Basic principles of data management have been implemented for a long time, and their best practices and foundations are extremely relevant. However, many companies face challenges related to data management that can hinder the effective utilization of data or even prevent the digitalization of the company. These challenges may include data fragmentation, data quality issues, lack of clear processes, ambiguity in data ownership, or a lack of tools in data management.
1. Data fragmentation
One of the most common problems in data management is data fragmentation. Companies collect data from different sources and in different formats. This data may be scattered across different systems, databases, and cloud services. Data fragmentation complicates unified data management and utilization, often resulting in unnecessary manual work either in data creation or in final reporting.
In this case, a centralized data repository or a more modern data platform can provide a single view of the organization’s data, making data usage, reporting, and analysis easier. When a standalone data solution is not enough, Master Data Management (MDM) solutions can help consolidate and integrate different data sources. At its best, MDM is a key process in centralized and organized data management, tailored to meet the requirements of your company’s strategy and operational needs.
Unfortunately, the everyday reality of data warehouse solutions often involves manual work. Data is either tinkered with in interim stages or at the end in Excel to produce desired reports and analyses. On the other hand, many MDM off-the-shelf solutions are challenged because they are designed for too narrow usage, don’t scale, and don’t meet evolving business needs. These have been made for 20 years, is there anything new here? Now is.
Solution: How do you create a centralized solution that reduces manual work in data management and reporting, while also flexibly serving future changing business needs? It’s difficult, even impossible, to achieve this with ready-made package solutions. Firstly, designing and implementing a quality data management solution requires vision, experience, and expertise. Secondly, the technology must be flexible enough to accommodate the company’s processes without breaking down. Thirdly, it requires an easy-to-use, familiar user experience. Using these methods, Lanttu.io’s experts have successfully implemented data management customer projects and development on the Power Platform since 2021.
2. Data quality issues
Many companies face challenges due to poor data quality. Required information may be missing, incorrect, or outdated. Data may contain duplicate entries or special characters, commas, and dashes, causing challenges in data transfer and interface interfaces. Inaccurate or poor-quality data undermines business efficiency and can lead to erroneous decisions. Who is responsible for data quality in your company?
Solution: Structural data models, data validation, quality control, and cleansing processes can help ensure that data is accurate and reliable. Success in these areas requires clear processes for data collection, management, and maintenance to be defined first.
3. Security and data privacy
Protecting data and ensuring data privacy are of paramount importance to companies. Laws and regulatory requirements, such as GDPR, impose requirements for protecting customers’ and employees’ personal data. At the same time, companies must do everything in their power to secure critical corporate secrets in terms of technical security, personnel, and their own operating models.
Solution: Organizations need to invest in reliable and secure technologies and processes, as well as establish clear data protection policies and practices. Staff training and awareness of data security and privacy are also crucial.
4. Data integration
Companies often use different software and systems for various functions, but integrating these systems can be challenging due to issues such as incomplete interfaces or incompatibility.
Solution: Use integration tools and platforms that facilitate data integration from different systems. Integration tools enable free movement of data within the organization. MDM solutions provide tools for both integrations and data maintenance and quality control.
5. Scalability and costs of data
As companies grow, the amount of data can grow exponentially. The costs associated with data management can be significant, including infrastructure, software licenses, and personnel resources.
Solution: Investing in the right technologies and automation can save costs in the long run. It’s important to assess which areas of data management require the most resources and prioritize them. Additionally, data management processes can also be improved through outsourcing services.
6. Utilization of data
Although companies collect vast amounts of data, they may not necessarily know how to effectively use it in decision-making and business development. Different stakeholders, such as customers or partners, have different expectations regarding data management and reporting. How do you meet these expectations and requirements?
Solution: Create data models that enable various uses. Train staff in data analysis and utilization. The use of data analytics and artificial intelligence can help companies make better decisions. Additionally, invest in reporting tools that make data visualization and interpretation easier.
Towards centralized and automated data management
Solving data management problems offers many opportunities to improve business and work. By investing correctly in processes, resources, and technology, companies can overcome these challenges. Quality data management and utilization can turn data into a strategic competitive advantage.
Where to start? Companies must first identify their own needs and problems. Then, a strategy and processes for data management must be planned, and tools that also cater to future needs must be selected.
Lanttu.io has developed an innovative, versatile, and scalable service range to meet these customer needs. Data management is a journey, not a destination, and its continuous improvement is key to success in the digital, modern business environment.
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