Data Analytics. Engineered.

Machine learning

Machine learning is the science of getting computers to act without being explicitly programmed. Machine Learning offers a deeper insight into collected data and allows the computers to find hidden patterns which human analysts are bound to miss.It is an iterative process in which models access behemoths of new data. In the process, the models learn to adapt independently without any human interaction At HorizonX, we expand upon Google’s modern machine learning services to develop be-spoke services for you to generate your own tailored models.

What is Machine Learning?

Analysis of high-value predictions can be performed autonomously by “machines”. Machine learning is a data analysis method that automates extrapolated model building algorithms. Machine learning offers a deeper insight into collected data and allows the computers to find hidden patterns which human analysts are bound to miss. This is an iterative process in which models access behemoths of new data. In the process, the models learn to adapt independently without any human interaction. Machine learning has evolved dramatically over the years. From simple analytical algorithms, it has evolved to automatic application of customized algorithms and mathematical calculations to big data. The speed and iteration are unique qualities of machine learning which make it an absolute necessity during data storage and management.

Why use Machine Learning?

Machine learning is needed for tasks that are too complex for humans to code directly. Some tasks are so complex that it is impractical, if not impossible, for humans to work out all of the nuances and code for them explicitly. So instead, we provide a large amount of data to a machine learning algorithm and let the algorithm work it out by exploring that data and searching for a model that will achieve what the programmers have set it out to achieve. How well the model performs is determined by a cost function provided by the programmer and the task of the algorithm is to find a model that minimises the cost function. According to Thomas H. Davenport, data analytics thought leader, “….you need rapid modelling systems to stay connected and machine learning gives you a way to do so. When human beings are capable of creating only one or two working models per week, machine learning can create a few thousand.” The most intuitive examples of this are the various applications for reinforcement learning, such as self-driving cars and self-flying helicopters.

How to enable Machine Learning?

Machine learning can be enabled through various techniques, two of the most widely adopted machine learning methods are supervised learning and unsupervised learning. Most machine learning – about 70 percent – is supervised learning. Unsupervised learning accounts for 10 to 20 percent. Semi-supervised and reinforcement learning are two other technologies that are sometimes used. Supervised learning algorithms are trained using labelled examples, such as an input where the desired output is known. The algorithms of supervised learning are trained using labels like R (runs) and F (failed). The algorithm receives inputs based on the actual output which trains the model to find errors. The model has the scope to evolve and modify accordingly. Unsupervised learning is used against data that has no historical labels. The system is not told the “right answer.” The algorithm must figure out what is being shown. The goal is to explore the data and find some structure within. Unsupervised learning works well on transactional data. Semi-supervised learning is used for the same applications as supervised learning. But it uses both labelled and unlabelled data for training – typically a small amount of labelled data with a large amount of unlabelled data (because unlabelled data is less expensive and takes less effort to acquire). Reinforcement learning is often used for robotics, gaming and navigation. With reinforcement learning, the algorithm discovers through trial and error which actions yield the greatest rewards. This type of learning has three primary components: the agent (the learner or decision maker), the environment (everything the agent interacts with) and actions (what the agent can do). The objective is for the agent to choose actions that maximize the expected reward over a given amount of time. The agent will reach the goal much faster by following a good policy. So the goal in reinforcement learning is to learn the best policy.

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DataOps (Data Operations)

DataOps (data operations) is an emerging discipline that brings together DevOps teams with data engineer, data consumers and data scientist roles to provide the tools, processes and organizational structures to support the data-focused enterprise. The DataOps approach is not limited to big data analytics & machine learning. DataOps fit well with microservices architectures and practices as well. The DataOps model is useful for any data-oriented work, making it easier to take advantage of the benefits offered by building a global data fabric At HorizonX, our talent pool has years of experience of Agile and DevOps, enables us to be the early adopters of DataOps.

What is DataOps?

Data Operations (DataOps) is a methodology consisting of people, processes, tools, and services for enterprises to rapidly, repeatedly, and reliably deliver production-ready data from the vast array of enterprise data sources.
DataOps (data operations) is an emerging discipline that brings together DevOps teams with data engineer and data scientist roles to provide the tools, processes and organizational structures to support the
data-focused enterprise.
At Horizonx with the DataOps approach we focus upon:

  •  Individuals and interactions over processes and tools
  •  Working analytics over comprehensive documentation
  • Customer collaboration over contract negotiation
  • Experimentation, iteration, and feedback over extensive upfront design
  • Cross-functional ownership of operations over siloed responsibilities

Why DataOps?

Enterprises today are increasingly injecting predictive analytics & machine learning into a vast array of products and services and DataOps is an approach geared to supporting the end-to- end needs of big data analytics & machine learning.
The DataOps approach is not limited to big data analytics & machine learning. DataOps fit well with microservices architectures and practices as well.
The DataOps model is useful for any data-oriented work, making it easier to take advantage of the benefits offered by building a global data fabric.

How to do DataOps?

At HorizonX, our talent pool has years of experience of Agile and DevOps, enables us to be the early adaptors of DataOps.
Like DevOps, the DataOps approach takes its cues from the agile methodology. The approach values continuous delivery of analytic insights with the primary goal of satisfying the customer.
DataOps teams value analytics that work; they measure the performance of data analytics by the insights they deliver. DataOps teams embrace change, and seek to constantly understand evolving customer needs. These teams self-organize around goals, and seek to reduce “heroism” in favor of sustainable and scalable teams and processes. DataOps teams seek to orchestrate data, tools, code, and environments from beginning to end. Reproducible results are essential. DataOps teams tend to view analytic pipelines as analogous to lean manufacturing lines.

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Streaming Analytics

Streaming Analytics performs actions on real-time data through the use of continuous queries. It connects to external data sources, enabling applications to integrate certain data into the application flow, or to update an external database with processed information. It enables so-called “real-time business intelligence” (RTBI) is the process of using real-time analytics to deliver information on business operations as they occur. At HorizonX, our team is experienced in delivering high quality, scalable and resilient streaming analytics platforms allowing customers to achieve real- time events, decisions, modelling and reporting.

What is Streaming Analytics?

Streaming analytics (also called real-time analytics) is comparatively newer than historical data analysis techniques. Stream processing analyses and performs actions on real-time data through the use of continuous queries. Streaming analytics connects to external data sources, enabling applications to integrate certain data into the application flow, or to update an external database with processed information.
The ability to extract information from operational data in real time is critical for a modern, agile enterprise. The faster you can harness insights from data, the greater your advantage in driving revenue, reducing costs, and increasing efficiency.

Why do Streaming Analytics?

Historical data tells us what happened in the past while real-time analytics tells you what is happening right now. This essential difference explains the advantages of looking at real-time data:

  • Data Visualization. A set of historical data can be placed into a single chart to communicate an overall point. But streaming data can be visualized in a way that updates in real time to show what is occurring at every single moment
  • Business Insights. Whenever an important business event occurs, it will first appear in the relevant dashboard. If the hourly sales at one of the aforementioned grocery stories plummets at an unusual time, then an alert can be triggered to tell management of a serious problem at that branch location
  • Increased competitiveness. Businesses can discern trends and set benchmarks much more quickly, allowing them to use this data to surpass competitors who are still using the slower process of batch analysis.

Real-time streaming analytics (RTSA), has the following value to businesses:

  • Cutting preventable losses. Streaming analytics can prevent or at least lessen the damage of incidents such as security breaches, stock exchange meltdowns, airplane crashes, manufacturing defects, customer churn, and social media meltdowns.
  • Analysing routine business operations. All of these can be monitored in real time: manufacturing closed-loop control systems; IT systems; field assets such as trucks, oil rigs, vending machines and radio towers; and financial transactions such as authentications and validations.
  • Finding missed opportunities. The streaming and analysing of Big Data can help companies to learn from customers as well as immediately recommend, upsell, and cross-sell to them based on what the information presents.
  • Create new opportunities. The existence of streaming data technology has led to the invention of new business models, product innovations, and revenue streams. Tractors could be implemented with soil sensors. Clothing manufacturers could add wearable health technology to its products.

Real-time streaming analytics (RTSA) enables so-called “real-time business intelligence” (RTBI) is the process of using real-time analytics to deliver information on business operations as they occur. (“Real-time” refers to near-zero latency. In practical terms, the phrase means that information becomes available anywhere from milliseconds to five seconds after the fact.)

How to do Streaming Analytics?

In RTSA, Data is analysed in motion as it arrives. Every incoming event is process-able. Events can be stored later or in parallel. Immediate actions are possible after processing.In descriptive, predictive, and prescriptive analytics, one exports a set of historical data for batch analysis. In streaming analytics, one analyses and visualizes data in real time. Real-time analytics can be used for purposes such as these:

  • To make operational decisions and apply them to business processes, transactions, or other production activities in real time and on an ongoing basis
  • To apply pre-existing predictive or prescriptive models
  • To report current and historical data concurrently
  • To receive alerts based on certain, predefined parameters
  • To view real-time displays or dashboards in real time on constantly-changing transactional data sets such as the hourly sales of a set of regional grocery stores
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