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Implementing Data

Driven Solutions


Gavin Sheehan, Executive: Data, Information and Analytics, Decision Inc.

Gavin Sheehan, Executive: Business Intelligence and Data Analytics at Decision Inc., examines the three fundamental pillars that comprise the strategic, sustainable and successful implementation of data-driven solutions within the organisation.


Data has been the gold of the digitalised organisation for many years, but with its potential has come pitfalls and complexities that few solutions have set out to solve. The lakes and pools of data that lie within the business remain a resource that can be tapped to fine-tune productivity and potential, but there is a need to build more stable and robust solutions that aren’t just ticked boxes on a data to-do list.

There is a reason why the title of Data Engineer is set to dominate in terms of the job title du jour, and why the Chief Data Officer will be playing a more integral role on the board and reporting directly to the CEO. This reason is that organisations will be looking to do more with data in more intelligent ways.

“More than 50% of CDOs will report to the CEO in 2018, an increase of nearly 16% in 2016.” [Forrester Predictions 2018: The Honeymoon for AI is Over]

The same report emphasises that when it comes to new technologies such as big data, cloud, and AI, there is still a lot of work to be done and that no business should continue to see any one of them as the panacea to cure all ills.

To benefit from what data has to offer, the organisation needs to build strong foundations of technology which then support the three pillars of Insight, Engagement, and Adoption.

The Foundation

The first step is to unwrap how an organisation’s data demands will look in the future. This will allow for the right technology investment that will provide an enriched, high-quality foundation.  This must adhere to the highest possible data standards, driving the line of business and creating a digital hub and digital experiences that define how the business engages with the data. This foundation is comprised of the architecture, the integration and the technology that brings data from the background to the forefront of the organisation.

Data cannot live in isolation. A robust foundation ensures that data is structured within a framework that’s aligned with the business strategy and supports the evolution of the digitalised organisation.

The foundation needs to be an on-demand and engaging platform that is accessible, contextual and relevant. The business must ask – how can data be compartmentalised to mean something to me?


The Value of Insight

Once the foundation is established, the organisation must enrich the information that this foundation provides and ensure that it has context and relevance. Most businesses operate in silos, data disappearing into different spaces without first being assessed and evaluated within these two critical frameworks. It has impacted the understanding of data and its value.

Big data slid off the Gartner hype cycle as far back as 2015 and this shift is largely due to the fact that few organisations were bridging the gap between the implementation of a data-driven foundation and deriving actual value from this implementation. The issue is this – how does the business get value out of big data?

The answer lies in developing a data strategy that invests in the tools required to collate the content, add expertise to the information, and glean insights that are of value and relevance.

Insight is critical to the successful implementation of any data strategy, providing a competitive edge that places the organisation ahead of the very demanding market curve.

Insight is the sieve that pulls the nuggets of value from the lakes of data and that ensures they land in front of the right people. It has also traditionally been ignored as organisations move from investing into a foundation straight to driving employee engagement, however, without insight there is limited value to be gained from engagement.

The Importance of Engagement

This pillar is the traditional business intelligence (BI) leg of the data process. It is the one that the business tends to leapfrog towards after implementing a foundation. It is in this phase that the user is physically interacting with the data, engaging with it and using it to drive innovation. Within the rich layers of data lie the insights that the engaged organisation can use to enhance research and development, redefine user experiences and interactions and invest in artificial intelligence (AI) solutions that map back to the business strategy.

In this level lies the technology that provides the user with the dashboards and interfaces needed to access the data, to pull insights into charts and reports and to refine business process and productivity. etc.

The Hook of Adoption

In the past, we have measured our success around how we delivered on time, how we met the budget and how we were always on spec. But we realised that there is a fourth driver around user adoption and truly benefitting from the data investment – what the user wants.

The business has spent a lot of money on its data. It has the foundation and has possibly invested in the tools that deliver genuine insight. It has delivered the software required by the end-user to pull the data and the insights into reports and analyses that deliver on the value proposition.

And still, businesses face the challenge of user adoption.

Users are not engaged, few use the system and those that do are usually the analysts or the power users and this cuts out a significant percentage of the workforce. The goal is to ensure 100% adoption, to ensure that the maximum number of users are engaged and to do so after the engagement tools have been installed.

To truly derive value from data the business has to invest in adoption, in ensuring that there is effective change management, that the user is empowered, that champions are created and employees heard. It is also vital that the complexities, and perception of complexity, are removed, making data and analytics as accessible as possible. This is where a truly integrated data strategy differentiates itself from the rest and delivers on the original promise.


The business needs to justify the expense and create a business case around why data is of value. There has to be a return on investment and a way to show efficiency before a business case can be implemented.

The key reason for developing a targeted and focused data management team is to help clients optimise how they work with data.

In Governance

A business must have a data steward who is completely focused on data quality and governance and who can provide clients with data governance frameworks and building architecture blueprints that aren’t structured in isolation.

In Aggregation

The team must have the technical qualifications and people who know how to aggregate the data, who can think two to three steps down the line to ensure that data is in the right format, fits the business and that it remains strategically viable.

In the Information Landscape Audit

It is critically important that the products selected to build the foundation are suited to the business and its needs. The team needs to ask the questions, assess the environment and provide the organisation with the information it needs to execute a fit for purpose data strategy.

In Ownership

There must be a dedicated consultant working with the organisation throughout the implementation of a data strategy. They ask the right questions, they ensure that data quality remains a priority, they reinforce good governance and they are the gatekeeper to prevent unnecessary expenditure on technology that doesn’t fit.


To fully deliver on the concept of an integrated and sustainable data strategy, the organisation needs to take specific steps:

  • Increase the touchpoints for end-users, opening up the data platform and making it easy and relevant. Users want the information in simple bites with a minimum of effort.
  • Use employee feedback and questionnaires to plot a maturity curve that will reveal the gaps and show the areas of the business that are hungry for change.
  • Enrich the data using embedded and intrinsic analytics and showcase how this affects business intelligence results and how this data can be used to create solutions that are easier to consume and understand.
  • Build dashboards that provide aggregated, real-time results and training that allows for end-users to become accustomed to the software without feeling as if they’ve been pulled out of their comfort zone. This has impressive returns on engagement and deliverables.
  • Delivering measurable results that show the value of data and a return on investment. Establish how data adds value, how it has changed the business, and ensure that every solution is carried through from implementation to adoption.
  • Invest in partnerships that are accountable for each platform and who are dedicated to changing your business and delivering real value.


The three pillars of insight, engagement, and adoption are essential to the strategic, sustainable and successful implementation of data-driven solutions within the organisation. Throughout this whitepaper, we’ve examined how each of these pillars supports the business in harnessing the true potential of data. For data that’s relevant, supports growth and transforms the business, there has to be technology that translates it and people that understand it. It’s an ecosystem that requires investment in the entire process to ensure the right results.


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