> For the complete documentation index, see [llms.txt](https://www.mlcompendium.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://www.mlcompendium.com/data/engineering-intro/data-strategy.md). # Data Strategy A company needs a strategy for what data work is for before it buys another tool. The page starts with the best places to get started, moves through step-by-step guides and frameworks, then the definitions and the reasons strategies fail, and ends with longer series and specific lenses such as ML, product, and competitive advantage. The (excellent) [how to get started](https://medium.com/data-science/data-strategy-716059d5c066) is the first read. Analytics 8 (excellent) is the second stop: [7 elements of data strategy](https://www.analytics8.com/blog/7-elements-of-a-data-strategy/) is Christina Salmi on the key elements of a data strategy framework that lets decisions rest on analysis rather than intuition, and you can deep dive into other articles from there. [7 tips for strong data infra](https://www.analytics8.com/blog/7-tips-for-strong-data-infrastructure/) covers the clean data and flexible infrastructure that analytics needs to drive decisions, and [step 2/5 how to develop it](https://www.analytics8.com/blog/how-to-develop-data-strategy-roadmap/) is the roadmap step, built to earn executive trust, drive adoption across departments, and prove ROI. Not every organization is a business. The (very good) [research data strategy - for research orgs](https://medium.com/data-science/research-data-strategy-2fd447f83719) starts from science having entered its big data era, in fields such as bioinformatics, astronomy, particle physics, and geology, while much of the data innovation happened in the tech sector. Once the goal is clear, the questions have to be asked in order. [how to create an effective DS](https://medium.com/method-perspectives/how-to-create-an-effective-data-strategy-ffedb1bdc71a) — helps to ask the right questions on several topics, starting from the point that gathering and storing data gives no advantage without a plan and a goal. A strategy also needs owners, which is why the (needed) [RACI matrix](https://en.wikipedia.org/wiki/Responsibility_assignment_matrix) is here, the Wikipedia page on the responsibility assignment matrix. [How to build an actionable data strategy phdata.io](https://www.phdata.io/blog/how-to-build-an-actionable-data-strategy-framework/) is Shane Robins's guide to a more practical and focused data strategy framework. The guides come next. A two-part article starts with [how to create a data strategy for your org](https://medium.com/data-science/how-to-create-a-data-strategy-for-your-organization-e0493110b2e7) and continues with [how to create a perfect data strategy](https://medium.com/data-science/how-to-create-a-perfect-data-strategy-7e8fd9bbfad0). [a step by step guide](https://medium.com/data-science/data-strategy-a-step-by-step-guide-91529a75c72b) is Arthur Mello on how data work fits the company's overall picture and how to draw a roadmap for getting value out of data. Guides assume the strategy will work; the next sources look at why it often does not. [why is your data strategy failing](https://medium.com/geekculture/why-your-data-strategy-is-failing-19e569b6b082) is Lewis Gavin on the failure side. [Become Data-Driven or Perish: Why your company needs a Data Strategy and not just more Data People](https://medium.com/data-science/become-data-driven-or-perish-why-your-company-needs-a-data-strategy-and-not-just-more-data-people-aa5d435c2f9) is Dwayne Gefferie's argument, drawn from fourteen years of working with data. [create a data strategy](https://medium.com/data-science/data-strategy-make-data-count-fa42b42d409) is a meta discussion, Adam Votava asking whether businesses really use data to decide better, refine operations, and create revenue. [data strategy for AI](https://medium.com/mmc-writes/the-ai-playbook-how-to-develop-a-data-strategy-for-ai-d74df9486c0e) is David Kelnar's blueprint, since executing an AI strategy needs an effective data strategy. [how to create a successful data strategy](https://medium.com/data-science/how-to-create-a-successful-data-strategy-1293bacf463c) is one more guide on getting it right. The vocabulary matters as well. Data Strategy describes a “set of choices and decisions that together, chart a high-level course of action to achieve high-level goals.” This includes business plans to use information to a competitive advantage and support enterprise goals. That is the [Definition taken from DAMA](http://web.archive.org/web/20221208205957/https://www.cleverrepublic.com/what-is/data-strategy/), read through the archived CleverRepublic page. The same site also defined what is [data management](http://web.archive.org/web/20221208204222/https://www.cleverrepublic.com/what-is/data-management/) and [intelligence](https://www.cleverrepublic.com/what-is/data-intelligence/); the second address now opens a junior data intelligence consultant posting about helping organisations get the maximum value from their data. Strategy then comes down to which data is worth having. [Data Strategy: Good Data vs. Bad Data](https://medium.com/data-science/data-strategy-good-data-vs-bad-data-d40f85d7ba4e) tells the story of Signet Bank in 1990, which turned its customer credit department into a laboratory that tested credit terms and collected data for years. [Data strategy in progress](https://dasbarrett.medium.com/data-strategy-in-progress-53e5e341ff7) is Dan Barrett on the data strategy work at Citizens Advice, a strategy seen while it is still being built. For a longer read, Francesco Corea's series: Big data strategy [Part 1](https://francesco-ai.medium.com/big-data-strategy-i-tips-for-analyzing-your-data-963292a43105) gives tips for analyzing your data with a goal in mind, [Part 2](https://francesco-ai.medium.com/big-data-strategy-part-ii-a-data-maturity-map-8e41db568625) is a data maturity map, and [Part 3](https://francesco-ai.medium.com/big-data-strategy-part-iii-is-your-company-data-driven-acf871c38001) asks whether your company is data-driven. The last lenses are specific. [Data before ML Strategy](https://medium.com/machine-learning-in-practice/first-things-first-you-need-a-data-strategy-before-you-can-have-a-machine-learning-strategy-366f8439aedf) answers the CEOs who are asked for a machine learning or AI strategy: you need a data strategy first. Connect the Dots in Data Strategy looks at it [from a product POV](https://medium.com/data-science/connect-the-dots-in-data-strategy-56f65a1e63a5), Ivy Liu's story of a new business whose annual forecast disappointed because nobody had put all the business data in one place. The [Data Advantage Matrix](https://medium.com/data-science/data-advantage-matrix-a-new-way-to-think-about-data-strategy-4178cd2f520a) is Prukalpa's new way to think about data strategy. To go back to the start, [create an effective strategy](https://medium.com/method-perspectives/how-to-create-an-effective-data-strategy-ffedb1bdc71a) is the same Method guide as above, and [how to get started](https://medium.com/data-science/data-strategy-716059d5c066) is the same first read. Three of those articles also exist at their original addresses. Adam Votava's Creating a strategy to put wind in your data sails, the create a data strategy piece, is at . Ivy Liu's from a product POV piece, which starts "A few years ago, I worked in a new business in a public company", is at . Prukalpa Sankar's Data Advantage Matrix, which says there's no one path to creating a data strategy, is at . ## Deprecated links {% hint style="warning" %} These links and images no longer work. The original wording is kept here. A same-resource copy, when one was checked, is used above. {% endhint %} * (excellent) how to get started. This address no longer opens: * (very good) research data strategy - for research orgs. This address no longer opens: * how to create a data strategy for your org. This address no longer opens: * how to create a perfect data strategy. This address no longer opens: * a step by step guide. This address no longer opens: * Become Data-Driven or Perish: Why your company needs a Data Strategy and not just more Data People. This address no longer opens: * how to create a successful data strategy. This address no longer opens: * Definition taken from DAMA. This address no longer opens: * what is data management. This address no longer opens: * Data Strategy: Good Data vs. Bad Data. This address no longer opens: * how to get started. This address no longer opens: --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation by asking a question. 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