What Is An Enterprise Data Strategy? - 7 Essential Principles

Introduction to Enterprise Data Strategy

Data regarding a business's operations, transactions, prices, and customer behavior are created and compiled each day. According to a study from Cisco, the average enterprise manages 347.56 terabytes of data. These metrics are key to understanding how well a company is performing and to making informed business decisions. To effectively leverage these insights, organizations need to establish an enterprise data strategy.

What Is An Enterprise Data Strategy?

what is an enterprise data strategy 7 essential principles

Enterprise data strategies are visions for how a business will use and manage data. It also outlines the various tools and solutions that they will use, such as big data and business intelligence. A data strategy will vary depending on the company's industry, clients, and goal. However, these plans generally must be practical and be updated frequently.

Most commonly, data strategies focus on financial, operational, or customer performance. For example, enterprises will create a plan for supply chain digitization or improve marketing. Data can also be used to enhance customers' shopping experiences or employee onboarding. The following are fundamental reasons why an enterprise data strategy is important.

  • Delineate a Business's Capabilities - A clear strategy will showcase what a business can do or employ. For example, it may demonstrate how teams acquire and manage data quality, as well as how a business manages its structure and regulatory compliance.
  • Define Long Term Plans - An enterprise data strategy will help companies plan the roadmap for their data management system.
  • Establish Expectations - Organizations can establish timeframes, costs, and deadlines for their strategies.
  • Identify Changes - Oftentimes, businesses will need to innovate their systems and processes to effectively utilize data. With a strategy, executives can determine what they need to change or how they can replace existing systems.

7 Key Enterprise Data Strategy Principles

An enterprise data strategy gives direction to business executives and helps teams progress towards their goals. The following are key best practices that enterprises should incorporate into their data plans.

1. Eliminate Silos by Integrating Data

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By reducing data silos in the workplace, different departments and teams can access information easily. It also enables employees to collaborate and work together towards common objectives. To prevent isolated data, many organizations should establish an effective data integration process. This will help make sure data is consolidated into one accessible location or system.

2. Simplify Data Gathering and Sharing

In order for a data strategy to be successful, business teams must efficiently gather data. This requires a process to ensure collected data is high quality, structured, and accurate. Doing so will help businesses easily extract insight and allow them to store the data in an organized manner. Additionally, the data strategy needs to outline how data will be named and accessed. This will make the information shareable among different authorized employees.

3. Define Objectives for Data Management

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By setting long-term objectives for how to manage data, business teams can work in the right direction. It is also important to define short-term goals so that executives can gauge their progress and make quick adjustments, if necessary. For example, a future goal for a business is to use marketing data to boost sales. A short-term object can be to improve data sharing by utilizing the cloud.

4. Improve Data Visibility

The enterprise data strategy should have processes for making data visible and digestible for different business teams. This may involve maintaining data in an accessible storage device, like the cloud. Improving data visibility will ensure businesses can quickly find the information they need without having to make duplicate copies.

5. Derive Actionable Insights From Data

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When raw data is collected, it is unstructured in its current format. This means businesses must first translate it into a legible form to effectively extract information. Data transformations are especially important when an enterprise uses multiple business systems. This is because data from disjointed software will arrive in different formats and types. Therefore, teams must correct and reformat the data into a consistent structure to properly analyze its insights.

6. Describe All Processes for Data Management

The data strategy should include regulations and standards on how to properly manage data. Also known as data governance, these rules will steer individuals and teams into accurately using, sharing, and accessing data. Having established standards will also eliminate any guesswork regarding data management. Additionally, it ensures that processes for maintaining insights are consistent throughout different teams.

7. Have Guidelines for Data Analytics

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There should be guidelines on how business teams can perform data analytics. The guidelines can also include goals on how users can interpret and apply data into their individual strategies. This will guarantee that teams are effectively leveraging all insights from the extracted data.

Key Takeaways - Enterprise Data Strategy

  • Businesses of all sizes and industries are generating massive volumes of data each day. These metrics include valuable information about performance, operations, customer experiences, and pricing.
  • Organizations can effectively capitalize on their data usage by establishing an enterprise data strategy.
  • An enterprise data strategy is a plan on how a business will use and manage data. It involves the use of various tools and solutions, such as big data and business intelligence.
  • Data strategies will differ for each company because it depends on their goals, industry, and consumer bases.
  • There are 7 pillars for a data strategy, which include eliminating silos, streamlining data sharing, and outlining rules and guidelines for data management.