As industries rapidly embrace Digital Transformations, the amount of data generated is also growing exponentially. This huge amount of data contains possibilities to improve the industry itself. With the right refinement process, data could be turned into insights, creating endless possibilities of improvement and optimization into your business needs and goals.
Data is the result of direct observation of an event or situation that has no actionable output . Then, information is a collection of data/facts that are organized or processed in a certain way. Knowledge is information that has been evaluated, compiled, or managed to be applied in supporting decisions. Insight is the ability to result from knowledge to gain a deep understanding of a problem
Define the Company’s Analytics needs.
Recruit, train, coach, and held workshops
for employees and stakeholders.
We provide End to End Data and Analytics solutions for your disposal. We understand that everybody has their own “Data Journey” and we will be your friend in The Journey. We also care not only about giving the best solutions for your business needs, but more than anything, we strive to forge healthy and prosperous strategic partnerships with you
AI development is the process of developing a company-specific AI solution targeting a particular problem. Since custom AI software (solution) is developed for a single business it needs to satisfy the business’ specifications and expectations. On the other hand, out-of-the-box (OOTB) AI software is a packaged solution sold by vendors to satisfy the needs of numerous organizations. AI Consulting Services help companies use AI technologies to improve businesses. We can help clients formulate an AI strategy, identify AI use cases and implement AI/ML solutions and provide training to your employees.
We evaluate the potential of your existing data, software infrastructure, and technology to deploy AI systems and workflows. We discover the best AI technology and tools not only to answer your Business goals but also optimize your Business potentials
Our AI consulting team reviews the existing workflow, processes, and operations to determine if an AI solution can bring efficiency. We do quick PoC and mockups to gather feedback from the stakeholders. Once the stakeholders can see the value, then we collect requirements and prepare a roadmap for your project that outlines the project timeline estimate, cost to build an AI app, and deliverables.
We provide a small-scale system/MVP to demonstrate the capability of AI technology for your business and analyze the possible improvement to the AI system.
Once all stakeholders see the value and approve the proposed solution, our AI developers and designers launch a live system. We also maintain and provide support for ongoing operations and changes
MLOps is fundamental. Machine Learning helps individuals and businesses deploy solutions that unlock previously untapped sources of revenue, save time, and reduce cost by creating more efficient workflows, leveraging data analytics for decision-making, and improving customer experience. These goals are hard to accomplish without a solid framework to follow. Automating model development and deployment with MLOps means faster go-to-market times and lower operational costs. It helps managers and developers be more agile and strategic in their decisions.
ML models always demand data transformation in some form, they can be difficult to run and manage reliably. Using proper data pipelines offers many benefits in machine learning operations management, run time visibility, code reuse, and scalability. ML is itself a form of data transformation, so by including steps specific to ML in the data pipeline, it becomes an ML pipeline. From specific data instances, the ML pipeline enables tracking versions in source control and automating deployment via a regular CI/CD pipeline. This allows for the automated, structured connection of data planes and code.
MLOps intend to automate as much as possible, eliminate waste, and produce deeper, more consistent insights using machine learning. When you develop your use-case for MLOps, it can help to visualize the stack of technology needed to make the process happen. Components like feature store, model registry, data analysis, and ML pipeline should all be considered.
Machine Learning grows with ML research applied to business solutions. MLOps helps deploy ML models within minutes rather than weeks and enables them to achieve a far faster value result than with homegrown deployments
Version control everything, including models, code, data, parameters, and environment. Enable anyone to trace how a model was produced.
Componentize the steps of the model creation process and build them into a pipeline. A single notebook is not a pipeline.
Codify testing. With checkpoints and safeguards in place, there is a standard that models have to adhere to.
Automate work to increase how much time can be spent on future development.
Data monetization is the process of using data to increase revenue.
Why data monetization is essential for your organization ?
How well do you truly know your customers?
Truly knowing your customers is about more than identity. It’s also about preferences, needs and the contextual knowledge of time and place. In the digital world, customers expect to be known as individuals with distinct preferences, not just as one member of a segment. Knowing your customer is powered 10 percent by your internal data and 90 percent by your ability to model the behavior of your customer
“KYC” refers to the steps taken by a financial institution (or business) to:
Give Personalized Recommendations for Individualized Experiences, Personalization is extremely important for customer loyalty. That’s because consumers are more likely to make a transaction if you give them something that they will actually like or are looking for. AI can help you discover exactly what your customers like quickly and easily. And then based on that, you can tailor the content you show them or the products you suggest for them.
The project is focused to optimize the promotion cost-efficiency for customer membership by providing recommendations of which customer group optimal for certain offer types, as well as generate more revenue through customer behavior insights.
Transaction data includes offers received, viewed, and completed by members as well as the time in hours needed to do the actions
The data is processed to perform a clustering model which produces customer groups based on their behavior.
Then, these groups can be interpreted in order to achieve targeted marketing and optimize the cost.
Cluster 0 is not optimal for BOGO offers, while cluster 1 is not optimal for discount offers. Cluster 2 could generate more revenue by providing offers that fit their behavior.
Before Optimization: Cost-to-Revenue Ratio: 2%
After Optimization: Cost-to-Revenue Ratio: 1.3% (35% more efficient)
Generate up to 16.7% more revenue (from the ‘not interested’ group)
The optimization performance can be improved through more data records and variables, thus, more granular behavior can be captured.
*cost refers to membership promotion cost
“A picture speaks a thousand words” is one of the most commonly used phrases. But the graphics speak more than that. The visual representation of data, in the form of graphs, helps us gain actionable insights and make better data-driven decisions based on that data.
The network consists of 2 elements, nodes, and edges. Nodes represent objects of interest, while edges represent relationships between them. Nodes can be of the same or different categories in the network graph. Thus, the network can show the relationship between people, while at the same time the network can show the connection.
The network is about the connections and relationships between different people, the nodes require (at a minimum) complete ID and Labels columns. The edges require Source, Target, and Weight.
Based on the mathematic of graph theory, graph algorithms use the relationships between nodes to infer the organization and dynamics of complex systems. Network scientists use these algorithms to uncover hidden information, test hypotheses, and make predictions about behavior.
Many businesses work with graphs. Some examples are:
Why is it important now?
Graph analytics is important due to the expected market growth. According to a recent graph analytics market report, the graph analytics market size was ~$600 million in 2019, and it is expected to reach ~$2.5 billion by 2024, at a Compound Annual Growth Rate (CAGR) of 34% during the forecast period.
Most companies have big data. Big data is a collection of data that is very large in volume but grows exponentially over time. Because you have a lot of data, you most likely don’t always know what’s happening with it and you may not know when your data behaving in a way it shouldn’t. Artificial intelligence (AI) anomaly detection will help your business identify issues around the clock faster and more reliably, increasing efficiency and driving performance.
An automated anomaly detection system uses machine learning to establish the normal patterns of time series metrics. Anomaly detection can detect attacks on a network that deviate from normal activities based on statistical probability. Statistical anomaly detection does not have an intelligent learning model which might cause a false alarm to have a high detection rate.
Here we will look at detecting anomalies with time series forecasting. Time series is any data related to time (daily, hourly, monthly, etc.). For example, daily store revenue is time-series data at the day level. Many use cases like demand estimation, sales forecasting are common time series forecasting problems that can be solved by algorithms like SARIMA, LSTM, Holtwinters, etc. Time series forecasting helps us prepare for future needs by forecasting them with current data. Once we have an estimate, we can use that data to detect anomalies by comparing them to actual ones.
The challenges for anomaly detection is selecting the appropriate anomaly detection method for each metric, tuning it, and constantly re-evaluating that choice in case the metric itself switched to a new normal behavior and looking for unusual data points by manually examining every metric, however, is impractical for more than a few dozen metrics. – all without human interaction.
An anomaly detection solution must be sophisticated and comprehensive enough to compute not only one outcome, but also tell the entire story and point to the root cause with considerably more benefits and faster time.