250% more business value

In a recent survey from Informatica “Driving Business Value from Data in the Face of Fragmentation and Complexity” (https://www.informatica.com/lp/driving-business-value-from-data-in-the-face-of-fragmentation-and-complexity_4241.html), there are a few numbers to highlight the importance of data management.

The most impressive number is this one — 250% more business value generated by enterprises with a high level of data maturity.

It’s important to see mentioned data maturity and not any technological maturity, so these are not necessarily organizations that are fully digitally transformed, that have fully migrated to the cloud, that have data mesh or data fabric architecture or just created their data lakehouse. No. These are organization with a high level of Data Maturity.

So, what organizations are these?

These are data-driven businesses, working with business-driven data.

  • Where data management is fully automated.
  • Where data is a strategic asset.
  • Where data is an intrinsic component of their decision processes.
  • Where there is a data culture aligned with a data strategy that is continually improved.
  • Where employee focus on high value work
  • Where AI/ML are effective.
  • Where new ways of working and new business models are emerging.
  • Where data brings the organization closer to their customers.
  • Where data is used to create a performance culture.
  • Where data-driven insights are imbedded into every business process and drive action.

These are organizations that can get breakthrough improvements not just through their data, but mostly through the way they manage and explore their data.

These are a subset of the 25% of organizations that have end-to-end data management in place.

What is happening to organizations with low data maturity?

These are organizations where data initiatives fall short from the objectives and will settle for dilution of value and mediocre performance, confronted with a situation where they simply assume that the investment was wasted and worse than that, accept to live with mediocre, under-performing solutions — expensive failures.

  • Where there is little or no data management.
  • Where there is no data strategy.
  • Where there is no data culture.
  • Where there is a reactive approach.
  • Where data initiatives are ad-hoc.
  • Where reporting is manual.
  • Where there is disagreement on how data is processed.
  • Where data is managed in silos.
  • Where data sources are unknown or uncontrolled.
  • Where there is little or no ownership.
  • Where there is no trust in data.
  • Where PoCs never make it to production.
  • Where spreadsheets rule.

Considering that 79% of the organizations are using more than 100 data sources, it’s easy to imagine that a vast majority of the organizations are losing money with their data.

Organizations where data far from being an asset is a liability.

The way up the ladder

Deriving value from the investment needed to become data-driven is a challenge, especially for organizations locked in legacy data environments, business processes, skill sets, and change resistant cultures, as they struggle to enable their data capabilities.

It’s critical to build the awareness that the transformation process that leads to a data-driven organization must be wholeheartedly supported on the business strategy and objectives — not on technology. The purpose is to create business value, so the transformation strategy, must be oriented towards the organization’s strategic priorities and key business objectives.

To understand that it’s the business prerogative to determine what are the priorities and objectives of the transformation, especially when most of the transformations tend to be customer oriented, and who better that the business to have the necessary awareness and knowledge of the customers’ expectations.

To understand that this is a business-driven transformation — where all initiatives are and oriented by the business units and grounded on clear business use cases — aligned with strategical business objectives.

Data-Driven is a priority

Whatever the reasons impairing data initiatives, analytical decisions and actions are generally better than those based on intuition and experience.

The need for data-driven organizations and cultures is real and it’s a priority.

Rather than undertaking massive change, organizations should concentrate on targeted efforts to build a data-driven culture. Don’t focus on overall data-driven transformation, identify specific projects and business initiatives that move the organization in the right direction.

Focus on the steppingstones not on a bridge to a data-driven organization.

A strict alignment with business goals and objectives — Keeping in mind that “data exists to serve business”, this means that any data governance process must be supported on strong business cases, with objectives anchored on business objectives, otherwise it will be viewed as another siloed IT project with no perceived value from the business side.

A strong focus on strategic data — At an operational level, most of the organizations rely on dozens of different systems, which handle massive volumes of data of every kind of typology daily. Approaching data in a global perspective will inevitably lead to a lack of focus, resulting on a misalignment with the business objectives and incapability to deliver value. Being supported on a strong business case that identifies and prioritizes business critical and strategical data is paramount for success.

Looking at any organization’s data landscape, to design a data strategy can be a challenge.

8 Steps for adoption

1. Vision

Frequently identified as a cause for data initiatives failures, the lack of leadership buy-in and commitment can be tracked back to the absence of a strong vision.

The role of top management it’s not simply to sanction a digital transformation.

It is essential that they communicate a vision of what and why is to be achieved — a strong purpose — to demonstrate that the transformation is an unquestionable priority, making other leaders accountable, and making it harder to back-track.

These processes need buy-in from all levels of the organization, it may start with strong executive sponsorship but needs the commitment of all the other stakeholders in the organization.

2. Clear, ambitious (business) objectives

Data’s purpose is to create value, so any data strategy must be oriented towards the organization’s strategic priorities and key business objectives, again — Data strategy is business strategy.

It is essential that clear, ambitious objectives are set from the beginning — objectives that can be clearly related to business objectives and evaluated by the business value they generate (cost savings, revenues, improved performance, or customer satisfaction).

3. Plan with the end in mind

Transforming into a data-driven organization is a long process, it needs planning.

A data journey roadmap is an essential tool. Mapping all the initiatives needed to complete the data strategy objectives, identifying the existing gaps between the current situation and the future situation, and most important the existing gap between business and the existing IT ecosystem.

It is critical to put business on the driver seat, to allow that all initiatives are driven and oriented by the business units. Transforming into a data driven organization is not an IT responsibility, it is a business responsibility, it is the business who better knows what their problems and objectives are. The role of IT in this process is to find the right technology and support the business units in this journey.

Planning the data journey roadmap must also create the conditions to ensure early efforts to thrive and gain traction. Choosing carefully on which initiatives to start with and support them with the necessary resources.

4. Start small

The priorities identified in the data journey roadmap allow to define the business cases to address first — business cases not use cases.

In an early stage, for effectiveness purposes, there should not be more than five business cases running in parallel, all with clear, achievable objectives and stakeholders that are aware of the importance and impact of data.

The choice must fall on small, targeted initiatives, where the impact and value of data can be clearly identified, business stakeholders that can passionately and effectively articulate the impacts of data in their business processes and that will be eager to defend the project.

Focus on success and on gaining traction. Look for initiatives that can be framed within a reasonable funding model, that are targeted, with focused effort, within short timeframes, able to increase internal engagement, and that can deliver targeted returns on short timeframes.

5. Who, then what

Once the roadmap is defined and the data journey about to start it’s important to gather a launch team, a team that gather the set of skills necessary to complete the first challenges, this core team should include business analysts, CX designers, data scientists, agile coaches to facilitate agile development, and developers with the necessary skills for the developing IT environment. This is the embryo team that gathers competencies in areas such as digital product and design, digital marketing, or data analytics.

6. Agile mindset

Apply an agile development mindset to all this process, start with a minimum viable solution and iterate, allow that visible results are presented in short time lapses.

Promoting agile product development, test-and-learn methods, and cross-functional teams that bring together specific types of expertise, will enhance the capabilities to create the business values necessary to build momentum and a sequence of successful data initiatives.

This agile mindset combined with the ongoing initiatives where the business stakeholders have an active role will accelerate the process of quickly move from the findings to specific actions.

7. Develop a data culture

Building a data culture within an organization is probably the biggest challenge in this process, and again, this change must be introduced in a progressive and incremental way. The same way that betting on small, focused initiatives is the best way to start, also the culture changes must be introduced in parallel and integrated with these initiatives.

Embracing innovative ways of thinking and working, it’s easier once the results and the produced value are in plain sight.

8. Build on success stories

A success story, even at a small scale will create the awareness within the organization and act as a motor to leverage the replication of that story in other business units.

When these initiatives are successful and deliver the intended benefits, business leaders will be encouraged to push to achieve more, not only focusing on what works well, but also on letting go of what doesn’t work.

Additionally, this focus on the involvement of business stakeholders driving these processes where visible business value is generated, will turn potential detractors in to change evangelists, even if only by sheer peer pressure.

Becoming Data-Driven

Becoming data-driven begins with establishing a strong data foundation, that will increase the quality and efficiency of corporate decision processes, positively affecting business operations, strategy, and performance.

Bottom line, business success depends on the execution and implementation of those decisions, and they are only as good as the data that supports them.

The true measure of success is the quality of the organization’s decision processes; the organizations best able to make the best insight-driven decisions faster will gain the competitive edge.



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