A Comprehensive Guide To Data Automation Implementation

Currently, businesses generate, process and store staggering volumes of data for serving insightful analytics. This analytical information enables faster and more efficient decision-making through Business Intelligence.

Data automation is a crucial process that can be incorporated into your business process to achieve successful management of sensitive data.

Additionally, to adhere to stringent security policies, data discovery and data classification should function symbiotically. Both functions represent the cornerstone of any well-informed data governance program and an integral part of the data compliance solutions.

However, the challenge is managing both these tasks manually. The issue is the complexity of these tasks. With manual interventions, achieving both desired quality and quantity simultaneously and within a stipulated timeline isn’t possible.

Automation in data workflows such as discovery, classification, remediation, and monitoring has become necessary.

In fact, data automation is a necessary requirement for the release management plan. When hiring a professional partner for data automation and compliance management, ensure they follow the 10 phase data automation implementation.

10 Phase Data Automation Implementation

End-to-end Evaluation And Discovery

The process to adopt automation involves auditing and evaluating your current systems. That includes data on the cloud platforms, stored on-prem, structured, unstructured, partner systems, SaaS systems, and legacy environments.

The objective of this assessment is to understand and trace your data’s footprint. Data scanning tools using automation and ML algorithms can be incredibly helpful for this process.

This in-depth discovery assessment will extract and analyze all your data. Leveraging insights from these analyses, you can start resolving the issues, addressing the challenges and understanding the faults you never knew existed.

For effective management of your release workflow, ensure to maintain a release calendar.

Tagging And Classifying Sensitive Data

• Once you know which are your sensitive datasets and which aren’t, time tagging and classifying is the next step to establish trusted sensitive data management.

• Creating a data inventory creates a centralised repository for your sensitive data. Implementing automation becomes easy when you know the A-Z of your datasets.

• Classifying encompasses categorising your data elements. Common data classification includes PHI (Protected Health Information), PII (Personally Identifiable Information), PCI (payment card industry), and CDEs (Critical data elements).

Perform Financial Risk Analysis For Sensitive Data

Operating and processing sensitive data is risky. But you need to understand how risky?

Performing a financial risk analysis through automated individual element’s risk calculation can be incredibly helpful in this step.

With such insightful risk analysis, you can determine the most vulnerable datasets, design specific governance policies and integrate efficient data compliance solutions.

Segregate The CDEs

CDEs or Critical Data Elements are vital components for organizational operations, sustainability and growth. Therefore these data sets need to be easily accessible but highly protected.

Confidently attaining this harmony between accessibility and security is possible through automated data management and efficient tools.

Remove Redundancy

Redundancy can improve your data system’s efficiency and streamline your data workflows.

Deleting data can be overwhelming, and even minor mistakes can result in huge financial losses. However, automated double and triple checks ensure that any redundant data is documented and eliminated.

All your data assets should be compared for determining duplications and redundancies. These outdated, trivial datasets don’t just take up space but can have an adverse impact on your business processes. Removing data redundancies contributes to the success of your release management plan.

Duplicates can return corrupted results, incorrect models, and inaccurate estimates, thus slowing your business growth and confusing your team with unnecessary information. All these complexities can increase the workload on your employees.

Assess Your Data Compliance Status

Data compliance isn’t a one-time process. Since laws and regulations are consistently updated with technological advancements, it is common to lose track of all the changes. This might lead to unintentional non-compliance.

To ensure you’re always compliant, automated alter systems are necessary. Sensitive data is life and soul for many businesses. Therefore efficient data compliance solutions can incorporate easy accessibility and security for those sensitive datasets.

Mitigate data breach risk through vulnerability categorization and proactive protection. It is mandatory for all sensitive data to be de-identified and encrypted, so in the unfortunate event of a data breach or hostile attack, your user data is protected from unauthorized access.

Active Data Monitoring

Actively monitoring your data systems is vital to maintaining the integrity of your privacy policies and security protocols. As mentioned previously, sensitive data management is an ongoing process. When done manually, the intricate process can be time-consuming and expensive without the assurance of accuracy.

Automation enables you to take your hands off the wheel and let the ML program manage the rest depending on your inputs.

After completing the assessment, activating monitoring and implementing data governance is necessary. The best part? Automation doesn’t apply for sick leaves.

Set KPIs And Generate Data Reports

Measuring data quality can be a complex task. However, it is frequently measured through qualitative analysis. However, machine learning-based solutions and professional data compliance agencies can help you create measurable metrics for evaluating data quality.

No more “pretty high quality” results. You can use insightful data quality results to make informed decisions. These reports can help you adhere to your release calendar.

Introduce Continuous Risk Monitoring

Today’s cybersecurity industry is highly evolved. Hundreds of potential attack types are now possible. Therefore, ensuring robust defense against any attacks and preventing breaches is necessary.

Data encryption ensures that lucrative data becomes useless to ransom demanders without your private key codes.

Keep The Cycle Ongoing

Data compliance solutions are always ongoing. Collaborate with an experienced data solutions company for ensuring compliance. As evolved attack methods are conceptualized, implementing immediate remediation can keep your security standards updated.

Data is the future of any industry. And the volume of data required for various IT processes such as release management, deployment, etc., will increase. Automation is the most efficient solution to reduce data-related stress.