Keep Your Transformation on Track
How a Data Strategy Powers Your Artificial Intelligence Journey

TRANSFORM YOUR WORLD

The Change Agent

Data-driven decision-making is a cornerstone of every modern business strategy. By leveraging data-driven insights, organisations can optimise processes, enhance customer experiences, make informed decisions, and even predict future events.

Data in Motion

In today’s rapidly evolving digital landscape, business use cases for AI technologies have sparked a wave of fervour among Fortune 500 companies. Mentions of AI on earnings calls are up over 100% compared with those from a year ago¹. Advanced technologies like AI promise to automate complex tasks, predict consumer behaviour and enable data-driven decision-making. However, the efficacy of AI hinges on an often underestimated factor: a data strategy.

A well-crafted data strategy serves as a North Star, guiding organisations toward successfully realising the full potential of AI and achieving transformative business outcomes through data-driven transformation. In a field that’s growing more crowded by the day, successful organisations are those that consider the significance of a data strategy and its pivotal role in driving successful AI adoption.

At its core, a data strategy is a comprehensive plan that outlines how an organisation acquires, stores, manages and uses data to achieve its objectives. Data-driven decision-making is a cornerstone of every modern business strategy. By leveraging data-driven insights, organsations can optimise processes, enhance customer experiences, make informed decisions and even predict future events. However, a significant amount of valuable information remains dormant and untapped within organisations. This dormant data often exists in disparate systems or unstructured formats or simply lacks integration. The challenge lies in converting this dormant data to an active, usable state.

To address this, organisations are increasingly investing in advanced data analytics, machine learning and AI. These technologies help unearth insights from an organisation’s internal and external data by identifying patterns, correlations and trends that might otherwise go unnoticed. By transforming data into actionable insights, businesses can make more informed decisions, optimise processes, enhance customer experiences and gain a competitive edge.

Unlocking the potential of data involves implementing robust data integration strategies, enhancing data quality and creating an organisational culture that values data-driven insights. Organisations that prioritise data activation can harness the full spectrum of their information resources, enabling them to make more proactive, agile and effective decisions in today’s rapidly evolving business landscape. There are a number of industry sectors where a holistic data strategy is driving AI insights and leading overall data-driven transformation.

In healthcare, a data strategy enables AI applications like predictive analytics to forecast disease outbreaks, personalise treatment plans and improve patient outcomes. This approach mitigates challenges, such as resource allocation inefficiencies, and enhances overall healthcare delivery.
In finance, data-driven AI algorithms analyse market trends, customer behaviours and risk factors to optimise investment strategies, detect fraudulent activities and predict market fluctuations. This helps financial institutions navigate volatile markets and ensure regulatory compliance while improving risk management practices.
In e-commerce and retail, AI-powered recommendation systems driven by data strategies can provide beneficial information for a better customer experience. These systems personalise product suggestions, enhancing user experiences and driving sales. Data-driven insights also enable inventory optimisation, ensuring that products are available when and where customers demand them.

In an era where artificial intelligence is poised to reshape industries, organisations cannot afford to overlook the role of a data strategy. It is the linchpin that ensures AI initiatives deliver on their promises. From data collection to model training and ongoing observation, a comprehensive AI data strategy aligns data practices with business goals, safeguarding against pitfalls and maximising potential value. As organisations embark on their AI journeys, a solid data analytics strategy is not just an option—it’s the driving force behind success in the age of data-driven transformation.

$300 billion+ Projected AI spending by 2026.²

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Points of view on how organisations can leverage automation to enhance customer experiences, reduce operating costs and increase profitability

Strategise the Data Revolution

Recent decades have proven the remarkable potential of data. This encompasses activities involving small data, big data, statistics, analytics and AI, undertaken by employees across all levels of an organisation. Such endeavors empower businesses to each enhance their performance in diverse ways. On the one hand, the focal point is AI, particularly generative AI, which has the potential to revolutionise the business landscape and is generating significant excitement. On the other hand, even basic analyses using modest data volumes are astonishingly effective at aiding companies in optimising decisions, refining business processes, gaining deeper customer insights and elevating products and services in response to customer demands. A quick scan of popular media might lead one to believe that data, analytics and AI are orchestrating a global takeover. But how do organisations seize upon this opportunity and use it to their advantage?

Every organisation has likely already embarked on a data journey. Their starting point depends on their past trajectory and the foundational work that has already been done. A winning data strategy must encompass the full spectrum of back-end technology, front-end strategy and all elements in between. Regardless of where the organisations are on this journey, the first crucial step is digitalisation. We generate petabytes of data each hour, and yet many processes still hinge on paper-based information. Consider a recent visit to the doctor, where filling out a paper form with personal details and medical history was necessary. While this information is critical for medical staff, its storage and security remain opaque. Before an organisation can unlock potent data insights, it must begin by digitising its business processes.

Subsequently, the journey evolves into a sequence of establishing the right foundation that empowers users to make decisions grounded in data. The organisation collects, stores, archives and governs data while ensuring the framework of its modern data platform is well-established. Effective data governance is critical. Without robust data governance, the organisation lacks a single source of truth from which strategic decisions can be derived. At this phase, much of the decision-making remains informed by reactive analytics—responses to events that have already happened. Users analyse year-over-year or quarter-over-quarter trends to glean insights from what’s occurred. This certainly holds value, but the organisation has yet to reach a state of agility where it can anticipate and quickly react to future and emergent developments. Data monetisation and transformation into an organisational asset remain unrealised.

21% increase in the share of global English-language job postings that mention new AI technologies such as GPT or ChatGPT since November 2022.³

Governance and Ethical Considerations

Artificial intelligence initiatives require access to vast amounts of data, necessitating scalable and efficient data storage solutions. A data strategy outlines the infrastructure needed to store, manage, and retrieve data seamlessly. Organisations must weigh the pros and cons of cloud-based and on-premises storage, considering factors such as cost, scalability, and data governance.

As data becomes the lifeblood of AI initiatives, governance, data security and privacy concerns come to the forefront. A comprehensive data strategy includes measures to safeguard sensitive data, adhere to regulatory standards, and address ethical considerations. Organisations need to strike a balance between data openness for AI and ensuring data protection.

By bringing together databases, analytics, AI and machine learning, successful organisations revolutionise the enterprise and deliver value across the life cycle of their data platform. Users gain unprecedented access to make data-driven strategic decisions, enhancing customer service and optimising operations through intelligent analytics. At this stage of the journey, data emerges as a prized asset. Across the enterprise, business users harness high-quality, reliable and timely data to uncover new revenue streams, enhance customer experiences and propel organisational growth.

Developing an effective data analytics strategy takes time and effort. Stay focused on your objectives and be prepared to iterate and adjust your approach as you learn from your experiences. By integrating AI considerations into your data strategy, you can harness the power of AI to drive innovation, improve efficiency and gain competitive advantages while maintaining ethical and responsible AI practices.

TEKsystems' Tips

Unlock the full potential of your organisation’s data with these essential tips for successfully implementing a comprehensive data strategy.

Clearly define objectives: Clearly outline the goals and objectives for your data analytics strategy. Determine what you want to achieve and how data can support those goals. Identify specific use cases where AI can bring value and enhance decision-making processes.

Secure executive support: Ensure that top-level executives are on board and supportive of the data strategy. Their buy-in is crucial for securing resources and driving cultural change.

icon of computer and arrows pointing to people

Embrace data diversity: Gather diverse and representative datasets to train AI models. Address ethical concerns surrounding AI such as fairness, transparency and accountability. Biased or incomplete data can lead to biased or inaccurate AI outcomes. Regularly audit AI models for potential biases and unintended consequences.

Encourage cross-functional collaboration: Involve various departments and teams across the organisation. Collaborative efforts ensure that the data strategy aligns with different business needs. Foster collaboration between data scientists, domain experts and business stakeholders. Their combined insights can lead to more effective data solutions.

Consider resource planning: AI requires sizable computational resources. Ensure you have the necessary hardware, software and cloud resources to support AI model training and inference.

Rigorously measure progress: Set key performance indicators (KPIs) to measure the success of your data strategy. Regularly assess and adjust your approach based on these metrics.

Sources

Companies Double Down on AI in June-Quarter Analyst Calls, Reuters
Worldwide Artificial Intelligence Spending Guides, IDC
Future of Work Report, LinkedIn

Keep Your Transformation on Track
How a Data Strategy Powers Your Artificial Intelligence Journey

TRANSFORM YOUR WORLD

The Change Agent

Data in Motion

Our Perspective