Lena drives international editorial and strategic marketing topics including social media, customer references, whitepapers, eBooks and the blog, where she writes about technological trends and digital transformation.
Despite considerable efforts, many companies still struggle to make progress in terms of data management. Based on what we know, there is sufficient awareness of the necessity to use data efficiently, but many companies fail to utilize the available potential. This blog post lists the five most important tips for a successful data management strategy and demonstrates how to implement such a strategy in practice.
Where do you get your data from? Where do you store it? What data types do you differentiate, and how do you analyze these types? By answering these questions, you make the first step towards obtaining an overview of your existing data management.
You can overcome these challenges by addressing several things in your data strategy.
The problem: If you try to find out where exactly you collect, store, and process your data, you will probably realize that these activities are not contained in a single location. In many companies, you have data lakes, IoT architectures, NoSQL, and graph databases, but also relational databases, which all exist in parallel to each other.
The reason: Various technologies are used, various objectives are pursued, and the scope of collected data varies accordingly.
Tip: Interconnected platforms and technological solutions, such as orchestration layers and interfaces that connect your systems to each other, become a must-have to avoid data fragmentation and make data available across systems.
The problem: Fast access to data is essential if you want to use your data in an expedient manner. On the one hand, data may change within a matter of seconds to indicate new developments. On the other hand, access to data must not be hampered by time-consuming obstacles so that you can optimally integrate it into your day-to-day work. However, if your company has established structured data governance, i.e., if it observes clearly defined rules and processes to maintain data quality and data security, you often encounter problems when it comes to fast and easy access to data.
The reason: The protection of personal and sensitive data should always be at the top of your priorities. This leads to the problem that data is often not accessible to everyone. At the same time, predefined data maintenance or compliance processes specify how you are allowed and required to process data. This can be very complex and time-consuming.
Tip: A key component of modern data governance is the ability to enable fast access to data while ensuring that all relevant data quality, data security, data maintenance, data protection, and corporate compliance requirements are observed. Data access here should be focused on achieving a specific objective. Do you want to adapt your product range to be more in line with customer behavior? If the answer is yes, your aim should be to enable fast access to customer data. For example, you can establish different permissions for different roles to ensure that not everyone can fully view or modify the relevant data.
The problem: There are usually high expectations when it comes to the analysis and use of data. Justifiably so, since these can offer incredible added value, however, many companies are completely unable to execute the required analyses or fully utilize their data because of old data architectures and legacy systems.
The reason: Unnecessarily complex, isolated data structures often fail to deliver the desired performance with regard to data quality, data processing, access capabilities, and analytics/the further use of data, which is at least in part attributable to the fact that the data structures are not properly interlinked with each other. As a result, companies often work with cobbled-together systems that combine to create a patchwork landscape and are supposed to cover all use cases. However, the output falls far short of the required performance, and the solution simply cannot keep up with the constantly growing requirements.
Tip: In this case, a redesign of the data architecture is unavoidable. This will not only enable you to achieve better results, gain more insights, and utilize the benefits of your data, but more importantly, it will also relieve your coworkers since they will no longer have to spend too much time on maintaining a fractured data architecture and can now focus on more important tasks.
In this blog post, we define what metadata is, examine what it is used for and discusse why we need a paradigm shift to find a new metadata management approach.
In this blog post, Pawel Wasowicz, Head of Data Engineering at Mimacom, explains the principles of metadata management in data fabric architectures. Read now!
Modern data management in practice
Data management has become an essential aspect of business strategy. In this whitepaper, we explain how to establish a high-quality data foundation to enable the analysis and effective use of your data.
