Enterprise Data Strategy: What is it? And why is it important?
According to a recent study, organizations that utilize data management resources, such as an enterprise data strategy, are 58% more likely to surpass revenue goals when compared to non-data-driven competitors.
If data is leveraged effectively, by way of a data strategy, it can be the single largest value driver for an organization.
So why are organizations still having trouble leveraging their data effectively? The top response we’ve heard is, “We’re stuck.” The truth of the matter is that most internal data and IT leaders are ill-equipped to create a strategy and business case to justify an investment that would change the fact that their data is trapped in silos.
Of equal concern, they are also overwhelmed by the abundance of technology solutions at their disposal. In today’s world, there are technological tools for each stage of the data journey. Given the pressures they are facing, it’s no surprise that they look for the “easy way out” and hastily incorporate a technology before solidifying a modern data foundation.
The reality is that organizations need to get unstuck from their data paradox by first implementing an enterprise data strategy that aligns with desired outcomes and goals. In doing so these entities define a modern approach to their persistent data dilemmas. A data strategy will help your organization extract value from your data assets and accelerate towards data-driven decisions that inspire growth.
Simply put, an enterprise data strategy will help your organization align People, Processes, Data, and Technology while establishing the necessary data culture to eliminate risk, support business objectives, and drive positive business outcomes.
Why do organizations need a data strategy?
Data and IT leaders are often overwhelmed with the magnitude of data produced from the various touchpoints accessible to internal and external stakeholders. This is compounded by the fact that they lack the ability to keep up with the velocity of business change.
Many leaders, therefore, need to transform and modernize their data capabilities in order to begin moving the needle forward with modern data management practices and advanced analytics use cases that support business growth.
However, this cannot happen without a stable data foundation established by an enterprise data strategy. Most organizations lack a cohesive plan or roadmap to move forward due to the complexity and scale of their challenges.
Some common pain points due to a lacking enterprise data strategy include:
Data Silos
In our experience we find that eliminating data silos are typically one of the primary challenges we are asked to help with. Data silos create an environment where it becomes very difficult to seize opportunities such as understanding customer habits and how that can drive a better customer journey.
Additionally, the inconsistent information delivered to executives by way of siloed data could prevent the necessary data exchange from one area of the enterprise to another. Often, this data is a critical component for business units to drive better outcomes.
Shadow IT
“Shadow IT” is a term often used in organizations when data has been largely decentralized and teams of data professionals branched off to replicate and replace traditional IT departments. These rogue operations often contribute to data silo challenges, strain data governance programs, create a wealth of redundancy in data assets, and generate tremendous amounts of technical debt.
One survey found that about 80 percent of workers use software without the IT department’s consent and knowledge. Eventually, this leads to different interpretations of data, leaving data consumers confused and customers dissatisfied.
Security, Privacy, and Risk
Protecting data from cyberattacks, loss, or corruption is a top priority for many organizations. A data strategy enables the design of efficient data management activities to enhance information security and privacy that eliminate risk and help maintain regulatory compliance. A focus on this area will continually provide peace of mind for not only the organization, but also consumers and clients knowing that more rigid practices will keep their data protected.
What are the benefits of an Enterprise Data Strategy?
An enterprise data strategy is important to a business because it can help transform the organization and the people within it by giving them the ability to make decisions that’ll provide positive business impact. Some of those areas include customer acquisition and retention, cash flow, and competitive analysis just to name a few.
The groups we find that benefit the most from a data strategy are:
Executive Leadership/IT & Data Managers:
- Defined roadmap
- Agile data-decision-making
- Market growth
- Improved processes
- ROI that enables future projects
A data strategy brings value to executive leadership and managers by providing a defined organizational roadmap. The roadmap outlines the set expectations as well as provides timelines to understand what stage the project is in. The data strategy also allows for understanding your current team and where you may need augmentation to accomplish projects, or if team growth is in the near future. With a defined roadmap, executives can better understand where the organization needs to go and make agile decisions to help stimulate growth and benefit from economies of scale by leveraging the data and strategy at hand.
Additionally, a data strategy can help improve processes within the enterprise. By improving processes, the organization can become more efficient, scale, and eliminate risk with its business actions. Process improvement seems like a completely different realm but focusing on how your data is created and moved through the organization provides efficiencies that most don’t realize can exist. Managers will also have the ability to track progress and make improvements as needed to ensure that the organization is on track to scale its data operations by executing against the data strategy that’s in place.
Furthermore, a data strategy that can provide leadership with a reasonable return on their investment will help facilitate the approval of future projects.
Employees/Data Culture:
- Data-informed to Data-Driven
- Self-reliant decision-making
- Regained trust in the data
A data strategy brings value to employees and the organization by first enabling a data-informed culture. This creates a path to introducing a more data-driven culture. The data culture at an organization will provide the attitude to be effective with data. With a more data-driven culture, employees are more likely to make decisions based on data rather than intuition. This will improve performance efficiencies, as employees will now have access to accurate and timely data and are able to effectively use it to make decisions.
Additionally, a data strategy provides a path for data consumers to learn and improve critical data skills that allow them to effectively work with data. Whether those skills lead to managing the quality of the data or more advanced techniques, employees will feel they are able to advance their skills because there is a focus on them. With honed skills, employees can now make the best decisions and provide optimal results by trusting the data they are working with.
Customers/Clients:
- Affordability
- Improved customer journey
- Brand loyalty
An enterprise data strategy brings value to customers and clients by uncovering insights that allow the organization to offer a customized consumer journey. When a business is able to provide a tailored experience by utilizing, for example, customer preference data, they can expect an increase in retention rates as well as brand loyalty. They may be able to uncover areas of improvement that can lead to lower prices for their consumers to enjoy while both the organization and customer benefit long term.
By implementing such an initiative, enterprises can properly collect, aggregate, and analyze multichannel data (i.e., marketing, order history, trend analysis, etc.) to further improve experiences and increase a customer’s lifetime value.
What other data services does a data strategy enable?
An enterprise data strategy enhances the intelligent use of data as a strategic asset and protects it from internal redundancies. It also enables other critical data services in data governance and self-service analytics.
Data governance:
- Data quality
- Security & privacy
- Data sharing
Data governance is an essential program to help ensure the quality of the data. Data quality is improved by most importantly establishing ownership and stewardship of the data. Having accurate information allows stakeholders to make informed choices around their business strategy. Data governance also ensures the security and privacy of data. Policies and procedures are established and monitored to ensure protection of your most valuable competitive asset. Business leaders must trust that their data is protected from unauthorized access.
Regardless of your industry, data sharing is an essential capability for modern organizations. It allows for collaboration amongst business units and external vendors to help improve workflows. By sharing data, stakeholders gain a holistic understanding of each area of the business and what data is being created by each respective unit. Understanding how and where data is coming from will lead to closing the gap on operational deficiencies.
Data governance, data quality, security and privacy, and data sharing are all enabled by an enterprise data strategy. By taking these factors into account and having a direct focus on certain areas, data and IT leaders can ensure that data consumers have the information they need to support business applications for making intelligent decisions on behalf of the enterprise.
Self-service analytics:
- Agile decision-making
- Employee empowerment
Self-service analytics is a cornerstone component of an enterprise data strategy. It allows end-users to be self-reliant with decision-making and provides refresh capabilities for near real-time data consumption. Self-service analytics, supported by trusted data, educate and empower employees with a data first mentality. It provides the agility employees need to make decisions in a timely and accurate manner.
With self-service analytics, employees can access the data they need to make informed decisions without waiting for someone else to provide it. This helps encourage employees to take ownership of their projects and make decisions best for customers and the organization. By taking advantage of self-service analytics, businesses can stay ahead of the competition while perpetuating a data-driven culture.
What Does a Data Strategy Look Like?
All organizations are unique, and their data strategies will differ accordingly. However, a strong enterprise data strategy should encompass various aspects of business processes, supported by data governance and analytics. Here’s a detailed look at what a comprehensive data strategy should include:
- Planning and Goal Setting:
- Define Business Objectives: Clearly articulate the goals the organization aims to achieve through its data strategy. This involves identifying key business drivers, motivations, and the overarching vision.
- Strategic Alignment: Ensure that the data strategy aligns with the broader business strategy. This includes integrating data initiatives with business goals to support growth and competitive advantage.
- Current State Assessment:
- Operational Analysis: Evaluate how the organization currently operates, both from a business and technical perspective. This involves understanding the existing data landscape, including data sources, systems, and processes.
- Technology Landscape: Assess the current technology infrastructure and identify gaps or inefficiencies that need to be addressed to support the data strategy.
- Future State Vision:
- Desired Outcomes: Define what the organization aims to achieve with its data initiatives. This includes outlining the future state of data operations, governance, and technology.
- Data Governance Framework: Develop a robust framework for data governance that includes policies, standards, and practices to ensure data quality, security, and compliance.
- Technology and Tools: Identify the technology stack and tools required to support the future state vision. This may include advanced analytics platforms, data integration tools, and data management solutions.
- Strategy and Roadmap:
- Action Plan: Develop a detailed action plan that outlines the steps needed to achieve the desired future state. This includes prioritizing initiatives, setting milestones, and defining success metrics.
- Capability Building: Focus on building the necessary capabilities within the organization to support data-driven decision-making. This may involve training and development programs for employees.
- Value Delivery: Implement a phased approach to deliver value early and often. This helps maintain momentum and demonstrates the tangible benefits of the data strategy to stakeholders.
- Continuous Improvement: Establish a feedback loop to continuously monitor, evaluate, and adjust the data strategy as business goals and objectives evolve.
Pillars of a Robust Data Strategy
Our experience has allowed us to develop four key pillars that underpin a successful data strategy:
- Business Goals & Strategy:
- Clearly define the business drivers, motivations, and vision.
- Ensure that data initiatives are aligned with these goals to drive business value.
- Current State:
- Conduct a thorough assessment of current operations, both from a business and technical perspective.
- Identify existing technology and data management practices.
- Future State:
- Develop a vision for the future state that aligns business, data, and technology strategies.
- Establish a comprehensive data governance framework to support this vision.
- Strategy & Roadmap:
- Create a detailed roadmap that outlines the steps needed to achieve the future state.
- Focus on building capabilities and delivering value through a phased approach.
Key Components of an Enterprise Data Strategy
A comprehensive data strategy will typically include the following components:
- Data Governance:
- Establish policies and procedures to ensure data quality, security, and compliance.
- Define roles and responsibilities for data stewardship and management.
- Data Architecture:
- Design a scalable and flexible data architecture that supports current and future data needs.
- Integrate data across different systems and departments to eliminate silos.
- Data Management:
- Implement best practices for data collection, storage, processing, and analysis.
- Utilize data management tools to automate and streamline data operations.
- Analytics and Insights:
- Develop advanced analytics capabilities to generate actionable insights.
- Use these insights to drive informed decision-making and business growth.
- Cultural Transformation:
- Foster a data-driven culture within the organization.
- Provide training and support to help employees become proficient in using data for decision-making.
- Performance Monitoring:
- Establish metrics and KPIs to monitor the performance of data initiatives.
- Use these metrics to continuously improve the data strategy and demonstrate its value.
By following these guidelines and focusing on the key components, organizations can develop a robust data strategy that drives business growth, enhances operational efficiency, and enables data-driven decision-making.
Partnering with Data Ideology – Experts in Data Strategy & Execution
Organizations maximize their chances of success by collaborating with data and analytics firms that possess extensive experience in implementing enterprise data strategies. While some companies attempt to develop a data strategy independently, they often struggle to provide a holistic perspective, adopt a proven process, and employ a modern approach. At Data Ideology, we have accumulated invaluable insights over the years, demonstrating that the do-it-yourself method rarely yields optimal results.
At Data Ideology, we have meticulously designed our Enterprise Data Strategy roadmap solution to cater specifically to the demands of data leaders. Our approach ensures that the resulting strategy is not only comprehensive but also tailored to align with your organization’s unique culture, investment capabilities, and resource constraints. Here’s what sets our solution apart:
- Holistic Perspective:
- We take a broad and inclusive view of your data landscape, considering all aspects of your business processes and goals. This ensures that your data strategy is well-rounded and integrated across all departments.
- Proven Process:
- Our approach is based on years of experience and proven methodologies. We utilize best practices and industry standards to develop a strategy that is both effective and efficient.
- Modern Approach:
- We stay ahead of the curve by adopting the latest technologies and innovative practices. This ensures that your data strategy is future-proof and capable of adapting to evolving business needs.
- Customized Roadmap:
- Our Enterprise Data Strategy roadmap is designed to be detailed and pragmatic, addressing your organization’s specific requirements. We consider your culture, investment potential, and resource availability to create a strategy that is feasible and actionable.
- Agile Frameworks:
- Our agile frameworks allow for flexibility and adaptability. We understand that business environments are dynamic, and our approach is designed to accommodate changes and enable continuous improvement.
- Accelerators for Quick Wins:
- We have developed accelerators that help achieve quick wins, allowing your organization to realize value early in the process. This not only builds momentum but also demonstrates the tangible benefits of a well-executed data strategy.
By partnering with Data Ideology, your organization gains access to a wealth of expertise and a strategic partner committed to your success. Our tailored solutions ensure that your data strategy is not only aligned with your business objectives but also capable of driving significant value and growth.
