The 4 pillars to create value from Data & Analytics


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


When data means value

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 & Analytics.

The common expression “Data is the new oil” defines data as an essential resource to power up companies’ business. Like oil, data can be an immensely valuable asset if you know how to extract and use it properly. Raw data by itself 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. (source: Laurent Fayet)

Data is hard to make valuable, and the 3Vs make the problem even harder

The 3Vs have long been used to characterize “big data”:

  • 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 those 3 dimensions. On top of that, the business context represented by those data is also evolving faster than ever.

All this makes it extremely difficult to identify the right area of focus, and requires moving forward with structured methods and frameworks to inventory, assess, and build value out of those data.

Data value involves impacting the business processes, and the 3Us challenge

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 impacts operational processes, aligned with the company objectives, which requires the delivered solution to address the 3Us:

  • Usable: integrated within the technical stack and connected with operational systems.

  • Useful: understood by business users who are able to interpret and act upon the results and recommendations.

  • Used: actually used by business users for decision making, and continuously improved to follow the business context and evolutions.

Data & Analytics really needs to be considered as a business capability

Fully supporting and integrating the core business functions and processes, and finally creating measurable value and impacts.

Only 32 percent of business executives surveyed said that they’re able to create measurable value from data, while just 27 percent said their data and analytics projects produce actionable insights. (source: Accenture)

… but companies struggle with this data-driven transformation — to push the business past disruptions and prove the value of data, and it is critical to put in place a comprehensive value management approach to tackle the challenge and achieve the benefits.


Data & Analytics as a business capability, to generate value & impact

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

  1. The right data strategy in order to align efforts and support the business goals.

  2. The right technology foundations and architecture to manage and operate the data challenges.

  3. The right operating model to effectively design, build, deploy and operate Data & Analytics initiatives.

  4. The right environment and change management to achieve greater data literacy and data-driven decision-making culture.



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1. Aligning on strategy

Data & Analytics are an enabler to support the business strategy… and not an objective in itself.

Without this clear frame for alignment, the efforts made on collecting, cleaning, preparing and analyzing data are vain, as they don’t lead to efficient decision making.

The end result is frustration amongst 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 & Analytics projects is fully focused on delivering outcomes and as such aligned with the business strategy:

  • To achieve this, organizations need to 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, in order to anticipate the transformational impact of some initiative 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 impacts.

This process of alignment needs to be performed by collecting contributions across the whole organization. In this context, collaboration from the very beginning is required to ensure actionability and buy-in.

It is also mandatory to ensure continued visibility and animation all along the lifecycle of initiatives to not lose track. Moreover, define a continuous process for managing new emerging ideas, qualifying them, and adapting the portfolio when needed.

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


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2. Tech-forward foundations

Data & Analytics initiatives require adapted tools and solutions to efficiently manage and use data: capture, store, transform, analyze and visualize them across all their different nature and serving 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 & Analytics is very dynamic with frequent innovations, the architecture needs to be designed with flexibility and evolution in mind. You want your architecture to scale and adapt with your maturity, and definitely don’t want to miss the next wave of data technology innovation!

The ideal data architecture also serves as the basis for a broader IT transformation, by connecting with operational systems not only as data sources but also for automated or manual decision making.

The goal of the Data & 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 making use of those foundations also requires a large and long-term investment in skills in order to leverage the new technologies and accompany both the data literacy and the methodology evolutions across the organization.



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3. An operating model for continuous improvement

Data & Analytics projects are not one-time, they require a continuous cycle of improvement:

  • They impact business processes that themselves need to be continuously improved.

  • They rely on various data sources all having their own evolution rate.

  • They are very sensitive to environmental changes (individuals behaviors, economical context, unexpected events, etc.).

  • They need to adapt to the evolutions of the business strategy.

As such, delivering Data & Analytics initiatives that have an impact requires to set up a proper operating model to manage and optimize the portfolio along the full lifecycle, from the emergence of initiatives to their qualification, prioritization, implementation, deployment, etc.

This operating model needs to include the ability to track costs, behavior, performance and finally impacts over time — in order to assess value but also the required maintenance and evolutions: 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 & Analytics initiatives over time, enabling continuous improvement.

The key factors of an effective Data & Analytics operating model:

  • Visible, enable visibility within the organization across all dimensions on initiatives, contributors, their expected vs effective value and impacts.

  • Comprehensive, to cover the full lifecycle, providing support and metrics tailored to each stage.

  • Adaptive, to take into account the differences of maturity within the organization, and the local specificities: being able to combine a common frame of reference to track initiatives and assets.

  • Collaborative, in order to efficiently include all actors involved in data projects along the lifecycle: target users, business experts, data providers, Data & Analytics teams, IT teams, etc. This includes in some context customers or suppliers.

  • Governed, in order to share clear processes, responsibilities, roles and manage risks.

  • Automated, so that all the monitoring and tracking information is gathered continuously and effortlessly from xOps pipelines.



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4. A value-driven data culture is crucial to achieve business value

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

75% of employees are uncomfortable working with data (source: Accenture)

The risk of individuals not understanding or not trusting data and analytics is huge, putting at risk the adoption and effective deployment of initiatives: they will either fail to correctly use the available data for decision making or revert to the previous way of operating and ignoring the available data.

Data literacy is also the key for innovation, to enable individuals to trust available data and delivered initiatives, identify and propose new initiatives that generate cost savings, efficiency gains, new revenue sources, etc.

Building trust is critical to achieve value. Combining visibility on ongoing activity, accessible and reusable knowledge, the skills and data literacy of workers enable trust! And with trust, organizations can become fully data-driven and boost their innovation capabilities.

This profound cultural change toward data literacy requires

  • Glossary, as it is essential to speak common language and definitions to build trust and encourage collaboration between teams.

  • Hiring and training, most organizations have identified the need to hire data talent but also to provide training to their existing employees with a non-data professional profile, in order to develop their skills and ability to contribute to Data & Analytics initiatives.

  • Collaboration, to involve the right people all along the Data & Analytics initiatives lifecycle so they deliver the right insights the right way (back to the 3Us), 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 get more autonomy, how to rely on existing Data & Analytics to put in action in their context, how to improve their own processes, or to identify new needs in term of training, tools or new initiatives to address their needs.



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Conclusion

Now that we have been through the key requirements to achieve value with Data & Analytics, what is the pathway?

The best way is to start with an assessment of your own maturity (or maturities as the responses could depend on parts of your organizations), and build your own plan on this basis.

As a recap, that Data & Analytics plan should always include those 4 pillars:

  1. Start with defining your 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 your operating model to manage and optimize your portfolio of initiatives and assets, and monitor the generated value over time.

  4. Invest in data literacy and in animating strong communities to achieve a data-driven culture.

And deploy a continuous improvement cycle, to enrich, evolve and adapt all there along with the growing maturity, the changing business conditions and new risks or opportunities being identified.



To help you ease and accelerate the deployment of your Data & Analytics operating model, we have designed YOOI, a SaaS platform combining portfolio and assets management in order to optimize the value generated by your projects.

With YOOI, you can build your cockpit to manage all your initiatives, from emergence to deployment. Fully connected with your ecosystem of tools to consolidate information, YOOI is providing alignment with the strategy, visibility on progress and risks, supporting animation of your communities.

Don’t hesitate to contact our team to learn more and schedule a demo!

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