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Data Management Development Company

Scale your Data Management development with our nearshore talent.

Our Data Management development services already power dozens of active engagements. We typically land our teams within 2 weeks, so you can start shipping top-quality software, fast.

Data Management Development Services We Provide

Data Architecture Design

Develop scalable and secure data solutions from the ground up. Ensure that your data systems are built on a solid foundation.

Utilizing tools like ER/Studio and Microsoft Visio, we design robust data architectures for managing complex data structures. 

Database Development & Integration

Drive better-informed decision-making. We build relational databases that integrate seamlessly into your current ecosystem. 

With platforms like MySQL or PostgreSQL, we optimize for transaction integrity and complex querying. Our developers use NoSQL databases like MongoDB, Cassandra, or Couchbase to power high-velocity applications.

Data Governance

Accuracy, accessibility, and security are the three pillars of data governance. We prioritize regulatory compliance, embedding rigorous data protection mechanisms like IBM Guardium or Symantec. 

We also ensure data consistency and transparency with tools like Collibra or Informatica.

Data Warehouses & ETL Services

Transform raw data into structured information ready for machine learning algorithms and accurate business intelligence. Enhance business analytics, proper data integration, regulatory compliance, and dating mining with systematic warehousing and ETL (Extract, Transform, Load) techniques.

Using tools like Amazon Redshift, Google BigQuery, and Snowflake, we build systems capable of handling petabyte-scale datasets. The result? Improved data management capabilities and better decision-making processes.

Data Migration & Transformation

Outgrowing your legacy systems?  Our developers transition your operations to modern, scalable cloud-based platforms. During your migration, we refine your data using ETL tools like Apache NiFi or SSIS to enhance data quality and utility, ensuring data integrity and uninterrupted service throughout.

Data Security & Compliance Auditing

Vulnerabilities can leave your systems at risk for hackers to exploit. We ensure your data is protected through a thorough security and compliance auditing process. 

Leveraging tools like IBM QRadar and Splunk, we conduct a comprehensive audit of your systems. Our approach includes detailed risk assessments and the development of standard security measures to safeguard your data.

Business Intelligence & Analytics

Data leads to better decisions and results—when it's interpreted accurately. We convert raw and unstructured data into actionable insights that drive performance and growth. 

Using platforms like Tableau, Microsoft Power BI, and Qlik, we build interactive visuals and real-time dashboards so stakeholders can see the whole picture. 

Cloud Data Management & Optimization

Maximize efficiency, resilience, and scalability, regardless of your cloud platform.

Through right-sizing resources and auto-scaling solutions, we identify unused or underutilized instances and dynamically adjust workload demands to optimize performance. 

Data Backup & Disaster Recovery Planning

Ensure business continuity and protect against data corruption with regular backups and effective disaster planning. Avoid downtime and keep your business running smoothly.  

Our developers design a blueprint for disaster recovery that minimizes the Recovery Time Objective (RTO) for quick restoration and extends the Recovery Point Objective (RPO) to reduce potential data loss. 

Big Data Solutions

Your data can drive innovation and growth. With proper management, governance, and analysis, you can see hidden patterns, anticipate market movements, and personalize the customer experience. 

We use Apache Hadoop for distributed storage and data processing, Apache Spark for fast in-memory analytics, and machine learning frameworks like TensorFlow, PyTorch, and SciKit-Learn for automation. From analyzing customer behavior to performing supply chain optimization, we ensure that you can use big data responsibly.

Pinterest case study

With a team of highly skilled data scientists, BairesDev reconstructed the Pinterest Ads Manager, delivering exceptional results. Pinterest Case Study.

Key Things to Know About Data Management

For data-driven organizations, implementing modern management techniques can offer significant benefits: 

  • Improved Decision-Making: High-quality, well-managed data means more accurate reporting, forecasting, and analytics. This leads to better decision-making. 
  • Increased Efficiency: Access and interpret data more easily, saving time and facilitating quicker responses to market changes. 
  • Enhanced Data Quality: Thorough data management involves cleansing, standardizing, and removing duplicates. You'll have access to more reliable data. 
  • Scalability: Cloud-based data management solutions and modern data architectures can facilitate growth without additional hardware investment.
  • Cost Savings: Data storage can be expensive, especially if redundancies aren’t dealt with. 

Data management services help businesses take advantage of their data's full potential and use it to inform their strategies. 

Best Practices for Data Management in 2024

Data Governance & Quality

Through advanced data quality management and strong governance policies, we ensure you’re making decisions based on accurate information. Here’s how:

Establish clear data governance frameworks

Use platforms like Collibra or Alation to integrate roles, responsibilities, and policies into a holistic framework. 

Implement comprehensive data quality standards

Tools like Talend Data Quality or IBM InfoSphere Information Analyzer help enforce stringent data quality standards. 

Define and enforce data privacy policies

Create, maintain, and enforce data privacy policies with tools like OneTrust or TrustArc. 

Regularly audit data for compliance and accuracy

Compliance audits using tools like IBM Guardium or Apache Atlas ensure data is accurate and consistent and meets governance standards. 

Architecture & Data Integration

When building or transitioning to new systems, we design robust data architectures. Here’s what we do:

Design scalable and flexible data architectures

Using Docker or Kubernetes for containerization and cloud services for dynamic scalability, plan for future growth or changing needs. 

Ensure seamless data integration across systems

A data integration platform like MuleSoft Anypoint will help facilitate the seamless flow and transformation of data across systems. 

Optimize data storage for performance and cost

Data storage solutions like Amazon S3 can provide inexpensive scalability and lifecycle policies to keep costs down. 

Adopt a robust data backup and disaster recovery strategy

Ensure business continuity and data protection through regular backups and disaster recovery services. 

Data Security & Privacy

Data breaches are expensive, disruptive, and sometimes even catastrophic. Here’s how we guard against them:

Enforce strict data access controls

Identity and Access Management (IAM) systems like Azure Active Directory will restrict access to only authorized users. 

Encrypt sensitive data both at rest and in transit

Use Transparent Data Encryption (TDE) for data at rest, and transport layer security (TLS) protocols for data in transit. 

Conduct regular security assessments and audits

Periodic security assessments with tools like Tenable Nessus or Qualys will scan for vulnerabilities. 

Stay compliant with relevant data protection regulations

Governance, risk, and compliance (GRC) platforms like RSA Archer can help monitor and meet data protection standards. 

Data Utilization & Analytics

These practices help you leverage your data in meaningful ways.

Promote a data-driven decision-making culture

Implement data visualization and reporting tools and encourage their use across all departments. 

Utilize advanced analytics and business intelligence tools

SAS Analytics or IBM Cognos provide sophisticated data modeling, forecasting, and analysis tools to inform strategic planning. 

Encourage collaboration and data sharing across teams

Integrate systems and create a centralized dataset to encourage teamwork and collaborative decision-making. 

100s of companies worldwide trust us for their Data Management services.

Why Choose BairesDev for Data Management Development

Why Choose BairesDev for Data Management Development
  • Nearshore, Timezone-Aligned Talent

    Data management is often time-sensitive. Because our LATAM data management professionals share overlapping time zones with the US, they can collaborate with your business in real-time.

  • Diverse Range of Solutions

    Whether you’re looking for governance to enhance data quality, migration to modernize systems, or security protocols to protect sensitive information, we deliver a diverse range of solutions. Our developers choose the best tools and platforms to optimize your data and inform your decision-making. 

  • Top 1% of Tech Talent

    We only hire the top 1% of LATAM tech talent. Our experts have deep knowledge of data management, as well as interpersonal and soft skills like communication, problem-solving, and collaboration. This leads to better project outcomes. 

Our process. Simple, seamless, streamlined.

Step 1Discuss Your Requirements

We'll start by discussing your goals for your Android app, including the engagement model that's best for your business. We'll also review your budget, timeline, and requirements.

Step 2Create a Plan and Build Your Team

After determining the approach we'll use, we will choose the best-fit Android developers and team members to build your app autonomously or work side-by-side with your internal team. 

Step 3Get to Work

Once we've assembled your team, we'll get to work. No matter which engagement model you choose, you'll retain oversight. We'll keep you fully informed throughout development.

Frequently Asked Questions (FAQ)

What is enterprise data management and how does it benefit a company?

Enterprise data management (EDM) is an approach to handling a business' data across its systems. EDM focuses on improving data quality, facilitating compliance with regulations, and improving decsion-making.

How does data profiling contribute to the effectiveness of data management?

Data profiling is a technique for analyzing a system's existing data and evaluating its quality and structure. It can help identify anomalies, inconsistencies, and deviations within the data. Data management teams leverage data profiling to improve data quality, better understand data attributes, and ensure that the data is useful for its intended purpose.

Who makes up a data management team and what are their responsibilities?

A data management team typically includes several roles, such as data architects, data engineers, data analysts, and database administrators. Some important responsibilities are overseeing data architecture, implementing data storage solutions, ensuring data quality, and facilitating data access. Team members collaborate closely, leveraging both structured and unstructured data, to meet the organization's needs.

What are the differences and relationships between structured and unstructured data in data management?

Structured data and unstructured data are both used in data management. However, they differ in terms of organization and format. Structured data is organized and formatted in a way that's searchable in relational databases. It usually involves predefined models, such as tables with rows and columns. Unstructured data, on the other hand, isn't organized in any predefined manner and may include various formats like text, images, and videos. Data management systems should handle both unstructured and structured data types to facilitate and support data-driven decision-making.

What tools and technologies are commonly used in enterprise data management to handle both structured and unstructured data?

There are several tools and technologies used to manage structured and unstructured data in data management. Examples include relational database management systems (RDBMS) for structured data and data lakes or NoSQL databases for unstructured data. Data integration tools, data quality management software, and advanced analytics platforms are also used for accurate data collection, storage, processing, and analysis in different formats.

How Businesses Can Overcome the Software Development Shortage

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