A Pivot for AI-powered strong organisations

The strength of an organisation is often measured by its ability to make informed decisions. This strength, however, is not solely dependent on visionary leadership or innovative products; it hinges on a less glamorous but equally vital element—analysis. Analysis can empower organisations to navigate complexities and uncertainties, especially with AI / ML. It is especially important if we want to identify the most important things of value quickly, model possible solutions, and monitor the results.

I have several questions for you :)

Do you think your organisation has enough analysis? Are you aware of the hidden messages? Or, more importantly, do you think your organisation, from top to bottom, scans the big data enough?

Why do I ask these questions? Because the secret is here, BIG DATA ANALYSIS. Poor analysis—or worse, the lack of analysis—can lead to decisions that are not just suboptimal but disastrous. It may cost millions of dollars for your organisation. In addition to that, investment in AI can go to waste. We need to know what should be first. Let me explain more...

BIG PICTURE VIEW

To make informed decisions, organizations must look beyond individual departments and consider the enterprise-wide impact of their actions. The "big picture view" involves aggregating and analyzing data across the entire organization rather than in isolated silos. This holistic perspective allows leaders to understand the full context in which their business operates, identify synergies, and make decisions that optimize the organization as a whole.

If your organization lacks value stream mapping, customer journey, and process analysis, you should think twice about its cost. I can almost hear you saying, 'We have our analyses". But are these usable? When you check them, will you be able to understand the whole picture? Are they up to date? It is not just the top level; the entire organisation (top to bottom) should understand and see the picture. Without this view, organizations risk making decisions based on incomplete information, leading to inefficiencies, missed opportunities, and potentially costly mistakes.

BIG DATA SYSTEMS

Big Data systems are the backbone of modern analysis, enabling organizations to handle vast amounts of data with the necessary speed, volume, and variety. These systems must be capable of processing petabytes of data from various sources and formats, including structured databases and unstructured media like video and sensor data.

Effective Big Data systems ensure that relevant information is readily available for analysis, supporting AI/ML initiatives and other data-driven strategies. When organizations lack robust Big Data systems, they may fail to capitalize on the full potential of their data, resulting in flawed insights and suboptimal decision-making.

CONTEXT

Context is critical in the analysis. Data without context is meaningless, and decisions made without understanding the context can be disastrous. Effective analysis considers the surrounding circumstances, historical data, and external factors influencing outcomes.

For example, a predictive model might suggest a course of action based on patterns in the data. Still, the recommendation could be irrelevant or harmful without considering recent market shifts or external disruptions. Organizations that neglect to contextualize their data analysis are more likely to encounter unexpected challenges and make costly errors.

ORGANIZING AROUND VALUE

To maximize the impact of Big Data, organizations must organize their efforts around delivering value. This involves aligning data collection, analysis, and application with the organization’s strategic goals. Companies can prioritize the most impactful data initiatives by focusing on value creation and allocating resources efficiently. Conversely, organizations that do not organize around value may spread their efforts too thin, failing to generate meaningful insights or drive significant improvements. This lack of focus can result in wasted investments and missed opportunities, ultimately costing the organization financially and strategically.

Why big data analysis is essential for AI implementation?

Everyone is talking about AI and how we can implement AI/ML in our business. We are taking courses about prompt engineering, AI/ML and learning new AI tools. The world is changing, and so are the rules of the game. In the realm of Artificial Intelligence, big data analysis plays a pivotal role.

AI systems thrive on vast amounts of data to learn, adapt, and make predictions. The accuracy and effectiveness of AI models are directly linked to the quality and comprehensiveness of the data they are trained on. Big data analysis ensures that AI algorithms are fed with rich, diverse, and relevant datasets, enabling them to uncover patterns, generate insights, and make accurate decisions. Without robust big data analysis, AI models risk being biased, incomplete, or inaccurate, leading to flawed outcomes and potentially costly errors.

In the AI-driven world, the success of AI initiatives hinges on the ability to harness and analyze big data effectively, making it a critical component of any AI strategy.