Most of the people living in this world earn money either by being employed by a company, getting their monthly salary, or running their own business. Now consider a situation when a person keeps spending all the money without thinking about monthly income. If it happens, then it is pretty sure sooner or later, that person will be without money. So, it is better to have at least insights on your income and to have a monthly budget to manage your money properly.
You can think of a data strategy like managing your monthly budget. You identify your income (data sources), prioritize expenses (key metrics), and track spending (analytics) to ensure you meet your goals. Without a plan, you risk overspending - just like businesses without a data strategy miss insights and value.
What is Data Strategy?
Data Strategy is an organization's well-defined approach to handling data to generate business value. It is not only about data but it is a framework to turn data into value by leveraging applications of data and analytics.
According to Bernard Marr, author of Data Strategy, has famously said: A good data strategy ensures that the right data is being used in the right way to drive the right outcomes. This highlights the essence of aligning data with business objectives to create meaningful impact.
Whether you are running a small business or a big business, you keep generating data. Irrespective of the data volume you generate, how you are able to use this data to increase/create more business counts.
Before we deep dive into the Data Strategy, let's see if you really need a data strategy for your organization.
- An IDC study shows that organizations with strong leadership support for data-centric initiatives are 4.5 times more likely to base major decisions on facts.
- The most recent report from Capgemini Research Institute’s data-powered enterprises series highlights the surge in organizations unlocking value from data for business and financial gains.
In short, A company without a data strategy is like planning your vacation without booking a ticket or identifying places you want to visit, etc.
- Data = facts and statistics collected together for reference by a company
- Strategy = a plan of action designed to achieve a goal
So, data strategies are all about the plans for managing, storing, collecting,g and analyzing data to achieve the business objectives. Let's understand the six pillars of Data Strategy.
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Image 1: Pillars of Data Strategy
1. Vision + Mission + Strategy
Vision
Focuses on leveraging data as a strategic asset that empowers decision-making, improves processes, and fosters innovation, while adhering to ethical standards. For example,
"To unlock the full potential of organizational data to drive innovation, competitive advantage, and sustainable growth while ensuring ethical usage and governance."
Mission
Emphasizes in overseeing the data strategy's execution, ensuring data is reliable, secure, and accessible to foster informed decision-making. For Example,
"To establish and oversee a comprehensive data strategy that ensures high-quality, secure, and accessible data for all stakeholders, enabling data-driven insights and decisions that align with organizational goals."
Strategy
It gives a direction to unlock the power of data in line with the business strategy of your organization.
2. People + Culture
The role of people and culture in shaping a data strategy is fundamental to its success. Organizations with strong data-driven cultures foster collaboration, trust, and innovation, which are essential for effectively implementing and utilizing data strategy.
- Creating a Data-Driven Culture: A strong culture of data literacy empowers employees across all levels to make data-driven decisions. This engagement leads to higher productivity, more innovative solutions, and better business outcomes. As noted by McKinsey, companies that build a data-driven culture are 23 times more likely to acquire customers, 6 times as likely to retain customers, and 19 times more likely to be profitable.
- Strategic Decision-Making: Executives, especially the Chief Data Officer (CDO), play a pivotal role in advocating for data strategy within the organization. Their involvement ensures that data initiatives align with overall business goals and objectives.
- Trust and Ethical Considerations: For a data strategy to succeed, employees and stakeholders must trust that data is accurate and used ethically. Ethical considerations and respect for privacy are paramount in a data strategy. Establishing a culture of transparency and accountability ensures compliance with laws like GDPR and builds trust with customers and employees alike.
3. Operating Model
An effective operating model for data strategy includes establishing policies, standards, and tools to ensure high data quality, security, and compliance across the organization. From choosing to either a centralized or a distributed data architecture to organizing people within the data team and also choosing the right agile framework are also a part of the operating model to execute a data strategy.
4. Data Management + Data Governance
Data Management and Data Governance are foundational pillars of an effective data strategy. Data management is the creation, storage, maintenance, archiving and preservation of data. Data governance is about the authority, control, privacy, accuracy, and availability of the data. It includes the actions people must take, the processes they must follow, and the technology that supports them throughout the data life cycle.
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Image 2: Data Management & Data Governance
5. Architecture + Technology
By carefully designing the architecture and selecting the right technology stack, organizations can maximize the value of their data and remain competitive in an increasingly data-driven world. It is about selecting the right tools, designing applications that are scalable, secure, cost-effective, and adhering to the future state architecture.
6. Roadmap
It provides a structured plan to guide the implementation of data initiatives over time. A clear roadmap ensures that the data strategy is aligned with broader business objectives, helping stakeholders understand how data initiatives drive value. It allows organizations to prioritize data initiatives based on their impact and feasibility. By phasing projects, companies can manage resources effectively, reduce risks, and make iterative improvements. It clarifies the resources : financial, technological, and human—needed for each stage of implementation. The roadmap acts as a tool for tracking progress, identifying bottlenecks, and making necessary adjustments.
After understanding the fundamentals of data strategy and its importance, Let's also explore how to create a successful data strategy.
- Define Clear Objectives: Align your data strategy with key business goals (e.g., increasing efficiency, improving customer experience, driving innovation). This ensures that data initiatives support organizational growth.
- Establish Robust Data Governance: Create policies and frameworks to ensure data is accurate, secure, and compliant with regulations. Define data ownership, quality standards, and security measures.
- Build a Scalable Data Architecture: Implement flexible, scalable infrastructure (e.g., cloud data platforms, data lakes) to integrate, store, and analyze data from various sources.
- Promote Data-Driven Culture: Treat data as a product or an asset. Provide training, promote data literacy, and engage stakeholders across all departments. It is all about adoption :)
- Leverage Advanced Analytics and AI: Invest in AI and machine learning tools to derive insights, predict trends, and automate decision-making processes.
- Ensure Data Quality and Consistency: Continuously monitor and clean data to ensure consistency, accuracy, and reliability across the organization.
- Create a Future Proof Roadmap for Implementation: Develop a clear, phased roadmap that outlines the timeline, resources, and milestones required for each data initiative.
- Measure and Adjust: Set KPIs to track progress and success. Regularly assess and refine your strategy based on outcomes and evolving business needs.
Andrew Ng Co-founder of Google Brain and Coursera says "AI is the new electricity." This emphasizes the transformative potential of AI, particularly generative AI, in reshaping industries and creating new opportunities. Data strategy plays a crucial role in accelerating and implementing generative AI solutions. To successfully leverage generative AI, businesses must have a solid foundation of data management, governance, and infrastructure.
I’d love to hear your thoughts on the importance of data strategy in the comments below. Stay tuned for my next article in the LADDER UP WITH SAGAR LAD series: Data Maturity Assessment: Where Does Your Company Stand? This article will explore how businesses can evaluate their data maturity and the next steps for growth in their data journey.