Top 7 Data Management Challenges for Businesses and Technology Solutions

Companies operating in the technology industry are among the most important data recipients. The rise of big data has greatly increased the volume of data that needs to be collected, processed and analyzed. These large volumes of data present numerous data management challenges for companies, especially those with outdated management systems.

Let's explore the 7 data management challenges facing technology companies and how to overcome them.

Data Management Challenges

Challenge #1: Accessing Organizational Data

An important aspect of a well-planned data management strategy involves knowing your organization's data sources and where business data resides. However, many companies experience problems in identifying critical data elements. More than 42% of technology professionals surveyed face difficulties in locating organizational data.

The solution is to first determine the analysis and reporting objectives. Based on these objectives, design a comprehensive strategy for data collection, preparation, storage and distribution. The goal should be to make the organization's data accessible throughout the organization. Once a plan is formulated, management must identify the human and IT resources to execute the processes successfully.

Challenge #2: Managing large data volumes

The limited storage capacity of local systems is a major obstacle in handling big data. Ever-increasing data volumes require additional storage space or more advanced storage systems, leading to higher costs.

The main culprit is the rise of big data, and the technology industry is one of the biggest consumers of big data. Data collection has increased tremendously due to the increasing digitization of information. It is further driven by the increasing use of social networks and smartphones. IoT systems are another important driver of Big Data.

Many companies move their data to the cloud to overcome this problem. Cloud-based data warehouses are becoming increasingly popular for storing large amounts of data. They offer powerful query capabilities for analytics and are highly scalable. Since data growth is not likely to slow down in the short term, a scalable storage solution provides long-term benefits.

Challenge #3: Consolidating Data Across Systems

Technology companies use multiple information systems, including accounting software, CRM and ERP systems. Each system has its own data repository, resulting in siloed systems. Contrasting systems can lead to duplication of data, lack of synergy and, consequently, can make data analysis difficult.

Companies must create a single source of truth by consolidating dispersed data into a centralized location to overcome this challenge. Bringing all data together in a unified system helps eliminate data silos and unlocks business information.

Challenge #4: Unstructured Data Analysis

More than 80% of business data is available in an unstructured format. This data contains valuable information that can drive effective decision making. For example, emails from potential leads contain information including their location, budget and use case. This information can help salespeople design more personalized and relevant demos.

Similarly, a technology company can extract unstructured data from PDF documents, including purchase orders and feedback forms, to gain meaningful information about the purchasing and sales departments.

Because unstructured data is not machine-readable, it must be converted into structured data, i.e., columns and rows for reporting and analysis. To leverage unstructured data to their advantage, companies must use an automated data extraction tool to instantly convert large volumes of unstructured documents into a structured format.

Challenge #5: Maintaining Data Quality

According to a Forrester survey, 80% of IT professionals cite poor data quality as a major data management challenge.

As companies face an onslaught of data from multiple directions, many data sets inevitably contain missing or incorrect data.

Leaving these data sets unattended can lead to misleading information and poor decision making. For example, missing contact data for a group of customers can lead to missed opportunities with potential customers.

To combat this challenge, it is imperative to introduce data validation checks with defined quality metrics. These checks will help identify duplicate values, missing fields, null values and overall data integrity. Ideally, a solution should have real-time data preparation functionality to ensure data quality.

Challenge #6: Ensuring Data Security

Cybersecurity is poised to become one of the major disruptors in the technology industry. The rise of data warehouses increases the risk of cyber-attacks and data leaks. Large volumes of data need protection, and IT executives have complained about the increasing severity of cyber attacks.

As a result, executives must devote resources to building a secure infrastructure. Companies can use established frameworks as a starting point. The ISO 27000 Series is an example of globally recognized best practices for IT security. However, the framework you choose will depend on multiple factors, such as the nature of your industry and your compliance requirements.

Building a centralized data repository with the right security protocols also minimizes the risk of a data breach. Companies should invest in an end-to-end ETL tool to integrate data sources and create a single, secure source of truth across the enterprise.

Challenge #7: Managing Multiple Data Solutions

Many organizations use separate software for different data management processes, such as extraction, integration, warehousing, etc. Using multiple data solutions can lead to data silos and inefficient data management, resulting in poor data monitoring and vulnerabilities.

The use of multiple programs has the following disadvantages:
  • Steep learning curve.
  • Lack of connectivity between systems.
  • Higher costs
  • Compliance risks
  • Lack of consistent quality
Unified data management solutions can help address and overcome these challenges. Companies should look for a single data management provider. A unified data platform helps eliminate data silos and data vulnerabilities so that companies can gain more control and visibility into their data.

A no-code approach to data management

A traditional approach to enterprise data management is very complex. It involves manual data entry and writing complex code, which is time-consuming and inefficient. To enable data-driven decision making, companies must be able to process data in seconds. Therefore, modern organizations are moving toward unified, code-free data management platforms.

Astera Data Stack

Astera Data Stack is a unified, zero-code platform that enables business users to simplify and optimize enterprise data management. It accelerates time to insight by automating the entire data journey, from data extraction and preparation to validation and loading. Using AsteraWith the enterprise-grade platform, companies can spend more time analyzing data and less time managing it.