Does your organization rely on data for decisions and actions? If so, you need Strategic Data Management (SDM) and an SDM plan.
SDM is a systematic process to collect, analyze, store, and use data to achieve business goals. A solid SDM plan aligns data activities with business objectives.
An effective SDM plan establishes accountability, policies, and procedures for managing data as an asset. It identifies, assesses, and mitigates data security, privacy, quality, and lifecycle risks through data and risk management. A comprehensive risk management plan is critical.
The SDM plan designs optimal data structure and flow, from databases to lakes, to meet business needs. It enables users to effectively access, interpret, and communicate insights from data through training and education.
You can build a robust data strategy to drive value with these core elements. Revisit and refine the plan as business needs change. SDM requires ongoing governance and commitment.
What Is Strategic Data Management?
Organizations need data to guide business strategies, spur innovation, and improve customer experiences. They also need data to understand the market and customers, make better business decisions, and gain a competitive advantage.
Considering that the correct quantity and type of data can make or break a business, firms must ensure that the data they collect is insightful, usable, and actionable. This is where a data management strategy enters the picture.
A data management strategy is an enterprise-wide plan, roadmap, or framework for collecting, organizing, storing, and using data. A robust strategy always considers the data volume, type, and location and considers its users’ data-related needs.
Strategic data management ensures that users can effectively access and use the data they need whenever they need it. SDM also helps organizations to:
- Reduce the costs of data storage, archival, and retrieval
- Establish a data governance plan
- Implement appropriate data analytics tools
- Achieve regulatory compliance with data privacy requirements
A well-planned data strategy also promotes and strengthens the organization’s data management culture.
Why Do I Need a Data Management or Governance Framework?
A data management or governance framework provides structure, policies, and procedures for handling data within an organization. This helps ensure high data quality, security, and accessibility to support business objectives. Here are some key reasons you need one:
- Promotes accountability and ownership of data assets. With clear roles and responsibilities, there is no confusion over who “owns” specific datasets.
- Supports regulatory compliance, such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA). Frameworks align data management processes with legal and industry regulations around data privacy, financial reporting, etc.
- Increases trust in data. Standard data collection, storage, and use process builds confidence that data is accurate and reliable.
- Improves data security. Policies for access controls, encryption, backups, etc., help safeguard data against breaches and loss.
- Enables data sharing. Common data standards and metadata definitions allow data to be combined across silos for analysis.
How Are Regulatory Compliance and Data Governance Frameworks Related?
While regulatory compliance and data governance have unique aims, they heavily influence one another:
- Compliance drives governance needs. Many regulations like GDPR mandate specific data management controls that data governance frameworks must address.
- Governance supports compliance. Organizations leverage data governance programs, metadata management, and data stewards to adhere to complex regulatory requirements.
- Non-compliance risks increase with poor governance. Weak or non-existent data management can lead to breaches of regulations around privacy, financial data, etc.
- Strong governance aids compliance audits. Documented policies, retention schedules, and metadata help demonstrate adherence during audits and inspections.
- Both require executive buy-in. Senior leaders must actively sponsor and participate in compliance and data governance initiatives.
- Ongoing alignment is required. As regulations and business needs evolve, frameworks must be updated to maintain compliance.
The Benefits of Strategic Data Management
Strategic Data Management (SDM) ensures that data is organized and accessible to the right people. It also improves data quality, maintains data integrity, makes it easier to mine the data for insights, and allows employees to use these insights for enterprise decisions and actions.
Organizations with robust SDM also gain other advantages, too:
- A deeper understanding of their operational landscape. They can better understand the market and their customer base.
- More excellent responsiveness and agility. They can respond faster to changes to increase competitiveness, revenues, and profits.
- Better adjustment to disruptions. They can adjust to disruptions (say, to the supply chain) with minimal harm to operations or revenues.
- Greater visibility. They get more visibility into internal systems and processes and can identify gaps and blind spots that must be fixed on priority.
- Data silos are removed. Data becomes available and shareable at every level so authorized users can access required data on-demand and with minimal friction.
A comprehensive data management strategy also eliminates many common challenges that hinder organizations from leveraging data, such as:
- Missing, duplicate, or non-standardized data that prevents users from gleaning insights for decision-making
- Inconsistently documented data sources that affect communication and collaboration
- Lack of data shareability resulting in effort duplication and increased costs
- Difficulties achieving business objectives from data activities
SDM is vital to achieving business objectives with descriptive, prescriptive, predictive, or customer data.
The Key Pillars of a Strategic Data Management Plan
Each universally accepted SDM plan will only work for some organizations because every company collects and uses different data for different purposes. Certain elements, however, are expected in every SDM plan. If your organization implements such a plan, ensure it contains the following.
Business Goals and Objectives
Every firm has its data-related objectives. Some examples include:
- Improve customers’ purchase journey
- Deliver personalized customer experiences
- Improve marketing and branding strategies
- Draw insights about the market
- Inform research and development
- Improve processes and workflows
Identifying the objective will ensure you collect and analyze the correct data types. Without a purpose, you will waste time, resources, and money on fussing over the wrong data. Keep your objectives in mind to determine what data to collect, from which sources, and what processes and tools are needed to manage data assets for your use cases.
It’s helpful to quantify your objectives with relevant metrics.
Metrics will tell you if the data is helpful to meet your objectives. Examples of some metrics include:
- Revenue increase
- Increase in conversions
- Reduced costs
- Increase in Average Order Value (AOV)
- Improvement in customer satisfaction
Once you know your overall objectives and have identified what enterprise data you need to meet those objectives, you will have to think about all these processes:
- Data collection
- Data preparation
- Data analysis
- Data distribution
- Data storage (including security)
- Data governance
For example, will you collect data directly from consumers or third-party data brokers? Will it come from your website, social media pages, paid ads, or other sources?
How will you analyze this data? With a machine learning-based tool? Or an advanced data analytics platform?
The data you collect may be a prime target for cyber attackers. How will you assure its integrity and prevent data breaches?
Think about all these aspects and implement processes to get everything right.
Since data is so valuable for your organization, you must protect it. Equally important, you must ensure that you (and all authorized stakeholders) have complete visibility into the data ecosystem. Only then will the data help the organization meet its objectives.
Defining the right data owners to increase visibility, accountability, and traceability. Think about these aspects:
- Who creates the data?
- Who owns the data?
- Who tracks, logs, and controls data access?
- Who responds if there is a data breach?
You can choose from various technology solutions to simplify data collection, analysis, storage, and more. The proper data management tools can also:
- Protect data against unauthorized access and tampering
- Improve data quality and transparency
- Increase data privacy
- Streamline analytics to convert raw data into useful, insightful information.
- Clean up data and remove duplicates for more accessible analysis
- Enable collaboration between multiple users
- Automate data backups, archival, and retrieval
Each tool or platform must satisfy all these requirements, so you will need multiple solutions depending on your data objectives, types, and sources. Make sure to select solutions that integrate well and form a unified data architecture that:
- Automates rote data management tasks
- Ensures that trusted data is always available when required
- Provides intuitive interfaces, controls, and dashboards for users
- Supports efficient data analytics
- Provides multi-user support for seamless collaboration
Many organizations struggle to use data effectively because their users aren’t aware of best practices or know how to best use the available tools. As part of your SDM plan, set aside time and resources to train users on using the appropriate data tool.
They should also understand the company’s data objectives and their role in helping to achieve those objectives. In addition, they should be made aware of:
- The organization’s data risk mitigation practices
- Applicable data privacy laws
- How to safely handle business-critical/sensitive data
- How to glean insights from the data for their role or business unit
Remember, data management is not a set-it-and-forget-it endeavor. After implementing your plan, assess its effectiveness regularly. If any metrics or processes are not working, investigate why. If required, replace them with other metrics or processes.
It’s also a good idea to:
- Implement a data risk management plan to assess the risk of data breaches
- Review data usage policies throughout the data lifecycle
- Maintain security compliance by watching for changes to the regulatory landscape
- Refine the training plan so users are always up-to-date on best practices
- Regularly back up data to prevent losses due to unforeseen event
Implementing a Data Management Plan at Your Organization
Executing an effective data management plan requires securing executive sponsorship, establishing data governance processes, focusing on data literacy, and choosing enabling technologies.
Leadership must commit resources to build a data-driven culture focused on data quality and security. Define critical roles like data stewards and a chief data officer to oversee the data governance strategy.
Training programs will improve data literacy at all levels so employees understand the value of data. Look to automation, AI, and tools like data catalogs to scale data management.
Measure progress with Key Performance Indicators (KPIs) for data completeness, security, and user adoption. Integrate frameworks like the Health Insurance Portability and Accountability Act (HIPAA) for regulated data.
With the right pillars, your data governance program can deliver business insights, bolster decision-making, and ensure regulatory compliance. But remember that effective data management requires persistence – view governance as an ongoing journey, not a one-time initiative.
Make ZenGRC Part of Your Data Management Strategy
Protect your data from intruders with Reciprocity’s Risk Observation, Assessment, and Remediation (ZenGRC) platform. This world-class cybersecurity risk management and strategic risk management platform will empower you to see and understand the risks to your business-critical data.
It will also reveal the contextual insights you need to mitigate cyber risks and protect your information assets from malicious adversaries. Understand the risk implications of your business processes, use expert-provided guidance to mature your risk management program, and automate manual workflows for a more efficient data management plan.
Get a demo to see how ZenGRC can strengthen your data security program.