July 19, 2021
min read

How to maximize Data & Analytics value with portfolio management?

Enterprises invest more and more in Data & Analytics but struggle to effectively manage their D&A assets, capture value and experience business transformation outcomes

Photo by Marcin Jozwiak on Unsplash
Photo by Marcin Jozwiak on Unsplash

NewVantage Partners’ Executive Survey (2021) reveals that 99% of enterprises reported active investment in data and AI. Yet, only 39% of them regard data as a business asset.

Enterprises already make great strides to embrace digitalization and transform their businesses.

However, the very wide range of diverse data and digital capabilities can easily disrupt an organization’s focus:

  • Having initiatives dissociate from business actions, organizations risk losing visibility and alignment.
  • Duplicated initiatives from different departments lower team productivity and project impacts.

In short, companies struggle to effectively manage their data and analytics assets, capture value and experience business transformation outcomes.

It has always been hard to track the value of IT investments. This need gains more and more attention with the growth of Data and Analytics, which combine significant investments with very high expectations in terms of impact. Organizations are at risk of running in circles despite increasing investments and as such of failing to remain competitive.

Therefore, companies need holistic Data and Analytics management to ensure focus, monitor value creation at every step, track costs and reduce misleading or underperforming projects.

Portfolio management comes in handy to enable companies to capture value from massive Data & Analytics initiatives, by clarifying the interdependencies between assets and projects, and managing their full lifecycle.

With portfolio management, Data and Analytics become an organization’s profit center.

In that context, portfolio management for Data & Analytics specifically need to drive both assets and project portfolio combined, in order to achieve:

  • Continuous visibility and control on ongoing initiatives, existing assets and their dependencies, in the context of the enterprise strategy
  • Monitoring of overall performance, costs and impacts over time
  • Keeping track and control of risks and compliance challenges
Data & Analytics portfolio management combines assets and projects. Illustration by author
Data & Analytics portfolio management combines assets and projects. Illustration by author
Photo by Jo Szczepanska on Unsplash
Photo by Jo Szczepanska on Unsplash

Portfolio management to balance the supply and demand

Portfolio management brings a bird’s-eye view on projects and programs in favor of creating a value stream. It helps leaders better select the right projects and resources, identify risks or impediments, to fulfill global goals.

By governing the value streams, enterprises can build and maintain suitable delivery workflows.

Having portfolio management break into a supply and demand system, it connects silos and supports organizational efficiency by matching both sides and helping managers manage the relationship between costs and profits.

In order to correctly apply portfolio management, a company needs a comprehensive understanding of its deliverability capacity (supply) and value expectations (demand). The end goal expands to ensuring coordinated planning, proposing realistic expectations and maximizing deliveries.

Well-governed IT portfolios result in a 30% increase in return on assets. (Gartner, 2020)

Portfolio management for Data and Analytics helps organizations use data meaningfully and measure value with transparency. It focuses on identifying the connections between initiatives and data assets for capturing both direct and indirect value. It leads to a long-term organizational value creation process around data and analytics. In other words, it strengthens the impacts of data and analytics from all aspects.

This opens new opportunities in value generation.

Each data asset and the existing budget fuel the supply system to enable business operations and accelerate project execution. Comparatively speaking, projects and initiatives constantly create Data and Analytics demands to solve their targeted business problems.

By matching both sides, companies can understand their investment level and competitive advantages in Data and Analytics. Eventually, companies can foster revenue growth opportunities, save expenses, leverage proactive deliverables and solidify data as a sustainable asset.

Photo by Alexander Schimmeck on Unsplash
Photo by Alexander Schimmeck on Unsplash

Manage data and analytics value stream efficiently

Optimizing value from data and analytics is a continuous process, not a once-a-year or quarterly activity. (Gartner, 2021)

Having a shared Data and Analytics vision from the start is the key, ensuring alignment with strategic goals and priorities. And having a strong operating model is a must-have to be able to deliver on that vision and harvest the expected benefits.

The operating model addresses the need for defining efficient governance (roles, responsibilities, risks), adaptive processes, achieving visibility and overall supporting an effective collaboration for all actors. It is based on an adaptive learning model to continuously improve and adapt to the situation (maturity, ambitions, etc.)… and continuously maximizes value.

At the core of the operating model, managing value streams is about managing initiatives and assets end-to-end: from ideation to delivery and all along their lifetime. We propose here to zoom into the 4 main stages of this lifecycle:

The 4-stages of Data & Analytics portfolio management. Illustration by author.
The 4-stages of Data & Analytics portfolio management. Illustration by author.

1. Ideation

The success of any change is when people within the organization adopt it. To achieve that, having business users contributing to ideas or proposing ideas is crucial from the start.

These business practitioners have the closest and latest understanding of business problems and opportunities in their area, as they face them every day. In turn, they might have the most suitable suggestions to solve existing issues, improve existing processes, or address new needs. Thus, the goal for ideation is to ensure the right people are involved in where it matters to them.

To initiate this, the base is to allow and enable everyone to view existing and ongoing initiatives and to freely propose their ideas.

Users need to be provided with a framework to guide them through framing their ideas and providing the right level of details, so they can be an easy context for collaboration and processing afterward.

Through the qualification process, individuals from across the organization can engage to give inputs, complete missing information, and provide approvals to an idea.

This collaborative process encourages engagement, communication and innovation. It allows to discover potential pain points early and to discover opportunities.

Qualification is also the ideal phase to identify and remove possible duplicated or conflicting ideas from the start, or on the opposite split large ideas into achievable and well-scoped initiatives.

And finally, users having contributed to ideating and qualifying ideas should be able to track their progress through the following phases, and continue being involved all along.

2. Roadmap

The post-pandemic economic boom leads to high demands of new Data and Analytics strategic initiatives, yet the budgets remain tight. After gathering enough ideas, decision-makers need to select suitable projects and allocate adequate resources.

Each organization may have different methods and processes for project selection, but the goal is always to form a feasible collection aiming for organizational growth and differentiation.

The decision-making process contains three factors:

  1. business value,
  2. risks and costs,
  3. the willingness to transform.

Keep in mind that high-value projects often come with high risks and complexity. It may be easier to execute the big plan starting from low impact and low barrier projects.

At the same time, be aware of the organizational acceptance on the transformation level.

Facing an unexpected transformation, an organization may show resistance and avoidances rather than the willingness to commit and execute the plans. Sometimes if the organization is not ready for the change, it is probably required to identify intermediary transformation steps to get there and wait until teams are prepared and onboard.

In addition, it is key for enterprises to accelerate the typical annual or bi-annual planning cycle, and incorporate more frequent changes. This enables them to cope with the global acceleration and be able to react fast to new market conditions, new risks, emerging opportunities.

In that context, the ability to connect the roadmap with the ongoing status of execution is required to be able to quickly assess feasibility and impacts of potential changes… continuously adapting supply vs. demand and maximizing the value vs. the risks.

3. Delivery

After obtaining a clear orientation and actionable portfolios, organizations need to maintain visibility regarding ongoing projects and initiatives for efficient and effective progress tracking at every stage.

When starting a project, the first step is to finalize alignment on the scope: deliverables, success criteria, costs, technical & functional requirements, data requirements, etc. With usually much more details than during ideation or roadmap phases.

Because of different objectives, team cultures, the scale of collaboration, or other reasons, projects may proceed with various execution methods.

Considering that there are many uncertainties during project execution, it is needed to have an adequate tracking system to consolidate progress and manage changes coherently. Portfolio management allows decision-makers to review and evaluate projects under the same measurements. With this global tracking mechanism, it is easier to follow and manage project uncertainties, track progress and take action.

This way, organizations ensure that the Data and Analytics supply capacity can still cover the changing needs.

The project delivery is about engaging the resources and people assigned to the initiative, but also ensuring they get proper support and expertise to be able to deliver efficiently.

Collaboration engages expert communities by exchanging ideas, sharing findings and tools, initiating workflows and leveraging diverse skillsets from other members. It facilitates cross-project and cross-department coordination to reduce conflicts and bring more added value to the deliverables.

Ultimately, successful and impactful project management anticipates and covers deployment needs, to prepare the users and stakeholders for operational adoption and use, and to prepare for ongoing care to maintain the initiative.

4. Monitoring

Sometimes Data and Analytics initiatives deliver an enabler or a new capability as a result instead of direct and instant value. Only after the tool has been deployed and used for a while, the true value of the initiative shows.

Monitoring focuses on long-term value generation and over-time quality tracking in order to identify how Data and Analytics initiatives perform in terms of impacts and costs.

Data and Analytics initiatives are sensitive to environmental changes, including macro/microeconomics events, data source changes, new behaviors, etc. Due to the swift changes in context or data, a model can be validated today but loses its accuracy and effectiveness tomorrow. Closely tracking these changes and updating models is the key to continuously deliver high-quality data and insights.

With high-quality information supporting business practitioners, teams will be more likely to use and reuse trustworthy data, as well as better explain achievements through impact metrics.

This eventually solidifies trust towards Data and Analytics, while supporting further continuous improvements… and favoring the emergence of new ideas!

Photo by Satheesh Sankaran on Unsplash

Conclusion: Data and Analytics are the backbones of company growth

Data and Analytics capabilities need to remain efficient, sustainable and effective to support the execution of the business strategy.

To an organization, the value of data starts when it is effectively used within operational processes and by users.

By incentivizing the use and reuse of data & analytics assets, their value and impact grow even further.

As a recap, portfolio management for Data and Analytics can help enterprises and Data leaders obtain a holistic view of all initiatives and manage multiple initiatives simultaneously. Business practitioners can also contribute and adapt better to digital transformation.

Portfolio management for Data & Analytics consists of those 4 steps:

  1. Provide an ideation hub for anyone to participate, propose ideas and innovate
  2. Select suitable projects and assets by balancing business value, risks and transformation readiness
  3. Execute projects and deliver assets with adequate tracking systems and collaboration to ensure the data capacity can always react to the changes
  4. Monitor project and asset deliveries to ensure long-term value creation and maintain data quality for building trust and enable reuse
The 4-stages of Data & Analytics portfolio management. Illustration by author.

By managing all the initiatives and assets, all along their lifecycle, enterprises can deploy an effective operating model to support their strategy and optimize the delivery and adoption.

Ultimately, portfolio management helps with managing and maximizing the value from Data & Analytics investments.

This continuously evolving cycle can help your organization to fully understand and develop your Data and Analytics assets, capabilities and potentials.

To help you consolidate, manage and optimize your portfolio of Data & Analytics initiatives and assets, 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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