For medium to large companies, implementing a robust data strategy is essential in today’s business landscape. Without one, there is a big risk of losing a competitive advantage. Inadequate resource utilization and a lesser understanding of customer needs compared to competitors are potential consequences. This risk amplifies as the world increasingly relies on data-driven decision-making. Even organizations already leveraging data could refine their strategies. But navigating this complex change can be intimidating.
In this blog post, we break down the steps to crafting a data strategy and offer practical tips. I’ve simplified the process into three concise steps for clarity and ease of implementation.
Alignment and Preparation
First and foremost, it is extremely important to align and prepare the complete organization, I call this the Alignment and Preparation stage. This starts by doing a lot of internal research by among others how much is the organization willing/able to spend, performing a data maturity assessment to see how much the organization is already data driven and asking important questions such as: What are the current business processes? How does the current IT landscape and IT practices look like? What current data types do we have much or will we receive much from? Think here about whether it is unstructured or structured data, if the data is received a lot from third parties (e.g. GS1 or TecDoc), if data streaming occurs (receiving data on a constant base, e.g. from IoT devices) as this will become important when deciding what software and presentation methods to be used. How ready and willing is the organization to change? What is the purpose of implementing this new data strategy? This could be for example leaner manufacturing processes, better customer understanding, increase in revenue, better retention rate of employees. An important reason for doing this is to make sure that all stakeholders see the reason and want the change to happen and are aligned.
Data Governance and Technology Planning
After the company has been aligned and the people are ready and excited to start this transformation. The next stage starts called the Data Governance and Technology Planning stage, here it is important to remove the data silos between each department, and to initially start with implementing some less invasive data governance practices, such as getting an common understanding what a customer is, which information is required for this, which date format is used, what common acronyms mean. This can already be done in existing information systems and data sources making it less invasive.
Sometimes implementing data governance practices already help a lot into getting better insight into companies operations, but it is often a stepping stone. After getting the data governance in order, the next step is to address technology for every stage of the data lifecycle and how the entire dataflow will look like in the future in order to achieve the set operational goals. Ask yourself questions about things such as: What data architecture do we want to use? Such as ETL or ELT strategy (this very much also depends on what your goals are).
Will the data be hosted on the cloud such as AWS, Azure, Google Cloud or on premise (this is also important to keep in account what potential new software you are going to use, as some work better or only on one platform, and not the other). Consider among others the in-house expertise, costs, accessibility, scalability, compliance and security. For example, as a hospital providing medical care, it might be a smarter idea to store your data as much as possible on premise to prevent security issues as you are working with highly sensitive data of patients.
Are there additional new software required? If the answer is yes, will you go for a single software vendor or multi-vendor strategy? (be careful for potential vendor lock in here). Does the software meet all the requirements: Can it handle your current and future data flow? Does it regularly get updates? Is it customizable enough? Does it have the APIs you will need currently and in the possible future? Does it provide good tools to monitor data quality? How about the security of the software? Does it provide solid Identity and Access management (IAM) policy (e.g. possibility to set up for example MFA2 and is it possible to evoke and manage users rights). As one of the worst things that could happen is a leakage of sensitive data.
Data Utilization and Presentation
Once all data is organized, stored, and of high quality due to the steps described above, it is time to consider how you will utilize and present the data you have received. This is the Data Utilization and Presentation part of the complete data strategy. Here, it is beneficial to create dashboards using tools such as Power BI or Tableau. Additionally, incorporating embedded analytics and exploring the possibilities of machine learning and the usage of RPAs (such as UiPath) or RAG LLMs, which utilize your data from documents or databases to retrieve data and provide answers to users, can significantly enhance data utilization and decision-making capabilities.
Lastly, continuously monitor and evaluate the effectiveness of data utilization efforts. Regularly assess key performance indicators (KPIs) related to data-driven initiatives to identify areas for improvement and optimization. This iterative approach ensures that data remains a valuable asset driving innovation and growth within the organization.
For further reading on the topics discussed, feel free to read the DAMA-DMBOK or “The Data Warehouse Toolkit,” or one of my other blogs.
In summary, becoming data-driven is key to staying ahead in today’s business world. The steps we’ve covered are just the beginning. To fully embrace data-driven decision-making, keep asking questions and exploring new ways to use data. If you want more in-depth strategic advice or technical information, feel free to get in touch. Let’s work together to make your business even better with data.