Using comprehensive data management to unlock AI success

Business leaders have widely accepted artificial intelligence (AI) as a key pillar of digital transformation. As such, it’s unsurprising to see so many organisations now attempt to accelerate the deployment and adoption of this technology.

However, most of these same organisations are still struggling to increase adoption and interest in analytics. Even with the emergence of business intelligence (BI) platforms, promises of better decision-making can go unfulfilled without widespread adoption.

>See also: Artificial or not, intelligence requires cleaned and mastered data

If organisations wish to succeed with AI, they must first have a sound BI strategy rooted in the core pillars of people, process, and platform. In recent years, many organisations have transcended basic descriptive analytics, moving into more diagnostic analysis. However, many are yet to create a true self-service environment capable of embracing the benefits and risks of AI.

Without this foundation in place, efforts to fast track an AI deployment could ultimately lead to negative outcomes like incorrect decisions. This can result in lost revenue opportunities, penalties, or even long-term damage to an organisation’s reputation. To avoid common pitfalls, organisations looking to boost investments in AI and accelerate its adoption should first assess the current state and foundational stability of their BI programme.

AI offers high stakes

Relative to BI, the stakes associated with AI are exponentially higher. BI is largely focused on understanding what has already happened, primarily via key performance indicators (KPIs), while the benefits of AI and machine learning reside in what they can offer in higher value predictive and prescriptive analytics.

As the adage goes, the greater the risk, the higher the potential reward. Inaccurately reporting a KPI via a report or dashboard would likely not be viewed as a catastrophic event, but the same would not be true if a critical business decision was ill-informed by a poorly trained algorithm.

>See also: Data management challenges and opportunities in 2018

Data is the foundation of an AI system. As a result, the quality and reliability of AI-enabled prescriptive recommendations or automated tasks are directly correlated to the quality and reliability of the data used to train the system. Organisations that have not invested in watertight data management practices or have struggled to build traction and confidence in their BI deployment will find it very difficult to successfully embrace AI.

Immature analytic adoption

A number of organisations have invested in and implemented sound data management techniques. But they can only advance in analytic maturity if employees analyse the data and use the resulting insights for decision making.

Unfortunately, BI adoption has been consistently low since its inception with a relatively small percentage of organisations’ users actually embracing BI and analytic capabilities. While the BI market is shifting towards a modern self-service model, extending analytical capabilities to a broader audience, most organisations are still in the infancy of overall analytic maturity. This is largely due to the time and complexity associated with creating a true culture of analytics.

>See also: Artificial intelligence is transforming the enterprise

For most companies, trying to leap from a state of low maturity to an AI-focused model is simply too wide of a gulf to successfully cross. An organisation’s users will ultimately train an AI system, so they must first have a vested interest in the outputs along with the aptitude and competence to properly manage the inputs. When they start increasing their data literacy levels by asking better questions and exploring new datasets, their need for more advanced analytic capabilities often follows in tandem. This fosters an environment where an AI implementation can thrive.

Addressing BI adoption

The extent to which AI will succeed within an organisation ultimately depends on the decision makers. If organisational decision-makers favour instinct over data, there is little chance that they would be willing to trust machine-generated insights and recommendations. In the face of business process decisions, a leader who has never embraced a data-driven mindset will likely reject any “black box” AI solution and revert to instinct.

When it comes to building a successful AI implementation, decision makers must address the underlying issues contributing to reluctant BI adoption. This begins with an honest assessment of an organisation’s data assets to determine if they are suitable to serve as an input into algorithms that power AI.

>See also: Is business data AI compatible?

A comprehensive data strategy should be developed and utilised to address any gaps or weaknesses in the areas of data governance, quality, cleansing, cataloging, security, or metadata management that surface during this assessment.

While your organisation is building this foundation, determine if there are departments or teams that have already established a solid BI programme or developed robust analytic processes that drive their decision processes. In these instances, the next step is to evaluate AI so it can further optimise decision making. These teams can serve as a blueprint for other areas of an organisation as they strive for analytic maturity.


Sourced by James Eiloart, SVP of EMEA at Tableau Software

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Nick Ismail

Nick Ismail is a former editor for Information Age (from 2018 to 2022) before moving on to become Global Head of Brand Journalism at HCLTech. He has a particular interest in smart technologies, AI and...