You're reading from Data Stewardship in Action

Product typeBook

Published inFeb 2024

PublisherPackt

ISBN-139781837636594

Edition1st Edition

Concepts

Data Science

Author (1)

Pui Shing Lee

Developing a Comprehensive Data Management Strategy

A data management strategy is crucial as it enables organizations to leverage their data as a valuable asset. It assists businesses in making informed decisions, improving operational efficiency, and driving business growth. Without a proper data management strategy, organizations run the risk of making decisions based on inaccurate, outdated, or irrelevant data. This could lead to financial losses, missed opportunities, and damage to their reputation. Furthermore, non-compliance with regulations related to data management can result in hefty fines and legal consequences.

A data management strategy addresses numerous challenges. Firstly, it ensures data quality by implementing processes to eliminate errors and inconsistencies in data. Secondly, it aids in data security, helping organizations protect sensitive information from breaches and cyberattacks. It also simplifies data integration, making it easier to combine data from different...

What is a data strategy?

A data strategy can be seen as the compass that directs an organization’s use, management, and protection of its data. It is a holistic plan that not only determines how data will be handled but also how it will be strategically employed to meet business goals.

A data strategy is not just about managing data, but also about leveraging it as a strategic asset to generate insights, drive decision-making, and create value for the organization. In essence, it ensures that data is reliable, accessible, secure, and used effectively to support the organization’s goals.

There are three elements involved when defining a data strategy, as depicted in Figure 4.1:

Figure 4.1 – Three considerations for your data strategy

Let’s discuss these three considerations in detail:

Business value: The primary purpose of a data strategy is to provide value to the business. This means that the strategy should align...

Assessing your current data environment for creating a data strategy – Where are you now?

I came across a discussion thread on Reddit (https://www.reddit.com/r/dataengineering/comments/14e8nuu/what_is_the_best_way_to_optimize_5000_dashboards/). Here the author wanted to understand how to go about BI Modernization if there are 5000 dashboards thrown at you. Would the approach include gathering information on dashboards that got maximum hits, or check on data sources, how they are consumed and patterns on consumption?

What would you do if you were given this task?

Probably you should first ask why we need 5,000 dashboards. It does not make sense at all for most organizations. Maybe you should just shut down all 5,000 dashboards and then take a vacation. When you are back from holiday, see if your boss’s boss comes to your desk with a solid dashboard use case. Then you have your fresh, new use case pipeline.

You need to clarify what exactly the problem statement...

Fulfilling the business and data strategy – Where do you want to go?

If you were granted three wishes by a wizard for your data roadmap, what would you ask for?

To answer the question, you need to define what good looks like for your organization.

Now imagine you have to call the wizard via your stakeholders. Defining the good is the key to asking for the apt wishes from the wizard.

As you ask for the three wishes you have to convince your stakeholders that the three wishes are valid, measurable, and most importantly, relevant to the organization’s goals.

So, what does it mean to stakeholders when the three wishes become true?

The three requirements for a good data roadmap are as follows:

Formalized data standards integrated into modern engineering processes so that business users can trust the data with confidence. For example, during the data ingestion stage, an automated script could check whether incoming currency data adheres to the prescribed...

Introducing the people, process, and technology – How do we get there?

After defining your starting point and your destination, how do you pave the way with the support of your data strategy?

Creating an effective data strategy is crucial for an enterprise to harness the full potential of its data, improve decision-making, and increase overall efficiency. Table 4.3 lists the steps to develop a solid data strategy and roadmap for your organization:

Making the impact visible to your stakeholders - Feedback loop to measure and report progress

Data stewardship is not a one-off exercise. Data stewardship is like your HR or Finance department. It exists for good reason. We need to foster a new culture of fail fast and relearn fast.

Before diving into the feedback loop mechanisms, it is essential to recognize the readiness to change within the organization. Change readiness is the ability to continuously initiate and respond to change in ways that create advantage, minimize risk, and sustain performance. It is an essential prerequisite to the successful implementation of data stewardship programs.

To evaluate the organization’s readiness to change, consider the following factors:

Awareness of the need for change: Ensure that there is a clear understanding across the organization of why change is necessary. This involves communicating the benefits and potential risks of not adapting to new data management strategies...

Summary

In this chapter, we walked through the steps to develop a data management strategy. The chapter began with an introduction to the data maturity review and setting the target state of the data stewardship program. We then went into the details of people, process, and technology considerations and saw how to implement a feedback loop to measure and report progress.

We also highlighted the importance of translating strategy into execution and explained the benefits of using the CDMC framework as your starter kit. The chapter concludes by discussing the importance of aligning the data roadmap with your stakeholders.

As we turn the page to the next chapter, we will delve into the symbiotic relationship between people, processes, and technology, and how these components collectively cultivate a data culture within an organization.

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Author (1)

Pui Shing Lee

Pui Shing Lee is a visionary leader with two decades' experience in FinTech, Data, AI, and Cloud across Europe, the US, and APAC. He is a Cloud Solution Strategist (Data & AI) at Microsoft. With a passion for deriving actionable insights from data, he provides comprehensive solutions for customers' journeys, ensuring tangible business outcomes. Shing holds industry-leading certifications like DAMA CDMP and EDMC CDMC V1. His professional experience includes roles as Chief Data Officer at Hang Seng Index, Head of Data Governance at HKEX, and APAC Director at IHS Markit. As the co-founder of the Data Literacy Association, Shing advocates for a culture-fit data strategy, self-service models, and automated governance on robust cloud platforms.
Read more about Pui Shing Lee

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Step	Milestone	Description
1	Define your business goal	The first step is to understand what you hope to achieve with your data. These goals should align with the overall business strategy. Whether it’s improving customer service, streamlining operations, or gaining a competitive advantage, it’s crucial to establish clear objectives. ...