Building a Resilient Data Management Strategy: Key Steps and Best Practices

Oleksandr Ieremchuk
Daria Iaskova

Predicting market trends, pinpointing emerging business opportunities, and building a reliable foundation for products’ and services’ personalization—these are just a few game-changing benefits data can bring to organizations. 

While most companies today fully realize the transformative impact of an efficient data management strategy, so far only 27% of them have managed to get measurable results from big data and analytics. Are they doing anything wrong? Possibly but not likely.

The objective reasons why investments in data analytics are not paying off are the speed and turbulence that characterize the modern world. Growing data volumes, changing industry regulations, and the competitive business landscape make both small businesses and mature global corporations put resilience on top of their data management strategy roadmap. 

What does this mean in practice and how to get started? We provide an expert view below in the article.

What is a data management strategy?

As a rule, speaking of a data management strategy, practitioners refer to well-planned consecutive steps aimed at organizing, storing, processing, and protecting data throughout its lifecycle to ensure its integrity, availability, and usability.


In other words, a data management strategy allows businesses to standardize procedures for data collection, storage, and collaboration. This, in turn, helps companies build the entire data management process around their specific business goals, enhancing decision-making and operational efficiency.

Why companies need a data management strategy in 2024

Over the past 10-20 years, the overall data concept has undergone a dramatic evolution. From 2 zettabytes in 2010 to the expected 181 zettabytes in 2025—the total amount of data created, captured, copied, and consumed is steadily growing at a CAGR of impressive 23% and shows no signs of stopping.

Not only do data volumes increase at an unprecedented speed—it’s their nature and complexity that are changing, triggering new policies and regulations and bringing in more rules related to its collection, processing, and management.

There is more to come in the next 5-10 years. With AI and machine learning becoming accessible and affordable to global customers, businesses are on their way to fully mastering them in the near future. When this happens, big data is likely to experience a new powerful surge. At that point, it’s either make or break—as hectic data management will reduce companies’ success to zero.

With the data landscape changing a lot, it’s critical to stay knowledgeable about the challenges and opportunities that appear as we navigate the data-driven age. 

Speaking about emerging trends, it’s natural to refer to the enterprise market—the environment where tendencies are born and developed. At the same time, the sign that a trend has gained prominence is its adoption within small and medium-sized businesses (SMBs). So, below we are listing some market tendencies that already apply to both enterprise and SMBs’ challenges with data management.

Data management made accessible 

Originally, data management was considered a costly investment that only large companies could afford. However, with time, data collection, storage, and processing have become more accessible—thanks to technology.

Today, with cloud hosting and AI-based tools for data processing available as services to millions of global customers, the price of efficient data management has decreased, making it affordable for businesses of different sizes. What is more, cloud computing and AI are, year over year, considered the most worthy investments when it comes to data and analytics.

All-in: surviving the competition

With data management becoming globally accessible, practically anyone can now use insights extracted from data to improve decision-making, tailor products and services to better match consumer needs, predict risks, or foresee future demand. Moreover, the value of insights grows, as data processing mechanisms become exclusively precise and sophisticated.

In recent years, this natural technology evolution has been putting the stakes sky-high, making efficient data management an undeniable imperative. A recent study conducted by Harvard Business Review revealed that companies investing in data and analytics outperformed their peers in operational efficiency (81% vs. 58%), revenues (77% vs. 61%), customer loyalty and retention (77% vs. 45%), employee satisfaction (68% vs. 39%), and IT cost predictability (59% vs. 44%).


Get measurable value from investments in data and analytics

Data culture is crucial 

The growth in data volumes, coupled with the excitement surrounding cutting-edge technology and the diversity of laws and regulations governing data processing and management, have collectively elevated data and analytics to a new level, yet made them hectic and complicated.

For this reason, over the past decade hiring a CDO (Chief Data Officer) has become a must-have of an average enterprise data management strategy. And if until recently assigning a CDO has been common only for large corporations, as of 2022, 27% of all global firms reported having a dedicated person in place.

The fact that companies are increasingly building in-house data departments and hiring Chief Data Officers clearly shows a major shift in corporate minds. This shift is recognizing the importance of a well-defined data management strategy, as the only way to keep moving forward. It’s no longer about approaching data analytics ad-hoc—on the contrary, today we see companies embracing an all-encompassing data culture aimed to help businesses thrive in the data-driven age.

KYD: Know Your Data

With the volume of all data generated growing, the majority of businesses find themselves trapped at the very beginning of a data management journey. Even though they usually have some data collected, it’s often the source of this data that remains unknown or the lack of proper access controls that prevent organizations from making actionable use of their data. Just like that, 17% of businesses simply don’t know how many data sources they use.

Considering this, in 2024, the Know Your Data (KYD) concept continues to be crucial for building the foundation of data implementation. Focusing on identifying and cataloging data assets, it helps organizations conduct an end-to-end assessment of the information they store and strategically approach data management.

Privacy and security on top

Data, particularly sensitive information like health records, financial data, and personally identifiable information (PII), is a double-edged sword for businesses. While it offers valuable insights and opportunities, it also poses significant risks if mishandled or exposed to unauthorized parties. 

In 2023, the cost of a data breach globally reached $4.45 million. Hacking, malware attacks, phishing scams, or insider threats make businesses take data protection seriously. At the same time, complying with related global laws and industry regulations, businessesbecome limited in their ability to collect, assess, retrieve, and manage sensitive information

Destination: resilience

Given the trends and challenges mentioned earlier, data management becomes increasingly complex, demanding organizations to foster heightened agility and implement proactive risk mitigation strategies to achieve resilience and become future-proof.

So, simply put, resilience is crucial in data management for several reasons.

  • As data volumes continue to grow exponentially, the complexity of managing and securing data also increases. Resilience in data management allows organizations to adapt to changing data environments and effectively handle the scale and diversity of data
  • Data systems are vulnerable to various threats such as cyberattacks, hardware failures, and natural disasters. A resilient approach to data management ensures thateven in the face of these challenges, data can be protected, recovered, and maintained with minimal disruption to business operations
  • Compliance requirements and regulations around data protection are becoming more stringent. A resilient data management strategy ensures that organizations can meet these regulatory requirements and safeguard sensitive information, thus mitigating legal and reputational risks

Overall, fostering resilience allows organizations to maintain data integrity, availability, and confidentiality, thereby safeguarding their business continuity and reputation.

Future-proof your business and maintain a competitive edge

with resilient data management

Understanding a resilient data management strategy 

Now that we are done with understanding why resilience is crucial for data governance, let’s proceed to the practical part of our article—the development and implementation of a dedicated data management strategy that helps companies support the evolution of business needs and growing data throughout time.

In practice, a resilient data management strategy includes several key components and approaches.

  • Redundant data storage 

Implementing multiple copies of critical data across different locations or systems to ensure availability in case of failures or disasters.

  • Robust Backup and Recovery 

Regularly backing up data and maintaining comprehensive recovery processes to restore data quickly in the event of data loss or corruption.

  • Proactive monitoring and detection 

Utilizing monitoring tools and techniques to continuously monitor data systems for anomalies, threats, or potential breaches, allowing for timely detection and response.

  • Data encryption and access controls

Employing encryption methods to protect data both in transit and at rest, as well as implementing access controls to restrict unauthorized access to sensitive data.

  • Compliance and governance

Establishing clear policies, procedures, and controls to ensure compliance with relevant regulations and industry standards, as well as maintaining proper documentation and audit trails.

  • Disaster recovery planning 

Developing comprehensive disaster recovery plans that outline procedures for responding to various types of disruptions, including natural disasters, cyberattacks, and system failures.

  • Regular testing and updates 

Conducting regular testing of data management systems and procedures to identify weaknesses or vulnerabilities, as well as staying current with updates and patches to mitigate known risks.

By incorporating these practices into their asset management data strategy, organizations can build resilience into their systems and processes, enabling them to maintain data integrity, availability, and security needed to go through continuously evolving threats and challenges.

Key steps it takes to build a data management strategy roadmap

Developing a hands-on data management roadmap, it’s important to focus on the long-term success this strategy can bring, yet start from the basics by thoroughly auditing existing processes and assets.

1. Assessment

Evaluate the current state of data within the organization, including data sources, quality, governance practices, and infrastructure.

Best practices
  • Analyzing the state of data, the first thing we focus on is understanding how the existing data ecosystem looks and works. This includes creating data models, structured representations of an organization’s data assets, and illustrating how data is organized, stored, and accessed.
  • While assessing all the data that a company stores, it’s crucial to categorize it into master data (entities such as customers, products, and suppliers) and statistical data (derived facts and figures that can be used for analysis and reporting purposes)—this enables organizations to optimize operational efficiency, decision-making, and business intelligence.
  • The results of this stage can be a data flow diagram (DFD), an illustration of how data moves between processes, data stores, and external entities or the data landscape, an overview of an organization's data environment, including databases, applications, data sources, data flows, and data storage mechanisms.
Working with enterprises, a common issue we’ve been facing from time to time is the lack of accountability for in-house data assets, as well as related policies and flows applied to data governance. For such cases, we often turn to process mining, a widely adopted technology that helps us understand how things are actually done in an organization by analyzing event logs and revealing insights into data processes and operations.
What is process mining and how to master it for optimal ROI?
2. Goal setting and planning

This stage involves defining clear objectives and outlining a strategic roadmap for how data will be collected, stored, processed, and utilized to meet organizational goals.

Best practices

Traditionally, there are two defined approaches to data collection and management:

  • A technological approach refers to organizations planning for the infrastructure and technology investments necessary to support their data management initiatives. This includes setting goals for implementing data storage solutions such as data lakes and data warehouses, integrating data from various sources into a centralized repository, and deploying analytics tools for extracting insights.
  • A cultural approach involves ensuring that everyone, from leadership to frontline employees, understands the value of data for decision-making and actively participates in data management practices. This approach might adopt concepts like data mesh, which encourages decentralized data ownership and governance, fostering a collaborative approach to data management across the organization.
3. Data governance and management 

Build and implement comprehensive data governance practices to ensure the integrity, security, quality, and compliance of data and develop the ability to effectively manage and leverage their data assets to drive business value.

Best practices

Common approaches applied at this stage include:

  • Master data management (MDM), which deals with establishing processes and technologies to ensure consistency and accuracy of core business data across the organization, such as customer, product, and employee information.
  • Building a data quality management strategy including the implementation of measures to monitor, cleanse, and improve the quality of data to ensure it is accurate, complete, and consistent.
  • Implementing policies, procedures, and technologies to protect data from unauthorized access, breaches, and cyber threats.
  • Ensuring that data management practices align with regulatory requirements and industry standards, such as GDPR, HIPAA, or CCPA, to protect sensitive information and maintain legal compliance.
  • Managing metadata, which provides context and description of data assets, to facilitate data discovery, lineage, and understanding across the organization.
  • Data lifecycle management that involves implementing processes to systematically manage data, including creation, usage, storage, archival, and deletion, optimizing storage costs and ensuring data availability.
Efficient data governance explained in 7 steps
4. Data analytics and insights

Develop capabilities for analyzing data to derive actionable insights, support decision-making, and drive business value.

Best practices

At this stage, there are several components that usually help businesses get insights from data to support decision-making and drive business value:

  • ​​Data analysis techniques such as descriptive analytics, diagnostic analytics, predictive analytics, and prescriptive analytics to understand patterns, trends, and relationships in the data.
  • Visual representations of data through charts, graphs, dashboards, and other visualizations to communicate insights effectively and facilitate understanding among stakeholders.
  • Leveraging machine learning algorithms and advanced analytics to uncover hidden patterns, make predictions, and optimize processes based on historical data and real-time information.
  • Utilizing business intelligence tools and platforms to explore data, generate reports, and gain insights into key performance indicators (KPIs) and metrics relevant to business objectives.
5. Monitoring and continuous improvement

Establish mechanisms for monitoring the effectiveness of the data management strategy, measuring progress against defined goals, and implementing continuous improvements to enhance data management practices over time.

Best practices

Getting started with data management in practice, a common mistake businesses tend to make is primarily focusing on the data quality attributes such as:

  • consistency
  • accuracy
  • completeness
  • usability
  • uniqueness and more 

As a result, they tend to overlook another important set of characteristics, which are software quality attributes including:

  • functionality suitability
  • performance efficiency
  • compatibility
  • usability
  • reliability
  • security
  • maintainability

We recommend keeping in focus both. This will allow companies to avoid costly errors during the development phase and ensure efficient and streamlined solution maintenance in the future.

How we approach data management at Trinetix 

At Trinetix, we’ve got years of experience helping enterprises and challenger businesses embrace a resilient data culture to gain a strong competitive advantage as they navigate the digital age. 

While the steps companies take to develop a data management strategy usually remain the same, the scale of business and the nature of challenges they face dealing with data and analytics often make improved team cooperation as well as technology and strategic maturity paramount.

Data consolidation, real-time visibility, and innovation?
Triple YES.

Working with global leaders including Fortune 500 companies, we developed a value-added approach that applies to solving both enterprise and SMBs’ challenges with data management. Below, we are sharing the highlights of this approach to give you an overview of what it takes to work with a strategic partner like Trinetix.

  • Applying industry best practices to achieve maximum efficiency

We begin by defining and implementing industry best practices and methodologies to assist organizations in identifying bottlenecks in their current data flows. From there, we guide them in taking actionable steps to establish a more efficient data management approach tailored to their specific business context.

  • Putting transparency on top 

Building a data management strategy requires clearly defining the expectations and responsibilities. As we are committed to guiding clients through complexities in their data assets, clearly communicating any challenges and limitations that might appear as we move toward data excellence. 

  • Bringing utmost tech literacy to create cost-efficient solutions 

The tech maturity and versatile expertise we’ve acquired while working with world-renowned enterprises allows us to remain agile when it comes to choosing the most cost-efficient data storage management solution— on-prem, cloud, or hybrid.  

  • Understanding privacy and mitigating reputational risks

Weacknowledge the value of sensitive data and treat it with utmost care so that you could focus on achieving your business goals and preserving industry leadership. 

  • Focusing on long-term value while providing fast results

Approaching data management, we focus on creating timeless solutions that help businesses prepare for global uncertainties. At the same time, we offer an extensive toolset that allows clients to get the first measurable results from the very first iterations.

Creating a resilient data management strategy and putting it on track requires time and effort. Partnering with a reliable tech partner makes the whole process easier and allows you to keep your business objectives first, while a dedicated team handles the process for you. If you feel like getting tailored advisory on managing your data from industry practitioners, let’s chat and discuss your next steps.

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