A data management strategy is essential to any IT environment, providing the structure and guidance for efficient data storage and retrieval. A well-developed data management governance model can offer numerous benefits, such as improved information accuracy, increased productivity through automation, and reduced costs from streamlined processes.
This article will delve into the comprehensive overview of a data analytics strategy, various types available, and best practices for successful implementation, helping you craft a practical approach tailored to your organization's needs. To further enhance your understanding and capabilities in this area, exploring data analytics services can provide additional insights and tools essential for creating a successful data strategy.
What is a data management strategy?
A data strategy refers to organizing, storing, and utilizing an organization’s data to meet its goals. It involves assessing the current state of the company’s data and developing policies and procedures to ensure it is used effectively. A successful data strategy can help organizations increase efficiency, reduce costs, improve customer service, and gain a competitive advantage.
Data management strategies are designed to help companies organize their data for quick assessment. The goal is to create a system where all relevant information is stored in one place so employees can easily access it when needed. A data management governance model includes using structured and unstructured sources of information and Cloud-based or on-premise solutions, depending on the organization's needs.
Having an effective data strategy can provide numerous benefits for businesses, including the following:
- Improved decision-making due to better access to accurate information;
- Increased operational efficiency through automation;
- Cost savings from reducing manual processes;
- Improved customer service by providing faster response times;
- Better compliance with regulations such as GDPR;
- More accurate forecasting based on real-time insights into trends;
- Greater agility in responding to shifting market conditions;
- Enhanced security measures for protecting sensitive information;
- Gaining a competitive advantage over rivals who do not have similar strategies in place.
A solid strategy can help streamline operations and ensure the security and integrity of your data. Organizations can leverage their resources more effectively by understanding how to develop a data strategy for their specific needs.
How to develop a data management strategy?
Data management is an essential component of any successful business. It involves collecting, storing, and analyzing data to help make informed decisions about operations and strategy. A well-developed data strategy can provide insights into customer behavior, identify opportunities for improvement, and ensure compliance with industry regulations.
The first step in developing a data strategy is to define your goals and objectives. What do you want to achieve by collecting this data? Are you looking for ways to improve customer service or increase sales? Once you have identified your goals, it will be easier to determine what data type needs to be collected and how you should analyze it.
After identifying your goals, assess the current state of your existing data sources, such as databases or spreadsheets. Determine which types of information are most important for achieving those objectives so that they can be prioritized when gathering new information from customers or other sources. Consider if any gaps in knowledge need filling before implementing a comprehensive plan for managing all available resources effectively.
Establishing policies and procedures around how data is stored, accessed, shared, and used within the organization will help ensure that everyone follows best practices. It’s especially true when handling sensitive information like customer records or financial transactions. Also, establish procedures on who has access rights, what security measures must be taken, and how often backups should occur. Businesses can protect their valuable assets by taking these steps while ensuring employees have access to necessary resources.
Having identified the goals and objectives, assessed the current state of your data, and established policies and procedures for data management, you are now ready to explore the different types of strategies available.
Types of data management systems
There are several types of data management systems, each with its benefits and challenges. The most widespread types include:
1. Structured vs. unstructured data strategies. Structured data refers to information organized into a specific format, such as a database or spreadsheet. This type of data can be easily searched and analyzed using software tools. Unstructured data, on the other hand, is more difficult to manage since it doesn’t have any predetermined structure or organization.
2. Cloud-based vs. on-premise strategies. Cloud-based solutions offer flexibility and scalability when managing large amounts of data across multiple locations. They also provide access to powerful analytics capabilities without upfront investment in expensive hardware infrastructure. On-premise solutions are better suited for organizations that need complete control over their environment due to security or compliance requirements. Still, they may need more resources for proper maintenance and upkeep of the system over time.
3. Centralized vs. decentralized strategies. Centralized approaches comprise storing all the organization's data in one place so that it can be accessed by everyone within the company quickly and efficiently while maintaining tight control over who has access rights at any given time. Decentralized approaches, on the other hand, involve distributing different parts of an organization's dataset across multiple locations to reduce latency issues.
The types of data management strategies discussed in this article provide a good starting point for understanding the different approaches available. Also, it’s important to understand best practices for implementing an effective strategy and selecting the right tools and technologies.
Read more: How ETL developers can elevate your data management strategy
Best practices for implementing a data management strategy
A solid data strategy helps organizations manage their data securely, efficiently, and cost-effectively. Implementing the following practices can help ensure your data is managed and securely.
- Automation and standardization of processes. Automating processes such as data collection, storage, retrieval, analysis, and reporting can help streamline operations while reducing costs associated with manual labor. Additionally, standardizing processes across departments reaches consistency in how data is handled throughout the organization. It also reduces potential errors due to inconsistent methods of handling information.
- Security and compliance considerations. Ensuring that all security protocols are up to date should be a priority when developing a data strategy. Data breaches can have serious financial and reputation consequences for businesses, so you must consider adequate measures to protect sensitive information from unauthorized access or manipulation. Additionally, compliance with relevant regulations must be considered when designing your strategy. Failure to do so could result in hefty fines or other penalties depending on the jurisdiction where you operate your business.
- Utilizing the right tools and technologies. This approach allows companies to quickly collect large amounts of structured or unstructured data from multiple sources. This data is further used to provide insights into customer behavior patterns that can inform decisions about product development or marketing campaigns. Furthermore, utilizing Cloud-based solutions allows companies to scale their operations without investing heavily upfront on hardware infrastructure, making them more agile than traditional on-premise systems would allow.
By following the mentioned guidelines, you can create an effective data management system and move on to measuring its success.
Read more: How to improve your business with efficient financial data management
Measuring the success of your data strategy
When it comes to any data management governance model, it’s essential to ensure that you achieve the desired results. Defining metrics for evaluation and analyzing performance reports is crucial to determine if changes need to be made.
Defining metrics for evaluation. Establishing measurable goals and objectives will help you track progress and evaluate success. This step includes setting targets such as cost savings, improved customer satisfaction, increased efficiency, or other relevant KPIs. Once these goals are reached, it’s time to measure them against actual outcomes. It can be done by collecting data from various sources, including customer feedback surveys, financial statements, and operational reports. Companies should analyze them regularly to identify discrepancies between expected and actual results.
Analyzing performance reports. Analyzing performance reports helps you gain insights into how well your data strategy performs over time. You can use this data to make informed decisions about improvements or adjustments within your organization's processes or systems. Additionally, tracking trends in key metrics such as cost savings or customer satisfaction can provide valuable insight into whether changes are necessary for your strategy to remain effective over time.
As with any business process or system implementation project, adjustments may be needed along the way based on changing conditions within the market environment or internal operations of an organization. By regularly monitoring performance reports and adjusting strategies accordingly when necessary, organizations can ensure that their data management initiatives remain successful while meeting evolving industry demands and consumer expectations.
Data management strategy FAQs
What are the 3 data management approaches?
1. On-premise data management. This approach involves hosting and managing data on physical hardware located within the organization’s premises. It provides complete control over the data but requires significant upfront investment in infrastructure and personnel resources.
2. Cloud-based data management. A Cloud-based model allows for storing and managing data on remote servers hosted by a third-party provider, such as AWS, Azure, or GCP. It offers scalability, cost savings, and high availability with minimal effort from the user's side.
3. Hybrid data management: A hybrid model combines elements of both on-premise and Cloud solutions to provide organizations with maximum flexibility regarding their needs. By leveraging both types of solutions, companies can benefit from lower costs while still having access to powerful computing capabilities when needed.
What are the data management steps?
1. Data collection. This step involves gathering data from various sources, including databases, surveys, and research. The data collected must be relevant, accurate, and sufficient for the intended purpose.
2. Data organization. Once the data has been collected, it needs to be structured in a meaningful format for more accessible analysis and storage. This step involves arranging the data into tables, spreadsheets, or other suitable formats for further processing.
3. Data storage. The organized data must be stored securely in systems designed for this purpose. The storage systems must have appropriate backup plans to ensure that the data remains safe and can be retrieved when needed.
4. Data processing. This step includes manipulating the stored data to extract useful information or insights. The processed data can then be used for decision-making or further analysis tasks.
5. Data analysis and visualization. The processed data is analyzed to identify patterns, trends, correlations, and other insights. The findings from the analysis phase can then be presented visually through charts, graphs, or other visual aids for better understanding by stakeholders.
6. Reporting and sharing results. The final step in the data management process is to generate reports based on the findings of the analysis phase and share them with relevant parties who may benefit from the information. The reports should be clear, concise, and easy to understand, and they should communicate the key insights and findings in a way that is relevant to the intended audience.
What are the types of database management systems?
1. Relational database management system (RDBMS). An RDBMS stores data in a structured format, allowing for easy retrieval and manipulation using SQL.
2. Object-oriented database management system (OODBMS). An OODBMS stores data as objects, making it easier to model real-world scenarios and complex relationships between different data types.
3. Cloud database management system. A Cloud database management system is an online service that allows users to store their data on remote servers hosted by third parties, providing scalability and access from anywhere with an internet connection.
4. NoSQL database management system. NoSQL databases are designed for storing large amounts of unstructured or semi-structured data without requiring a predefined schema structure as relational databases do.
Read more: From migration to optimization: Streamlining your database development workflow
Data management strategy: Success stories
Designing a data strategy can be a top priority for leading industry players. Enterprises with effective data management practices are better equipped to make informed decisions based on the existing data. Let’s highlight two real-world examples of how a focus on data management has led to successful business outcomes. We will explore how the N-iX team assisted a Fortune 500 industrial supply company in developing a Cloud-agnostic strategy and building a unified AWS data platform. Also, we’ll explore how the Data experts at N-iX helped cleverbridge to design an effective data strategy.
Scalable Big Data analytics platform for a leading industrial supply company
Our client is a leading industrial supply company listed in the Fortune 500 that offers over 1.6 million high-quality products in categories such as safety, material handling, and metalworking. The company also provides inventory management and technical support to over 3 million customers in North America. The client needed to modernize its existing on-premise data solution by moving it to the Cloud to increase scalability and reliability, as well as reduce costs.
The N-iX data team assisted the client in developing a Cloud-agnostic strategy for migration to the Cloud and implemented a unified data platform on AWS. The main challenge was to build an effective data management strategy. As the project is still ongoing, the N-iX team has worked on the data pipeline unification, integrating more than 100 various data sources into a unified data platform.
By designing a data management governance model, the N-iX team helped the client to achieve the following:
- Reduced infrastructure expenses through Cloud migration;
- Boosted data management efficiency with a centralized data platform that keeps all data in one location;
- Enabled predictive analytics, such as the ability for the finance department to forecast inventory expenses;
- Adopted Cloud-agnostic technology to avoid vendor lock-in and take advantage of various Cloud providers;
- Lowered software development overhead by substituting costly on-site contractors with alternative solutions.
Read more: Scaling Big Data analytics platform for a leading industrial supply company
Designing a comprehensive data analytics solution for cleverbridge
Germany-based cleverbridge offers comprehensive e-commerce and subscription management solutions to monetize digital goods, online services, and SaaS across industries. With a Cloud-based ecommerce platform, they simplify recurring billing, enhance the customer experience, and provide global compliance and payment capabilities. The client needed to upgrade their solution to reach more customers and improve the user experience.
The N-iX Data unit assisted the client in migrating their desktop solution to the Web, designing a new UX, and creating clear BI reports to increase customer value. In particular, we helped the client to modernize the app by migrating it from desktop to web, with backend integration. Regarding the data strategy, the N-iX team designed a data strategy with a clear implementation plan. Moreover, the obtained implementation plan was used to build a modern data platform on AWS and establish effective data governance for this project.
The client has gained many benefits from the cooperation between N-iX and cleverbridge. More precisely, the client:
- Strengthened their value offering through a comprehensive reporting solution, attracting a wider customer base;
- Elevated information security through implementing effective data governance in the data platform;
- Improved decision-making capabilities by utilizing the solution for in-depth analysis of client performance.
Read more: Designing a comprehensive data analytics solution for cleverbridge
Wrap-up
A data strategy can be essential for companies and enterprises looking to get the most out of their data-related operations. By understanding the different types of data management strategies, best practices for implementation, and how to measure success, organizations can ensure that they can manage their data to maximize its value effectively. Thus, the data management governance model should be considered when making decisions about IT outsourcing services.
Do you need help with developing a data strategy? N-iX is here to provide an encompassing range of outsourcing services to ensure your business runs smoothly. Our Data specialists can assist in creating, implementing, and managing the right data strategy for your organization's success. Build a strong foundation for efficient operations through optimized data solutions.
Why choose N-iX as your data management strategy partner?
- N-iX is a global software development service company boasting 21+ years of experience in custom software development, technology consulting, and digital transformation;
- The company’s data unit consists of 200+ experts who delivered 30+ data projects;
- With strong Cloud expertise, N-iX partners with top Cloud providers, such as Microsoft Gold Partner, Amazon Consulting Partner, and Google Cloud Partner;
- N-iX operates under various industry-standard regulatory systems for infrastructure, information security, and quality management, including ISO27001, ISO9001, ISO 27001:2013, PCI/DSS, and GDPR.
- N-iX has a global presence, with offices and development centers in Europe, the US, and LATAM.
