An effective data strategy  empowers an enterprise to make technology choices aligned with business priorities to get the most value from its data. Our team of business leaders with deep experience in the data science domain can help you at every step of the way.


A modern data strategy is a roadmap toward data-driven decision making, applications and automation. It also includes the process of transforming a business and acquiring the skillsets required – all with one overarching goal: to enable an enterprise to achieve its strategic ambitions.

An effective data strategy always incorporates:


  • strategic business goals
  • data and technology, current and future
  • constraints and priorities
  • a plan to update capabilities and skills
  • a plan to update organizational structure
  • a roadmap of investments to make
  • a KPI dashboard to assess progress
  • a communication plan

Data and analytics are playing a new role in enterprises – one that highlights value creation and business outcomes. Your data transformation should be based on a comprehensive view of the “ideal future” of your company, not change for the sake of change.


Nearly every digital age company owns valuable data. To put it to the best use and generate the most value from it, it must do three things:

  1. ensure progress toward strategic goals;
  2. connect investments in technology with quantifiable business value;
  3. articulate the business impact of data and technology.


First, explore and fully understand what drives your business – this is always the starting point. If you discover that:


  • you are unable to clearly link the cost of your data systems to the benefits that they bring to your business, or;
  • you are unable to describe how your technology choices help you achieve your business goals…


…your business would absolutely benefit from data strategy.

Your data will never be perfect. Instead of first questioning the type, quality and volume of the data your organization has, follow five logical steps to ensure that the data you have is deeply linked with your goals as a company.


  1. Identify the business objectives you want to achieve.
  2. Explore how you can use data to achieve these objectives or boost your results.
  3. Define the gaps and challenges in your organization to reach your objectives – these can be skills, tools, processes and data.
  4. Create a realistic plan that includes pragmatic investments, improvements and changes in technology, people, processes and organizational structure.
  5. Commit to achieving every step of the plan within a set timeframe.


At the highest level, data maturity refers to an organization’s ability to derive value from its data. This doesn’t just encompass the technological tools needed to harness that value, but also the necessary people, processes and systems.


In a data-mature company, the data itself is making decisions without any interpretation from people. Machine-learning algorithms take on new roles as copilots to their human counterparts. Without organization-wide understanding of and trust in these intelligent tools – and the changes in jobs and responsibilities that they bring –, an organization’s data maturity evolution will come to standstill.

Infographic: Maturity = Degree of being datadriven

A strategic view of data maturity starts with creating value before addressing underlying operational processes.


Surprisingly, data isn’t usually the main reason why data projects often fall short. Data availability, volume and quality as well as a lack of the right tools or technologies can play a role in failure, but the key culprit is less obvious.


Data ownership conflicts across departments, intra-company politics and a “what’s in it for me” mindset are in fact the biggest roadblocks for data projects. A lack of leadership knowledge and support will also cause projects to lose momentum and fizzle out before they ever deliver results – even though these projects are rarely cancelled.


In order to develop and execute an effective data strategy, it’s important to understand that data projects:


  • require business transformation;
  • require investments and well-considered governance;
  • touch every aspect of your company.


A data project that fails to gain the buy-in of the C-level will never lead to a real data-driven transformation. Company leadership must be heavily involved in the project, leading the change by inspiring people, setting targets, giving direction and making decisions – as they are the only ones with the scope of responsibility to do so on an organization-wide scale.


Depending on where you stand on your roadmap, the impacts of your data strategy cover many domains, including:


  • your business results, both top and bottom line
  • the quality of business decisions
  • project outcomes
  • teams and organizational structures
  • skills and resources available
  • capturing data and data storage
  • data analytics
  • communication and follow up

To define your KPIs, create a list of the metrics you need to keep track of in order to gauge how well your data strategy is working, and how to improve it. Review the metrics you are already tracking and identify the ones you need to keep and those that aren’t driving the right behavior.

In addition to tracking metrics, also track your organization’s data maturity level. Make sure you know where you are on your data strategy implementation roadmap. Measure the quality of your business decisions, and continuously evaluate and review your key metrics as well as your data strategy – it’s an iterative process.

A few tips on metrics:


  • Approach them both from the top down and the bottom up
  • Choose a handful at most – or you risk them losing their importance
  • Clearly assign responsibility and ownership of each metric
  • Communicate them with the wider team


The enterprise architecture of the future is lightweight. It will rely on rich data insights to make decisions and to move quickly in order to respond to a dynamic, global business environment. As a result, traditional, outdated waterfall-style IT flows to design your applications and architecture just won’t cut it.


Becoming an ‘experimental enterprise’ means transforming the way you operate on a fundamental level. This means embracing agile approaches, laying the foundation for a ‘fail fast’ technology platform and, of course, investing in the right technologies – but don’t panic, it always starts on a small scale.

  • Data strategy workshops
  • Data strategy development
  • Data roadmaps
  • Market discovery
  • Tool selection
  • Program management
  • Change management
  • Transformation office setup

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