Our data science consultants can help you solve your business challenges. From Optimising Stock Inventory, Increasing Customer Satisfaction, Personalised Recommendations Engines, Financial Fraud Detection.
Data Science & Machine Learning
Machine Learning Statistics
China is the biggest nation implementing AI and machine learning, Europe is close behind the United States regarding implementation. From discussions with organisations, information from data events, and reading technical articles we have identified that globally:
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47%
of organisations are considered AI and ML projects as a priority. In contrast only 28% of organisations considered AI and ML projects in 2021. This shows that year on year a higher percentage of organisations are looking at, or plan to implement machine learning.
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35%
In 2023 we estimate 35% of companies are using AI or machine learning in their business.
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52%
of organisations are planning to implement machine learning in the next three years.
What Is Data Science?
Data science is the method to extract meaningful insights from business data in the form of predictions. Data science combines math, statistics, specialised programming using a language like Python, and advanced analytics. Businesses use data science to provide answers to questions like what happened, why it happened, what will happened.
What are the benefits of Machine Learning?
Machine Learning helps in extracting meaningful information from a huge set of raw data. If implemented in the right way, machine learning can serve as a solution to a variety of business problems and predict complex customer behaviours. Here are some of the ways in which machine learning could help your business:
- Customer Lifetime Value and Customer Segmentation Predictions
- Increasing Customer Satisfaction
- Predictive Maintenance
- Product Recommendations
- Financial Analysis
- Fraud Detection
- Image Recognition
- Medical Diagnosis
- Improving Cybersecurity
Frequently Asked Questions
Machine Learning is an application that is a subset of Artificial Intelligence.
Machine Learning is the process of using mathematical models created using past know outcomes to help a computer learn without direct instruction. As machine learning algorithms are used, they learn and improve based on the data and the outcomes.
AI is a broader term of many applications that does includes machine learning. AI is the “capability of a computer to replicate human cognitive functions”, such as learning and problem-solving. AI will simulate reasoning in the same way that a human does. AI includes applications like natural language processing (chat bots), autonomous vehicle technology, human like interaction in gaming etc.
If an organisation has historic data used to train a model, then machine learning is possible.
With any machine learning project one of the first stages is to assess the data. Clean accurate data will provide better accuracy with machine learning models. Any outliers which may skew the model will be highlighted and removed from the data.
If you are asking yourself “are we ready to start using machine learning” have a look at our blog – What is the Analytics Maturity Curve?
Business Intelligence (BI) analyses historic data to find insight that describe the business trends and performance. BI enables you to take data from both internal and external sources, prepare it, run queries on it, and create dashboards to answer the questions like quarterly revenue analysis or bespoke business problems.
Data Science is a more forward-looking approach, an exploratory way with the focus on analysing past or current data to train a model. Then using the model predict the future outcomes with the aim of making informed decisions. It answers the open-ended questions as to “what” and “how”. For example,
- if we sell a new line of products what will our expected sales be?
- If we Increase our customer service team by 10%, how will that affect our customer retention?
- If we change our opening times how will that affect customer satisfaction?
With Machine Learning your data is secure. There are various ways to make predictions using open-source code with R or Python on a local server.
You also have the option to use a secure cloud platform like the Microsoft Azure Machine Learning Services. Datahub Consulting can provide solutions that is secure and will liaise with your IT team to ensure security and any IT policies and procedures are adhered too.
With the implementation of EU GDPR there is an issue that would need to be addressed on automated processing of personal information where there is no human interaction. This is a topic that would need to be addressed on a case-by-case basis for an accurate answer. But EU GDPR Article 6 refers to the lawful reasons for processing personal information.
- If a data subject has given explicit consent, then the use of machine learning is possible. But the organisation would need to be transparent with the data subject as Principle 1 of EU GDPR refers to Lawfulness, Fairness, and Transparency.
- Where processing is necessary as part of a contractual agreement.
- Finally, where it’s explicitly authorised by another law. EU GDPR does not override other country laws, and there are use cases where this may applicable.
In all the above cases, when machine learning is used with personal information then a Data Protection Impact Assessment (DPIA) will be required. This is to assess the process involved, assess any possible risks, and to mitigate those risks.
Datahub have a dedicated data compliance and risk management team that can support any customers that are worried about implementing machine learning. Please speak to us about this.
Our Approach
Datahub Consulting has a team of experienced Data Scientists that can take organisational data, integrate it with external data sources like social media feeds and provide insightful predictions. Most predictive models the organisations historic data to train the model. So, the data science team will initially identify the problem that you will want to overcome. Then look at the quality and the amount of data relating to the required outcome.
From this point the team will develop a concept model, and test using your dataset. Once that a successful predictive model is created with a good percentage accuracy then this model will be deployed for you to use. Our analytics team will create a dashboard that interprets the predictive model results and visualise them in a meaningful way to the end users with Power BI.
To use a data science application, you don’t need to be technically minded. The services that Datahub provide are all integrated. A data science project can connect directly to a database with no manual intervention. Also, the project can include data visualisations and insights in a meaningful dashboard. All the user needs to do is to view the dashboard and see the results, then make decisions based on the outcomes.
Our Achievements
Here are some examples of our success stories where predictive analytics has helped organisations improve their services or production.
Improved retention rates
We have supported organisations to improve customer retention rates using churn analysis.
Increased conversion rates
We have helped marketing teams increase conversion rates on events.
Improved efficiency
Using machine learning we have helped organisations improve performance and efficiency by making predictions using weather data.
Find out how we can help
We would be happy to sit down over a coffee and let you know more about our growing business and how Datahub could support you with your data science requirements.
Contact us