Artificial Intelligence

What is Machine Learning?

Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.

Classification Classify/label visual objects Identify objects, faces in images and videos
Classify/label writing and text Identify letters, symbols, words in writing sample
Classify/label audio Classify and label songs from audio sample
Cluster,group other data Segment objects (e.g., customers, product features) into categories, clusters
Discover Associations Identify that people who watch certain TV shows also certain books
Prediction Predict probability of outcomes Predict the probability that a customer will use another provider
Forecast Trained on historical data, forecast demand for a product
Value function estimation Trained on thousands of games played, predict/estimate rewards from actions from future states for dynamic games
Generation Generate visual objects Trained on a set of artist's paintings, generate a new painting in the same style
Generate writing and text Trained on a historical text, fill in missing parts of a single page
Generate audio Generate a new potential recording in the same style/genre
Generate other data Trained on certain countries weather data, fill in missing data points for countries with low data quality

Why Invest in Machine Learning?

  1. Data and analytics capabilities have made a leap forward in recent years. The volume of available data has grown exponentially, more sophisticated algorithms have been developed, and computational power and storage have steadily improved.
  2. The convergence of these trends is fueling rapid technology advances and business disruptions. Most companies are capturing only a fraction of the potential value from data and analytics
  3. Globally, The greatest progress has occurred in location-based services and in retail, both areas with digital native competitors. In contrast, manufacturing, the public sector, and health care have captured less than 30 percent of the potential value.
  4. The biggest barriers companies face in extracting value from data and analytics are organizational; many struggle to incorporate data-driven insights into day-to-day business processes. Another challenge is attracting and retaining the right talent—not only data scientists but business translators who combine data savvy with industry and functional expertise.
  5. Data and analytics are changing the basis of competition. Leading companies are using their capabilities not only to improve their core operations but to launch entirely new business models. The network effects of digital platforms are creating a winner-take-most dynamic in some markets.
  6. Data is now a critical corporate asset. It comes from the web, billions of phones, sensors, payment systems, cameras, and a huge array of other sources—and its value is tied to its ultimate use. While data itself will become increasingly commoditized, value is likely to accrue to the owners of scarce data, to players that aggregate data in unique ways, and especially to providers of valuable analytics.
  7. Data and analytics underpin several disruptive models. Introducing new types of data sets (“orthogonal data”) can disrupt industries, and massive data integration capabilities can break through organizational and technological silos, enabling new insights and models. Hyperscale digital platforms can match buyers and sellers in real time, transforming inefficient markets. Granular data can be used to personalize products and services—and, most intriguingly, health care. New analytical techniques can fuel discovery and innovation.
  8. Above all, data and analytics can enable faster and more evidencebased decision making. Recent advances in machine learning can be used to solve a tremendous variety of problems—and deep learning is pushing the boundaries even further. Systems enabled by machine learning can provide customer service, manage logistics, analyze medical records, or even write news stories. The value potential is everywhere, even in industries that have been slow to digitize. These technologies could generate productivity gains and an improved quality of life—along with job losses and other disruptions.
  9. Previous MGI research found that 45 percent of work activities could potentially be automated by currently demonstrated technologies; machine learning can be an enabling technology for the automation of 80 percent of those activities.
  10. Breakthroughs in natural language processing could expand that impact even further. Data and analytics are already shaking up multiple industries, and the effects will only become more pronounced as adoption reaches critical mass. An even bigger wave of change is looming on the horizon as deep learning reaches maturity, giving machines unprecedented capabilities to think, problem-solve, and understand language. Organizations that are able to harness these capabilities effectively will be able to create significant value and differentiate themselves, while others will find themselves increasingly at a disadvantage

Machine Learning Stages

The process of applying a machine learning method can be broken into 5 main steps.
  1. Data Analysis – This is where you produce descriptive statistics and carry out exploratory data analysis.
  2. Data Preparation – This is where you clean, reformat, munge, and aggregate your data.
  3. Algorithm Testing and Evaluation – In step 3, you deploy various algorithms and then compare and evaluate the results of each. Select the appropriate algorithm based on your findings.
  4. Model Fine-Tuning – This is where you make small tweaks to your model, in order to improve its performance.
  5. Data Presentation – In the final step, you present your findings to your team and begin working to get the model into production.

Machine Learning Stages

These use cases are very important because they clearly articulate the business need and projected impact of using machine learning, and helps outline a clear vision of how the business would use the solution.


Data & Analytics Strategy

Today’s global digital economy demands that every organisation do more with less to remain competitive.
We help leading businesses find value and advantage in their data, and chart a course of action for them to reap the rewards.


Without data you are just another person with an opinion. Gaining a competitive edge starts with informed decision making.


In this age of digital disruption, analytics defines the competitive battlefield.


Successful businesses know that to stay ahead of the competition, they must look to the future.

Custom Software Development

We design and build completely custom, fully-automated software that solves unique business problems in ways generic software can’t.

Technology Auditing & C-Level Consulting

We conduct extensive, top-to-bottom audits of your algorithms, architecture and code, revealing opportunities to cut costs, increase accuracy and improve your processes across the board.