
# AI and Energy: Navigating the Complexities of Efficiency and Consumption

![AI and Energy:Navigating the Complexities of Efficiency and Consumption image](/img/blog/AI-and-Energy-Navigating-the-Complexities-of-Efficiency-and-Consumption/hero.webp)

***When we think about the rapid advancements in Artificial Intelligence (AI), it's easy to get swept up in the excitement of all the possibilities. But as someone who has spent a lot of time working with AI, I've come to realise that the story of AI and energy is a complex one, full of both promise and challenges.***

On one side, we have the incredible potential of AI to revolutionise how we manage and consume energy across industries. But on the flip side, the energy demands of AI itself, especially when it comes to training and running large language models (LLMs), are pretty staggering. It's a paradox of progress - the very technology that could help us become more energy-efficient is itself a significant energy consumer.

### The Paradox of Progress: AI's Energy Appetite

Let's dive into the numbers for a moment. According to a study by the [MIT Technology Review](https://www.technologyreview.com/2019/06/06/239031/training-a-single-ai-model-can-emit-as-much-carbon-as-five-cars-in-their-lifetimes/), training a single AI model can emit more than 626,000 pounds of carbon dioxide equivalent. To put that into perspective, that's nearly five times the lifetime emissions of the average car, including the manufacture of the car itself. It's a sobering statistic that highlights the environmental impact of AI development.

But it's not just the training of AI models that's energy-intensive. Running these models, particularly LLMs, also requires a lot of computational power and energy. [A recent study](https://itif.org/publications/2020/07/06/beyond-energy-techlash-real-climate-impacts-information-technology/) found that the carbon footprint of training and running a large language model is equivalent to around 300 round-trip flights between New York and San Francisco.

![Carbon Footprint of AI vs. Other Activities](/img/blog/AI-and-Energy-Navigating-the-Complexities-of-Efficiency-and-Consumption/carbon_footprint_comparison.png)
*Data compiled 9/10/2019. Source: College of Information and Computer Sciences at University of Massachusetters Amherst*
### Harnessing AI for Good: The Efficiency Crusade

Despite these challenges, I remain optimistic about the potential of AI to help us tackle our energy challenges. Across various sectors, AI applications are already being used to optimise operations, improve renewable energy forecasting, and even reduce maintenance downtime through predictive maintenance.

In the renewable energy sector, for example, Google and DeepMind have [developed a neural network](https://www.iea.org/commentaries/why-ai-and-energy-are-the-new-power-couple) that can predict the future output of their wind power fleet with impressively high accuracy. This allows for more efficient energy use and selling strategies, and has increased the financial value of their wind power by 20%. That's a big deal, and it's just one example of how AI is encouraging further investment in renewable energy sources.

Another exciting area where AI is making a difference is in predictive maintenance for utilities. Companies like E.ON are using machine learning algorithms like gradient boosting and random forests to predict when medium voltage cables in the grid need to be replaced. This approach has cut down outages by up to 30% compared to conventional methods, which not only enhances the reliability of energy infrastructures but also contributes to more efficient and economical energy use.

![Predictive Maintenance with AI](/img/blog/AI-and-Energy-Navigating-the-Complexities-of-Efficiency-and-Consumption/predictive_maintenance.png)
*A futuristic representation of Predictive Maintenance*

Despite the substantial energy requirements of AI, its potential to enhance energy efficiency across various sectors is equally compelling. AI applications have been integral in optimising operations, improving renewable energy forecasting, and even reducing maintenance downtime through predictive maintenance.

### AI in Action: Predictive Maintenance and Power Saving

The potential of AI to revolutionise energy efficiency isn't just theoretical - it's already happening in real-world applications. A great example of this is the work being done by [Voltvision](https://www.voltvision.live/), a company that digitises high and medium voltage industrial power networks to transform power data into actionable information.

I've had the privilege of being sponsored by Voltvision in my PhD research, where I'm focusing specifically on using AI for predictive maintenance. By analysing the digitised power data from Voltvision's systems, we're able to identify anomalies in operation and classify potential issues before they cause downtime or inefficiencies. We use techniques like Transformers and autoencoders to learn normal patterns of operation and detect deviations that could indicate impending failures, and use AI to deal with diverse data from different motors operating with different specifications and in different environments.

This predictive maintenance approach has significant implications for power saving and sustainability. By keeping induction motors operating at maximum efficiency and preventing unexpected downtime, we can reduce energy waste and extend the lifespan of industrial machinery. In one case study, a manufacturing plant was able to reduce its energy consumption by 15% and avoid several costly outages by implementing an AI-powered predictive maintenance system.

It's a powerful demonstration of how AI can be applied to real-world energy challenges. And it's not just about saving energy - it's also about saving money. By reducing downtime and improving efficiency, companies can see significant cost savings over time. [A recent report by PwC](https://www.pwc.com/gx/en/industries/energy-utilities-resources/publications/embracing-ai-power-utilities-sector.html) estimates that AI could help the global power and utilities sector save up to $130 billion annually by 2025.

What's exciting to me is that this is just the beginning. As we continue to develop and refine these AI-powered predictive maintenance systems, I believe we'll see even greater gains in energy efficiency and sustainability across industries.

Of course, there are still challenges to overcome. Implementing these systems requires an upfront investment, and there can be a learning curve for organisations as they adapt to new technologies and processes. Ensuring the security and privacy of the massive amounts of energy data required for these AI models is also a critical consideration. But from what I've seen in my work with Voltvision, the benefits far outweigh the challenges.

### The Future of AI and Energy: Neuromorphic Computing and Beyond
As we look to the future, there are some really exciting developments on the horizon that could help address the energy challenges of AI. One area that I'm particularly excited about is neuromorphic computing.

Neuromorphic computing is an approach to AI that takes inspiration from the way the human brain processes information. Unlike traditional AI systems that rely on power-hungry GPUs, neuromorphic chips are designed to be much more energy-efficient. Companies like Intel and IBM are already developing neuromorphic chips, and I think we'll see a lot more progress in this area in the coming years.

Another promising development is the rise of new AI chips, like those being developed by companies like [Groq](https://groq.com/wp-content/uploads/2022/10/GroqChip%E2%84%A2-Processor-Product-Brief-v1.5.pdf). These chips are specifically designed for AI workloads and can offer significant improvements in performance and energy efficiency compared to traditional processors. Groq's tensor streaming processor, for example, is capable of delivering 1 PetaOp/s of compute performance while consuming only 50 watts of power.

![Energy Efficiency of AI Hardware](/img/blog/AI-and-Energy-Navigating-the-Complexities-of-Efficiency-and-Consumption/ai_hardware_efficiency.jpg)
*A benchmark of new AI chips. [Source](https://github.com/ray-project/llmperf-leaderboard?tab=readme-ov-file)*

And it's not just dedicated AI chips that are getting more efficient. Even mainstream CPUs are starting to incorporate neural processing units (NPUs) to better handle AI workloads. Apple's M1 chip, for example, includes a 16-core NPU that can perform up to 11 trillion operations per second while being incredibly energy-efficient.

Government policies and regulations will also play a critical role in shaping the future of AI and energy. Carbon pricing schemes, renewable energy incentives, and standards for energy-efficient AI hardware and software can all help drive the development and adoption of more sustainable AI technologies. The European Union's proposed [Artificial Intelligence Act](https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai/), for example, includes provisions for promoting the development of energy-efficient AI systems.

## Conclusion

As I reflect on the complex relationship between AI and energy, I'm struck by the importance of finding a balance. Yes, the energy consumption of AI, particularly LLMs, presents a real challenge as we strive for sustainability. But at the same time, the potential of AI to revolutionise energy efficiency across industries is simply too great to ignore.

I believe the future of AI and energy will be shaped by our ability to advance AI's capabilities while minimising its environmental impact. This will require innovations in energy-efficient AI algorithms, greener data centres, and more sustainable practices in technology deployment.

It's a journey that will be full of complexities and contrasts, but it's one that I'm excited to be a part of. As someone who has seen firsthand the challenges and opportunities of AI, I'm convinced that by working together and staying focused on the goal of sustainability, we can write a new chapter in the story of AI and energy - one where AI is not just a consumer of energy, but a powerful tool for saving it.

**So what can you do to be a part of this journey?** If you're a researcher or developer, consider focusing your efforts on creating more energy-efficient AI models and hardware. If you're a business leader, look for opportunities to implement AI-powered efficiency solutions in your operations. And as consumers, we can all do our part by supporting companies and products that prioritise sustainability and energy efficiency.

Together, let's navigate the complexities of AI and energy towards a cleaner, more sustainable future.


Explore the complex relationship between AI and energy consumption, and discover how AI can be harnessed to improve energy efficiency across industries despite its own significant energy demands.

AI and Energy:Navigating the Complexities of Efficiency and Consumption


Top AI-Powered Analytics Tools: Assisting or Replacing Data Analyst Jobs?
=========================================================================

![A chessboard with chess pieces with a human data facing off against a robot representing AI-powered analytics tools](/img/blog/Top-AI-powered-analytics-tools/hero.jpg)
*The metaphorical face-off between the AI-powered analytics tools and the human data analyst. Generated by DALLE*

***Where do we stand between AI acting as a complex toolkit for analysis specialists vs. AI as an assistant to ease or replace parts of the analyst's work? For this, we will have a run through the chronological developments in AI affecting data analysis as well as recent tools on the market to establish the current state of the art in the automation of data analysis.***

Data analysis is the process of extracting meaningful insights and patterns from raw data. It is a critical component in a wide range of fields, as it enables informed decision-making, problem-solving, and the identification of opportunities for improvement. The field of data analysis has benefitted from the new capabilities provided by AI tools. More recently, however, AI is increasingly capable of assisting in the end-to-end process of data analysis and performing required data analysis steps by itself. On one hand, this increases the productivity of data analysts employing such tools and on the other hand less technical users become more independent from specialised data analysts and can benefit from enhanced decision-making support and newfound data insights.

What is data analysis and why is it important?
----------------------------------------------

Broadly speaking, data analysis is the process of inspecting, cleansing, transforming, and modelling, and visualising data to derive meaningful insights and inform decision-making. It is out of scope for a deep-dive into the main branches of data analysis or common procedures but please feel free to subscribe to follow upcoming news and articles in this domain.

In today's digital age, where vast amounts of data are generated, the role of data analysis has become indispensable across industries. From businesses seeking to optimise operations and understand consumer behaviour to scientists exploring complex phenomena and policymakers formulating evidence-based strategies, data analysis provides a foundation in virtually every aspect of modern society.

With the rise of automation and artificial intelligence (AI), the relevance of data analysis surged yet again. AI-driven systems rely heavily on data analysis and corresponding processing for tasks such as model training, fine-tuning, and validation. Conversely, AI technologies also have an impact on the field of data analysis itself. Initially, this expanded the analyst's toolbox with advanced tools such as machine learning or clustering methods, allowing to extract deeper insights from increasingly complex datasets. Advancements in AI have enabled these systems to play a more hands-on role in the end-to-end process of data analysis, autonomously applying reasoning to run various analytical procedures independently.

Regardless of the way or by whom the data analysis is performed and insights are extracted, ultimately, effective data analytics provides the potential for the following benefits according to [Oxagile](https://www.oxagile.com/article/data-analytics-use-cases-how-data-is-transformed-into-better-business-opportunities/):

-   Increased profit

-   Cost reductions

-   Higher operating margins

-   More effective strategic decisions

-   Improved control of operational processes

-   A better understanding of customers

-   Increased workforce productivity

-   Enhanced security

The rise of AI in data analysis
-------------------------------

The evolution of data analysis has been greatly influenced by the steady advancements in artificial intelligence (AI) over the past few decades. Starting from the early beginnings in the 1990s and 2000s, the integration of machine learning algorithms into data analysis tools paved the way for more sophisticated techniques. During the Big Data era of the 2000s and 2010s, the introduction of technologies like Apache Hadoop running the MapReduce programming model enabled the processing of large-scale, unstructured data. Concurrently, the rise of the Python data science ecosystem, including libraries like NumPy, SciPy, Pandas, and Scikit-learn, empowered users to build increasingly sophisticated custom data analysis pipelines.

The true AI revolution, however, took place in the 2010s and 2020s. Breakthroughs in deep learning, with the development of powerful neural network architectures like convolutional neural networks (CNNs) and recurrent neural networks (RNNs), revolutionised fields like computer vision and natural language processing (NLP), opening up new frontiers for data analysis, particularly for unstructured data such as text. The emergence of open-source AI frameworks, such as TensorFlow, PyTorch, and scikit-learn, further democratised access to advanced machine learning capabilities, accelerating innovation in data analysis.

More recently, the advent of Automated Machine Learning (AutoML) tools, pioneered by companies like Google and Amazon, has made it easier for non-expert users to leverage powerful AI-driven data analysis techniques without the need for extensive technical expertise. Alongside these advancements, there has been a growing emphasis on Explainable AI (XAI) and responsible data analysis practices, ensuring transparency, fairness, and ethical AI-driven decision-making.

While many of these advancements in AI have been more closely related to enhanced data modelling and predictive capabilities, they have yet significantly improved the core domain of data analysis. Techniques like deep learning have enabled the automated extraction of complex features and patterns from structured and unstructured data (audio-visual and textual data), allowing analysts to uncover hidden insights that would have been difficult to detect through traditional statistical methods alone. Furthermore, AI-powered anomaly detection algorithms can identify outliers and anomalies in large datasets with greater accuracy, supporting the data exploration and sense-making process.

AI-based automation of data analysis and reports
------------------------------------------------

The previous section highlighted how the developments in AI have expanded the data analyst's toolkit. In this section, we will go further and look into more independent assistance AI can provide in data analysis and report writing, as well as currently existing tools and their capabilities.

When searching the web for automated data analysis and reports, one is likely to come across ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) services. Some of the most common examples of these tools include Apache NiFi, Talend, and Microsoft Azure Data Factory. ETL/ELT services essentially facilitate the process of moving data from source systems to a data warehouse or data lake, transforming it into a usable format, and then loading it into a destination system. These tools vastly facilitate handling tasks, substituting efforts which otherwise would have to be covered by designing and developing software architecture/backend, data storage, deployment, and other DevOps procedures.

However, it's crucial to note that while ETL/ELT tools excel at automating and deploying data processing pipelines, they fall short when it comes to the iterative process of data analysis and thus do not necessarily assist with the core essence of data analysis -- extracting meaningful insights and patterns from data. This is where the distinction lies between automated data processing and true autonomous data analysis.

With the rise of large language models like ChatGPT and GPT agents, AI models now provide the capability to summarise extensive analysis results and perform a level of data querying using natural language. Let's have a look at the already existing tools and the level of autonomous data analysis and reports generation provided by these AI-based tools.

Best AI-driven tools for data analysis automation
-------------------------------------------------

### APEX by Numvio

[https://numvio.com/](https://numvio.com)

[APEX](https://numvio.com/apex) is an AI-powered platform by Numvio aiming to empower businesses with data analytics and tailored AI modelling without the complexities of coding, extensive setups, and data prompting. The platform requires minimal setup and querying, providing comprehensive data analysis from just uploading a dataset file or spreadsheet. The platform then generates a data analysis report, which not only visualises and describes key features of the dataset but also highlights the business value potential of the data with specific suggestions for further AI model applications to benefit the business. A remarkable feature of APEX is that the user can provide custom requests or simply select one of the suggested AI use cases, which will prompt APEX to automatically create a data pipeline, train corresponding AI models, and generate a dashboard for the tailored use case. The data pipeline, AI model processing and dashboard are then ready for deployment via the web UI or APIs that facilitate streamlined decision-making. The benefit of this platform is that it makes the wide range of data analysis (from EDA to AI modelling) accessible to users with diverse backgrounds with minimal time effort.

### Julius

[https://julius.ai/](https://julius.ai/)

Julius offers a text-based (chat) interface to analyse and visualise data. It can create graphs, run statistical tests, and answer questions about the uploaded data. It is also capable of performing common data analysis tasks, such as [EDA](https://julius.ai/use_cases/eda). It appears that this system needs to be precisely instructed on each step of the calculation and processing, making this tool suitable to professionals or experienced users in the field of data analysis. Some capabilities in the generation of forecasting models is also provided, albeit with a rather experimental or academic nature, as the service appears to lack deployment features and the ability to process data and run AI models in the form of data pipelines.

### DataGPT

[https://datagpt.com/](https://datagpt.com/)

Similar to Julius, DataGPT offers a user-friendly data querying service where users can ask data-related questions to receive AI-generated answers accompanied by relevant graphs. With its AI onboarding assistant, DataGPT provides a simple setup by suggesting suitable metrics and dimensions from the data source. However, it primarily relies on user queries, seems to lack report generation, and currently lacks AI modelling capabilities, making it more suited for straightforward data interactions rather than in-depth analytics or reporting.

### Tableau AI

[https://www.tableau.com/products/tableau-ai](https://www.tableau.com/products/tableau-ai)

Tableau AI features the "Einstein Copilot", which is a text-based AI chatbot. Based on user queries, it can provide information about the data, show charts, and support with code examples to set up a data analytics procedure. The module "Tableau Pulse" also adds similar functionality to previously created charts and dashboards. A benefit of this system is the access to the large Tableau ecosystem (e.g., offering support for a wide range of databases). The downside is however, that this requires a considerable amount of manual setup, despite a level of AI assistance. While the AI assistant is also capable of suggesting data queries, this approach assumes that the user has existing knowledge about the analysed data and its potential in order to write queries. [User reviews of Tableau](https://www.g2.com/products/tableau) praise the visual appeal and configurability of the charts while noting the high pricing and effort due to a steep learning curve for more advanced features.

### Microsoft Fabric

[https://www.microsoft.com/en-us/microsoft-fabric](https://www.microsoft.com/en-us/microsoft-fabric)

Microsoft Fabric provides access to new and existing components from the Microsoft services Power BI (for data visualisation and reporting), Azure Synapse (to query datasets and develop code for data analysis), and Azure Data Factory (for data preparation by constructing ETL/ELT pipelines). Fabric offers "[Copilot](https://learn.microsoft.com/en-us/fabric/get-started/copilot-fabric-overview)", another chat-based AI assistant. You might be familiar with it in the form of GitHub Copilot or Copilot in the Microsoft Office suite (Excel, Word, etc.). In Fabric, it can provide code completion, generate code templates, create charts in response to questions about the data, and it can summarise previously configured reports. Therefore, similar to Tableau AI, a-priori knowledge is required both for the analysed data as well as technical coding skills, in particular for more advanced capabilities such as AI model training. Another factor to consider is the potentially inaccessible [pricing](https://powerbi.microsoft.com/en-us/pricing/) since Copilot is only available for the paid (non-trial) workspace SKU capacities using F64, Premium P1, or higher.

### Polymer

[https://www.polymersearch.com/](https://www.polymersearch.com/)

Polymer uses AI to automatically analyse data and generate tailored dashboards from various sources or pre-made templates. Another feature is the ability to embed Polymer's functionality, catering app developers looking to integrate data analytics into their software. As a notable mention, [Luzmo](https://www.luzmo.com/) is another service that also specialises in the software embedding feature. While its chatbot interface allows for querying of the created charts, users may find the initial analytics limited in comprehensive insights that would be expected from an EDA and lacking detailed explanations. Polymer shines in creating visually appealing dashboards with added textual insights but also doesn't offer AI model generation or data processing pipeline deployment, suggesting potential need for supplementary tools for more advanced tasks.

### Akkio

[https://www.akkio.com/](https://www.akkio.com/)

Akkio is an AI data platform, which features "[Chat Explore](https://www.akkio.com/chat-explore)" to query data and generate graphs using GPT-4. The platform allows users to copy these visuals into Akkio reports. While Akkio supports the extra functionality of model training and deployment, its reports offer rather basic insights with brief chart descriptions, often lacking summary metrics and conclusions of the comprehensive dataset provided to the system. Additionally, like most of the previously listed services, the platform demands specific data queries, as well as a manual data preprocessing setup (supported by an AI assistant), catering more to technically experienced users.

Is AI replacing data analyst jobs?
----------------------------------

The emergence of AI-driven tools in data analysis automation suggests that truly automated data analysis is becoming a reality. Platforms like APEX by Numvio, Tableau AI, and Microsoft Fabric exemplify this trend by offering advanced capabilities to automate data processing, report generation, and even model training in some cases, significantly reducing the manual effort traditionally associated with these tasks. Most of the existing tools however are on a spectrum: on one end requiring high effort and skill to provide extensive functionality and on the other end offering ease of use but providing limited analysis depth or features. I.e, these tools often lack the interpretive depth, and strategic insight that human analysts bring to the table. Moreover, these platforms typically require specific data queries, manual data preprocessing, and a level of technical expertise, making them more suitable for technically inclined users or professionals in the field of data analysis.

At this stage, both human data analysts and AI-based data analysis automation complement each other. Expert data analysts are required to operate or query AI-assisted systems, fine-tune preprocessing steps, and add the iterative element, which is essential in data analysis, such as adjusting a preprocessing step once the analysis yields a certain insight or providing feedback on the AI-generated results. The collaboration between AI and human analysts can lead to more insightful, efficient, and actionable insights, driving business growth and efficiency.

In summary, while AI-driven tools are revolutionising the data analysis landscape, they are not replacing data analyst jobs but rather augmenting the capabilities of data analysts and enhancing the overall data analysis process. However, technology is moving fast and it will be exciting to see the future leaps that it brings.

Explore the rising impact of AI on data analysis, see the capabilities and decide if these recent tools are assistants or rather potential replacements for data analyst roles.

Top AI-Powered Analytics Tools:Assisting or Replacing Data Analyst Jobs?


# Turning Data into Dollars: How You Can Actually Make Money with Data

![Cover](/img/blog/Turning-Data-Into-Dollars/cover.webp)
*Data as the New Gold: Navigating the Digital Frontier. Generated by DALLE*

***It seems like everywhere I turn, people are talking about how data is the new gold. Companies are collecting tons of information from all sorts of places, hoping to strike it rich. But here's the thing: just having data doesn't guarantee success. The real question is, how do you actually make money with it? In this blog post, I'll share some strategies and approaches that can help you turn data into a profitable asset.***

But first, let's take a step back and talk about what we mean by "data." In simple terms, data is just a collection of facts, observations, or information that can be processed, analysed, or used to gain insights. It can come in all sorts of forms, like numbers, text, images, audio, or video. Data can be neatly organised, like in a spreadsheet, or it can be more unstructured, like social media posts or customer reviews.

You might be be surprised by some of the things that count as data:

- Every click, scroll, and interaction on your website
- The information about your email communications, like who sent it, who received it, and when
- Social media interactions, such as likes, shares, comments, and mentions
- Customer support logs, including questions, complaints, and resolutions
- The content inside PDFs, reports, and other documents
- Transcripts from customer calls, meeting recordings, or video content

These are just a few examples, but the point is that businesses can collect and use all sorts of data to make money. The key is to recognise the value of data in all its forms and to come up with strategies to turn that data into insights and opportunities that generate revenue.

## The Data Gold Rush

[A study by IDC](https://www.readkong.com/page/the-digitization-of-the-world-from-edge-to-core-8666239) predicts that the global data sphere will grow from 33 zettabytes in 2018 to a whopping 175 zettabytes by 2025. To give you an idea of how much that is, if you stored 175 zettabytes on DVDs, your stack of DVDs would circle the Earth 222 times!

![DVDs DALLE graphic](/img/blog/Turning-Data-Into-Dollars/cds.webp)
*An exaggerated visual representing the data on the planet using DVDs. Generated by DALLE*

This massive growth in data is thanks to the rise of connected devices, social media, digital transactions, and the Internet of Things (IoT). Every interaction, every click, and every sensor reading adds to the ever-expanding universe of data. Companies across all sorts of industries are investing heavily in collecting, storing, and analysing data, hoping to uncover valuable insights that can help them make better decisions, improve efficiency, and gain an edge over their competitors.

But here's the thing: just having a ton of data is only the first step. The real challenge is figuring out how to extract value from that data and turn it into something profitable. A lot of companies make the mistake of collecting data just for the sake of it, without a clear plan for how to monetise it. They might have impressive data warehouses and fancy analytics tools, but if they can't use those insights to drive real business outcomes, their data is nothing more than an untapped resource.

To truly strike it rich in the data gold rush, companies need to treat data as a strategic asset. They need to have a clear understanding of what data they have, what insights they can gain from it, and how those insights can be translated into tangible business value. This takes a mix of technical know-how, industry knowledge, and business savviness. Later, I'll cover some of the strategies and methods you can use to start extracting value from your data.

## The Power of AI

Artificial Intelligence (AI) has been a real game-changer when it comes to monetising data. AI helps businesses find patterns, correlations, and anomalies in their data that would be impossible to spot through manual analysis. In fact, [a study by McKinsey Global Institute](https://www.nexford.edu/insights/how-will-ai-affect-jobs#:~:text=McKinsey%20global%20institute%20says%20that,16%25%20higher%20cumulative%20GDP%20compared) found that AI could potentially create $13 trillion of additional economic output by 2030.

One of the most exciting ways AI is helping businesses is through predictive maintenance. Imagine you're running a factory with hundreds of machines, each one generating data on things like temperature, vibration, and pressure. In the old days, you'd have to manually analyse all this data to figure out if a machine was about to break down. But with AI, you can automate this process and predict when a machine needs maintenance before it even starts to show signs of wear and tear.

But AI isn't just useful for keeping machines running smoothly. It's also a powerful tool for fighting fraud. Banks and financial institutions are using AI to analyse transactional data and customer behavior to spot any suspicious activity. [Mastercard has taken this to the next level](https://www.mastercard.com/news/press/2024/february/mastercard-supercharges-consumer-protection-with-gen-ai/) with their AI-powered fraud detection system. This system can analyse billions of transactions and detect fraudulent activities, boosting fraud detection rates on average by 20%, and reducing the number of false positives by more than 85%.

And let's not forget about the magic of personalised recommendations. E-commerce giants like Amazon and Netflix have turned this into an art form. They use AI to analyse every little thing their customers do, from what they click on to what they buy, and then use that data to provide scarily accurate product or content recommendations.

Netflix, for example, [attributes over 80% of the content watched on their platform to their AI-powered recommendation system](https://www.wired.com/story/how-do-netflixs-algorithms-work-machine-learning-helps-to-predict-what-viewers-will-like/). That's right, AI is basically deciding what you should binge-watch next. And the craziest part? You probably love every minute of it.

But AI isn't just for the big players. Even smaller businesses are getting in on the action with chatbots for customer support. These AI-powered helpers can analyse customer questions and provide instant answers, taking a huge load off of human support staff. And the more they interact with customers, the smarter they get.

H&M, the fashion retailer, has seen great success with their AI chatbot. This virtual fashion guru can give personalised style advice, recommend products, and even help with order tracking and returns. It's like having a personal shopper at your fingertips, 24/7.

The bottom line is, AI is an incredibly powerful tool for monetising data. But it's not a magic wand. Businesses need to invest in the right infrastructure, talent, and governance to really harness its potential. They need data scientists and machine learning experts who can build and train these AI systems, and they need to make sure these systems are transparent, explainable, and aligned with ethical principles.


## Strategies for Monetising Data

Alright, so how can you actually make money with data? Here are a few strategies to consider:

### Data-Driven Products and Services

#### Personalised Recommendations
Imagine you're an e-commerce company sitting on a goldmine of customer data. By analysing purchase history, browsing behaviour, and demographic information, you can create highly personalised product recommendations that cater to each individual's preferences. Amazon's "Customers who bought this also bought" feature is a prime example of how data-driven recommendations can drive sales and enhance the customer experience. 

![DVDs DALLE graphic](/img/blog/Turning-Data-Into-Dollars/amazon.png)
*An example of Amazon's "Also Boughts" for a gardening book*

To implement personalised recommendations, start by collecting and integrating customer data from various touchpoints, such as website interactions, purchase history, and customer support inquiries. Use collaborative filtering algorithms to identify patterns and similarities between customers. Develop a recommendation engine that suggests products based on individual preferences and behaviour. Continuously monitor and refine your recommendations based on customer feedback and engagement metrics.

#### Predictive Maintenance
In industries like manufacturing and transportation, equipment downtime can lead to significant financial losses. Data-driven predictive maintenance can help you stay ahead of the game by identifying potential issues before they occur.

By collecting sensor data from equipment, such as vibration, temperature, and pressure readings, you can build machine learning models that predict when maintenance is required. This proactive approach can reduce downtime, extend equipment lifespan, and optimise maintenance schedules. Companies like GE and Siemens have successfully implemented predictive maintenance solutions, saving millions of dollars in repair costs and lost productivity.
  
To get started with predictive maintenance, identify the critical equipment in your operations and install sensors to collect relevant data. Develop machine learning models that analyse the data and predict potential failures. Integrate the predictive insights into your maintenance workflows and use them to schedule proactive repairs and optimise resource allocation.

### Data Licensing and Partnerships

#### Location Data Licensing

Companies in industries such as retail, real estate, and tourism are willing to pay top dollar for accurate and comprehensive location data. They use this data to analyse foot traffic patterns, optimise store locations, and target location-based advertising. [Foursquare](https://foursquare.com/), a location technology platform, has successfully monetised its location data through partnerships with companies like Apple, Uber, and Microsoft.
  
To pursue location data licensing, ensure that you have obtained proper consent from users and comply with privacy regulations such as GDPR. Anonymise and aggregate the data to protect individual privacy. Package the data in a format that is easy for partners to integrate and use. Establish licensing agreements that outline data usage rights, pricing, and performance metrics.

#### Data Marketplace Participation

Data marketplaces are platforms that facilitate the buying and selling of data between companies. By participating in these marketplaces, you can monetise your data assets and access valuable data from other providers.

Marketplaces like [Snowflake Data Marketplace](https://app.snowflake.com/marketplace) and [AWS Data Exchange](https://aws.amazon.com/data-exchange/?adx-cards2.sort-by=item.additionalFields.eventDate&adx-cards2.sort-order=desc) enable companies to securely share and consume data in a centralised platform. They offer a wide range of data categories, including financial data, healthcare data, and consumer insights. By listing your data on these marketplaces, you can reach a broader audience and generate new revenue streams.
  
To succeed in data marketplaces, ensure that your data is high-quality, accurate, and well-documented. Provide sample datasets and clear pricing information to attract potential buyers. Leverage the marketplace's tools and services to manage data access, billing, and customer support.

### Data-Driven Decision Making

In today's competitive landscape, making decisions based on gut instincts is no longer enough. To stay ahead of the curve, businesses must harness the power of data to inform their strategies and drive growth. Data-driven decision-making is the process of leveraging insights from data to guide business actions and optimise outcomes.

I've seen firsthand the transformative power of data-driven decision-making in my role as a researcher and as a co-founder of Numvio. In today's fast-paced business environment, relying solely on intuition is no longer sufficient. To stay competitive and drive growth, businesses must leverage the insights hidden within their data.

However, through my experiences working with various companies, I've realised that the journey from raw data to actionable insights is often riddled with obstacles:

- **Data Silos**: Data is often scattered across different departments and systems, making it difficult to gain a holistic view of the business.
- **Data Quality**: Poor data quality, including inaccuracies, duplicates, and missing values, can lead to flawed insights and decision-making.
- **Skill Gaps**: Analysing complex data sets requires specialised skills and expertise that many businesses lack in-house.
- **Time Constraints**: Extracting insights from data can be a time-consuming process, especially when dealing with large volumes of information.

(you can read more about my perspective on the challenges to becoming data driven [here](https://numvio.com/blog/stories/Why-Businesses-are-Grappling-with-Becoming-Data-Driven))

To address these challenges, various solutions have emerged in the market. Business intelligence (BI) tools like Tableau and Power BI provide data visualisation and reporting capabilities, enabling businesses to gain a better understanding of their data. However, these tools often require significant manual effort to prepare and analyse the data, and they may not provide the advanced analytics capabilities needed for complex decision-making.

Data science platforms like DataRobot and H2O.ai offer machine learning and predictive analytics capabilities, allowing businesses to build and deploy advanced models for decision-making. While these platforms are powerful, they often require a high level of technical expertise to use effectively, limiting their accessibility to many organisations.

At Numvio, we recognised the need for a more comprehensive and user-friendly solution for data-driven decision-making. That's why [we developed APEX](https://numvio.com), an automated data analyst that simplifies the process of turning raw data into actionable insights.

![APEX Auto-reports feature](/img/apex/features/report.png)
*APEX Auto-Reports*


One of the key advantages of APEX is its ability to automate many of the time-consuming and complex tasks associated with data analysis. From data cleaning and preparation to feature engineering and model selection, APEX handles the heavy lifting, allowing businesses to focus on making informed decisions based on the insights provided.

![APEX Auto-reports feature](/img/apex/features/use-cases-frame.png)
*APEX Auto-Use-cases*

The results speak for themselves. Companies that have adopted APEX have reported significant improvements in their decision-making processes and substantial time and cost savings. They can now make informed choices based on real-time data insights rather than relying on gut instincts or outdated reports.

For example, let's say you're a retail company looking to optimise your inventory management. With APEX, you can simply upload your sales data, and the platform will automatically identify patterns and trends, predict future demand, and provide recommendations on optimal stock levels. By leveraging these insights, you can make data-driven decisions that reduce costs, improve efficiency, and enhance customer satisfaction.

But when done right, AI can be a real game-changer. It can help businesses streamline their operations, boost efficiency, and provide unparalleled customer experiences. So if you're not already thinking about how AI can help you monetise your data, you might want to start. Because one thing's for sure: **AI is here to stay, and it's only getting smarter.**
## Conclusion

Turning data into dollars is no easy feat, but it's definitely achievable with the right approach. By leveraging the power of AI, implementing effective monetisation strategies, and having a solid data strategy in place, businesses can unlock the true value of their data. Remember, data alone isn't enough; it's what you do with it that counts.

As you embark on your data monetisation journey, keep in mind that success requires a strategic approach. Start by defining your data monetisation goals, assessing your data assets, and developing a roadmap for implementation. Foster a data-driven culture within your organisation and empower your team with the tools and skills necessary to harness the power of data.

The opportunities for data monetisation are endless, limited only by your imagination and ability to execute. So, seize the moment, dive into your data, and unlock the plethora of insights waiting to be discovered. The future belongs to those who can turn data into dollars, and with the right strategies and mindset, that future can be yours.

Now, I invite you to share your own experiences and insights on data monetisation. Which strategies have you found most effective? What challenges have you encountered along the way? Let's engage in a dialogue and learn from each other's successes and failures.

Discover practical strategies and approaches to monetise your data assets and turn them into a profitable resource for your business.

Turning Data into Dollars:How You Can Actually Make Money with Data


# The Struggle is Real: Why Businesses are Grappling with Becoming Data-Driven

![Cover](/img/blog/Why-Businesses-are-Grappling-with-Becoming-Data-Driven/cover.webp)
*An overly dramatised depiction of the journey to data-driven success. Generated by DALLE*

***In the fast-paced world of business, data has emerged as the new gold. It's no secret that companies that effectively leverage data to guide their decisions and strategies often outperform their competitors. In fact, according to [a study by McKinsey Global Institute](https://www.mckinsey.com/capabilities/growth-marketing-and-sales/our-insights/five-facts-how-customer-analytics-boosts-corporate-performance), data-driven organisations are 23 times more likely to acquire customers, six times as likely to retain them, and 19 times more likely to be profitable. However, despite the clear benefits, many businesses find themselves struggling to become truly data-driven.***

## The Importance of Being Data-Driven

Before we delve into the challenges, let's take a moment to understand why being data-driven is so crucial. In today's digital landscape, businesses generate vast amounts of data from various sources, including customer interactions, operational processes, and market trends. This data holds valuable insights that can help organisations:

- Make informed decisions
- Optimise their strategies
- Drive innovation

By harnessing the power of data, businesses can gain a competitive edge, improve efficiency, and ultimately, boost their bottom line. According to [BARC research](https://bi-survey.com/big-data-benefits), businesses using their data saw an 8 percent increase in profit and a 10 percent reduction in cost.


## The Hurdles on the Path to Data-Driven Success

![Data Quality DALLE graphic](/img/blog/Why-Businesses-are-Grappling-with-Becoming-Data-Driven/data-quality.webp)
*The difference between good and bad quality data. Generated by DALLE*

So, why do businesses struggle to become data-driven? Let's explore some of the common obstacles:

### Data Quality

Firstly, data quality is a major hurdle. Poor data quality, characterised by inaccuracies, inconsistencies, and incompleteness, can lead to flawed insights and decision-making. It's like trying to build a house on a shaky foundation - no matter how beautiful the design, it's bound to crumble. [Studies from Gartner](https://www.dataversity.net/the-impact-of-poor-data-quality-and-how-to-fix-it/#:~:text=Poor%20data%20quality%20is%20considered,shipped%20to%20the%20wrong%20place) show that poor data quality costs businesses an average of $15 million per year. It's a silent killer that undermines the very essence of being data-driven.

*In my own experience conducting research, I encountered this challenge firsthand. While researching generalisation across datasets, I found that the data often came in different formats, and the quality of many datasets was subpar, rendering them unusable for my analysis and experimentation. This issue is not limited to academic research; I've noticed the same problem in industrial projects. The data collected is not always suitable for AI and analysis, forcing teams to get creative in finding solutions.*

### Data Structure

Secondly, businesses often grapple with unstructured or poorly structured data. [Oracle claims](https://www.oracle.com/big-data/structured-vs-unstructured-data/) that around 80% of business data is unstructured. When data is scattered across different systems and formats, it becomes a nightmare to integrate and analyse effectively. It's like trying to solve a puzzle with pieces from different boxes - it's frustrating and time-consuming. This lack of a unified data structure hinders the ability to extract meaningful insights and derive value from the data.

*In one particular project, our team faced a situation where the client had data stored in various formats across multiple databases. Some data was in CSV files, others in SQL databases, and some even in plain text documents. We had to invest significant time and effort in data preprocessing and transformation before we could even begin the analysis and AI pipeline designs. This experience highlighted the importance of having a well-structured and consistent data architecture.*

### Data Strategy

Thirdly, many organisations lack a clear and comprehensive data strategy. Without a roadmap for collecting, managing, and utilising data, businesses find themselves drowning in a sea of information. A well-defined data strategy is the compass that guides businesses towards data-driven success.

To add to this, data siloing can also be a significant barrier to becoming data-driven, if not done right. When data is isolated within different departments or systems unnecessarily, it prevents a holistic view of the business. Siloed data leads to inefficiencies, duplication of efforts, and missed opportunities for collaboration and innovation.

## Overcoming the Obstacles: Strategies for Becoming Data-Driven

Now that we've identified the challenges, let's explore some strategies to overcome them:

### Data Governance

Implement a robust data governance framework to ensure data quality and consistency. This involves setting data standards, implementing validation processes, and assigning clear roles and responsibilities. Regular data audits and cleansing exercises can help maintain the integrity of your data.

*Speaking from personal experience, at the start of our journey as a company, we actually neglected data auditing. Surprising, considering we are all data professionals, right? When we did start needing this data as we began growing, we realised that we had to do it anyway. Not only that, but we also had to spend time converting the data we already collected to adhere to the structure we had formulated. It was a valuable lesson in the importance of proactive data governance.*

### Data Strategy and Integrations

* Invest in data integration solutions to break down data silos and enable seamless analysis. By connecting disparate data sources and creating a unified view, businesses can unlock the full potential of their data. It's like assembling the pieces of the puzzle to reveal the complete picture.

* Develop a comprehensive data strategy that aligns with your business objectives. This roadmap should encompass data collection, storage, security, and utilisation guidelines. It should also define clear metrics to measure the success of your data-driven initiatives.

Several data integration solutions exist, including ETL (Extract, Transform, Load) tools that automate the process of extracting data from various sources, transforming it into a consistent format, and loading it into a central repository (shameless plug: our upcoming platform [APEX by Numvio](https://numvio.com) automates this step). 

*This is one area where we at Numvio actually excelled from the start. Two key aspects contributed to our success: effective communication within our team about what we needed to store and for what purpose, and our technical expertise in designing an infrastructure that was scalable for our needs, manageable for our small team, secure, and unified for easy analysis across datasets. Getting the data strategy right from the outset can save a lot of headaches down the line.*

### Culture and Education

Foster a data-driven culture that encourages data literacy and data-informed decision-making. This is a key point that most companies neglect, but is actually the key barrier to being truly effective with your data. What's the point of setting up systems and processes if your employees don’t understand how to use them, and why they need them? Provide training and resources to empower your employees with the skills to leverage data effectively. Celebrate successes and showcase the impact of data-driven decisions to reinforce the importance of this approach.

*Creating a data-driven culture is not just about tools and processes; it's about people. At Numvio, we made a conscious effort to promote data literacy at all levels. We conducted regular training sessions, shared success stories, and encouraged cross-functional collaboration around data initiatives. By empowering our employees with the skills and mindset to leverage data effectively, we were able to create a culture that truly embraced data-driven decision-making.*

These strategies, when implemented effectively, can help businesses overcome the obstacles on their path to becoming data-driven. It's not always an easy journey, but the rewards are well worth the effort. By prioritising data governance, investing in the right solutions, developing a comprehensive data strategy, and fostering a data-driven culture, businesses can unlock the full potential of their data and gain a competitive edge in today's digital landscape.

## Conclusion

Becoming data-driven is not a destination; it's a journey. It requires overcoming challenges related to data quality, structure, strategy, and siloing. But the rewards are well worth the effort. By implementing the right strategies and fostering a data-driven culture, businesses can unlock the full potential of their data and stay ahead in today's competitive landscape.

*Even we at [Numvio](https://numvio.com), as an AI company, didn't have a perfect journey. It's a common challenge that businesses face, regardless of their industry or size.*

In larger organisations, collaboration is key. When teams operate in isolation, chances are that data will also be isolated. Breaking down these silos and fostering cross-functional collaboration is essential to achieving a truly data-driven approach.

Remember, Rome wasn't built in a day, and neither is a data-driven organisation. It takes time, persistence, and continuous improvement. But with the right mindset and approach, you can transform your business into a data-powered engine of growth and success. So, embrace the power of data and let it guide you towards a brighter future. The struggle may be real, but so are the opportunities that await.

Now, that’s me done. I want to hear from you now: Have you faced this struggle in your own business? How did you tackle it? Did you have any failures or mind-blowing successes along the way? Share your stories, tips, and battle scars in the comments below.


Discover why many businesses struggle to become data-driven despite its clear benefits.

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