What Is a Data Strategy?
What is an enterprise data strategy? A data strategy is a vision for how a company will collect, store, manage, share and use data. The MIT CISR Data Board provides the following data strategy definition: “a central, integrated concept that articulates how data will enable and inspire business strategy.” A company’s data strategy sets the foundation for everything it does related to data.
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Every organization’s data strategy will look a bit different, but generally, a data strategy will do the following:
- 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 outlines 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.
Why Is Data Strategy Important?
So why is it essential for a business to have a data strategy, and what benefits does having one provide?
1. Data Strategy Helps Unlock the Power of Data
Today, the value of data is well-understood. Rather than seeing data as a byproduct of various business activities, most organizations understand it’s a valuable resource. Unlocking that value, however, can be a challenge. Organizations might not know how to answer the question, “What is big data?” They also might not understand what data to collect or how to effectively capture it. Once they collect data, they also face the challenge of converting it into a useful form, sharing it across the organization and deriving insights from it. A data strategy gives employees guidance on how to do these things and helps ensure people across the organization do so in a consistent manner.
2. The Volume of Data Is Increasing
The volume of data that exists in the world and that’s available to businesses is increasing rapidly — 90% of the data in the world became available in just the last two years. As the volume of data increases, managing that data becomes more challenging and the need for a data strategy grows.
Previously, some companies may have been able to manage their data using common knowledge. For instance, if a developer needed data, they would contact the person in charge of overseeing it. They may have to hold meetings, send emails and have conversations to find a source for it. While this method wasn’t the most efficient, it didn’t take too much time because the volume of data was low.
As the amount of data grows, though, it becomes impossible for any individual to keep track of all the data relevant to them. Instead, the company needs to put a data strategy in place. Relying on an informal approach in today’s data-centric world can lead to inefficient data usage, lost data and incorrect results.
3. Data Strategy Improves Data Management Across the Entire Organization
When an issue related to data comes up, the first reaction many companies have is to implement a solution to solve that specific problem. This point approach may work in the short term, but it is not the most efficient method of fixing data-related issues. It also doesn’t address issues that may span department and project boundaries — and when it comes to data, many things do.
Data access and usage are organization-wide needs, and they affect every group and seniority level within a company. That’s why it’s beneficial to implement a company-wide solution such as a data strategy. Creating a company-wide strategy improves data management across the whole organization and ensures various departments work in alignment with each other, rather than against each other.
4. Data Strategy Helps You Use Resources Efficiently
If you don’t have a data strategy, different departments and individuals will solve data issues on their own. Each department, for example, may put raw data into whatever format they think is best. Every department has to dedicate time and resources to this task, and they may put it into a format that isn’t ideal for another project. This approach leads to a lot of wasted resources.
With a data management strategy, every department and individual will have guidelines to follow related to what format data should be in. While they may make adjustments as needed, the data should be in a usable format when someone goes to retrieve it. Having the data in a standard format also means departments can easily send it back and forth. Having a company-wide strategy helps you use your resources more efficiently, reducing your data-related costs.
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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 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 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 in a consistent way that makes 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. Establishing Guidelines for Data Analysis and Application
Similarly, your data 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 building 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, earning buy-in can take some time. You may need to revise your proposal several times to convince people 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. You should establish both long-term and short-term goals, as well as both overarching objectives and ones that apply to individual tasks and departments.
Ultimately, 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. Doing so will help you determine your 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.
Your data strategy will help you identify goals for each discipline area. Set specific, short-term goals for implementing your policy related to the collection, storage, sharing and use of data. These short-term goals will help you keep track of your progress when implementing your data strategy and enable you to update your plans as needed as implementation continues.
Each department can also develop objectives for how they plan to use data. They can keep track of their progress and report on it 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 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.
The technology you use, as well as the partners you work with, can have a significant impact on the success of your data strategy. To get the most out of your data strategy and make your implementation easier, partner with Spherical, powered by Lotame. Its suite of solutions extract the maximum value of a brand’s first-party data by connecting with leading CDPs to enable persona development, enrichment, analysis and advertising activation. 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
To learn more about how Spherical helps marketers and media owners drive growth and revenue with actionable customer intelligence, data informed audiences, and identity powered activation, contact us today.
