12 min read

Data Management: 10 Real-World Problems and How to Solve Them

Data is the foundation of almost all core processes in modern companies. Businesses need to use it effectively but in a fast-changing pace where petabytes of data have to be handled daily, data management becomes a real struggle. In this article, we reviewed 10 common data management problems with real-world solutions to each of them.
Written by
Mahira Chahine
Published on
February 13, 2024
Read time
12 min read

Data has become the main asset for any business, driving every essential decision and process. Each day, companies operate gigabytes of data and its amount is increasing faster than we can imagine, making data management a challenging task. Security concerns, integration issues, data inaccuracy, and lack of centralized processes – all of it can ruin a business if not addressed properly. How do you take control of the data that keeps growing?

At Modeso, we help our clients build and maintain complex data-driven systems. Drawing from our expertise, we recognize that data management consistently poses challenges. In this article, we have compiled a list of 10 primary data management challenges along with corresponding solutions, illustrated through our case studies.

But first, let’s see what makes data management the priority task for business.

Why is data management crucial?

Proper data management is crucial for scaling operations, enhancing the efficiency of the company’s workflow through streamlined processes, facilitating improved decision-making, and enabling the optimal use of new technologies.

On the other hand, poor data management negatively impacts decision-making and company performance. According to a recent survey, 47% of IT leaders report that poor data management hinders their ability to make strategic decisions and 31% state that poor data management doesn’t allow them to win the competition. Finally, 45% of IT leaders admitted that inefficient data management means they can’t leverage emerging technology.

As you can see, data management can’t be overlooked if you want to build a successful business. However, managing data properly remains a challenge due to various reasons. Having said that, here are the exact data management challenges companies may encounter and how to address them.

10 data management problems and solutions

Based on our experience, here are the main challenges related to data management and the methods to overcome them.

1. Data silos and integration

Data silos refer to isolated data storage systems that hinder a smooth flow of information between different departments or teams. Each silo contains specific types of data that can’t be shared seamlessly. Breaking down data silos involves the implementation of data integration tools, standardized APIs, and cohesive data governance policies. This helps businesses create a more unified and accessible data environment where data is shared effortlessly.

Manufacturing companies often have to deal with data silos. Obliged to control the quality of the goods they produce by law, manufacturers have to collect and track data from various sources but their quality management processes are often manual and inefficient. Managing quality in manufacturing labs becomes time-consuming and results in data loss when juggling paper-based tools, and diverse systems such as Excel, ERP, and laboratory measurement devices. This disjointed workflow complicates the identification of root causes in the event of any issues.
To help manufacturing companies overcome their data struggles, we helped develop 1LIMS, an information management SaaS that automates quality management tasks in the laboratories by offering a centralized system that integrates with ERP systems like SAP, service labs, and laboratory devices allowing quality managers to simplify their workflow.

1LIMS automatically generates quality control checks, making sure each production step follows set quality standards.

Similarly to 1LIMS, you can solve the data silos problem by integrating a data management system that streamlines operations by connecting different tools, automating tasks, and leading to seamless workflows across different business functions.

2. Lack of processes and systems

The next challenge that often arises in data management is the absence of a streamlined system. Without clear processes, businesses find it hard to manage and use their data efficiently. As an outcome, data quality and consistency are affected, making it difficult for a business to make informed decisions.

In the dental sector, handling data flow for clear aligner production poses notable challenges. The process is inefficient, involving the exchange of vital data through various channels like Dropbox, email, or Google Drive. As a result, dental clinics encounter issues such as misplacement, delays, and a lack of synchronization between patient data and manufacturing data.
To solve this problem, in collaboration with Dental Axess, Modeso developed Xflow, a unified workflow management platform that streamlines the entire process of clear aligner production. Seamlessly tracking every step in the clear aligner supply chain, the system streamlines manufacturing workflows, helping to eliminate human errors and improve collaboration between different stakeholders. At the end of the day, it speeds up clear aligner treatment.

We streamlined the clear aligner production by integrating patient information, 3D scans, and other critical data into one system, centralizing the data management and enhancing workflow efficiency.

Xflow allowed clear aligner production companies to significantly improve the overall speed and efficiency of the data management process. With the help of streamlined data management, companies can now scale further without the risk of sudden data-related bottlenecks.

3. Low-quality data and inaccuracy

The third challenge many companies encounter is inaccurate and low-quality data. This issue, caused by human errors, system glitches, and the overwhelming volume of data, can greatly damage the company’s reputation and erode stakeholder confidence.

To avoid such consequences, you can:

  • Implement data validation at entry points
  • Use automated error checks
  • Conduct regular audits
  • Focus on key data to ensure accuracy and high quality.

We adopted these solutions when working with Albin Kistler, a prominent wealth management company in Switzerland. They approached Modeso to revolutionize their financial analytics platform, demystify algorithms, and build a modern investment analysis platform. One of the main challenges was to move a substantial dataset from the legacy system to a newly developed one. To smoothly transition data and at the same time guarantee its quality, we implemented a data validation mechanism that solved the problem of inconsistencies in the input files. The system automatically validated the data, identifying issues like missing information and prompting our team to make changes, ensuring an acceptable data quality level. Adopting an automatic data validation system, we successfully transitioned all the data to the new application in the correct format.

4. Data security and compliance

Security is paramount when it comes to data management, especially for domains that operate with sensitive data such as finance or healthcare. It’s important to ensure that the data is protected from unauthorized access, breaches, and misuse, otherwise, the lack of data protection can lead to financial losses and ruin a company’s reputation because of fraud, theft, or data breaches.

To prevent such a scenario, strengthening data security is a must. We understood it during our collaboration with TWINT,  a company that owns Switzerland’s most popular payment app. For this project, we had to strengthen its standing in the mobile payments market through the development of three projects: Digital Voucher, Super Deals, and Storefinder.

To ensure secure transactions in these projects, we implemented:

  • “Security by design" development approach
  • Adherence to the state of art security standards and protocols like OWASP
  • Implementation of industry-standard fraud prevention measurements
  • Penetration tests performed by independent third parties

Additionally, we implemented KYC guidelines to verify customer identity and comply with Anti-Money Laundering (AML) regulations. As a result, we managed to achieve complete security of every transaction and helped TWINT make their payment offerings available to millions of users.

5. Resource constraints and skill gaps

As the data grows and managing it becomes more intricate, many companies find themselves grappling with limited resources, both in terms of team members and technology. Moreover, the rapid evolution of data management tools often outpaces the development of skills among experts.

To overcome this challenge, businesses can try several strategies:

  • Invest in training programs. Comprehensive training programs can bridge the skill gaps of the employees, helping them polish their skills and develop new ones. These programs should encompass the latest data management techniques, tools, and technologies, empowering team members to adopt data-related technologies effortlessly.
  • Outsource specific projects. As building an in-house team with specific skills can take a lot of time, outsourcing specific projects to third-party experts is a viable solution. External partners with niche expertise in data management can provide targeted support, allowing you to solve data management needs faster and more efficiently. At Modeso, we delve deep into each client’s needs and build tailored solutions that help them streamline their data management processes.
  • Leverage user-friendly tools. Implementing user-friendly data management tools can empower non-technical staff to handle routine data tasks. Intuitive interfaces and guided functionalities can reduce the reliance on specialized skills, making data management more accessible and easing the work burden of data experts.

Overcoming resource constraints and skill gaps is not only about acquiring new skills but also about acquiring experienced professionals to solve intricate data issues for you.

6. Data consolidation

Another common data-related challenge is the fragmentation of data across various sources which leads to data consolidation issues. It happens when data is scattered across different platforms, departments, or software. As a result, the lack of cohesion prevents a unified view of the company’s information. For example, customer details might be stored in one system, sales data in another, and inventory information elsewhere.

To solve this problem, consider the next steps:

  • Adopting a centralized data warehouse or platform that acts as a one-stop repository for all your business data. This allows you to bring together information from various sources into a unified and accessible location.
  • Implementing Master Data Management (MDM) practices to ensure consistency in key data across the company. MDM involves data standardization, data quality management, data integration, and governance practices and helps maintain a standardized version of critical data, such as customer information, across different systems.

Investing in data visualization tools that can help you make sense of consolidated data. Such tools as Tableau, Microsoft Power BI, or Looker present information in a visually comprehensible manner, helping decision-makers understand complex datasets.

7. Data migration

Data migration challenge arises when a company moves to new software, upgrades existing systems, or undergoes a structural shift. However, the challenge lies not just in transporting data but in making this transfer to a new environment seamless, accurate, and organized.

The best strategy for this challenge is to make a comprehensive plan for smooth data transfer and follow it step by step, paying particular attention to data accuracy. As we already mentioned, for Albin Kistler we had to conduct the data migration from the old platform to the new one, which involved moving a substantial dataset. The challenge arose from changes in the input files because the new format differed greatly from that of the old application.

Here’s how we managed to transfer data step by step:

  1. The old files got uploaded into the new system.
  2. The system validated the data, addressing issues like missing information.
  3. Rejected uploads prompted the responsible data analyst from the client side to make changes, ensuring an acceptable data quality level.
  4. The corrected files were then uploaded to the new application in the correct format.

This approach ensured not only the migration of data but also a significant improvement in data quality.

8. Data integration

Data integration involves the seamless blending of information from diverse sources. These sources often use different formats, structures, and standards, creating a fragmented landscape that impedes a unified view. Additionally, there are security concerns as the information flows between different parts of the organization. Addressing the security and privacy of sensitive data during this process is a constant challenge to prevent unauthorized access or data breaches.

Here are several aspects to pay particular attention to:

  • Data governance. Establish robust data governance policies. Clearly define roles, responsibilities, and standards for data quality to ensure consistency and reliability across integrated datasets.
  • Advanced integration tools. Leverage advanced data integration tools that offer automation, real-time capabilities, and compatibility with various data formats.
  • Data quality management. Implement data quality checks and validations to maintain the accuracy and reliability of integrated data. Regularly audit and clean datasets to address inconsistencies.

Security measures. Prioritize data security during integration. Implement encryption, access controls, and monitoring mechanisms to safeguard sensitive information as it flows between systems.

9. Data overload

The abundance of data brings its own set of challenges, known as data overload. Businesses are flooded with vast amounts of data from multiple sources – customer interactions, transactions, market trends, and more. The sheer volume can overwhelm decision-makers, making it difficult to discern valuable insights from the noise.

How to solve it and work with abundant data efficiently? To instantly see the essential data and make insightful decisions, user-centered interfaces are a must. Building a platform for investment portfolio analysis for Albin Kistler, we implemented a user-friendly interface that seamlessly integrated the complex functionality of the application.

The first challenge was to present numerous parameters in intuitive tables so we implemented a sophisticated grid view allowing users to save filters and column preferences for customization. Then, we had to showcase to users significant changes in data like stock prices or shifts in credit ratings for bonds. For this, we developed a dashboard that allows daily monitoring of crucial changes and simplifies data analysis. Finally, we replicated the structure and complexity of Excel reports, ensuring responsiveness across various screen resolutions.

10. Data accessibility

The challenge of data accessibility revolves around ensuring that relevant stakeholders can easily access and use the data they need. This challenge often arises due to data being stored in disparate systems, formats, or locations. The goal is to break down these barriers, making data readily available for users across various departments, fostering collaboration and enabling informed decision-making.

When working with Dental Axess on Xflow, a workflow management platform for clear aligner manufacturing, our ultimate goal was to enable users to complete the entire journey from scanning to receiving the final product without leaving the platform. To unify a multistep workflow on a single platform, we made data accessible only to responsible users at each stage of production, from scanning to design and production.

As a result of transparent data sharing, all three stages are executed in a single place. A practitioner initiates the workflow by scanning and uploading data, managing orders, and collaborating with labs. Then a designer creates 3D treatment plans within the software, creating an aligner design. Finally, a manufacturer receives scans and uses the platform for aligner production.

So overall, various data management challenges can prevent business efficiency but all of them can be solved with a comprehensive approach and tailored solutions.

Bottom line

Numerous factors make data management challenging, from security measures to transparent data visualization and its accessibility for all involved parties. However, each of these data management issues can be effectively addressed with tailored solutions that respond to business needs and prevent any possible data-related bottlenecks.

The main aspect of data management is to pay particular attention to specific challenges that are relevant to your domain. Ensuring that data is always accurate and easily accessible, both technically and visually, is a big challenge but an expert partner can help you overcome it.
At Modeso, we build data management systems that streamline core business processes and centralize data use for all parties. If you need a dedicated partner with expertise in data management, contact us, and we’ll be glad to help you.

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