For A Data Shystrategy
Management Summary
This article explains step by step how companies can create a customized oneData strategycan develop.
What exactly is data strategy?
All organizations work with data, but only very few actually deal with it beyond the operational or legal requirements – but their own data is a real treasure trove of insights for sensible preparation and optimization.
This potential can be harnessed with a data strategy: It covers the collection, organization, analysis and activation of data in order to achieve organizational goals.
You could also call it the key to the effective use of data: It helps to use existing data efficiently, identify gaps and discover new potential.
A roadmap is created that includes technological and organizational steps to establish data as a valuable resource for the company.
Step 1: Purpose: Why a data strategy?
A data strategy should contribute to the organizational goals. This can be sales, the efficient design of processes, customer loyalty or simply the fulfillment of regulatory requirements.
Often it simply means being able to make better, data-driven decisions.
It’s important to work out exactly what you want to achieve and use that purpose as a starting point. This can also mean that different strategies are required depending on the area and topic, but they should form a big picture within the organization.
In order to break these down more precisely, it is advisable to work in use cases.
Step 2: Use Case(s) – Use data profitably
In order to develop a successful data strategy, concrete use cases must be identified. Defining a use case helps narrow down topics and structures. There is an extremely large amount of data, different types of data, and trying to capture or use ‘everything’ often leads to overly long processes that come to nothing without results. A clearly defined use case can usually be implemented quickly and allows a piece of the puzzle to be inserted into the overall strategy.
Step 3: Inventory and Gaps
The use cases then also define what information and actions are required. What data does the organization already have? Is it structured or unstructured data? What quality do they have?
A data inventory not only provides information about the current status, but also shows possible gaps. A systematic inventory can be used to decide whether new data sources need to be developed and how the existing data can be used.
Step 4: Governance, Consent & Compliance
It’s not enough to have data, you also have to be allowed to use it. Data governance is a core topic of every data strategy: processes, policies, roles and standards that ensure the quality, security and compliance of the data. Consent and consent management are probably the best-known topics in this complex, but in terms of data economy, what is fundamentally crucial is which data is needed for which purposes and how it should or can be processed and used.
Step 5: Technology – the right tools
Is an Excel spreadsheet the ideal place for data and data processing? Sometimes sure, but for large amounts of data and many use cases there are much more suitable tools. Choosing the right tools and platforms is a central pillar of data strategy.
- Data Collection Tools (Collection): To collect data from various sources
- Data storage solutions (storage): For the (permanent) storage and management of data
- Data processing tools (processing): For cleaning, transforming and analyzing data
- Data visualization: Presenting data and insights in an easy-to-understand, visual form
- Data activation: The use of insights in automatic processes, for example in the form of segments or decisions.
The right platform depends on the individual use cases as well as the type and volume of data. Data warehouses and cloud technologies are often the best option today to respond scalably and flexibly to future requirements.
Step 6: Skills – Data is nothing without people
Although AI systems can now analyze a lot of data quickly and precisely, decisions are still made by humans. And as smart as they are, LLMs need an input, a mandate, to take action. It doesn’t work without human employees (yet) – and they must have the necessary skills to work with data. This is also referred to as data literacy, meaning the ability to work with data in a planned and conscious manner. This includes knowledge of data analysis, data protection and how to deal with the technologies used, especially in the areas of data science, artificial intelligence and machine learning. The hurdles here have become smaller in recent years, and instead of pivot, prompt engineering is now a relevant topic – but what is important is to ensure the qualifications of employees, expand them through training and keep them up to date with further training.
Step 7: Activation – From Insight to Profit
Once the data strategy is implemented, it’s time to activate the data. By concentrating on use cases that form puzzle pieces of an overall strategy, initial insights are generated quickly and can be implemented immediately. The data strategy can then be validated and refined with the results from the use cases – it is not a one-time process, but is based on continuous development. It is important to remain flexible and continually analyze what is successful and where adjustments should be made – data is a dynamic resource that can create new added value, but can also change through regulation or technical development.
In summary:
A data strategy is essential for every organization today. Whether business analysis or the right marketing segment for the next campaign: Instead of collecting data in silos and doing nothing with it, a targeted data strategy can create the basis for data-driven decisions.
This isn’t always possible overnight – the systematic use of data requires some thought and often investment in people and technology. Defining a clear strategy enables the development of a roadmap with the next steps towards the vision.
