What is a data strategy?
A data strategy is a long-term plan that defines the technology, processes, people, and rules required to manage an organization's information assets. All types of businesses collect large amounts of raw data today. However, they need a well-thought-out data management and analysis plan if they want to use this information to make informed decisions. A data strategy outlines an organization's long-term vision for collecting, storing, sharing, and usage of its data.
Why is a data strategy important?
Building a data strategy is essential for organizations to stay relevant, competitive, and innovative amidst constant change. You must collect, organize, and act on your data to meet business goals and unlock new value for your organization, such as the following:
- Operational efficiency
- Process optimization
- Faster decision-making
- Increased revenue streams
- Improved customer satisfaction
Your data strategy gives you a competitive advantage because it aligns data management with business strategy and data governance. It has two primary purposes.
Improve data architecture decisions
A company's data architecture describes how the company collects, stores, transforms, distributes, and consumes data. It also includes the technical aspects of data management, such as the following:
- Databases and file systems
- Rules governing data storage formats
- System connections between applications and databases
For example, data architecture might input daily marketing and sales data into applications like marketing dashboards, which then further integrate and analyze the information to reveal relationships between ad spend and sales by region. Your data strategy provides the framework within which data engineers make architectural decisions that meet business goals.
Manage data consistently
An effective data strategy supports the entire organization for collaborative and consistent data management. It gives everyone the answers to five key questions:
- What data is appropriate?
- What data operations are approved?
- What is the purpose of data storage and collection?
- What is the data governance policy for business processes?
- What insights can you get from your existing data?
What are the advantages of implementing a data strategy?
There are more benefits of having a good data strategy:
Solve data management challenges
Most organizations experience data management challenges like data silos, data duplication across business units, inefficient data flow between departments, and lack of clarity around data priorities. A data strategy allows companies to solve these challenges by making data accessible and shared in a secure way. You can unlock the value of data to meet business requirements. Better alignment around data and access to the right data at the right time enables organizations to prepare for the future or unknown.
Improve customer experience
Organizations use data and analytics to better understand customers and improve customer experience. From online experiences to contact centers, organizations can use data to create more value for customers and address unmet needs proactively. Data also helps organizations create new business or monetization opportunities and build hyper-personalized products and services based on customer needs.
Attain analytical maturity
The Gartner Analytic Ascendancy Model defines four steps in analytical maturity. Organizations typically start with descriptive and diagnostic analytics to understand what happened and why. Analytical maturity comes when the organization transitions to predictive analytics that use data to answer what will happen. Organizations in the final stage of maturity use prescriptive analytics to achieve predetermined results. A data strategy thus lays out a detailed plan to help your organization move from making decisions based on foresight instead of hindsight. It lays the foundation for implementing advanced technology like artificial intelligence (AI) and machine learning (ML) for improved business intelligence.
Create an organization-wide data culture
A data strategy presents a roadmap to improve data literacy and efficiency in usage across the organization. Diverse teams can work in alignment to enhance data quality and the accuracy of data collection. In addition, you can develop customized training and create learning pathways for collaborators to go from beginners to experts in data management and usage.
Achieve regulatory compliance
An effective data strategy improves data security by implementing measures to limit unauthorized data access. You can consider all data governance rules and regulations while defining policies and processes. All operations can be carefully planned to ensure enterprise data management maintains the privacy, security, and integrity of data at all times.
What are the key components of an effective data strategy?
You can present your data strategy as a sequence of steps and a timeline to implement these steps. This data strategy roadmap includes guidelines to maintain your organization's current data maturity and action items that take it to the next level.
The following are some common data strategy components to include in your roadmap:
Data catalog tools
Data management tools
Several tools exist for data visualization, reporting, dashboards, and data analytics. A data strategy helps identify the best tools that meet business needs and support both IT teams and business users. You can also verify that the tools meet all data governance policies, ensuring compliance with regulations.
Successful data strategies typically include plans for both data and analytics management within an organization. Data analysis requires existing datasets as input for ML and AI software. An enterprise data strategy aims to minimize bias by outlining the best datasets to use for analytics and how to train employees in data operations. For example, suppose your organization plans to use AI to sort job applications automatically. In that case, you will need to carefully select a diverse dataset of past and present employees to avoid creating unconscious bias in the software.
Your data strategy should include a review process to assess and improve existing data management systems and the data strategy itself. This includes tasks like the following:
- Periodically auditing existing data architecture
- Verifying that data collection processes remain compliant
- Measuring data quality against comparable market data
You can use such review documentation to improve your existing data strategy and revisit strategic goals.
What are the different approaches to creating a data strategy?
There are two main approaches to creating a data strategy for your organization.
Data defense is a highly centralized, control-oriented approach to data management. The data architecture typically includes a single source of truth for every broad data category. For example, there is one primary source of revenue, customer, or sales data. The data systems gather data from several sources, clean it, and store it in this central repository. Data defense thus minimizes downside risk by identifying, standardizing, and governing authoritative data sources to maintain the integrity of the data flowing through the company's internal systems. It prioritizes activities, such as the following:
- Compliance and regulations
- Fraud detection using analytics
- Security measures for theft prevention
Data offense focuses on adding more flexibility to centrally governed data management systems. It recognizes that multiple business units interpret the same data differently. It accommodates those different interpretations by permitting controlled data transformations for reporting that can be reliably mapped back to the single source of truth.
For example, consider a scenario where both the finance and marketing departments produce monthly social media ad spending reports. Marketing, interested in analyzing ad effectiveness, reports on the impact of spending on clicks and views. Finance reports on the effect of spending on cash flow. The reports contain different numbers, but both reports represent an accurate version of the truth.
Offense defense spectrum
Every company needs to incorporate both offense and defense for its data strategy to succeed, but getting the balance right can be complicated. Offensive activities tend to be real-time operations and are more relevant for customer-focused business functions, such as marketing and sales. Defensive activities are more important for legal, financial, compliance, and IT departments. A balanced corporate strategy gives business leaders the flexibility to diverge from the single source of truth in consistent ways to better meet business needs.
Who builds a data strategy?
The data strategy team typically includes representatives from upper management and business analytics and IT teams. The following are some examples of users who come together to create and implement a data strategy.
Data engineers are responsible for building a reliable and efficient data architecture. They oversee and administer several data pipeline tasks like data collection, processing, storage, and analytics. This role includes specialists who implement data security and governance requirements.
Data scientists take the data processed by data engineers and use it for further analysis. They use the data to create different ML models and generate reports for business intelligence.
Data analysts specialize in interpreting and analyzing data. They work closely with data scientists to ensure that business intelligence tasks align with organizational requirements.
Business managers review data reports and help manage data operations. They ensure data strategy aligns with overall business strategy and regulatory requirements.
What are the steps to building a data strategy?
We suggest the following steps to create an actionable data strategy.
Prepare a proposal
The first step is to create a proposal that outlines the advantages of having a coherent strategy for your organization. Your recommendation can include aspects like the following:
- Economic benefits of implementing a strategy
- Competitor analysis
- Business objectives you plan to achieve
- Data strategy roadmap
A comprehensive proposal will help you get a faster buy-in from leadership, IT departments, and key stakeholders.
Build a team
Finding the right people who bring a diverse range of viewpoints is crucial for success in data strategy. Your team will be responsible for several tasks, which may include the following:
- Resource allocation and distribution
- Establishing and improving policies
- Dealing with data-related issues as they arise
You can also assign data governance roles for determining who is responsible for deploying technologies, ensuring compliance with standards, and providing updates to everyone about policy changes.
Optimize your data architecture
Any data strategy requires the right tools and technologies to succeed practically. You will need to review your existing data infrastructure, analyze how different teams currently use data, and identify any gaps to be resolved. This step typically involves making technology-centric decisions based on your requirements, which may include the following:
- Data volume and type
- Data quality and analysis
- Security and compliance
Ultimately, your goal is to create a data strategy that makes your data as accessible, shareable, and actionable as possible for all stakeholders who need it, with the right security controls in place.
How can AWS help with your data strategy?
AWS has several services that help you reinvent your business with data. You can join over 1.5 million customers in bringing your data to the world's most trusted, secure, and scalable cloud community. For example, you can use AWS to do the following:
- Upgrade existing systems with AWS Data Infrastructure Modernization services
- Select and implement the best data analytics strategies with Analytics on AWS
- Invent new experiences and reimagine old processes with Machine Learning on AWS
Get started implementing your data strategy using AWS by creating a free account today.
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