Cloud-based Data Management: Strategies for Moving Past Using Excel or Google Sheets

All data starts out on some type of spreadsheet.

Initially this approach serves its purpose.

The amount of Data is small, the number of people are small, & decisions are made quickly. Only having a couple of google sheets or excel sheets is sufficient to manage operations and/or reporting. (Typically speaking)

However, at some point, companies will see an increase in growth.

The volume of Data will grow, the size of teams will grow, and conflicting metrics will occur resulting in broken reports; spreadsheets will quickly become a hurdle instead of convenience.

This is the point in time (Hense the title: Inflection Point) in which teams need to rethink how they approach cloud-based Data Management.

For a Data Engineering Lead

For a Data Engineering Lead, this "Evolution" will be more than simply moving data from an on-premise solution to a cloud-based one; they will also have to implement systems that are:

Scalable
Reliable
Provide Real Time Insight
Enable Governance & Compliance

This Guide will provide you with an understanding of

The definition of Cloud Data Management
Why Spreadsheets fail when used at scale
Best Practices when building a Cloud Data Management system
Name at least 2/3 Common Mistakes made by Teams
3 Key Tools/Services to Consider When Building a Cloud Data Management System
How to Ensure that your Cloud Data Management App will meet your future needs

Cloud Data Management Definition

Cloud Data Management is the Process of Storing, Organizing, Integr rules

Data protection
Data access
How data management using the Cloud works

How data management using the Cloud works

At a high level the components of cloud data management systems are multiple layers that work together to allow data to:

Be taken from different places
Be kept in a cloud computer
Change form and learn something from the data
Be used by users to understand their businesses and make decisions

What Does this Mean

Companies today use real time data to:

Have current data about their business
Make decisions using AI and machine learning
Utilize a combination of multiple computers

You can’t use a spreadsheet to analyze data as required.

Clarity

Cloud data management is more than just hardware or software.

Cloud data management is not hardware or software; it is the ground level of how your organization manages and uses data.

Spreadsheets do not work at Scale

To discuss best practices to manage your data effectively, it is important to understand why spreadsheets fail to deliver.

1. Cannot Scale

Spreadsheets do NOT scale for:

Large volumes of data
Multiple users working on the same data at the same time

2. Your Data is Inconsistent

Multiple versions mean you may have:

Different numbers/icons for the same data
People do not trust the data in a spreadsheet

3. Limited Automation

Manual processes slow down workflow.

4. No Governance

With spreadsheets there is no way to control access or to know the history of changes made.

5. Poorly Integrated

Spreadsheets do not integrate with modern systems.

Practical Example

A rapidly growing software as a service (SaaS) company uses spreadsheets for reporting.

As a result they have:

Delayed results or insights
Errors
Increases in the number of employees to handle day to day operations

Key Takeaway: A spreadsheet is not a good solution to manage and use modern data effectively.

Cloud Data Management Core Components

For teams to move beyond spreadsheets they must create a solid infrastructure.

1. Data Ingestion Layer

Taking in the data:

Data that is batch processed
Data that is processed real-time

2. Storage Layer

The location that the data resides:

Data Lakes – where large amounts of data are stored (unstructured)
Data Warehouses – where small amounts of data are stored (structured)

3. Processing Layer

Where data is manipulated and transformed for reporting purposes as well as the data modeling.

4. Governance Layer

To ensure that data is used properly:

Quality processes
Compliance with laws and regulations

5. Access Layer

Allows users to access data via:

Dashboards
Application Programming Interface (API)

Key Takeaway: A solid infrastructure provides the necessary foundation for continually effective enterprise level cloud data management.

Best Practices for Cloud Data Management

1. Develop your data strategy

What are the principles of an effective cloud-based data strategy?

Some of the key components include:

Data ownership
Data governance policies
Scalability plans

2. Select a cloud data management platform based on

Scalability
Integration
Cost structure

Some popular cloud platforms include:

Snowflake
BigQuery
RedShift

3. Design with scalability in mind at the beginning

Do not design for scalability after the fact.

Plan for potential:

Data growth
Increased workloads

4. Implement effective data governance policies

Stay safe with:

Access controls
Data lineage
Regulatory compliance

5. Automate your data pipelines

Use:

ETL/ELT tools
Orchestration tools

6. Prioritize data quality

Bad data produces bad decisions.

You should implement:

Validation checks
Monitoring

7. Optimize costs

Cloud systems can be expensive when not managed properly.

8. Provide real-time access to your data

Modern applications require:

Low latency
Streaming capabilities

Key insight
Good practices are key to effective data management, and as such, they will influence the scalability of your system.

Cloud data management in cloud computing

Native cloud environments have both advantages and disadvantages.

Benefits

Elastic scaling
Less infrastructure management
Fast deployment

Drawbacks

Cost control
Vendor lock-in
Security

Hybrid/cloud multi cloud approaches

Organizations typically have:

Multi cloud data management strategies
Hybrid architectures

Key Insight
Flexibility is critical when dealing with today’s cloud-based infrastructures.

Security and Compliance in Cloud Data Management

A common question is:

How can I secure sensitive data that is stored in

Main Features

Encryption at rest and during transit
Role Based Access Control
Regular audits
Regulatory Compliance

Industries must comply with the following laws:

GDPR
HIPAA
SOC 2

Main Insight
Security should be integrated into your architecture, rather than being a bolt-on.

Migrating from Microsoft Excel to Cloud-Based Systems

Another frequently asked question is:

How to move an enterprise from an Excel-based data model to a cloud storage service?

Steps to Implement Migration

Audit Existing Data Sources
Cleanse and Standardize Data
Select a Cloud Platform
Create Ingestion Pipelines
Confirm Accuracy of Data

Challenges

Data Quality Issues
Schema Mismatch Issues
User Adoption Issues

Main Insight
Migration is a joint technical and organizational process.

Selecting Effective Cloud Data Management Tools

Tool Classifications

Data Integration Tools
Data Storage Platforms
Data Governance Tools

Examples

Informatica
Talend
AI Enabled Tools

Currently Available Features

Automated Data Discovery
Automation for Smart Data Quality Controls

Main Insight
Supporting Tools for Your Architectural Framework, Not Defining It

Comparing Cloud Data Management Solutions

A critical decision point for most organizations:

Comparison Between The Leading Cloud Storage Solutions

Important Considerations

Performance
Cost
Integration
Security

Strategic Planning

Tools should be selected according to particular parameters including:

Specific Use Cases
Team Level of Expertise
Growth Strategy

Common Mistakes

1. Lifting and Shifting from Microsoft Excel to Cloud

This does not fix what is broken.

2. Not Paying Attention to Governance

This will create chaos with your data.

3. Over-Engineering in the Early Phases of the Project

Start simple, then scale.

4. Failing to Budget for Cloud Pricing

Cloud costs can quickly get out of control.

5. No Defined Ownership of Data

Have clear expectations for accountability.

Main Insight
Most failures occur as a result of not having a good plan, not because of technology.

Cloud Data Management Trends

Rapid change is driving emerging developments in cloud storage.

Emerging Developments

Artificial Intelligence driven data platforms
Real-time analytics
Data fabric architecture

What This Means

Cloud Data Management is becoming:

More automated
More intelligent
More integrated

Strategic Insight

Early adoption of new trends will allow teams to:

Move faster
Scale better
Implement AI programs

Frequently Asked Questions

What does Cloud Data Management mean?

The Use Of Cloud Products To Store, Organize and Manage Data.

How does Cloud Data Management Operate?

Data Is Ingested, Stored, Transformed, Governed And Made Accessible.

What Are The Advantages To Cloud Data Management?

Scalability, Flexibility, Cost Efficiency and Advanced Analytics Capabilities.

What Are The Issues With Cloud Data Management?

Cost Control, Security, and Integration Complexity.

How To Migrate From Microsoft Excel To Cloud Systems?

Audit Data, Clean Data, Select A Cloud Provider, Create Ingestion Pipelines.

Conclusion

Spreadsheets Are A Good Starting Point – But They Do Not Scale As Your Organization Grows.

Cloud Data Management Offers A Solution.

Logiciel Solutions Point Of View

Logiciel Solutions works with data engineering teams to move from fragmented, spreadsheet-based processes to scalable AI focused cloud-based solution.

Logiciel Solutions focuses on intelligent data platforms that integrate seamlessly with analytics, automation and AI capabilities to provide a long-term foundation for scalability and performance.

If your organization is outgrowing Excel spreadsheets, now is the time to create a solution that supports your next phase of growth.

Let’s develop a cloud data management strategy to grow with you and your business goals.