Data-driven business means business-driven data

Data-driven business means business-driven data

Being a data-driven organization - This is an objective that is continuously formulated over the past decade – yet continuously failed.

The current context pushes towards it - we’re living in an increasingly digitized economy – being data-driven can play a critical strategic role in how organizations pursue gaining the necessary competitive edge to be at the forefront of digital disruption.

There is a serious effort to manage data as a critical corporate asset, to transform organizational cultures into data-driven cultures, to build business strategies supported on data and analytics.

There is a serious effort in the increasing investment in big data and analytics, seen as an urgent to face a disrupting business context.

There is a serious effort in organizational changes, with more organizations appointing Chief Data Offices and supporting structures.

Still, most organizations seem to be falling short in their efforts to become data-driven.

Data-Driven is not about data

Data-driven business means business-driven data - it’s about business.

Business is the driver, it’s about finding the right balance between people, processes and technology and combining them to derive business value from data.

Business adoption is most of the times the real challenge to be faced, and this is rarely a technological problem, most frequently is about people and processes.

In organization that invested heavily in technology as a first step toward becoming data-oriented, these transformations are still hampered by ill-defined processes and business roadblocks.

The success of any data related initiative is measured on how it impacts business performance.

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.

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.