What Is The Best Data Management Software for SMBs

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    Data has become the key driving force for modern businesses. Organizations spend significant time and resources gathering data to make the right decisions. However, with the acceleration of data collection and its growing complexity made data management challenging, especially for smaller businesses with limited in-house resources and budgets. 

    Challenging, not impossible. 

    With this article, we want to give you the keys to make sense of your data. We will provide you with a shortlist of the best data management softwares on the market. We will discuss each tool’s key functionalities and offerings and look into add-ons like plugins, integrations, and customer support.

    We will also discuss critical factors to consider before deciding. But before that, let’s first discuss some key elements of evaluating a data management tool.

    Key Criteria To Find The Best Data Management Software For Your Business

    The proficiency of the data management software directly impacts data quality and availability. Here are some critical factors for evaluating data management tools.

    1. Scalability

    The longevity of a data management tool is best judged by its ability to scale according to growing data needs. 

    Growing businesses are often faced with the challenge of managing an expanding user base. The challenge comes in the form of increasing data volume, velocity, and processing complexity. 

    If the data management tool cannot keep up with the information influx, it impacts customer experience and retention.

    2. Data Security and Compliance

    Data security protocols protect user information against cyberattacks and unauthorized access. A good data management tool must incorporate state-of-the-art security measures, which include data encryption, access control, and regulatory compliances such as GDPR and HIPAA. Security integrations help build client trust and protect organizations from potential lawsuits.

    3. Integration Capabilities

    It is likely that your organization has an existing tech stack to manage day-to-day tasks and team collaborations. 

    The selection of a data management software will significantly depend on how well it can integrate with your business tech stack. 

    The platform must be compatible with your existing business tools and allow seamless access to data throughout the organization. This is the only efficient way to have a single source for your data and avoid silos. 

    Frictionless integration capabilities prevent any delays in organizational processes and cut down on the learning curve of the new tool.

    Shortlist Of The Best Data Management Softwares

    Now that we understand the key capabilities that a good data management tool should have, let’s look at some of the best data management softwares on the market.

    1. ClicData – Data Management and Analytics Platform

    best data management software clicdata

    ClicData is the go-to data management software for mid-market businesses since 2015. It is an all-in-one data management platform that offers extensive data integration, data analytics, visualization, and efficient collaboration between members across your organization.

    ClicData offers connections to extract data directly from over 250 platforms, APIs, databases, storage, and applications. It also offers individual modules for handling data loading and processing, providing users with fine-grain control over their data hierarchies and models.

    ClicData’s data management software has benefited various industries, including retail, marketing, healthcare, and finance. The tool powers diverse use cases across several industries, including:

    • Customer Satisfaction Monitoring
    • ROI Analytics
    • Forecasting
    • Operations Optimization

    ClicData offers a free trial with all the data management and analytics features available.

    2. Amazon Redshift – Data Warehousing and Big Data Processing Tool 

    blog amazon redshift

    Amazon Redshift is a fully managed data warehousing platform integrating machine learning (ML) and a massive parallel processing (MPP) architecture. It is a high-performing service that provides powerful compute clusters for processing petabytes of data.

    Pros of Amazon Redshift’s ETL for Small and Medium Businesses:

    1. Performance: Redshift is optimized for fast query performance, providing quick and efficient analysis, which is crucial for businesses requiring timely insights.
    2. Integration with AWS Ecosystem: It seamlessly integrates with other AWS services, offering a comprehensive ecosystem that can be advantageous for businesses already using or considering AWS for various components of their operations.
    3. Ease of Use: Amazon Redshift is designed to be user-friendly with a familiar SQL interface, reducing the learning curve for analysts and data professionals.
    4. Cost-Effective: With a pay-as-you-go pricing model, small and medium businesses can manage costs effectively, paying only for the resources they consume.

    Cons of Amazon Redshift’s ETL for Small and Medium Businesses:

    1. Cost Considerations: While the pay-as-you-go model is cost-effective, the overall costs may still be relatively high for businesses with limited budgets, especially as data volumes grow.
    2. Complexity of Management: Although Redshift is a managed service, it still requires ongoing management and optimization to ensure optimal performance, which may be challenging for smaller teams.
    3. Learning Curve: While Redshift is designed to be user-friendly, the optimization and advanced features may require a learning curve for analysts unfamiliar with cloud data warehouses.
    4. Limited On-Premises Options: Amazon Redshift is a cloud-based solution, which may not be suitable for businesses with strict on-premises data requirements due to regulatory or security concerns.

    3. Domo – Drag-and-Drop ETL

    blog domo etl

    Domo, a comprehensive business intelligence (BI) platform, offers Extract, Transform, Load (ETL) capabilities designed to empower analysts in managing and analyzing data efficiently. The platform supports seamless data integration from diverse sources, including databases and cloud applications, with pre-built connectors facilitating connectivity. Domo’s ETL tools enable analysts to transform and clean data, ensuring it is ready for insightful analysis through operations like filtering, aggregating, and applying business logic.

    Pros of Domo’s ETL for Small and Medium Businesses:

    1. Cloud Integration: Being a cloud-based solution, Domo seamlessly integrates with cloud services, providing flexibility in data storage and accessibility without the need for significant infrastructure investment.
    2. Data Quality Management: Domo’s ETL tools include features for data validation, cleanup, and enrichment, contributing to improved data quality and reliability for analysis.
    3. Parallel Processing: The platform’s support for parallel processing enhances performance, particularly beneficial for small and medium businesses dealing with large datasets.

    Cons of Domo’s ETL for Small and Medium Businesses:

    1. Cost: Domo’s comprehensive features may come at a higher cost, which could be a consideration for smaller businesses with budget constraints.
    2. Learning Curve: Despite its user-friendly interface, there may still be a learning curve for users unfamiliar with BI tools, potentially impacting the speed of adoption.
    3. Dependency on Internet Connectivity: As a cloud-based solution, Domo relies on internet connectivity, and disruptions may impact real-time data access and analytics.
    4. Limited On-Premises Options: While Domo offers cloud-based solutions, there may be limitations for businesses preferring on-premises ETL solutions due to regulatory or security considerations.
    5. Customization Complexity: Advanced customization may require a deeper understanding of the platform, posing a challenge for small and medium businesses without dedicated technical resources.

    Challenges and Considerations

    As mentioned, adopting a data management software is essential to support your business growth. 

    However, implementing the tool across your business comes with several challenges. Let’s look at some of these in detail.

    1. Downtimes During Implementation

    There is no perfect plug-and-play tool that seamlessly integrates with all your workflows. 

    Many existing processes must be halted and migrated towards the new platform. The entire setup procedure, from administrative configuration to data and pipeline migration, is also time-consuming. During this preliminary phase, several existing procedures will face disruptions and downtimes, resulting in delays and potentially loss of business.


    Opt for a platform that easily integrates with most workflows with minimal changes, such as ClicData. 

    Making the right choice will require extensive planning, research, and demos with the potential vendor to ensure all boxes are checked, but the effort is worthwhile in the long run. The planning phase must also include alternate workflows to keep the business running while the integrations happen.

    2. Learning Curve

    Any new tool or platform will come with a learning curve for your business employees. It may be a new user interface (UI), workflows, or back-end implementations. Either way, users must familiarize themselves with the new environment and catch up to the new methodologies. The learning process will result in work delays and possible delivery quality issues.


    While the learning curve is unavoidable, it can be minimized by opting for a tool that follows workflows similar to your organization’s existing infrastructure. If your business has any existing solutions implemented, it is ideal to opt for the same vendor for data management since your team will be familiar with the UI elements and workflows. You should also plan employee training sessions to speed up the learning process.

    → Learn more about ClicData’s onboarding program

    3. Data Quality Issues

    A new infrastructure may have errors during implementation, resulting in poor data quality in the initial phases of the migration. The errors will have to be back-tracked, verified, and fixed individually.


    Involve your data analysts or data engineers to verify all workflows and logic during implementation. Any implementation must have the data team’s approval and keep them in sync with any required changes. 

    Additionally, you must ensure that the new workflows are deployed in a testing environment and extensively tested before production to avoid any impact on the business.

    4. Customer Support and Updates

    Constant updates are vital in the fast-moving IT world. As technology progresses, hacks and cyberattacks are also evolving. Vendors must constantly implement the latest security protocols and algorithms to prevent attacks and data leaks. Software updates are also important for application optimization and maintaining robustness.

    Active customer support is important in promptly registering and resolving bugs. It also allows the client to request new features and get clarifications regarding workflows. Customer support is important in building a healthy client-vendor relationship and maintaining smooth operations.

    5. Budget

    Despite the features and integrations, selecting a data management tool will ultimately come down to budget. 

    A feature-packed platform will likely be costly compared to others. As a small business, you must understand your budgetary constraints and opt for a tool that balances cost and functionality. Many vendors also offer additional functionality at extra cost. You must evaluate the importance of these add-ons to avoid any unnecessary bills.

    data templates accélérer et enrichir analyses
    clicdata logo and name round 600x143

    No hardware. No installation. No maintenance cost. All included.

    Future Trends in Data Management

    Data is growing at an unprecedented rate, and the present systems aren’t ready for it. Organizations now collect data in various formats, including text, tables, audio, videos, etc., and each is fetched from a different source. This change demands an evolution in the data management ecosystem. Here are a few future trends for data management to look out for.

    Adoption of Data Lakes

    Data volume and velocity are growing beyond the capabilities of conventional data management solutions. Moving forward, we will see a wider adoption of the data lake architecture incorporating parallel storage and processing for efficient processing. The data lake infrastructure will also help unify multiple data types from various touch points and help organizations utilize the information seamlessly.

    blog data lake architecture
    Architecture of a Data Lake – Source

    AI and Automation

    AI has already left its mark on various industry processes. The future will experience significant AI involvement in the automation of data processes. These include capturing data errors, improving observability, and generating automated insights and visualizations. Moreover, as AI applications grow across industries, more data management platforms will include native tools and algorithms for machine learning processing and training.

    Focus on Real-Time Analytics

    Data management will focus more on data streaming and real-time analytics to keep up with the fast-paced market dynamics. Data management practices will be optimized to reduce processing and streaming latencies to capture real-time trends. This will allow business leaders to make instantaneous decisions based on changing user requirements.

    Select the Best Data Management Software for Your Needs

    In recent times, data has emerged as one of the key factors in the prosperity of growing businesses and large enterprises alike. Effective data consumption is made possible by leveraging data management solutions for storage, processing, and analytics.

    The IT market is flooded with data management solutions, but not all of these are the right fit for you. To make your decision easier, you must focus on some key evaluation criteria, such as

    • Scalability: Can the tool handle increasing workloads?
    • Integration: How well does the tool fit with your existing infrastructure?
    • Security: Does the software vendor take data security seriously? What security protocols and regulatory compliances are in place?

    Moreover, migrating to a new data infrastructure accompanies various challenges that you must remain ready for. Some of these include

    • Workflow Downtimes: Migrating pipelines might require them to be halted for some time.
    • Learning Curve: Employees will have to go through a learning phase for using the new platform
    • Preliminary Errors: The new implementations will likely have bugs that must be resolved.

    Selecting the best data management software can be quite a dilemma. ClicData appears to be best data management software for smaller businesses. Our 250+ data connectors ensures seamless integration with all your data sources, efficient data pipelines management and interactive dashboards make it ideal for various use cases. ClicData makes data management accessible and more efficient in many industries including retail, hospitality, healthcare, as well as in consulting.

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