"Data strategy is one of the most ubiquitous and misunderstood topics in the information technology (IT) industry. Most corporations' data strategy and IT infrastructure were not planned, but grew out of "stovepipe" applications over time with little to no regard for the goals and objectives of the enterprise. This stovepipe approach has produced the highly convoluted and inflexible IT architectures so prevalent in corporations today." (Sid Adelman et al, "Data Strategy", 2005)
"The chaos without a data strategy is not as obvious, but the indicators abound: dirty data, redundant data, inconsistent data, the inability to integrate, poor performance, terrible availability, little accountability, users who are increasingly dissatisfied with the performance of IT, and the general feeling that things are out of control." (Sid Adelman et al, "Data Strategy", 2005)
"The vision of a data strategy that fits your organization has to conform to the overall strategy of IT, which in turn must conform to the strategy of the business. Therefore, the vision should conform to and support where the organization wants to be in 5 years." (Sid Adelman et al, "Data Strategy", 2005)
"Working without a data strategy is analogous to a company allowing each department and each person within each department to develop its own financial chart of accounts. This empowerment allows each person in the organization to choose his own numbering scheme. Existing charts of accounts would be ignored as each person exercises his or her own creativity." (Sid Adelman et al, "Data Strategy" 1st Ed., 2005)
"Data is great, but strategy is better!" (Steven Sinofsky, Harvard Business School, 2013)
"Strategy is everything. Without it, data, big or otherwise, is essentially useless. A bad strategy is worse than useless because it can be highly damaging to the organization. A bad strategy can divert resources, waste time, and demoralize employees. This would seem to be self-evident but in practice, strategy development is not quite so straightforward. There are numerous reasons why a strategy is MIA from the beginning, falls apart mid-project, or is destroyed in a head-on collision with another conflicting business strategy." (Pam Baker, "Data Divination: Big Data Strategies", 2015)
"The overall data strategy should be focused on continuously discovering ways to improve the business through refinement, innovation, and solid returns, both in the short and long terms. Project-specific strategies should lead to a specific measurable and actionable end for that effort. This should be immediately followed with ideas about what can be done from there, which in turn should ultimately lead to satisfying the goals in the overall big data strategy and reshaping it as necessary too." (Pam Baker, "Data Divination: Big Data Strategies", 2015)
"A data strategy should include business plans to use information to competitive advantage and support enterprise goals. Data strategy must come from an understanding of the data needs inherent in the business strategy: what data the organization needs, how it will get the data, how it will manage it and ensure its reliability over time, and how it will utilize it. Typically, a data strategy requires a supporting Data Management program strategy – a plan for maintaining and improving the quality of data, data integrity, access, and security while mitigating known and implied risks. The strategy must also address known challenges related to data management." (DAMA International, "DAMA-DMBOK: Data Management Body of Knowledge", 2017)
"A good data strategy is not determined by what data is readily or potentially available - it’s about what your business wants to achieve, and how data can help you get there." (Bernard Marr, "Data Strategy", 2017)
"A sound data strategy requires that the data contained in a
company’s single source of truth (SSOT) is of high quality, granular, and
standardized, and that multiple versions of the truth (MVOTs) are carefully
controlled."
"Companies that have not yet built a data strategy and a
strong data-management function need to catch up very fast or start planning
for their exit." (Leandro DalleMule & Thomas H Davenport, "What’s Your Data
Strategy?", Harvard Business Review, 2017)
"How a company’s data strategy changes in direction and velocity will be a function of its overall strategy, culture, competition, and market." (Leandro DalleMule & Thomas H Davenport, "What’s Your Data Strategy?", Harvard Business Review, 2017) [link]
"[…] if companies want to avoid drowning in data, they need to develop a smart [data] strategy that focuses on the data they really need to achieve their goals. In other words, this means defining the business-critical questions that need answering and then collecting and analysing only that data which will answer those questions.
"Start by reviewing existing data management activities, such
as who creates and manages data, who measures data quality, or even who has
‘data’ in their job title. Survey the organization to find out who may already
be fulfilling needed roles and responsibilities. Such individuals may hold
different titles. They are likely part of a distributed organization and not
necessarily recognized by the enterprise. After compiling a list of ‘data
people,’ identify gaps. What additional roles and skill sets are required to
execute the data strategy? In many cases, people in other parts of the
organization have analogous, transferrable skill sets. Remember, people already
in the organization bring valuable knowledge and experience to a data
management effort." (DAMA International, "DAMA-DMBOK: Data Management Body of
Knowledge", 2017)
"In truth, all three of these perspectives - process, technology, and data - are needed to create a good data strategy. Each type of person approaches things differently and brings different perspectives to the table. Think of this as another aspect of diversity. Just as a multicultural team and a team with different educational backgrounds will produce a better result, so will a team that includes people with process, technology and data perspectives." (Mike Fleckenstein & Lorraine Fellows, "Modern Data Strategy", 2018)
"A data strategy is the opportunity to bring data, one of the most important assets your organisation has, to the fore and to drive the future direction of the organisation." (Ian Wallis, "Data Strategy: From definition to execution", 2021)
"Data strategy is even less understood [thank business strategy], so the chances of success can be further decreased, simply because you need organisation-wide commitment and buy-in to succeed. Data does not exist in a bubble; it is not the preserve of a function that can fix it for all, detached from touching everyone else. It is core to how you run the organisation, and without a focus on where you are heading, it is going to trip the organisation up at every turn – regulatory compliance; operational effectiveness; financial performance; customer and employee experience; essentially, the efficiency in managing virtually every activity in the organisation." (Ian Wallis, "Data Strategy: From definition to execution", 2021)
"I am using ‘data strategy’ as an overarching term to describe a far broader set of capabilities from which sub-strategies can be developed to focus on particular facets of the strategy, such as management information (MI) and reporting; analytics, machine learning and AI; insight; and, of course, data management." (Ian Wallis, "Data Strategy: From definition to execution", 2021)
"It is also important to regard the data strategy as a living document. Do not regard it as a masterpiece, never to be reviewed, amended or critiqued within the time frame it covers, but instead see it as a strategy that can flex to the changing demands of an organisation." (Ian Wallis, "Data Strategy: From definition to execution", 2021)
"In the same vein, data strategy is often a misnomer for a much wider scope of coverage, but the lack of coherence in how we use the language has led to data strategy being perceived to cover data management activities all the way through to exploitation of data in the broadest sense. The occasional use of information strategy, intelligence strategy or even data exploitation strategy may differentiate, but the lack of a common definition on what we mean tends to lead to data strategy being used as a catch-all for the more widespread coverage such a document would typically include. Much of this is due to the generic use of the term ‘data’ to cover everything from its capture, management, governance through to reporting, analytics and insight." (Ian Wallis, "Data Strategy: From definition to execution", 2021)
"Many organisations start a data strategy from a need to get data into some sort of organised state in which it is feasible to demonstrate compliance. In my opinion, compliance should be a component of a data strategy, not the data strategy in itself." (Ian Wallis, "Data Strategy: From definition to execution", 2021)
"The data strategy should answer the questions: Where are we going? What are we trying to achieve? How does this data strategy fit with the vision, mission and strategy of the organisation? The digital strategy should answer the overarching question: How are we are planning to achieve this?" (Alison Holt [Ed.], Data Governance: Governing data for sustainable business", 2021)
"The key for a successful data strategy is to align it clearly with the corporate strategy. The data strategy is a crucial enabler of the corporate strategy, and the data strategy should clearly call out those components that have a clear line of sight to delivering, or enabling, the corporate goals. If the data strategy does not align to the corporate goals it will be a much more challenging task to get the wider organisation to buy into it, not least because it will fail to have any resonance with the objectives of the organisational leaders and be regarded as optional at best." (Ian Wallis, "Data Strategy: From definition to execution", 2021)
"Right now, the biggest challenge for organizations working on their data strategy might not have to do with technology at all. [...] It’s an understandable problem: to a degree that is perpetually underestimated, becoming data-driven is about the ability of people and organizations to adapt to change." (Randy Bean, "Why Becoming a Data-Driven Organization Is So Hard", Harvard Business Review, 2022) [link]
