Key Pillars of a Strategic Data Management Plan

Does your organization collect and process data? Do you rely on data to refine your business plan? Does data affect your strategic decisions and/or tactical actions?

If you answered “yes” to any one of these questions, you need strategic data management (SDM), as well as a SDM plan to manage your data, protect its integrity, and keep it out of the wrong hands.

Strategic data management is a systematic process to collect, analyze, store, and use data to achieve business goals. A robust SDM plan can help to assure that your data management activities work well together and are aligned with your ultimate objectives.

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 right quantity and type of data can make or break a business, firms must assure 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 takes into account its users’ data-related needs.

Strategic data management assures 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.

The Benefits of Strategic Data Management

Disciplined data management (that is, SDM) is vital to assure 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:

• Deeper understanding of their operational landscape. They can better understand the market and their customer base.
• Greater 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 their internal systems and processes, and can identify gaps and blind spots that need to 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

All in all, SDM is vital to achieve business objectives with data, whether that data is descriptive, prescriptive, predictive, or customer data.

The Key Components of a Strategic Data Management Plan

There is no single, universally accepted SDM plan that will work for all organizations, because every company collects and uses different data for different purposes. Certain elements, however, are common to every SDM plan. If your organization is implementing such a plan, make sure it contains the following.

1. Business Goals and Objectives

   Every firm has its own 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 assure that you collect and analyze the right types of data. Without an objective, you will end up wasting 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 collect data assets for your use cases.

   It’s useful to quantify your objectives with relevant metrics. Metrics will tell you if the data is useful 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

2. Data Processes

   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 from 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.

3. Data Owners

   Since data is so valuable for your organization, you must protect it. Equally important, you must ensure that you (and all authorized stakeholders) have full visibility into the data ecosystem. Only then will the data help the organization meet its objectives.

   To increase data visibility, accountability, and traceability, define the right data owners. 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?

4. Data Technology

   You can choose from a wide range of technology solutions to simplify data collection, analysis, storage, and more. The right 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 easier analysis
   • Enable collaboration between multiple users
   • Automate data backups, archival, and retrieval

   No single tool or platform can 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

5. User Training

   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 how to use 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 an unforeseen event

Make ZenGRC Part of Your Data Management Strategy

Protect your data and keep it safe 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.