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4 Pillars That Create Data and Analytics Power


The power of data and analytics comes from four essential components – generating value and impact for any organization.

From collecting data to making it actionable knowledge and seeing the effects on the organization, the path to effective analytics could be challenging, especially if the organization has not yet engaged its data-driven transformation or is not fully equipped to power it correctly.

Data is crucial when it comes to business strategy across every sector and is the catalyst for innovation and productivity. Nearly all companies are now investing in Data and Analytics since data equates to value for most organizations.

The common expression “Data is the new oil” defines data as an essential resource to fuel business operations. Like oil, data can be an immensely valuable asset if it can be extracted and used properly. Raw data, without effective data management, doesn’t bring any value.

A data asset is any data owned by an organization that, when exploited adequately and efficiently, can generate value for the organization. Data is hard to make valuable, and its increasing complexity aggravates the challenge.

Data increases in complexity

The 3Vs have long been used to characterize “big data” but they apply across the data landscape.

  • The volume of data to be collected, stored, and analyzed.
  • The variety of data through the different types of formats and characteristics to manage, but also the very diverse sources they are from.
  • The velocity of data covering both the speed of collection and the speed of change of its sources and structure.

The complexity of data managed by enterprises has never stopped growing on all these dimensions. Additionally, the business context represented by data is evolving faster than ever, making it extremely difficult to identify the right area of focus.  The complexity requires proceeding with structured methods and frameworks to inventory, assess, and build value out from data.

The value of data assets comes from how it is used within an organization, which determines how important it is, and ultimately what monetary value can be determined.  Indeed, the success of a data-driven initiative is when it affects operational processes, aligned with the company objectives.  This challenge calls for the delivered solution to address the 3Us:

  • Usable: integrated within the technical stack and connected with operational systems and data management processes.
  • Useful: understood by business users who can interpret and act upon the results and recommendations.
  • Used: consumed by business users for decision making, and continuously improved to follow the business context and evolutions.

For true value, data and analytics must be considered as a business capability, through fully supporting and integrating the core business functions and processes and creating measurable value and impacts. Only 32 percent of business executives surveyed by Accenture said that they’re able to create measurable value from data, while just 27 percent said their data and analytics projects produce actionable insights. 

These statistics show that companies struggle with this data-driven transformation.  They are challenged to push the business past disruptions and prove the value of data, and it is critical to establish a comprehensive value management approach to tackle the challenge and achieve the benefits.

Data and Analytics as a business capability to generate value and impact

Data and Analytics value management relies on 4 strong pillars, to address coherently all the various dimensions:

  • The right data strategy to align efforts and support the business goals.
  • The right technology foundations and architecture to manage and operate the data challenges.
  • The right operating model to effectively design, build, deploy and operate Data & Analytics initiatives.
  • The right environment and change management to achieve greater data literacy and data-driven decision-making culture.

Steps to data and analytics value generation

1. Aligning on strategy

Data and Analytics are an enabler to support the business strategy… and not an objective.  Without this clear frame for alignment, the efforts made on collecting, cleaning, preparing, and analyzing data do not lead to efficient decision making.  Focusing on the implementation of analytics results in frustration among executives on the lack of benefits vs the significant investments in people and technology.

It is therefore critical to ensure that the portfolio of Data and Analytics projects is fully focused on delivering outcomes and aligned with the business strategy.  To achieve this, organizations must have a well-defined set of priorities at the enterprise level and define the KPIs that will be used to measure success.

Then, they carefully map all their initiatives to their strategic priorities, identifying their associated risks and expected outcomes. They also need to be conscious of their current maturity level to anticipate the transformational impact of some initiatives and account for this dimension in the qualification process.

They can then optimize the portfolio by selecting the right set of initiatives, matching their investment capability, their appetite for transformation, and the expected business results.

This alignment process should be performed by collecting contributions across the whole organization. In this context, collaboration from the beginning is required to ensure actionability and active support.  Doing so will help define a continuous process for managing new emerging ideas, qualifying them, and adapting the portfolio when needed.

A Data and Analytics strategy serves as a framework to select the right areas of focus and investments along time, to build, manage and deliver the optimal portfolio of Data and Analytics initiatives.

2. Tech-forward foundations

Data and Analytics initiatives require adapted tools and solutions to efficiently manage and use data.  Tools to capture, store, transform, analyze, visualize, and serve the different needs of all users, from occasional users requiring reports and self-service visualizations to experts requiring advanced analytics capabilities.

And because the technology market for Data and Analytics is dynamic with frequent innovations, the architecture must be flexible and able to evolve smoothly, scaling as the organization matures.

The optimal data architecture also serves as the basis for a broader IT transformation, by connecting with operational systems as data sources and for automated or manual decision making.

The goal of the Data and Analytics architecture is to define the key organizational and operational guidelines to deploy tools, operate and manage data storage and pipelines, and evolve, similar to an urbanism plan for a city.

Deploying and using those foundations requires a large and long-term investment in skills to leverage the new technologies and accompany both the data literacy and the methodology evolutions across the organization.

3. An operating model for continuous improvement

Data and Analytics projects are not one-time, they require a continuous cycle of improvement since they affect business processes that must be continuously improved.  They rely on various data sources all having their own evolution rate.  They are sensitive to environmental changes (individuals’ behaviors, economical context, unexpected events, etc.).   They must adapt to the evolutions of the business strategy.

So, delivering Data and Analytics initiatives that have an impact requires a proper operating model (or methodology) to manage and optimize the portfolio along the full lifecycle, from the emergence of initiatives to their qualification, prioritization, implementation, deployment, maintenance, retirement.

This operating model should include the ability to track costs, behavior, performance and results over time — to assess value and support the required maintenance and evolutions.  These adaptations prevent model decay or drift, incorporate additional data, manage evolution in data sources, adapt to changing business context, etc.   Continuous monitoring creates a feedback loop that is key to ensure reliability and accuracy of Data and Analytics initiatives over time, enabling continuous improvement.

The key factors of an effective Data and Analytics operating model or methodology:

  • Visible within the organization across all dimensions on initiatives, contributors, including expected vs effective value and results.
  • Comprehensive across the full lifecycle, providing support and metrics tailored to each stage.
  • Adaptive to acknowledge differences of maturity within the organization, and the local specificities; able to combine a common frame of reference to track initiatives and assets.
  • Collaborative to include all actors involved in data projects along the lifecycle: target users, business experts, data providers, Data and Analytics teams, IT teams, etc. This includes in some context customers or suppliers.
  • Governed, to share clear processes, responsibilities, roles and manage risks.
  • Automated, so that all the monitoring and tracking information is gathered continuously and effortlessly from various pipelines.

4. A value-driven data culture

While companies invest in defining their strategy, in setting the right technology foundations, and in deploying an effective operating model, they need to ensure that every employee has the skills and training to understand and use data and analytics properly. Otherwise, the analytics-driven organizational concept might remain in a stage of an idea instead of reality.

According to research by Accenture, 75% of employees are uncomfortable working with data.  The risk of individuals not understanding or not trusting data and analytics is huge, affecting the adoption and effective deployment of initiatives.  Users will either fail to use the available data correctly for decision making or revert to the previous way of operating and ignore the available data.

Data literacy is a key for innovation, to enable individuals to trust available data and how to use it properly, support initiatives, identify and propose new efforts that generate cost savings, efficiency gains, new revenue sources, etc.  Data literacy training is an essential aspect of any successful organization’s approach to data and analytics.

Building trust in a data-focused culture is critical to achieving value. Combining visibility for activities with accessible and reusable knowledge, using the skills and data literacy of workers enable trust.  With trust, organizations can become fully data-driven and boost their innovation capabilities.

Data Literacy Requirements

This profound cultural change toward data literacy requires:

  • Business Glossary, to speak a common language and use definitions to build trust and encourage collaboration across teams.
  • Hiring and training, to hire new data talent and to support existing employees to develop their skills and ability to contribute to Data and Analytics initiatives.
  • Collaboration, to involve the right people through the Data and Analytics initiatives’ lifecycle so they deliver the right insights the right way and build communities to trigger knowledge sharing and enrichment.
  • Support and change management, to ensure individuals get the help they need to understand how to use available tools to achieve more autonomy, how to rely on existing Data and Analytics efforts, how to improve processes, or to identify new needs for training, tools, or new initiatives.


There is an optimal pathway for effective data analytics initiatives across any organization.  Start with an assessment of the organization’s maturity, its competencies in each area, desired target states, and build a plan using this assessment. That Data and Analytics plan should always include those 4 pillars:

  1. Start with defining a data strategy as the way to align everyone on the objectives and the points of focus.
  2. Define and deploy strong technology and architecture foundations.
  3. Define or refine an operating model to manage and optimize the data and analytics portfolio and monitor the generated value over time.
  4. Invest in data literacy and in animating strong communities to achieve a data-driven culture.

Finally, deploy a continuous improvement cycle, to enrich, evolve and adapt with the growing maturity, the changing business conditions, and new risks or opportunities.

A version of this article appears at


Nicolas Averseng

Nicolas Averseng is a data and analytics software developer, technology adviser, and CTO for Software as Service (SaaS) firms.  He has provided strategic consulting on data architectures and data platforms (assessments, recommendations, implementation), and has designed and deployed large-scale data & analytics systems.  Nicolas co-founded SaaS provider Yooi after leading technology and analytics efforts at several large organizations, focusing on the project management as well as the technical aspects of analytics endeavors.  Nicolas studied computer science engineering at National Institute of Applied Sciences (INSA) of Lyon.

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