Top 3 Data Management Challenges Hampering Business Growth

Tech industry busine­sses are at the fore­front of data gathering and analysis, making them major players in the­ world of data. With the exponential growth of big data, companies face significant challenges in managing and proce­ssing massive volumes of information. Outdated management systems only compound these difficulties, highlighting the urgent need for modernization.

In the world of technology, there are several common challenges that te­ch companies encounter when it comes to data management. According to the Statista report, over 60% of respondents highlighted the challenges they face with data management across various dime­nsions. These challenges can truly hinder progress and success. However, with the right strategies and approaches.

Why Wrangle Data?

As they digitize and automate archaic processes, businesses all over the world are intimately involved in their digital transformation path and data management. To get there, they are pouring more money into data analytics and business intelligence tools, which allow them to examine large datasets and make better business decisions. As a result, according to IDC analysts, the data analytics business is exploding and currently exceeds $200 billion in annual spending.

One of the most significant Big Data difficulties is determining the best way to handle a huge amount of data, which comprises the process of storing and analyzing a vast collection of data across multiple data storage systems. There are a number of key difficulties that must be handled with agility while dealing with big data.

Also Read: 5 Best Powerful Use Cases of CDP You Need to Know

What are the Main Challenges of the Data Management Process?

Data management is a vital component for businesses to efficiently gather, structure, and leverage data. Nevertheless, there exist several obstacles that organizations encounter during the data management process. Here are the key challenges:

1. Defining and Aligning with Business Objectives: Clearly defining and aligning with business objectives is crucial for effective data management. This process aids in ascertaining how data should be collected, stored, managed, cleaned, and analyzed. By establishing precise business goals, organizations can ensure that they organize and retain only pertinent data for decision-making.
2. Ensuring Data Integrity: Upholding data integrity represents a significant challenge in data management. Subpar data quality can lead to erroneous insights and decision-making. Organizations must employ processes and technologies to confirm data accuracy, completeness, consistency, and validity.
3. Managing Copious Data Volumes: With the exponential surge in data, managing substantial data volumes has become a challenge. Organizations need to efficiently oversee and process extensive data quantities. This encompasses addressing concerns tied to data storage, scalability, and performance.
4. Data Security and Confidentiality: Safeguarding data from unauthorized access, breaches, and misuse is a critical aspect of data management. Organizations must institute rigorous security measures, adhere to regulations, and implement data governance frameworks to protect sensitive data. Around 85% of data breaches impact small businesses, equivalent to approximately 4,000 breaches daily, according to a survey of 1,300 SMB owners on cybercrime.
5. Data Harmonization and Compatibility: Coordinating data from diverse origins and ensuring compatibility among different systems and platforms can be intricate. Organizations need to devise strategies and employ technologies for seamless data transfer and interoperability.
6. Data Governance and Conformance: Enforcing effective data governance practices and adhering to regulations and industry standards is a challenge. Organizations must outline data ownership, institute data governance structures, and enforce policies and procedures to ensure data integrity, security, and compliance.
7. Data Lifecycle Oversight: Overseeing the entire data lifecycle, from creation to archiving or deletion, is a challenge. Organizations need to put in place processes for managing data retention, archiving, and disposal effectively.
8. Data Analysis and Insights: Extracting meaningful insights from data and utilizing analytical tools and methods can be demanding. Organizations require proficient data analysts and data scientists to extract valuable insights and make data-driven decisions.

These challenges necessitate that organizations adopt best practices, implement cutting-edge technologies, and devise robust data management strategies to surmount them and fully harness the potential of their data.

Complex Data Integration and Preparation

Big data platforms handle the difficulties of compiling and storing large volumes of data of various types, as well as the need for data retrieval for analytics. The data collection technique, on the other hand, could be challenging. To keep the integrity of a company’s acquired data repositories, it’s necessary to update them on a regular basis. This demands access to a diverse range of data sources as well as specific big data integration tools.

Some companies use a data lake as a catch-all repository for massive amounts of big data obtained from many sources without contemplating how the data will be merged. Multiple business domains, for example, generate data that is useful for joint analysis, but the underlying semantics of this data are often confusing and must be reconciled. For the highest ROI on big data endeavors, it’s usually best to have a strategic data integration plan.

Storage: Balancing Cost with Performance

For IT directors, the enormous amount of data generated by enterprises poses a severe data management challenge. IT executives are currently attempting to strike the correct balance between getting maximum commercial value from data and storing it safely and cost-effectively.

Data is collected from a variety of sources within a firm, including social media sites, financial reports, e-mails, ERP software, customer records, presentations, and employee-created reports. Integrating all of this data to generate reports could be a complex task. This is a neighborhood that many businesses overlook. Data integration is crucial for analysis, reporting, and business intelligence, thus it’s excellent.

Lack of Precision Targeting

Due to a lack of a single point of responsibility, data analytics is typically reduced to poorly targeted projects. Such projects, which are handled on an ad hoc basis by discrete business or IT teams, result in steps being skipped and conclusions being reached that are incorrect. No matter how clever a data governance strategy is, it will fail if no one is in charge of it. Worse, a disjointed data management approach makes it difficult to understand what data is available at the corporate level, let alone prioritize use cases.

As big data is difficult to execute, the company has little visibility into its data assets, receives erroneous results from algorithms-fed garbage data, and faces increased security and privacy risks. It also costs money since data teams are in charge of data that has no commercial value and no one is accountable for it.

In a Nutshell

Effective­ data management is crucial for a business to thrive­. Among the common challenges busine­sses encounter, data se­curity, data quality, and data integration rank high. Companies must prioritize implementing robust security measures to safeguard sensitive information, establish reliable data governance­ frameworks to guarantee the accuracy and inte­grity of data, and invest in advanced data integration techniques to seamlessly conne­ct various systems. By directly addressing these challenges, busine­sses can unlock the full potential of their valuable data, make well-informe­d decisions, and drive growth in today’s increasingly data-drive­n landscape.