How to Build a Data Strategy

For today’s businesses, data is an invaluable resource. With so much data being produced, it’s easy for organizations to struggle with maximizing that data’s potential. Gathering, sorting and using data becomes difficult without an effective strategy for processing and optimizing it in place. Developing a business data strategy can help businesses overcome these challenges and access the value of their data while efficiently using their resources.

• What Is a Data Strategy?
• Examples of Data Strategies
• Why are Data Strategies important?
• Essential Data Strategy Principles
• How to Build a Data Strategy (7 Steps)
• How to Support Your Data Strategy
• Creating a Data-Centric Culture
• How Lotame Can Help

 

What Is a Data Strategy?

A data strategy is a vision for how a company will collect, store, manage, share and use data. Creating a data strategy involves outlining how your organization will use data to maximize productivity and efficiency. With an effective data strategy, data becomes a resource that works to meet your organization’s goals.

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Every organization’s data strategy will look a bit different, but generally, the main elements of a data strategy will mean you should:

• Define how data will help the company meet business goals.
• Lay out how the company will complete the desired data activities to achieve its objectives.
• Describe the changes the organization needs to make to maximize the value of its data activities and outline plans for how the company will make those changes.
• Establish a timeline for completing the proposed activities, define milestones and priorities and describe a strategy for moving forward.
• Discuss the financial justification of the suggested data activities and how the company will benefit from them, and use insights to increase its profits and monetize its data.

A data strategy should be specific and actionable, but you should also be able to adjust it as circumstances change.

Examples of Data Strategies

You can see examples of effective data strategies across various industries.
A retail giant might implement a data strategy focusing on customer behavior analysis to personalize marketing efforts, enhance customer experiences and optimize inventory management. In health care, a data strategy could involve leveraging patient data to improve treatment outcomes, streamline operations and enhance research initiatives.

Financial institutions often deploy data strategies to improve risk management and tailor financial services. Technology companies might adopt a data strategy that harnesses user data for product innovation and enhances user satisfaction. Governments implement data strategies for policy-making, resource allocation and improving public services.

Each data strategy aligns with specific business goals, incorporates actionable plans for data activities, outlines organizational changes and more. These strategies are highly adaptable to changing circumstances, making them crucial for maximizing the value of data activities and propelling organizations toward increased profitability and sustainable growth.

Why Are Data Strategies Important?

So why is it essential for a business to have a data strategy, and what benefits does having one provide? Data strategy helps organizations:

• Unlock the power of data: Today, the value of data is well-understood. However, unlocking that value can be a challenge. Understanding what big data is and how to collect and use it can be difficult for some organizations. A data strategy provides employees with guidance, ensuring consistency in data collection, transformation, sharing and understanding.
• Handle increasing data volumes: With data creation predicted to hit 180 zettabytes by 2025, businesses are working to grapple with the increasing volume. While common knowledge practices were enough when data volumes were lower, the current situation requires a formalized data strategy. Relying on an an individual, informal approach in this data-centric era leads to inefficiencies, lost data and inaccurate results.

• Improve data management: Addressing data issues through point solutions might work in the short term, but it lacks efficiency. Many data-related challenges span department and project boundaries. Implementing a company-wide data strategy is crucial, ensuring cohesive data management across the organization and fostering alignment among departments.
• Use resources efficiently: If you don’t have a data strategy, different departments and individuals will solve data issues on their own. This setup leads to varied data formats and wasted resources. A data management strategy provides guidelines, ensuring data is in a usable format and creating seamless data exchange among departments.

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What Are Some Essential Data Strategy Principles?

Incorporating certain data strategy principles into your plans can help give them direction and move the company toward its goals. Some essential data strategy principles include the following.

1. Integrating Data and Eliminating Silos

Eliminating data silos makes data more accessible and fosters collaboration between different departments within an organization. One goal of your data strategy should be to integrate all your organization’s data into one system that people across the company can access and use. Doing so helps people get data more efficiently and can enable new data-driven projects that, otherwise, no one would have even thought of.

2. Streamlining Data Collection and Sharing

A data strategy can help you establish consistent processes for collecting and sharing data. Having established procedures means you can collect more data more efficiently and that the data you collect will likely be of a  higher quality. It also keeps your information consistent and well-organized, which makes it easier to use and helps you derive value from it.

One way to make your data more shareable and usable is to establish rules for naming element data and representing data value. You also need a way to reference and access metadata. Creating a glossary of business data terminology can help with this.

3. Setting Clear Goals and Objectives for Data Management and Use

Defining goals for data management and use is crucial to data strategy success. Your goals will drive your data strategy and activities and help you improve how you handle data. You’ll likely set both short-term and long-term goals. Short-term goals enable you to monitor your progress, while long-term objectives create a vision that guides and informs your activities. You can set goals related to each component of your data strategy, including data collection, storage, sharing and more.

4. Making Data More Visible and Accessible

Your data strategy should also outline steps for ensuring your information is accessible. How you store your data is essential for increasing accessibility. It’s crucial that you find a way to store data so people can quickly find and access the information they need without having to create copies of it themselves.

5. Making Data More Actionable and Easily Shared

Raw data has tremendous potential, but you won’t be able to take advantage of it until you convert it into a usable format. Data typically comes from many different sources, meaning it’s in various forms. To make data more actionable, you need to transform, correct and format it consistently, making it easy to use. Putting your data in a consistent, usable format will reduce the number of steps employees need to take before they can use it and make it easy to share within your company.

6. Establishing Clear Processes for Data Management

Data governance refers to setting rules and standards for how individuals and groups within an organization manage data. When such rules are in place, individual developers don’t have to make decisions about how to process, manipulate or share data every time they work on a project. Instead, they can use data governance rules to make these decisions. The goal of data governance is to make data easier to access, use and share.

7. Prioritizing Security and Compliance

Your data strategy should also prioritize data security and compliance. Your organization must follow the relevant regulations to avoid fines and legal issues. A robust data strategy incorporates measures to protect sensitive information, establish access controls and adhere to legal requirements.

Your data strategy should also employ encryption and authentication mechanisms to fortify data security. Finally, developing a plan that outlines what to do in the event of a data breach will help identify the issue, recover data and ensure the issue does not happen again.

8. Establishing Guidelines for Data Analysis and Application

Similarly, your data analytics strategy should define guidelines for how employees should analyze and use data. Data governance can address this, and your business goals should inform how you interpret and apply your data. Although learning new rules may slow developers down at first, the long-term benefits will make up for this initial learning curve.

How to Build a Data Strategy (7 Steps)

Below, you’ll find seven steps for creating a data strategy. Through every stage of developing your strategy, your company’s data goals and how you plan to use data, whether for targeting ads, content personalization or another purpose, will drive your process. The seven steps are:

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1. Create a Proposal and Earn Buy-In

The first step to building a data strategy is creating a proposal and using it to earn buy-in from across your organization. You’ll need buy-in from executives to get the approval and resources you need to implement the strategy. Obtaining buy-in from others at all levels of your organization is necessary for getting the participation you need for your implementation to be a success.

To get buy-in from company leadership, you’ll need to show how the strategy will benefit the organization. The economic logic included in your report will be crucial for this. It can also be helpful to show how competitors are using data to gain an advantage. Provide clear examples and use data to back up your claims.

You’ll also need to get department leads, technology partners and other employees who will use or help manage data onboard. Getting as much buy-in as possible is crucial, as the more people actively participate in your strategy, the more successful it will be. Giving people a sense of ownership over the process will help improve your results as well.

Keep in mind, that earning buy-in can take some time. You may need to revise your proposal several times to convince people that building a data strategy is worthwhile and feasible.

2. Build a Data Management Team and Assign Data Governance Roles

Once you get the agreement you need, you can start to create your data management team. Choose senior-level managers and department heads who have an understanding of the value of data and the data-related challenges you may face, as well as the business’ technological and organizational capabilities, opportunities and limitations. The team should consist of people from across the organization with a wide range of viewpoints — not just people from the technical side of things. Assess your in-house talent to find the right fits and hire from the outside if you don’t have the necessary expertise or you want a dedicated team member who focuses exclusively on data governance.

This board will help create and implement the data strategy. The data management team will be in charge of allocating resources, establishing and improving policies and dealing with data-related issues as they arise, as well as various other data-related tasks.

Once you’ve assembled your team, assign data governance roles to its members. This step includes determining who is responsible for ensuring compliance with standards, deploying technologies, providing updates to employees about policy changes and more. Having well-defined roles helps ensure the necessary tasks get done and promotes a sense of ownership among your team.

3. Identify the Types of Data You Want to Collect and Where It Will Come From

The next step is to determine what data you’ll collect and how you’ll get it. Your business goals will determine the kinds of data you need. For example, if you’re a publisher and you want to tailor your articles to the topics your audience is most interested in, you’ll need to gather data on their interests and the types of posts they read. You might get that information from observing which articles different groups of readers click on most on your site. You could also check your viewers’ social media profiles to see what pages they like and what topics they post about.

On the other hand, your goal may be to target online ads to gain new customers for your business. To do this, you could collect demographic data from customers who visit your website and purchase products from it. You could then purchase second-party or third-party data that matches those demographics and target ads to those people. Because they are similar to your current customers, there’s a higher chance they’ll become customers.

4. Set Goals for Data Collection and Distribution

A cornerstone of building a data strategy is determining specific, measurable goals. Your goal-making process should strive to:

• Establish measurable goals: Define short-term and long-term goals. Then, tailor these goals to align with overarching organizational targets and individual departmental tasks.
• Link data to business goals: Your data should help you achieve your overall business goals. Describe how data can help you achieve those objectives and how each department will benefit from data.
• Craft a vision statement: Use your data to develop a vision statement, which should be a high-level description of how data will improve the organization in the next five years. This vision statement should align with the corporate strategy to ensure it’s executed effectively.
• Make discipline-specific goals: Use your data strategy to identify goals for each discipline area. Set specific, short-term goals for implementing your policy related to the collection, storage, sharing and data use.
• Use progress tracking and adaptability: Your short-term goals will help you track progress during strategy implementation. They’ll also allow you to remain flexible, updating plans as needed as implementation continues.
• Create departmental objectives: Encourage individual departments to define their own objectives for using data. This allows them to track and report progress to the data management team to help provide insight into how the company is using data.

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5. Create a Data Strategy Roadmap

Once you’ve set goals, you need to outline plans for how you will achieve them. These plans will make up the roadmap for your data strategy.

You should have a plan for how you’ll accomplish every goal you’ve set. These plans should be specific and include who owns the goal, what process and technology they will use, how much it will cost, how long it will take and the intended outcome. These plans should also be relatively flexible so you can adjust them if you determine something isn’t working as you expected or circumstances change. As your projects progress, you should regularly evaluate them to determine what’s working well and what is not.

Your data strategy roadmap will outline how you plan to achieve your ultimate long-term goal and vision, as well as how you will achieve the smaller short-term goals that will help you fulfill your vision.

6. Plan for Data Storage and Organization

Your data strategy should also include policies related to data storage and organization. These aspects of data management are crucial, as they help determine how actionable and shareable your data is.

Data storage is a relatively simple technology capability, but methods for storing data can vary significantly from company to company. When creating your storage plans, you need to consider how much storage capacity you need, but you should also consider how your approach to storage will impact data sharing and usage. The way you organize your data impacts how easy it is to access, understand and use. Your storage solution also influences how easy it is for different departments to share data.

Ultimately, your goal in creating a data storage and organization plan is to make your data as accessible, shareable and actionable as possible for the parties that may need it. Different approaches may work best for different companies, but, generally, you should store your data in an easily accessible system in a consistent format.

7. Earn Approval and Begin Implementing Your Data Strategy

Once you have your goals and roadmap laid out, your data strategy is ready to go. You can then package it into a business plan and present it to company leadership for approval.

This business plan should include all the strategies you’ll use to achieve the company’s data goals and the resources you’ll need to implement the strategy, including capital investments, new hires, new processes and new organizational structures.

Once you gain final approval from company leadership, you can begin implementing and improving your strategy. As mentioned earlier, this will be an ongoing process. You should regularly review the plans and your company’s progress toward your goals, and adjust as needed.

How to Support Your Data Strategy

In the dynamic landscape of data management, fostering a culture of continuous improvement and adaptation is essential for ensuring long-term success. Once you’ve planned out and implemented your data strategy, you need to create a framework for supporting it.

Monitoring Key Metrics

With metric data, your organization can see where improvement needs to occur and how your strategy is succeeding. Here are two of the ways monitoring metrics can ensure alignment with organizational goals and overcoming challenges:

1. Tracking KPIs: Key performance indicators (KPIs) act as the compass that guides organizations toward their business objectives. Effective tracking involves selecting relevant KPIs aligned with defined goals. Whether it’s enhancing data quality, optimizing processing times or improving decision-making, meticulous monitoring provides actionable insights, enabling stakeholders to stay informed and make data-driven decisions promptly.
2. Utilizing feedback loops: Feedback loops help identify improvement areas and ensure an adaptable response to emerging challenges. Regular surveys, user forums and collaborative discussions contribute to a responsive data ecosystem, encouraging a culture of adaptability and customer-centricity.

Implementing Frequent Updates

As organizations strive for excellence in their data initiatives, the notion of a static, one-size-fits-all strategy becomes obsolete. Instead, organizations should embrace flexibility and proactive responses to emerging trends and challenges. Let’s look at how iterative updates help your data strategy maximize its potential:

1. Flexibility in data strategy: Data strategy needs to be flexible to ensure success. Once you acknowledge that unforeseen challenges and opportunities will pop up, your organization will need to create a strategy that will adapt to those situations. This involves building a framework that allows for adjustments in response to changing business needs, emerging technologies or shifts in market dynamics. A flexible data strategy empowers organizations to pivot swiftly without compromising their data governance and infrastructure integrity.
2. Embracing technological advancements: Rapid technological advancement requires a proactive stance on innovation. Data strategy should evolve with emerging technologies to ensure your organization remains at the front of industry standards. Strategies for rapid evolution include adopting new data storage solutions, integrating advanced analytics tools and exploring the potential of artificial intelligence and machine learning.

Creating a Data-Centric Culture

Implementing a new data strategy requires proper employee preparation. Creating a culture that values data and integrates it seamlessly into organizational operations is key to sustaining data strategy success. Here are some tips to help you get your organization on the same page about data.

Employee Training and Awareness

All of your organization’s employees need to understand your data strategy and the tools necessary for carrying it out. Encourage employee mastery through:

1. Promoting data literacy: In the pursuit of a data-centric culture, organizations must invest in equipping their workforce with the skills to navigate and interpret data effectively. Promoting data literacy goes beyond basic technical proficiency — it involves ensuring employees across various departments understand the significance of data in their roles. Training programs, workshops and accessible resources contribute to nurturing a data-literate workforce capable of leveraging data for informed decision-making.
2. Fostering a data-driven mindset: Beyond literacy, installing a data-driven mindset requires a cultural shift that encourages employees to view data as a strategic asset. This mindset enables individuals to seek data-supported insights, challenge assumptions and continually explore ways to optimize processes. By fostering a collective understanding that decisions should be rooted in data, organizations empower their teams to contribute actively to their data-centric goals.

Encouraging Collaboration

Data strategy requires support from everyone in an organization. Building this collaboration helps your data strategy to be as flexible and productive as possible. Invest in collaboration through:

1. Breaking data silos: Siloed data, confined to specific departments or teams, limits your data use potential. Breaking down these data silos involves creating channels for seamless data sharing and collaboration. This might mean using centralized data repositories, supporting transparent data governance practices and promoting a culture where data is seen as a shared resource.
2. Cross-functional teams: Collaboration flourishes when diverse perspectives converge. Establishing cross-functional teams for data projects ensures individuals from different departments collaborate on initiatives. By dismantling departmental barriers, organizations tap into the collective intelligence of their workforce, promoting innovation and a unified approach to achieving data goals.

How Lotame Can Help

As data becomes more valuable to businesses and the volume of data continues to increase, building a data strategy is becoming increasingly crucial for companies of all sizes. A good data strategy will help you use your resources more efficiently and maximize the value you can get from your data.

Lotame’s Data Empowered solutions can help you optimize your data and take the next step toward unlocking your organization’s full potential. Our end-to-end data collaboration platform, Spherical allows you to unify marketing and advertising data to provide actionable insights. Whatever your goals, our advanced machine learning and lookalike modeling capabilities empower your growth initiatives.

With Spherical, brands and agencies can: 

• Unify fragmented marketing and advertising data 
• Expand audiences for advanced prospecting using machine learning, lookalike modeling and access to a trusted data marketplace
• Preserve and improve activation and measurement with a proven cookieless identity framework
• Ensure regional and global compliance with auditable privacy management tools

At Lotame, we recognize the pivotal role a data strategy plays in optimizing your organization’s mission. Drive growth, enhance revenue and stay ahead in the dynamic digital space — make Lotame your trusted partner in navigating data-driven strategies and goals. Partner with us today and let’s start finding smarter, faster, and easier ways to drive growth and derive value from your customer data – whether you have it or need it.