Organizations that treat data as a business asset look for ways to transform the entire business ecosystem into a data-driven entity. Yet, aligning business goals with data goals is neither easy nor straightforward; a systematic and well-defined Data Strategy must be conceived and developed to take the data-enabled business practices forward. In this Data Strategy 101 article, we will look at the definition of Data Strategy, as well as best practices, challenges, and roadmaps.
What Is Data Strategy?
Data Strategy is a master plan or blueprint for confronting day-to-day business challenges and meeting pre-defined business goals using data. To that end, the Data Strategy spells out the interrelationships between technologies, processes, and people – all linked through their data roles and responsibilities.
The vision embedded in Data Strategy guides daily business decisions through data-powered insights and data-backed intelligence. The Data Strategy explicitly decodes how data will help achieve pre-defined business goals. The goal of such a master plan is to ensure that all business units throughout an organization are using data consistently and transparently.
While laying the foundations of data and analytics strategy, it is common to explore existing capabilities from the perspectives of people, processes, and technologies. After understanding the business strategy (goals), as well as the opportunities and challenges that lie ahead, data and analytics use cases are identified.
The enterprise strategy master plan usually includes all strategies that will be used to meet the business goals, as well as the resources needed to execute those strategies, such as capital investments, talent acquisitions, rehauled processes, and a modified organizational structure.
Surprisingly, McKinsey research finds that fewer than a third of polled organizations have their data strategies aligned with their organizational strategy.
According to David Benigson, CEO of Signal AI, “Truly impactful data strategies are only just coming into their own due to the advancement of technology and maturing of things like enterprise artificial intelligence (AI).”
The guiding principles of an enterprise Data Strategy include:
- Streamlining data acquisition processes
- Making data easily accessible and shareable
- Eliminating data silos
- Integrating disparate data
- Developing consistent data usage and management goals
- Setting up clear guidelines for data analysis
What Is a Data Strategy Roadmap?
While the Data Strategy spells out data usage policies, a Data Strategy roadmap offers a set of granular steps and action plans that business staff are expected to follow to achieve predefined business goals.
An effective roadmap includes exact implementation plans: actual resources, schedules, costs, and deliverables. A Data Strategy serves as a blueprint for data policies, and the strategy roadmap serves as an implementation guide.
In the roadmap, every defined business goal will have a corresponding “data action plan” describing how the goal is achieved. The strategy roadmap is much more detailed and exhaustive compared to the strategy plan, detailing the ownerships, technologies, processes, projected costs, timelines, and the desired outcome.
To some extent, the roadmap, though granular, must keep room for unforeseen problems. The Data Strategy roadmap will serve as a checklist for achieving long-term business goals with data.
Finally, the roadmap aligns organizational processes with the desired business goals.
Therefore, in order to ensure your strategy roadmap is aligned with requirements:
- Tools and technologies must be chosen with an eye on precise requirements, available resources, cost estimates, project timelines, and infrastructure capabilities.
- The current data systems, processes, and workforce must be assessed and evaluated for necessary changes or modifications.
- Roles and responsibilities related to data ownerships, data repositories, and Data Governance must be clearly defined.
What Are Data Strategy Best Practices?
Data Management is a key driver for businesses, so businesses must ensure that data is captured, verified, stored, and protected systematically. Data Management best practices establish policies and procedures that guarantee data is used consistently and efficiently across the entire organization.
Data Governance, a key component of Data Strategy, controls the policies and procedures for managing Data Quality and data security throughout the organization. Making Data Quality the priority, closely followed by data security, helps organizations to ensure all aspects of the data are pure and trustworthy.
Data Strategy, which holds the blueprint for managing enterprise data, follows these best practices:
- Setting up data-enabled, business goals based on current requirements
- Setting up uniform data processes to ensure all business units are accessing and using data consistently
- Setting up policies and procedures for sharing data across the enterprise
- Setting up measurement metrics based on specific business objectives
- Defining measurable metrics related to data policy implementations
- Defining data privacy rules to be used both by C-suite and Data Governance teams
The ultimate goal of Data Strategy best practices is to validate the policies developed for collecting, integrating, processing, sharing, and measuring data in an organization.
What Are Data Strategy Challenges?
A Data Strategy is a long-term, guided plan that defines the people, processes, and technologies involved in handling the day-to-day challenges in data within the organization. But the data must address particular business problems in order to meet strategic business goals.
When designing the first Data Strategy for an enterprise or modifying an existing one, the strategy team may come across the following challenges:
- The absence of a shared business vocabulary among data users across the entire organization, which makes it hard for users to find and easily understand data.
- Data is trapped in silos within the separate enterprise business units; it cannot be shared or is of poor quality.
- Ensuring Data Quality and access across the organization – as well as setting up and maintaining appropriate governance processes – is a daunting challenge.
- With an increasing number of data sources, organizations often lack seamless data integration facilities.
- CDOs can struggle to identify, capture, and provide the right data to the right users at the right time.
- The growth of multi-cloud environments has given rise to data replication between different cloud systems, which leads to bad data.
Data security and privacy concerns are paramount for the optimal use of data in enterprises. Thus, policies related to data access, data security, data ethics, and Data Quality cannot be overlooked.
In recent times, data breaches have caught global attention. Safe use of data is crucial to remain globally competitive and relevant. Automated data engineering processes have partially addressed the security concerns, but much work is still to be done.
Data Strategy 101: Further Reading
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