The Ultimate Guide to Data Management: Comprehensive Overview and Best Practices

Each day, 2.5 quintillion bytes of data are generated. And this growth shows no signs of slowing down.

While the numbers themselves may be difficult to grasp, what matters most is how well companies are able to tap into this exponential growth in data and use it to their advantage. Data management helps companies improve their decision-making, enhance customer experience, facilitate collaboration, and transform their data from mere figures and facts into valuable business assets.

However, managing data effectively is no easy feat. It comes with careful planning, robust systems, and a deep understanding of its complexity. Moreover, effective data management is an ongoing process. Businesses evolve, new technologies emerge, and so do data management strategies.

In this article, we’ll cover the why, how, and what of data management, preparing you for what could be one of the most strategically significant topics on your agenda going forward.

Data is far from a static element confined to spreadsheets. It’s an asset in constant motion, and understanding its lifecycle is essential to being able to manage and extract value from it effectively.

In its raw form, data holds no intrinsic value. It exists as a collection of unprocessed numbers, text, or symbols lacking any inherent meaning. Only when enough data points are gathered to measure a specific process does it transform into information. But information also has limited value because it's not actionable. In other words, although the information has been collected and stored, it brings very little utility since it can't be used to draw conclusions or to take further action. 

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Only after analyzing and understanding the particular situation that the aggregated data describes, can we draw insights that lead to better decision-making.

Once the data has been analyzed, generating insights, the next stage of data lifecycle management comes into play: data validation, sharing, and usage. This stage ensures coordination and communication between all relevant parties. Whether through reports, dashboards, or presentations, these insights are shared with relevant audiences and may even serve as input for further decision-making processes. At this point, the data may have fulfilled its purpose and can be archived, destroyed, or reused, restarting the cycle.

Data management is the process of ingesting, storing, organizing and maintaining the data created and collected by an organization. It begins with defining a data strategy that sets the vision for how the company uses its data to improve business outcomes. The strategy defines the roadmap for implementing the pillars of data management. At the very low level, it defines company-wide governance elements like organizational roles that guarantee ownership, access policies, and overall data quality processes. Other important pillars to complement governance are data architecture, data quality, and metadata management, which in turn are the basis for more complex features of the strategy like business intelligence, analytics, or artificial intelligence.

The key to effective data management is to develop a tailored data management strategy and establish corresponding processes that align with the organization's unique characteristics and operational objectives. It involves adjusting for various factors such as business needs, company vision, scalability requirements, and resource availability. 

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As a result, data management extends beyond the IT department and becomes a responsibility shared by all aspects of the business. Gone are the days when data management was solely the domain of a secluded IT team managing databases in isolation. Today, companies generate vast volumes of data that permeate across different business functions. And this shift needs active involvement and participation from various stakeholders throughout the organization.

The Data Management Association (DAMA) defines several large knowledge areas included in the end-to-end data management strategy. While these areas deserve in-depth attention, we will only give a brief overview here.

• Data Architecture

Data architecture is a starting point for any data management model. It defines the blueprint for managing data assets by aligning with organizational strategy to establish data requirements and design solutions to meet those requirements.

• Data Governance and Master Data Management

Data governance establishes guidelines and protocols to guarantee the consistency and efficient utilization of data across an organization. It plays a crucial role in preventing errors, mitigating the potential misuse of sensitive data, and ensuring compliance with data-related regulations like the EU's GDPR.

Data governance includes master data management. Master data is critical enterprise data related to customers, products, staff, and technologies. Master data management ensures its consistent use, fixing any duplicated, incomplete, or controversial data.

• Data Modeling & Design

Data modeling and data design are concerned with the organization of data, such as the layout and design of the databases and the programming languages used for managing data.

• Data Security

Data security covers all practices, processes, and technologies preventing unauthorized access to information assets and inappropriate use of them. A relevant data security plan must consider gathering only the required data, keeping it safe, and erasing information once it is no longer needed. When data is about to undergo either archiving or destruction, it’s necessary to retain data intelligently and avoid redundant archived copies.

• Data Integration & Interoperability

Companies acquire data from multiple sources — manual entries, IoT devices, payment processors, CRMs, CMSs, eCommerce platforms, web and mobile analytics tools. Scattered across different storages in various formats, data values don’t talk to each other.

We need data integration and interoperability to achieve connectivity between systems and consolidate content from disparate places into a single dataset to use for analysis and reporting.

• Data Quality

Data quality management (DQM) aims at ensuring that data aligns with specific business requirements. It utilizes various technologies and methodologies to achieve this goal. DQM has a continuous and proactive nature. By ongoing observation, analysis, and improvement of information, DQM maintains the health state of data instead of fixing the consequences of the flawed data.

It might be tempting to focus on all these components. But rather than attempting to tackle all areas simultaneously, prioritizing key areas of improvement ensures a targeted and effective approach aligned with the company's goals and current data maturity level. For example, organizations may prioritize data security after a cyber attack, metadata management to enhance operational efficiency, or data quality to address inaccuracies in production reports.

The success of AI/ML models depends on high-quality, well-structured data. So assessing your data readiness is a key point in identifying the right AI use cases. 

An effective data strategy clarifies what data the organization requires, how to collect it, manage and how to use it. Developing a successful data management strategy requires many tactical steps. And here are some points to consider for any organization starting from scratch.

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1. Appointing Data Management Roles

The process of data management covers a wide range of tasks, responsibilities, and expertise. In smaller organizations with fewer resources, these responsibilities may fall upon individual employees. However, in general, data management professionals comprise various roles such as data architects, data modelers, database administrators, database developers, data quality analysts and engineers, data governance managers, and data engineers. They collaborate with analytics teams to build data pipelines and ensure data is well-prepared for analysis.

2. Defining Data Processes

With your business objective at top of mind, it’s time to think through the processes in place for collecting, preparing, storing, and distributing the data.

You may find it useful to answer the following questions:

• Data Sourcing - How and when will you collect the required data? This involves identifying the appropriate data sources to use or acquire.

• Data Enablement - Where and how will your data be stored and integrated? How will you ensure data quality, integrity, accessibility, and security, and what measures will you take to continuously improve them?

• Data Use - Who will use the data and for what specific purposes? This includes considering various applications such as predictive analytics, prescriptive analytics, operational analysis, etc.

3. Finding the Right People and Technology

If the data strategy involves leveraging a cloud-based data platform, Python or Spark for advanced analytics, having a team member proficient in these tools can drive the need for additional team members and teams with similar expertise.

Adopting new technologies and tools almost always affects hiring. Finding individuals with the skills and knowledge required to work with a specific technology quickly becomes a priority. But balancing specialization and flexibility is also something to not lose sight of.

On one hand, you may want to build a team around your services and solutions, deepening their expertise in a specific area. On the other hand, you’re looking to remain agile and adaptable to keep pace with evolving technologies. The right balance ensures your team can handle a diverse range of data management tasks without sacrificing competitiveness.

4. Establishing Data Governance

With the increased use of data and the growth of your data infrastructure, the manner in which the organization handles this data matters more than ever. Having a set of policies, procedures, and guidelines that maintain the accuracy of your data, protect sensitive information, and assign responsibilities and accountability for data management will ensure you’re effectively managing and leveraging your data assets.

Proper data management allows organizations to improve decision-making, increase efficiency, and reduce costs. However, navigating different options and approaches to data management initiatives can be challenging. Here are a few best practices to help you get started.

1. Foster Data Quality

Fostering data quality involves implementing data validation processes to identify errors, integrating data from various sources for a complete view, standardizing formats for consistency, implementing data governance, profiling data for improvement, cleansing data to remove duplicates and inconsistencies, maintaining comprehensive documentation, and monitoring data quality. By maintaining high data quality standards, you can trust the integrity of your data, enhance decision-making capabilities, and improve operational efficiency.

2. Encourage Data Accessibility and Collaboration 

With so many different teams and departments leveraging data, establishing communication channels, whether live, virtual, or asynchronously, ensures that all teams are on the same page when it comes to data management. Set clear roles and expectations, use jargon-free messages, and communicate on a regular basis. 

3. Prioritize Data Protection and Security 

Data protection and security should be at the forefront of your data management practices. Implementing robust security measures, such as encryption, access controls, and regular security audits, safeguards your data from unauthorized access, breaches, and data loss. By prioritizing data protection, you can instill trust in your customers and stakeholders while complying with data privacy regulations.

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Companies can benefit from starting by implementing foundational data capabilities such as data quality, governance, or metadata management, as these are the basis of advanced data analytics, big data, and artificial intelligence. These foundational data management practices rely on each other and are only as good as the weakest link. But narrowing and prioritizing the elements addressed allows you to incrementally mature your data management practices.