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Data Strategy by Bernard Marr or why a data-informed culture must begin at the top

written by human, not by AI

A lot of buzz in the data & analytics world revolves around tools and technology. But many of the clients I’ve worked with, and most companies I’ve worked for before going on my own path, are not ready for the latest, most advanced technology. Most of their pain points revolve around things like data that is too messy to be of any use, proper definition and alignment of KPIs meaning and targets, situations where one team doesn’t want to give business users access to particular datasets, or even more fundamentally a general lack of visibility on “how do I know if I’m on the right track or not”. Tools are powerful accessories that can help with much of the heavy lifting of analysing data, but none of these pain points can be solved by technology alone. All of these situations arise from something else - a factor that is unfortunately often overlooked - the lack of a well-thought-out Data Strategy.

Business strategy is generally about aligning company goals (such as profitability, revenue, customer success, etc.) to its execution stream (the teams that will make the strategy happen). But if you ask a room full of executives how they plan to include data into their strategy streams, you’ll likely draw blank faces, a few apologies and some blurbs about how that’s just part of operational execution and not necessarily linked to a strategic level. This is where all those pain points originally stem from: a lack of acknowledgment that data should be treated as the strategic asset it is, starting with a good strategy.

Most books about data I come across tend to be directed at one of three audiences:

  • Those who work in an operational or business area now need to learn general skills to interpret, analyse and communicate data more effectively;

  • Those who want to become data analysts and want to learn the nitty-gritty details of the field and its tools;

  • Those in management roles who need to understand data to make decisions.

But a fourth and critical audience could benefit from more data-related content specifically written for them: leaders, directors and executives tasked with creating and executing a business strategy. This is the gap Bernard Marr’s book, aptly named Data Strategy, intends to close. The book is concise and straight to the point. It is written from a broad, general viewpoint - it doesn’t go into the weeds of data work. But it serves as a fantastic starting point for leaders looking for information on how to include data as part of their strategic planning and goals. The best little detail? It doesn’t even mention a technology stack until chapter 8 (of 11).

The ingredients of a Data Strategy

Embarking on the journey of incorporating data into a company's strategic planning can be daunting for many people at the helm of a business. There’s so much to tackle, and here I come telling them there’s this one more thing permeating everything they do. They often recognise the importance of data but grapple with how it connects to their organisation's broader goals. Bernard Marr's book, Data Strategy, serves as a friendly guide tailored to those in executive and leadership positions, providing a comprehensive understanding of data's role in business strategy and offering practical steps for weaving data into the fabric of their plans.

Marr starts the book by emphasising that in today's business world, every organisation, regardless of size or sector, is fundamentally a data-driven business. Consequently, collecting, analysing, and interpreting data are indispensable elements of any business strategy. A good strategy, according to Marr, effectively bridges the gap between data and business strategy.

The book's core outlines a concise yet complete roadmap to create and implement a successful Data Strategy.

  1. Identify key questions and objectives related to data: Begin by understanding why you're collecting and analysing data. Determine the questions you want to answer and the objectives you aim to achieve. This requires scrutinising your company's goals and pinpointing areas where data can offer invaluable insights and informed decision-making. Often businesses start collecting data before understanding if or how the data will be used. This can have all sorts of implications, like issues with governance, privacy and security.

  2. Align data objectives with the overall business strategy: Make sure your data objectives resonate with your organisation's overarching strategy. The author explains that data is often used in one of 3 central cases across a business: to better understand customers, markets, and competitors; to enhance operational efficiency; or to monetise data itself. Understanding which objectives apply to your overall strategy will help you define the data initiatives that will support and enable your success in these areas.

  3. Create a data-driven culture and community within the company: A successful data strategy needs a solid foundation, clear objectives, and widespread organisational trust and support. You should generate enthusiasm for data initiatives and foster a sense of community among employees through workshops, training sessions, or other events that educate and empower them to succeed in a data-driven environment. This is not just addressing a tech skills gap: it is about getting people excited to be part of the transformation that will inevitably happen once everyone embarks on the data journey outlined by the strategy. It helps build trust and ensures data access and democratisation can happen at all levels.

  4. Determine the appropriate technology and infrastructure for data collection and analysis: With the basics in place, you can focus on the practical aspects of implementing a data strategy. Decide on the appropriate technology and infrastructure for data collection, storage, and analysis. Marr's book briefly offers an overview of various data types and techniques to derive insights. It works as a high-level clarification of basic definitions that are often confused and miscommunicated. My favourite little nugget is when the author briefly explains the differences between a Data Warehouse and a Data Lake and how that affects and changes the whole strategy underpinning either choice. It serves well to demonstrate that strategic alignment to goals and business pains supersedes technology for various reasons, but mostly because tech stacks are built to fit into specific strategies - and doing it the other way around is much more difficult!

  5. Promote data stewardship and provide access to data within the organisation: Democratising data access within the organisation is crucial, but it must be balanced with proper data governance to ensure quality and accountability. Marr highlights the significance of data stewardship, which involves defining guidelines, processes, and responsibilities for data management and usage.

  6. Establish tools and approaches for data communication: establish tools and approaches to communicate data findings clearly and efficiently from the get-go - visualisation guidelines, templates, and design systems are all tools to enable people to get used to communicating and consuming data in varied ways, embedding the practice in their day to day.

  7. Build data competencies and skills in the organisation through in-house talent development or outsourcing: A data-driven organisation requires a workforce with the skills to effectively work with data. Invest in building these capabilities by enhancing in-house talent or outsourcing technical work. A hybrid approach may be the most efficient strategy for rapidly building data competencies - outsource while you build skills in-house. The exchange with practices from outside the company can benefit everyone by bringing different views and skill sets into the mix.

  8. Implement data governance, including data ownership, privacy, and security: As data collection and storage grow, legal and ethical considerations become increasingly important. Marr's book delves into data governance topics, such as data ownership, privacy, and security, underscoring the need to address these issues as part of a well-rounded data strategy. This is one of the best parts of the book. The author goes deeper into the consequences of hoarding data and later figuring out what to do with it. Nobody wants to end up with heaps of data that isn’t in usable form while also being a liability.

  9. Execute and revisit the data strategy, making adjustments as needed: Marr emphasises the importance of executing the data strategy and continually revisiting and adjusting it as needed. This is not a one-and-done process. As business priorities shift and new technologies emerge, the data strategy must adapt to stay relevant. This ongoing evaluation and adjustment process is crucial for a data-driven organisation's long-term success. It also means that shifts in current technology stacks - like phasing out legacy tools and implementing new ones - demand reviews in data strategy and how these transformations will impact and fit into the overall business goals.

Fail-proof your data strategy

Despite the clear benefits of a data strategy, many organisations still struggle to implement one successfully. Changing habits and ways of working means more profound transformations must happen at the top: without leadership buy-in to model behaviour, implementing a strategy can be a steep climb. Strategies can fail for a range of reasons, but Bernard Marr's book identifies a few common ones why data strategies fail, offering insights on how to avoid them and ensure the successful execution of a data-driven vision.

  1. Vague or ill-defined objectives: A data strategy must be built on clear and well-defined goals. When a strategy lacks direction or specificity, it becomes challenging for employees to know where to start or how to contribute to the initiative. To avoid this issue, executives should invest time in articulating the goals of their data strategy and ensuring that these objectives are aligned with the organisation's overall strategy.

  2. Poor communication: Communication is a critical aspect of any successful data strategy. If employees are not informed about the importance of the strategy, its objectives, and how it ties into the broader goals of the organisation, they may be less likely to engage with and support the initiative. To foster a data-driven culture, executives must communicate the value and importance of data strategy clearly and consistently throughout the organisation.

  3. Lack of collaboration between departments: Siloed departments can be a significant barrier to the success of a data strategy. When different teams or departments fail to collaborate and share information, valuable insights may be overlooked, hindering the organisation's overall data capabilities. To overcome this challenge, leaders should encourage cross-departmental collaboration and work to break down any existing silos within the organisation. Building a strong, shared data community can do wonders to tear down these walls.

  4. Senior management resistance: A data strategy can only be successful if it is supported by the organisation's leadership. Senior managers may hesitate to invest in or support the strategy when they do not trust or understand the data and technology. This lack of buy-in can quickly spread throughout the organisation, leading to a general lack of engagement with the data strategy. Leaders must work to educate themselves and their teams about the value of data and the technology that supports it, ensuring that all levels of the organisation are aligned and committed to the data strategy's success. The author makes the point for the role of a CDO (Chief Data Officer). If your data is as strategic as you say, someone should be officially at the helm, ensuring everyone is on board.

  5. Not addressing skills gaps: Building a data-driven organisation requires employees with the necessary skills and competencies to work with data effectively. If an organisation does not invest in developing these skills within its workforce, the data strategy is likely to falter. This includes helping people get comfortable with using data beyond software. A data-driven culture requires teams capable of reading, working with, analysing and communicating with data at different levels. Leaders must proactively identify skills gaps within their teams and take steps to address them through training, development or enabling a thriving data community.

Making a case for business cases

Another essential point Bernard Marr makes in his book is the need to define business cases to help give the strategy a more practical scope. Marr shares a couple of templates on his website to help you capture and organise how the strategy may come to reality. He also breaks it down a little further in this Youtube video:

Should you read it?

Bernard Marr's book, Data Strategy, highlights the critical role of data in business success and emphasises the importance of incorporating data into a company's strategic planning. The author outlines key components and addresses common challenges, providing a valuable guide for executives and leaders to weave data into their organisation's fabric.

The book serves as a reminder that effective data strategy begins at the top, with a leadership team that fully understands and appreciates the value of data. By ensuring clear communication, fostering collaboration, addressing skills gaps, and establishing a data-informed culture, organisations can harness the power of data to drive their strategic goals and thrive in an increasingly competitive business landscape.

Data Strategy is a book about the forest, not the trees. You won’t find deeply detailed techniques, but you will find an excellent introduction to the main ingredients that compose a successful data strategy. For all C-level executives and leaders alike out there, if you’re looking for guidance on how to include data in your next strategic review, this is a worthwhile and informative read.

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written by human, not by AI

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