“Data strategy,” is a term that is thrown around a lot, especially now that companies are trying to catch up on the AI/ML, predictive analytics, and big data race.
But what does data strategy really imply? How do you build a data strategy implementation plan and how do you measure KPIs, time and effort, and priorities?
In this insightful webinar with Dora Boussias, a transformational leader in data and digital architecture, we learn the framework and five pillars to adhere to when building a data strategy plan.
Dora also speaks about priorities, KPIs, and the importance of aligning people with processes and technology.
The article below is a summary of the questions and discussions in the webinar.
You can watch the complete webinar here 👇
What does data strategy really mean?
Generally, ‘data strategy’ implies a plan that organizations develop and implement to effectively manage and leverage their data assets. It involves considering various pillars or areas, such as data governance, data quality, analytics, information security, privacy regulations, and tools/platforms.
In this context, the “strategy” aims to: address business objectives and challenges by establishing processes, frameworks, and guidelines for data management, decision-making, and organizational culture. It involves aligning data initiatives with the specific needs and goals of the organization, considering factors like data governance, stewardship, architecture, and data literacy.
But this makes data strategy sound like a vague business plan that takes ages to implement.
In the webinar, Dora stresses the fact that a data strategy plan is not solely about the data itself but rather about the business.
The ultimate aim is to leverage the potential of data to improve customer experiences, attract and retain customers, streamline operations, reduce costs, and mitigate risks. Dora emphasizes that the implementation of data strategy should prioritize the pain points that align with the desired business outcomes. It involves identifying the key stakeholders, determining the necessary actions, and considering the specific aspects of implementation.
The true meaning of data strategy, therefore, lies in its alignment with business objectives, and successful implementation requires prioritizing and addressing the pain points that contribute to achieving those objectives.
However, systems and technologies are not enough.
A data strategy plan must break down silos and foster collaboration. Dora emphasizes that implementing the best technology is not enough if people are not actively engaged and feel like they are part of the solution.
Companies must understand that data strategy implementation is not a one-time event but a continuous endeavor that involves the active participation and engagement of people within the organization.
Dora Boussias’s Five Pillars of a Data Strategy Implementation Plan:
Dora breaks down a data strategy implementation plan into five key pillars. These are:
Data Governance and Stewardship:
This pillar involves considerations such as data management, data architecture, data governance, and master data management (MDM). It focuses on ensuring data quality, integrity, and proper management.
Analytics and Meaningful Insights:
This pillar encompasses leveraging analytics to derive valuable insights from data. It involves analyzing and interpreting data to make informed decisions and drive business growth.
The third pillar emphasizes securing data and managing access rights. It includes implementing measures to protect data from breaches, ensuring compliance with security protocols, and safeguarding sensitive information.
Data Privacy and Regulatory Compliance:
This pillar addresses adherence to data privacy regulations and staying compliant with legal requirements. It involves understanding and implementing appropriate measures to protect customer data and ensure privacy.
Tools and Platforms:
The final pillar pertains to the tools, platforms, and technologies used in a data strategy implementation. This includes data lakes, data quality standards, monitoring frameworks, and other relevant tools necessary for effective data management and analysis.
Additionally, Dora highlights the importance of data literacy and organizational culture. Data literacy involves raising awareness and fostering a culture of collaboration and understanding within the organization. She emphasizes the need for the right people in the right roles and the involvement of diverse stakeholders across the organization.
KPIs to Measure For Strategy Implementation
Like most things in life, your strategy must have an end goal; aka KPIs. What are the core objectives you’re trying to achieve with this strategy? Dora suggests key KPIs to measure strategy implementation include:
Data Quality Metrics:
This KPI focuses on measuring the quality of data and ensuring its accuracy, consistency, and completeness. It may include metrics related to data governance, data stewardship, and data architecture.
Data Literacy and Cultural Transformation Metrics:
This KPI evaluates the level of data literacy and the success of cultural transformation within the organization. Metrics could include user adoption rates of data catalog tools, collaboration levels among stakeholders, and the effectiveness of implementing common definitions and practices.
Operational Efficiency Metrics:
These KPIs measure the effectiveness and efficiency of data strategy implementation. They may include metrics such as faster delivery of products or services, increased repeatability of data services or tools, and the implementation of best practices to streamline operations.
Privacy and Security Metrics:
This KPI assesses the organization’s compliance with data privacy regulations and the effectiveness of information security measures. Metrics could include data breach incidents, data access controls, and adherence to privacy regulations.
Each pillar of the data strategy has its own set of KPIs, specific to its objectives. Dora highlights the importance of having relatable and specific metrics that align with each pillar. These metrics provide insights into the progress, effectiveness, and impact of the data strategy implementation.
How to Align People, Process, and Technology
It must be reiterated that a data strategy is only effective when people, processes, and technology are aligned. How do you ensure this holy trinity?
Dora suggests some key priority points:
👉 Be cautious not to prioritize tools solely based on buzzwords but ensure they are the right fit for the purpose.
👉Situational awareness is crucial in understanding the specific environment, including the people, processes, and data maturity, to tailor the strategy accordingly.
👉 Bring everything together in a thoughtful and integrated approach that aligns with the business environment and objectives.
👉 Balance tools and people within the context of the overall data strategy through a practical and tailored approach that involves employee training, measuring how tools are impacting (or driving) decision-making, and making people feel like they are contributors to the strategy.
Organizational change management and the active participation of individuals are key factors in successful implementation and achieving business success.
How to Measure ROI on a Data Strategy Plan?
Dora explains how to measure the return on investment (ROI) from implementing a data strategy with the objective of making better business decisions. Her insightful answer is summarized in key points below:
✅ ROI measurement varies across different pillars or areas of the data strategy.
✅ Metrics should be specific to the initiatives being implemented, considering factors such as business value and return on investment.
✅ It can be easier to measure metrics related to implementing analytics compared to data strategy or data governance initiatives.
✅ Consider metrics such as data quality, user adoption, and the ROI of building a data lake.
✅ Assess the impact over time, including factors like speed of delivery, resilience, and reduction in costs.
✅ Benchmark the implementation timeline and aim to shorten it by implementing best practices and repeatable processes.
✅ Calculate the ROI in terms of cost savings and operational efficiency resulting from the implementation of effective data management practices.
Measuring ROI must not be limited to monetary value. ROI is also the time saved on replacing a manual method with an automated one or training employees to handle data more effectively are all examples of measuring ROI for data strategy realistically.
Setting Realistic Objectives
In answer to a question raised on setting realistic objectives, Dora shared an experience where an organization faced delays in shipping products due to a mismatch in data for label printing. The objective, in this case, would be to improve data management, particularly in the area of enterprise master data, to prevent such delays and ensure accurate labeling. This objective directly impacts business processes, customer experience, and cash flows. Dora emphasizes the importance of focusing on the right data, and its quality and ensuring a consistent understanding across the organization. She also highlights the need to consider the end-to-end impact of data management initiatives, designing resilient solutions that can adapt and scale over time.
Expected Time and Cost to Build a Data Strategy Plan
In terms of time, the duration required to build a data strategy plan varies depending on several factors. It depends on who is leading the effort and their level of experience and understanding. If someone is starting from scratch, it may take longer compared to someone who has previous experience in data strategy implementation. Additionally, the organization’s appetite for discussing and embracing data strategy plays a role. If there is resistance or lack of interest, more effort is needed to engage stakeholders and gain their support.
There is no definitive timeline, as it depends on the dynamics of the organization. However, generally, it could take a couple of months to a couple of years!
In terms of cost, again, there is no definitive amount. The goal is to showcase tangible value through pilot projects and quick wins. By demonstrating the direct impact of data initiatives, businesses, especially those where the board may initially be uninterested, can gradually build confidence and trust in the strategy. Companies must prioritize the data strategy as an ongoing program rather than a one-time project, which would involve allocating funding accordingly.
Furthermore, Dora emphasizes on the significance of understanding the business and speaking the language of different stakeholders. Tailoring the data strategy’s communication to different functional perspectives within the organization helps ensure that the strategy resonates with the target audience and facilitates their understanding. The focus is on presenting business use cases rather than specific tools or technologies, emphasizing the practical application of data strategy in solving business problems.
Implementing a data strategy is crucial for organizations to unlock the full potential of their data and make informed business decisions but it’s a complicated task that involves aligning business objectives, people, processes, tools, and metrics. By focusing on these aspects, organizations can improve data governance, data quality, analytics capabilities, and overall data culture.
How To Use WinPure in Your Data Strategy Plan?
In the context of tools and efficiency in a data strategy plan, WinPure can be a valuable asset.
By incorporating WinPure’s data quality solution into your strategy, you can enhance the accuracy and integrity of your data, leading to improved decision-making and business outcomes.
WinPure’s no-code solution empowers human resources by providing an efficient and user-friendly platform to cleanse, standardize, and deduplicate data, saving valuable time and resources. With clean and reliable data, your organization can optimize processes, minimize errors, and increase productivity.
You could drastically cut back the time spent on the quality component of a data strategy plan simply by using a no-code solution to take away the manual effort of matching, cleaning, and deduplicating data.
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