n today’s business world, being data-driven is no longer a question; it is a necessity. Organisations that don’t understand how to work with data and leverage it risk falling behind or even going out of business. However, merely being data-driven is not enough anymore. The rapid growth of access to artificial intelligence (AI) and lowered computing cost has amplified the significance of data, driving a shift towards predictive (and even prescriptive) intelligence to stay ahead of the competition.  

Transitioning from a data-driven to an AI-driven organisation presents immense opportunities, enabling companies to understand the competitive landscape better, and leverage both market predictions to gain an edge, as well as improving operations to lower operating expenses. This transition requires a fundamental change in how we operate and organise the company. Secondly, we need to decide where to start, and whether to build, or buy a solution.

Here we share five, simple, steps to ensure your organisations success in this transition.  

1. Management must clearly state that it is a goal

Achieving success with a transition is a strategic choice and an executional leadership challenge. It is crucial for management, whether top-level executives, business unit leaders, or team managers, to clearly communicate that the goal is to capitalise on the benefits of being data-, AI-, or analytics-driven, and where these benefits will have an impact, and why the transition is imperative for the organisation’s success. Leaders should:

• Clearly state the necessity of this transition for organisational success.
• Be transparent about the potential challenges of the transition.
• Accept that the transition might take longer time than anticipated, especially if immediate benefits are not apparent.
• Repeat the goal, ensure regular follow-ups on the agenda, at least monthly, preferably weekly, and support.

2. Organise the transition

Clarifying responsibility is essential as well as identifying the right person to lead the operational work of the transition. Allocate funding centrally rather than locally to prevent initiatives from being perceived as competing with short-term operational needs. By centralizing funding and clarifying responsibility, organisations can ensure that the transition to an AI-driven approach is viewed as a strategic investment rather than an operational cost.

3. Disseminate the solution broadly

It is unfortunate when initiatives become confined to a single department or individual. The benefits of an AI-driven approach are significant and extend across the entire organisation. Therefore, it is crucial to integrate solutions into as many teams as possible where there is a business case. Engaging more teams in the adoption phase offers several benefits:

• Shared costs across more teams.
• A unified language and collaborative efforts towards success.
• Accelerated transition and higher combined ROI.

Avoid placing the burden on a single individual. Employ the innovative power of the entire organisation to achieve greater success.

4. Embed new solutions in daily routines

For new solutions and strategies to work, they must be integrated into daily operations. Overcoming existing habits and ways of working requires repetition until the new practices become habits. Incorporate the use of data and analytics tools into the organisational rhythm, such as in weekly meetings or daily stand-ups. Measure the impact of these new practices and share the progress with the entire organisation. Highlight how the transition is improving efficiency compared to previous methods.  

5. Embrace an Adaptive Mentality

Fostering an adaptive mindset is crucial for the transition to an AI-driven organisation. This mindset should infiltrate the company culture, regardless of role. Here are three tips for building a stronger adaptive mindset:

• Identify and support superusers who can inspire and motivate others.
• Hire individuals with an innovative mindset, both leaders and employees.
• Promote a supportive culture through promotions, celebrating successes, and sharing positive results.

It might sound simple, but actively working on lifting and promoting the right people is very often overlooked. Make sure it is part of the leaderships action plan so this practice doesn’t fall between two chairs, or is forgotten within a couple of quarters.

Conclusion

Building a data and AI-driven organisation is essential for maintaining competitiveness in today’s business environment. Transitioning from being merely data-driven to embracing AI and predictive intelligence offers significant advantages, including a better understanding of the competitive landscape, leveraging market predictions, and improving operational efficiencies.

To ensure success in this transition, organisations should follow five key steps. First, management must clearly articulate that becoming an AI-driven organisation is a strategic goal. This involves transparent communication about the importance and challenges of the transition, along with regular follow-ups and continuous leadership support.

Second, organising the transition is crucial. This includes clarifying responsibilities and centralizing funding to ensure that AI initiatives are viewed as strategic investments rather than operational costs.

Third, disseminating the solution broadly across the organisation is vital. Integrating AI solutions into multiple teams enhances collaboration, shares costs, and accelerates the transition, leading to a higher overall ROI.

Fourth, embedding new solutions into daily routines ensures that these practices become ingrained in the organisation’s operations. Regular use and measurement of the impact help highlight the efficiency improvements over previous methods.

Finally, fostering an adaptive mentality is essential. This involves supporting superusers, hiring individuals with an innovative mindset, and promoting a culture that celebrates successes. An adaptive mentality ensures the organisation remains agile and responsive to new opportunities.

By following these steps, organisations can effectively leverage data and AI, achieving sustained success in an increasingly AI-driven world.

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ata analytics has become the cornerstone of strategic business decision-making. But what is the difference between diagnostic and predictive analytics? This visual and simple guide represents the evolutionary journey of analytics, from basic understanding to advanced prediction and optimisation.

🔍 Descriptive Analytics: "What happened?"

This is our starting point, where we use historical data to understand past performance.

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🔎 Diagnostic Analytics: "Why did it happen?"

Here, we dig deeper, using the data to uncover the root causes of past events.

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📈 Predictive Analytics: "What will happen?"

Leveraging statistical models and machine learning, we forecast future trends and behaviors.

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🎯 Prescriptive Analytics: "How can we make it happen?"

This is the pinnacle of analytics maturity, where we not only predict the future but also provide actionable recommendations to shape desired outcomes.

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As we move up the ladder from hindsight through insight and towards foresight, the difficulty increases, as well as the data requirement - but it significantly amplifies the value and optimisation capacity of our decision-making processes.

aking a decision is easy but knowing how to make the right decision at the moment of choice, now that is tricky. As the outcomes and consequences are only known after the decision has been made, we try hard to mitigate the risk of making a wrong one.  

Like a game of probability, we weigh different information and data, and play out the possible outcomes against each other to narrow down our choices, and, well, make a bet. Given the vast amount of information and data available, gathering the needed and relevant information can be a challenge. For the human mind it is impossible to grasp all inputs and data at once. And it is practically impossible. Additionally, as we learn new information, we may create new connections and gain new insights that open new possibilities. Which often leads to the question, "What if...?"

Lastly, before executing the decision, we weigh our options and evidence, and filter it through the personal and/or corporate value filter. By repeating this process, and adding a decision-review step, we learn how to make better decisions. The more we know, the more experience we have, the better our chances of making the best possible choice. And that is how it has been for the last ten of thousands of years.  

While we have evolved our ability to gather and access information with software, and made the analytical part simpler and more accessible, machine-assisted decision making and execution is about to change the decision-making process.

AI and decision making

The human brain can process 11 million bits of information per second, but our conscious minds can handle only 40 to 50 bits per second. And while we do not always forget, retrieving the right information at the right time is not straightforward.

Our ability to gather and analyse data is limited by our knowledge, time, and “computational power.” However, if we know what information we need, there are now thousands of tools that can help us gather the data and connect it with other data sources to uncover new insights and patterns.

Predicting the future based on historical patterns is not a complicated science, but rarely a trustworthy one. Machine learning algorithms have increased the accuracy and given us a better foresight of how decisions and events might unfold, making it possible to simulate different scenarios and study decision consequences without having to execute a decision. The possibility of setting up “What-if” scenarios and playing them against each other, pushes us closer of being able to make the right, rational decision.  

Building on the previous point about the importance of good data, let us talk about the challenge of data diversity. Machine learning models are only as good as the data they are trained on. If you train a model on a narrow dataset, it will only be able to make predictions that are relevant to that dataset. For example, an automated script writer that is only trained on movies and books written by Quentin Tarantino will always produce scripts that are similar to Tarantino's work. The same thing happens if you run your analytics only based on your company's internal data without considering external data such as market and competitor data.  

Powerful and accurate models combine data from a variety of sources to reduce bias, improve generalisation, and identify new patterns and insights. For example, a company that is developing a model to predict customer churn could combine data from its internal CRM system with data from external sources such as social media and customer reviews. This would help the company to identify patterns and insights that it would not be able to see by looking at its internal data alone.

Prescriptive analytics  

The one type of analytics that will profoundly change our decision-making process, and profoundly change how we work, is prescriptive analytics.

Prescriptive analytics is (currently) the final stage in the analytics spectrum, which includes descriptive, diagnostic, predictive, and prescriptive analytics. Descriptive analytics answers the question "What happened?", diagnostic analytics explain “Why it happened!”, predictive analytics addresses "What might happen?", and prescriptive analytics tackles "What should we do about it?", including all former analytics in its process.  

When we make decisions, all these analyses happen naturally in our brain and are part of our decision process. The extent of how much we analyse depends on the time we have, the number of people involved, and the consequences of the decision. If we have little time or the stakes are low, we may make a quick decision with minimal analysis. However, if we have more time or the stakes are high, we will spend more time trying to analyse the situation and considering our (imagined) options.

If we turn to machine-assisted decision making powered by prescriptive analytics many of parts of decision process become automated. Using machine learning, algorithms, and computational modelling, prescriptive analytics provide insights, simulates different scenarios, and suggest actionable steps in response to a predicted outcome or scenario.

For example, in supply chain management, prescriptive analytics might suggest optimal routes for delivery based on predicted weather conditions, anticipated traffic patterns, and historical accident data. Or, in finance, it could recommend investment strategies based on a forecasted economic downturn.

A New Paradigm of Decision-Making with Prescriptive Intelligence

A step-by-step decision-making process includes most commonly these seven parts:

1. Identify the decision
2. Gather information
3. Identify alternatives
4. Weigh the evidence
5. Choose among the alternatives
6. Take action
7. Review the decision

Imagine that you have a data foundation that gathers all your data in one place, both external open data (market, competitors etc.) and internal. You have billions of rows of present and historical data, cleaned, enriched, and contextualised. You are a Business Development Manager at a Last-Mile delivery company, and you are tasked with expanding sales to a new area. Where do you start?

1. Identify the decision

In which geographical area can we increase our revenue the most?  

2. Gather information

Where are our competitors present?  
What are our competitors' prices?  
Where are our terminals?  
How much are we today delivering in each area?
What delivery options are the most popular in which area?  
What investment will be needed for each area?  
Etc.  

3. Identify alternatives

All areas and options are listed. Business cases are presented.

4. Weigh the evidence

Alternatives are weighed against each other. Pros and cons are discussed.

5. Choose among the alternatives

Once you have weighed all the evidence, you are ready to select the alternative that seems best for the company. You may even choose a combination of alternatives.

6. Take action

You implement the chosen alternative. It is time for execution.

7. Review the decision

You review the results of the decision and see how your expansion plan is working out and iterate.  

With prescriptive intelligence in place, the machine assisted decision-making process is similar, but at the same quite different as the effort lies in the beginning, and not the collection of information. We assume here you have access to a tool that combines market data with internal data.

1. Identify the decision

In which geographical area can we increase our revenue the most?  

2. Goal formulation (prompting)

What are the results that you are looking to achieve and through what means. List interesting areas for exploration and factors you think are relevant.

3. Scenario evaluation

Alternatives and scenarios are simulated and presented by the AI describing the steps needed to reach formulated goal. Costs and risks are listed based on data that is available. You have the possibility to deep dive into areas to expand your analysis or follow the recommended path.

4. Weigh the scenarios

Recommendation is weighed against the other scenarios.

5. Scenario implementation

You implement the chosen scenario and measure against milestones and goals set by the AI.

6. Review the chosen scenario

The decision and chosen scenario are evaluated in real time with the AI to ensure ongoing learning and optimisation.

If we look past the fact that much of the decision-making process is automated, we move from hypothetical discussions around outcomes and consequences to an evaluation of the proposed steps to reach the decision and set goal. The proposed scenario is not unbiased and unemotional, it is guiding force explaining how to reach that goal with what is available.  

Science fiction?  

Prescriptive intelligence is not something we imagine anymore, it is being worked on today, and there are already solutions in the market for specific use cases. Our decision-making process will not only be faster (timewise), but we will also be able to be much more accurate in understanding outcomes and the decisions in between we need to make to reach a certain goal.

Finding the competitive edge

If everyone can afford the same tools and have access to the same data, isn't there a risk that we will all pull towards the same goals in our respective fields? Isn’t it all about increasing profit through expansion or decreasing costs?

The chances of that scenario are limited.

Not one company has the same data as another one. We can acquire datasets, predictions, but in the end how we operate, they people we employ, the decision we made, and our assets and business models are not the same. Each company has its own strategy, so even if we all access the same market intelligence, the outcome will be different. But just as generative AI has shown with ChatGPT and Midjourney, the playfield has become much more even.  

Market analysis and expensive data is becoming less expensive and available to a larger extent of companies, and not only the big ones.

Market intelligence

A general prescriptive analytics platform is still a couple of years in the future. At Tembi, we have built the data foundation for it, and are constantly working on adding new machine learning based prediction and econometric models to create better insights and foresights for our clients based on open data.

While companies have their internal data, we provide extensive access to open data, and ready-to-go-analytics – or market intelligence – that provide actionable insights to the decision-making process. Many of our clients use our API to connect their data with our data to examine and understand (i.e.) volume fluctuations (revenue drivers) with external events, and hence be able to understand how external factors impact their business, mitigate risk, or uncover new business possibilities.

The more we connect the world's Information the better we will understand the future, and the more impact our decisions will have. And that is why we work here at Tembi. Until we provide a general prescriptive intelligence platform for executing successful business decisions, we focus on providing market intelligence that is beyond what can be seen by a person online. We combine data from multiple industries and build market predictions models based on changes across different industries.  

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he amounts of available data is growing in an overwhelming speed, on one hand presenting an increased difficulty to collect and access the data, on the other hand an increased opportunity to better understand markets and competitors.  

With continuously increased computing power and a steadily growing democratisation of access to advanced analytics, the way we approach decision-making is evolving.  What has been historically a process of intuition and experience is now increasingly guided by data-driven insights. This transformation is enabling companies to not only understand past and present trends but also to predict and shape future outcomes.  

Let’s dive into how data and analytics are reshaping business decision-making, from traditional methods to the advanced analytics techniques of the future.

The evolution of decision-making processes

Traditionally, business decisions were often made based on intuition, experience, and a limited set of data. Executives relied heavily on their gut feelings or the historical knowledge of their industry. While this approach worked in the past, it more than often led to suboptimal outcomes due to the lack of comprehensive information and understanding of the market.

The emergence of data-driven decision-making marked a significant shift in this process. Businesses began to collect and analyse large internal and external datasets, to inform their strategies and tactics. A development that has been rapidly accelerated by the introduction of BI software. Decisions were no longer solely based on instinct but were supported by quantitative evidence.

As technology advanced, so did the decision-making process. We have now entered an era of analytics-driven decisions, where businesses use sophisticated analytical tools to forecast future trends (predictive analytics) and even prescribe specific actions to achieve desired outcomes (prescriptive analytics). For instance, Amazon uses predictive analytics to manage inventory, ensuring that products are in stock when customers want them while minimising storage costs. Our company, Tembi, has developed a beta product that uses prescriptive analytics to recommend development and construction companies what to build in certain locations to reach full capacity. And this is the only beginning of how data and analytics will assist us in making better decisions.

The Analytics Value Escalator

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To understand the full impact of analytics on decision-making, it’s essential to explore the concept of the Analytics Value Escalator developed by Gartner. This model describes the progression of analytical methods, each offering increasing value and complexity.

1. Descriptive Analytics

Descriptive analytics answers the question, “What happened?” It involves summarising historical data to understand past performance. For example, sales reports, web analytics, and financial statements fall into this category. While descriptive analytics provides valuable insights, it is often limited to hindsight and does not explain the reasons behind the data.

2. Diagnostic Analytics

Diagnostic analytics delves deeper, addressing the question, “Why did it happen?” By identifying correlations and patterns within the data, businesses can uncover the root causes of specific outcomes. This method is more powerful than descriptive analytics but still focuses on past events.

3. Predictive Analytics

Moving up the escalator, predictive analytics answers the question, “What is likely to happen?” It uses historical data, machine learning algorithms, and statistical models to forecast future trends and behaviors. For example, retailers might use predictive analytics to anticipate customer demand or optimise inventory levels.

4. Prescriptive Analytics

At the top of the escalator is prescriptive analytics, which addresses the question, “What should we do?” This advanced method not only predicts future outcomes but also recommends specific actions to achieve the best possible results. For instance, a logistics company might use prescriptive analytics to determine the most efficient delivery routes, considering variables like traffic, weather, and fuel costs.  

The importance of quality data

No matter how advanced the analytics methods are, their effectiveness is fundamentally dependent on the quality of the data they analyse. Poor quality data or analytics conducted on incomplete data-sets can lead to misleading conclusions and can hence create unreliable insights.    

Common data issues include data silos, where information is trapped in isolated systems; inconsistent data formats; and incomplete or outdated data.  

To ensure data quality, businesses must adopt best practices such as regular data cleaning, integration across departments, and robust data governance policies.  

For instance, Procter & Gamble invested in a comprehensive data governance framework to ensure consistency and accuracy across its global operations, which has been crucial in maintaining the integrity of their analytics initiatives.  

“We’re also now able to take our data analytics and AI to the next level because we have a solid, reliable base of product data that can be matched with external consumer data. That possibility gets our business leaders really excited!”
Laura Becker, President of Global Business Services at Procter & Gamble

Generative AI’s limitations in business decision-making

Generative AI, a cutting-edge technology that enables machines to create new and original content, has revolutionised various industries by producing text, images, music, and even complex data patterns. Its ability to generate content that mimics human creativity has opened up exciting possibilities in fields like marketing, design, entertainment, and more. However, despite its remarkable capabilities, generative AI faces notable limitations, particularly in the context of business decision-making.

In business environments, decision-making often requires a deep understanding of nuanced contexts, the ability to interpret complex and sometimes ambiguous data, and the capacity to foresee the broader implications of certain choices. While generative AI can assist by providing insights, generating scenarios, or offering creative solutions, it lacks the human intuition and judgment needed to fully comprehend the strategic, ethical, and long-term consequences of business decisions.

Another significant limitation is the lack of transparency in how generative AI models arrive at their outputs. These models often function as "black boxes," where the decision-making processes are not easily interpretable or understandable, even to those with technical expertise. This opacity can be problematic in business settings, where leaders need to understand the rationale behind decisions and recommendations. Without transparency, it becomes challenging to trust and validate the AI's outputs, increasing the risk of relying on potentially flawed or biased information. For example, in finance, where decisions can have significant consequences, the lack of transparency in generative AI’s recommendations might lead to regulatory concerns.

Moreover, generative AI relies heavily on the quality and scope of the data it has been trained on. If the training data is biased, incomplete, or not representative of the current environment, the AI’s output may be flawed or misleading. This can be particularly problematic in business, where decisions based on inaccurate or biased data can lead to significant financial losses, reputational damage, or other unintended negative outcomes.  

The future of decision-making with Prescriptive Analytics

Looking ahead, prescriptive analytics is set to further transform how businesses make decisions, enabling them to be more proactive and confident in their choices. By processing large amounts of data—both historical and real-time—using advanced algorithms, prescriptive analytics not only analyses past events and predicts future trends but also recommends the best actions to take. This empowers everyone in an organisation, from managers to frontline employees, to make quicker and more informed decisions.

For example, industries like healthcare, finance, and supply chain management are already beginning to harness the power of prescriptive analytics. In healthcare, it can optimize treatment plans for patients by analyzing a wide range of factors, from medical history to genetic data. The Mayo Clinic is one institution exploring how prescriptive analytics can personalise treatments that hopefully can lead to better patient outcomes and reduced costs. By using simulations, companies can test different strategies in a virtual environment before implementing them, ensuring that decisions are more likely to lead to successful outcomes.

A key advantage of prescriptive analytics is its ability to combine internal data with external market intelligence. By integrating data from sources like customer feedback, industry trends, and competitive analysis, businesses can gain a more comprehensive view of the environment in which they operate. This broader perspective allows companies to better understand market dynamics, customer needs, and emerging opportunities. When internal data is enriched with external insights, businesses can make more informed decisions about where to allocate resources, how to optimise operations, and where to focus strategic efforts. This combination of internal and external data enhances the ability to deploy resources effectively, ensuring that efforts are aligned with both internal capabilities and market demands.

However, not every company will immediately or fully adopt prescriptive analytics. The extent to which businesses can leverage this technology depends on the quality of their data, the sophistication of their existing analytical capabilities, and their willingness to embrace advanced analytics. Companies with strong internal data and analytical resources will be the first to take full advantage of prescriptive analytics. In contrast, smaller businesses or those with less advanced data strategies may begin with specific applications and gradually expand its use. Alternatively, they can utilise Intelligence-as-a-Service providers such as Tembi to gain access to market data, analytics, and actionable insights, allowing them to benefit from advanced analytics without the need for extensive in-house capabilities.

The success of prescriptive analytics also hinges on the quality of internal data and the company’s analytical skills. To implement it effectively, businesses need to ensure their data is accurate, comprehensive, and up-to-date, requiring investment in data management and infrastructure. Skilled data scientists and analysts are essential for developing and maintaining the models that drive prescriptive analytics. Moreover, fostering a data-driven culture within the organisation is crucial, so that decision-makers understand and trust the recommendations provided by these tools.

As prescriptive analytics becomes more widespread, companies must also consider the ethical implications of relying on these advanced technologies. The potential for algorithmic bias, the need for transparency in decision-making processes, and concerns around data privacy and security are all critical issues, especially in industries handling sensitive information. Businesses will need to strike a balance between leveraging the capabilities of prescriptive analytics and maintaining human oversight to ensure responsible and effective decision-making.

Conclusion

The journey from traditional decision-making to an analytics-driven approach represents an important evolution in the business world. As data and analytics continue to advance, businesses are better equipped than ever to make informed, strategic decisions. However, the effectiveness of these decisions depends on the quality of the data, the appropriate use of analytical methods, and a clear understanding of the limitations of emerging technologies like generative AI.

To navigate this new landscape, businesses should consider the following steps:

Audit your data quality: Ensure that your data is clean, integrated, and well-governed.

Invest in analytics training: Equip your team with the skills needed to leverage advanced analytics tools.

Balance AI with human judgment: Use AI tools like generative AI and prescriptive analytics wisely, keeping human oversight in place.

As we look to the future, prescriptive analytics offers a promising glimpse into how businesses can navigate an increasingly complex world with confidence and foresight. By embracing these tools and strategies, companies can stay ahead of the curve and achieve sustained success in a data-driven world.

For further reading, consider exploring the ethical challenges of AI in business or case studies on successful data-driven decision-making in various industries.

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Invitation for Discussion: How are you incorporating analytics into your decision-making process? What challenges or successes have you experienced? Share your thoughts with us at mbu@tembi.io.

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