Managing data is hard. Automation makes it easier. Here’s how:
- Common Problems: Businesses face data silos, manual errors, and high costs. Workers lose 12 hours weekly finding data, and errors cost companies $12.9M annually.
- Automation Benefits: Automation reduces errors, connects systems, and cuts costs. Examples include saving $2.1M annually (Atlassian) and improving data quality by 98% (Acceldata).
- Tools That Help: Use cloud-based integration (e.g., Hevo Data), low-code platforms (e.g., Peliqan.io), or open-source tools (e.g., Talend) to streamline workflows.
- Results: Better data accuracy, lower costs, and stronger security. Small businesses using automation compete better, with 88% reporting improved outcomes.
Automation transforms how businesses manage data, saving time, money, and reducing risks.
Common IT Data Management Problems
Managing data lifecycles is a critical challenge for modern businesses. These issues often lead to higher costs and increased security vulnerabilities.
Data Silos and Scattered Information
Data silos are one of the biggest hurdles in IT management. A staggering 72% of organizations struggle to manage multiple CRM systems spread across locations and siloed technologies . On average, knowledge workers lose 12 hours each week searching for and consolidating data across different platforms .
"The greatest challenge organizations face in meeting their marketing and sales objectives is managing data and sharing insights that drive actions across organizational silos." – Dun & Bradstreet/Forrester Consulting report
"Only when you have context and semantics assigned to data, that’s what brings that data closer to a business process. Without that, it’s just a bunch of bits and bytes."
Adding to the problem, manual processes often lead to data inaccuracies, creating further inefficiencies.
Human Error in Data Handling
Manual data entry is prone to mistakes, with error rates reaching 10% due to typing and transcription errors . These mistakes often affect critical asset identifiers like tags and serial numbers, making it harder to track assets throughout their lifecycle. Asset discovery systems can miss up to 30% of assets, falling short of the 95% accuracy standard . Such inaccuracies contribute to poor data quality, which costs companies an average of $12.9 million annually .
Inefficient data handling not only wastes resources but also increases operational and security risks.
Resource Costs and System Complexity
Poor data management comes with steep financial consequences:
| Cost Category | Impact |
|---|---|
| Cloud Storage Waste | $62 billion annually across industries |
| Legacy System Maintenance | Consumes 60–80% of IT budgets |
| Technology Complexity | Costs 25% more than average |
Complex IT environments demand 27% more staff, while top-performing IT teams manage 44% fewer applications per user .
"Companies that want to compete as digital enterprises need to operate at the speed of change. Any level of complexity will slow that speed down and compromise your organization’s ability to outperform your competition. Being able to reduce complexity and increase agility is fast becoming the new standard of operating excellence." – Peter Moore, President at Wild Oak Enterprises, LLC
On top of these costs, poor data management exposes organizations to security risks, with the average cost of a data breach reaching $4.35 million .
Data Lifecycle Automation Solutions
Connected Data Systems
Automation bridges gaps between isolated systems, addressing the challenge of scattered data. With integration technology, processes can be completed up to 80% faster , freeing IT teams from repetitive tasks. A great example is Centivo’s healthcare data management overhaul using Adeptia‘s automation tools. These tools streamlined connections among employers, payors, care providers, and regulators. Michael Mann, Centivo’s Integration Architect, highlighted the benefits:
"It’s really about maintaining the tool and getting it set up in the first place, and how easy and quick it is for your teams to do that."
This strategy empowers business users to handle data integration through no-code templates and one-to-many app connectors, cutting down reliance on IT specialists. It also ensures consistent data normalization across systems while reducing manual errors – something automation is particularly effective at addressing.
Automated Error Prevention
Frameworks like AWS Lambda and Step Functions play a critical role in preventing errors and optimizing processes.
AWS Step Functions stand out in key areas:
| Automation Category | Benefits | Impact |
|---|---|---|
| ETL Processes | Automatic sequencing and completion tracking | Reduces manual errors in orchestration |
| Security Workflows | Automated incident response with manual approval options | Speeds up response time and lowers mistakes |
| Large-scale Data Processing | Parallel workload orchestration | Ensures accurate handling of security logs |
These workflows come with built-in retry mechanisms and step tracking, ensuring tasks are completed without manual input . Such features help create smoother, more reliable processes.
AI-Driven Data Management
Artificial intelligence is reshaping data management by improving accuracy and efficiency.
For example, IBM used AI to enhance its agile development process. By analyzing developer profiles, skills, and workloads, they achieved:
- A 30% increase in sprint completion rates
- A 25% reduction in code review time
- Greater developer satisfaction
Siemens also leveraged AI for resource allocation in smart factories, resulting in:
- A 20% boost in production efficiency
- An 18% drop in energy use
- A 35% decrease in unplanned downtime
AI’s impact extends to improving data quality. Acceldata reported a 98% improvement in data quality and a 30% cut in infrastructure costs . This is especially important, as 75% of executives admit they lack full confidence in their data for critical decisions .
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Results of Data Lifecycle Automation
Automation brings clear advantages, tackling data silos and manual errors while delivering measurable outcomes.
Improved Data Quality
Data Lifecycle Management (DLM) ensures better accuracy and consistency through standardized processes. For example, Tide, a UK-based digital bank, revamped its data management using Atlan‘s metadata platform. By automating data identification and tagging, they cut a 50-day manual process down to just hours. This not only improved data quality but also ensured compliance with GDPR regulations, saving both time and money.
Reduced Costs and Time Savings
Automation leads to notable cost reductions and efficiency improvements. Here are some examples:
| Company | Implementation | Results |
|---|---|---|
| Atlassian | Migrated 2.3 petabytes to Amazon FSx for NetApp ONTAP | Saved $2.1M annually, with a 17% reduction in application response times |
| Johnson & Johnson | Automated snapshot archival | Achieved over $1M in yearly storage savings, managing over 5 petabytes |
"NetApp came up with a process that was safe and effective, and it resulted in zero customer impact."
Beyond financial and operational gains, automation plays a key role in enhancing security and meeting regulatory requirements.
Strengthened Security and Compliance
With 65% of personal data falling under privacy regulations , automated data governance is essential. It enables features like column-level access control, policy propagation, detailed audit logs, and standardized handling protocols. Gartner forecasts that by 2025, 80% of organizations aiming to scale digital operations will struggle without modern governance tools . A cautionary tale comes from Deutsche Wohnen, a housing association fined heavily for failing to manage data deletion properly . This highlights how robust governance is critical for managing data throughout its lifecycle.
Setting Up Data Lifecycle Automation
Small businesses can implement data lifecycle automation effectively by focusing on three key steps.
Review Current Data Processes
Begin by analyzing your current data workflows. For example, a German automotive OEM boosted process efficiency by 10% using the Data Quality Navigator .
Look for:
- Inconsistent or siloed data
- Bottlenecks in data entry
- Redundant steps
- Areas where data quality impacts operations
"The Data Quality Navigator guides us to a future where we can proactively optimize our processes and limit process disruptions to an absolute minimum. Like this, we can improve our process efficiency by 10%!"
- Innovation Manager of a German Automotive OEM
Use these findings to build a clear action plan.
Create an Action Plan
As many organizations explore automation, choosing the right tools is essential .
| Tool Type | Best For | Example Solution | Key Features |
|---|---|---|---|
| Cloud-based Integration | Real-time data integration | Hevo Data | Free tier, automated pipelines |
| Low-code Platform | Limited technical expertise | Peliqan.io | User-friendly, visual workflows |
| Open-source Solution | Budget constraints | Talend Open Studio | Free (open-source) |
| Workflow Automation | Task automation | Zapier | Broad integrations, free plan |
When selecting tools, focus on:
- Integration compatibility
- Monitoring data quality
- Security measures
- Scalability
If you need specialized help, consider working with a managed services provider like InfraZen (https://infrazen.tech) to align your automation strategy with business and IT goals. Once your plan is in place, track performance to make adjustments as needed.
Track and Improve Results
Measure automation performance to ensure continuous improvement. For instance, an international German automotive supplier saw impressive outcomes:
"The implementation of the Data Quality Navigator was a turnaround for our project. Before the DQN we were not aware of the poor data quality in our ERP system which putted our go-lives regularly on high risk. Now, with the DQN we can clearly measure and improve it with a streamlined data cleansing approach and go-lives became finally smooth."
- S4 Program Manager
Focus on these metrics:
- Data quality scores
- Process completion times
- Error reduction rates
- System performance analytics
- Compliance tracking
Monitoring these factors will help refine and improve your automation efforts over time.
Conclusion: Solving IT Problems Through Automation
Automation in managing the data lifecycle addresses long-standing IT challenges. For instance, data entry errors cost businesses over $600 billion each year . Small businesses are particularly vulnerable, with poor data management often leading to closures – nearly 60% shut down within six months of a breach costing between $36,000 and $50,000 . These numbers highlight why automation isn’t just helpful; it’s a critical step forward.
"It’s far less expensive to implement solutions that prevent bad data from entering a system than it is to fix bad data or to suffer the consequences. Investment in automation is almost always a net benefit to a business that relies on accurate data."
- Brady Behrman, CEO and founding partner of PunchOut2Go
The benefits of automation are clear and measurable. A striking 88% of small businesses report that automation helps them compete with larger companies . This competitive advantage is evident in multiple areas:
| Area of Impact | Improvement | Supporting Data |
|---|---|---|
| Data Accuracy | Fewer errors in data entry | AT&T found 40% of invoicing data contained mistakes |
| Cost Savings | High return on investment | Businesses gain $35 for every $1 spent on automation |
| Security | Lower breach risks | SMB data breaches average $120,000 in costs |
These results show that automation goes beyond being an IT tool – it’s a strategic move. However, to fully realize these benefits, businesses need a strong data governance framework. This includes assigning clear roles, monitoring data usage, and promoting accountability among team members . The takeaway is simple: automation isn’t just a technical upgrade – it’s a critical investment for improving efficiency, cutting costs, and ensuring growth in today’s digital world.
