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How AWS can help you jump start your AI journey?

The Hype is real!!! If you still think AI is the future then you are mistaken, AI is the present.

Amazon Web Services (AWS) SageMaker Studio

ChatGPTGenerative AI– you might have heard these words pop-up in many online articles, social media trends, news articles etc.,

Generative AI describes AI algorithms which can learn from vast amounts of existing data points and create new data. It can generate text, images, video and audio. One application of Generative AI algorithms is ChatGPT.

ChatGPT is a Generative AI based platform (subset of AI) trained on terabytes of data to generate new and close to original content, it works so well that it becomes hard to distinguish whether the content created or conversation you are having is coming from a person or machine. While this article is not about ChatGPT but it definitely shows the benefits that businesses and individuals can reap from Generative AI. You can learn more about Generative AI from these articles by McKinseyBCG & Gartner.

Usecases of Generative AI are growing at fast pace and it is important that you step into the waters to try this technology and see how it can help you or your business grow.

Usecases of Generative AI

  1. Improved chatbots or virtual assistants.
  2. Create code for your applications, code review, bug fixes.
  3. Create summarized content for marketing campaigns, webpages etc.
  4. Generate media (Audio, Video or Images).
  5. Brainstorming product ideas or designs.

The usecases are growing each day. Check this post written by Cem Dilmegani for more use cases based on Generative AI.

How to get started with Generative AI using AWS SageMaker

To build sophisticated AI products such as ChatGPT, data is crucial and we need a lot, I mean massive amounts of data to train our programs. Cost is also a big factor as the amount of compute power you would need to train your ML program is really expensive.

AWS SageMaker JumpStart provides public or proprietary Foundation Models (pre trained ML models on vast amounts of data) and you can tune them to fit your use case at a lower cost.

AWS SageMaker Studio Setup

1. To get started, create your Amazon Web Services account here. If you already have one, login to continue with next steps.

Amazon Web Services (AWS) Sign in Page

2. Search for SageMaker and go to the service.

Amazon Web Services (AWS) Console Home
Amazon Web Services (AWS) SageMaker Service Page

3. To setup SageMaker environment, we should first create a “Domain”. To create domain, Click Domain -> Create Domain.

SageMaker Domain — A domain includes an associated Amazon Elastic File System (EFS) volume; a list of authorized users; and a variety of security, application, policy, and Amazon Virtual Private Cloud (VPC) configurations. Each user in a domain receives a personal and private home directory within the EFS for notebooks, Git repositories, and data files.

4. In the Setup SageMaker Domain page, Select Quick Setup option, Give the domain a name (ex: sagemakerdomain) and a name for user profile (ex: SageMakerUser). Select “create a new role” option under execution role, this role will be utilized by the users in this domain.

Amazon Web Services (AWS) SageMaker Domain

5. In the “Create an I AM role” pop-up, select “Any S3 bucket” and click “Create role”.

Amazon Web Services (AWS) SageMaker Domain

6. A new execution role will be created and click create domain (it will take few minutes for domain to be created and ready for use).

Amazon Web Services (AWS) SageMaker Domain

7. Once domain is ready, you can find it in the domains section. Click on the newly created domain.

Amazon Web Services (AWS) SageMaker Domain

8. In the domain, go to “User Profile” tab and click on “Launch -> Studio” with the new user created during domain setup.

Amazon Web Services (AWS) SageMaker Domain

9. It may take a while for SageMaker Studio to initialize. (If you see any error screen asking to clear workspaces, closing any other tabs like Jupiter notebooks, SageMaker studio labs etc., and reload- that did work for me ;)

Amazon Web Services (AWS) SageMaker Studio

10. Once SageMaker Studio is ready for use, you land into it’s home screen. Select “SageMaker JumpStart -> Models” option.

Amazon Web Services (AWS) SageMaker Studio

11. You can see multiple ML options from end-to-end solutions, foundation models, vision models etc., For this demo we are going to use a foundation model to (text2text generation) generate text responses based on our input and (text2image generation) image generation based on text input.

Amazon Web Services (AWS) SageMaker JumpStart

Generate Text Responses based on Text Input (TEXT To TEXT Generation)

1. There are hundreds of options to select from but for the sake of this demo and to get you familiar with JumpStart service, search for “Flan-T5 XL” and click view model.

Generative AI Text Generation using Amazon Web Services (AWS) SageMaker JumpStart

2. On model tab you can read the details about how the model works, deployment configuration, security configuration. You can leave the defaults as is for the demo purpose. Click “Deploy”.

Generative AI Text Generation using Amazon Web Services (AWS) SageMaker JumpStart

3. It may take a while for SageMaker to deploy the model and setup the endpoint for inference.

Generative AI Text Generation using Amazon Web Services (AWS) SageMaker JumpStart

4. Once the endpoint is ready, you can access to use the endpoint from a notebook inside studio itself. To start querying your endpoint, click on “Open Notebook”.

Generative AI Text Generation using Amazon Web Services (AWS) SageMaker JumpStart

5. It should open a new notebook with details on how to run inference on the endpoint we just created. For a quick trial you can run the cells (select the cell and click on play button above to execute the code from the selected cell).

Generative AI Text Generation using Amazon Web Services (AWS) SageMaker JumpStart
Generative AI Text Generation using Amazon Web Services (AWS) SageMaker JumpStart

6. You can replace the input text text1 & text2 to any other text query and run it again with endpoint.

Generate Image based on Text Input (TEXT To IMAGE Generation)

1. Go back to SageMaker JumpStart or click on the models, notebooks and Solutions option under SageMaker JumpStart On left pane of the screen.

Generative AI Image Generation using Amazon Web Services (AWS) SageMaker JumpStart

2. Search for “Stable Diffusion 2.1 base” model and click view model.

Generative AI Image Generation using Amazon Web Services (AWS) SageMaker JumpStart

3. Once again repeat the steps 2–4 as mentioned in the text 2 text generation endpoint deployment above.

4. Open notebook once the endpoint is ready. Run cells (select the cell and click on play button above to execute the code from the selected cell).

Generative AI Image Generation using Amazon Web Services (AWS) SageMaker JumpStart

5. You can replace the input text/argument for “query_endpoint” function to any other text and run it again with endpoint.

Embedding into your application

1. Go to “Inference -> Endpoints” and select endpoint to get the API for the model we just deployed. Using the API you can run the inference programmatically.

Resource Cleanup

1. Go back to respective model tabs where we created endpoints. Select “delete” the endpoint.

Amazon Web Services (AWS)

2. If you have closed that tab, go back to the SageMaker dashboard in AWS, click “endpoints” under “Inference” option. select the endpoint and click “Actions -> Delete”.

Amazon Web Services (AWS)

3. Repeat the same steps for models under “Inference -> Models”.

4. To delete the domain we must first the delete the user profile & any apps associated with the user profile. Go to “Domains -> select the created domain -> select User profile tab -> select the created user -> delete the apps”.

Amazon Web Services (AWS)

5. Once all apps are deleted, click edit on the bottom right side and go ahead with deleting the user.

6. After deleting the user, go to edit option on the domain and delete the domain.

7. You can continue to remove other artifacts like S3 buckets, roles, policies, EFS volume created by SageMaker.

Conclusion

Hope this demo helped you start your Generative AI or AI journey in general using Amazon Web Services (AWS). Foundation models in AWS also provides option to train the model using your data sets to fit to your business cases.

Written by Sai Sameer Syed, Senior Software Engineer at Ness Digital Engineering

NESS DIGITAL ENGINEERING ACQUIRES MVP FACTORY – A LEADING GERMAN HEADQUARTERED PRODUCT DESIGN, DIGITAL INNOVATION AND VENTURE BUILDER

ACCELERATES LEADERSHIP IN END-END DIGITAL TRANSFORMATION

TEANECK, N.J. – June 29, 2023 – Ness Digital Engineering is pleased to announce its acquisition of MVP Factory – a leading German headquartered corporate venture builder & product design studio.

“MVP Factory’s expertise in helping clients incubate and scale digital ventures and design software products further strengthens our strategy to be a specialist in digital engineering” said Ranjit Tinaikar, Chief Executive Officer, Ness. “We have been deeply impressed by MVP Factory’s leadership and team in their distinctiveness and design-based approach to digital innovation. We are truly excited to have them join our team”.

Headquartered in Berlin, Germany, MVP Factory provides an end-to-end service for digital venture and product building and has successfully pioneered the venture studio model with global clients such as DB Schenker. It allows clients to ideate, validate, launch and scale digital ventures using lean and agile methodologies. Additionally, MVP Factory’s cross-functional team offers an entrepreneurial mindset helping identify new opportunities and business models in rapidly changing industries.

“Joining Ness Digital Engineering, backed by KKR, offers a fascinating opportunity for the global expansion of MVP Factory,” said Philipp Petrescu, MVP Factory’s Founder & CEO. “Through our combined offering, Ness can further solidify its position as one of the few at-scale pure digital engineering players that seamlessly manages projects from new venture ideation and incubation to technical execution. The synergies will offer immediate value to our clients”.

The transaction is expected to close at the end of June 2023. Financial details were not disclosed.

About Ness Digital Engineering

Ness Digital Engineering which funds managed by global investment firm KKR acquired in 2022 is a full life-cycle digital engineering firm offering digital advisory through scaled engineering services. Combining our core competence in engineering with the latest in digital strategy and technology, we seamlessly manage Digital Transformation journeys from strategy through execution to help businesses thrive in the digital economy.For more information, visit www.ness.com.

For more information on this release contact
Vivek Kangath
Global Head of PR – Media Relations
vivek.kangath@ness.com
+91 9742565583

Leverage the digital twin situational awareness, so generative AI knows what is happening.

Generative AI has garnered significant attention in recent months, captivating people with the remarkable level of conversation they can engage in with ChatGPT. Simultaneously, professionals are actively seeking ways to harness this tool’s potential. Many blog posts and videos abound, asserting that generative AI is a game changer poised to revolutionize numerous domains while introducing novel services and approaches.

The rapid pace at which this technology advances has instilled a sense of urgency, as nobody wants to miss out on the opportunities it presents. The ease of use and versatile range of applications contribute to its swift adoption, positioning it as a multipurpose tool.
People quickly realized that ChatGPT 3.5 and the underlying generative AI model have limitations in answering questions related to recent months, given its training cut-off in 2021.

However, professionals are eager for a generative AI system to comprehend company-specific data. Fortunately, we are lucky, as generative AI is a versatile tool. It offers the opportunity to incorporate custom data, enabling it to provide more accurate answers and perform tasks relevant to company-specific information.

Two main methods to incorporate custom data are:

  • Prompt engineering 
  • Large Language Models fine-tuning

While both methods can be useful simultaneously, each has advantages and disadvantages for the particular use case. The rest of the article will focus on the prompt engineering option. Various generative AI models can be of use. We are using GPT-3.5 Turbo as part of the solution.

In numerous scenarios, we seek to integrate generative AI into our processes, allowing it to answer queries, gather and analyze data, create steps to achieve a goal, or perform specific tasks. Most of these tasks require that generative AI is “aware “of the context. To achieve this, we can turn to a concept that excels in situational awareness—the digital twin.

Digital twins and generative AI

The digital twin concept offers numerous features that we can leverage. This article will focus on refining the digital twin to function as a system of systems with situational awareness. Situational awareness software built on top of the digital twin concept provides data that reflect the current state. For our exploration implementation, we have chosen the fleet management domain. Our simplified fleet management digital twin model takes the following form:

The digital twins graph for three vehicles and a couple of destinations looks like this:

In addition to its various applications, the primary advantage of generative AI lies in its ability to facilitate human-like interactions with different users. In the context of our fleet management solution, these users include the fleet operator, driver, and customer. Each interaction necessitates real-time contextual information by retrieving data from the digital twin graph. There is no reason to use technology without business benefits:

  • Customer-oriented use cases
    1. reducing customer service costs
    2. introducing new services
    3. enhancing customer experience
    4. improving customer satisfaction
  • Driver-oriented use cases
    1. increasing driver productivity
    2. improving interaction with customers
  • Fleet operator-oriented use cases
    1. increasing fleet operator productivity
    2. extending application usability 

Let’s delve deeper into particular use cases.

Customer use case – delivery is late

Location data from vehicles undergo processing via standard IoT pipelines, and the vehicle position samples get directed to the digital twin service. When a vehicle’s digital twin receives a position update, it triggers the estimated arrival time (ETA) calculation for scheduled destinations. After a few position updates, the business and decision logic detects that the vehicle is moving slower than anticipated (e.g., due to a traffic jam), resulting in an ETA that exceeds the delivery window communicated to the customer earlier in the day.

Depending on the customer’s status, we will automatically contact the customer to provide updated information regarding the actual state of affairs. We will inquire whether the customer will wait, cancel, or reschedule the delivery. This particular task aligns well with the capabilities of generative AI. Our responsibility is to construct prompts instructing the generative AI on how it should behave and furnishing it with relevant contextual information.
In this case, the generation of prompts is from three different sources:

  • Digital Twin Graph: current delay, contact to the driver, the reason for the delay, customer status
  • Delivery scheduling service: provide information about available time slots for rescheduling
  • Company data: Information about the benefits of platinum, premium, and standard customer statuses.

Once the chat session completes, generative AI is utilized for a second time to assess the conversation and deliver the customer’s final decision. Following that, employing additional services helps to execute the customer’s chosen course of action and make necessary adjustments to the state of the digital twin.

As we observe from the prompt composition, it consists of data sourced from multiple channels. Static data encompasses general information about customer status types and associated company policies that do not undergo frequent updates. Adding the entire dataset in the prompt might be unnecessary since it is extensive, but it is vital to incorporate the customer’s query with relevant data. Employing text embedding indexed in a vector database is a suitable solution in this scenario.

The digital twin graph serves as a valuable source of real-time contextual data. In this customer-oriented use case, a predefined set of digital twin graph queries retrieves all the relevant data. By employing these fixed queries, we ensure that the user receives only the customer-specific information, eliminating the need for a separate filtering or data security layer.

The scheduling service offers a list of available rescheduling slots and verifies the validity of the chosen time slot.

Driver use case – be aware of changes.

The primary responsibility of a driver is to safely operate the vehicle, execute planned tasks, and provide a positive customer experience. The interaction between the driver and the customer is often the only personal contact between a company representative and the customer. They require support to alleviate the workload and ensure they can fulfill their responsibilities effectively.

The service execution or delivery process is prone to changes. For instance, rescheduling, altering the contact person’s phone number (where a relative will pick up the package), or changing the delivery point and contact person (where a neighbor will handle the package) are common occurrences. The process described in previous customer-oriented use cases is useful in managing such changes. The digital twin graph captures these changes.

The interaction between the driver and the generative AI system is voice-based. The driver is informed about upcoming stops and can receive contextual information to adapt to changes. This approach presents an opportunity to brief the driver with customer-related data, thus enhancing the overall customer experience.

Regarding implementation, most of the data originates from the digital twin graph. However, there may be instances where the driver has additional questions that necessitate modifying a graph query. We use generative AI to directly translate the driver’s question into a graph query to address this. A data security layer is required to ensure data security and filter queries beyond the information boundary.

Fleet operator use case – be aware of the current state.

The fleet management solution encompasses business and decision logic, which monitors operational processes and initiates alerts or automated actions. The fleet operator notifies the outcomes through a management dashboard. The fleet operator can benefit from generative AI by posing questions translated into graph queries.

The query results can provide customized answers specific to the current situation beyond what the dashboard captures.

Generative AI can effectively assist the fleet operator in achieving a goal to address the current fleet operation state, and breaking the goal into specific actions help in the execution of successful attainment. This assistance ensures that operators recognize all crucial steps and facilitate the discovery of novel approaches to accomplish them. Acting as a copilot, the generative AI offers suggestions and can carry out tasks as an agent.

Conclusion

We have successfully incorporated a customer use case into our fleet operation exploration solution. By extracting contextual information from a digital twin graph and leveraging generative AI, we have significantly enhanced the capabilities of our solution. It enables each role in our schema to access up-to-date information and make informed decisions. The generative AI serves as a human language interface, information extractor, and query transformer.

Unleash the true potential of your business with the dynamic combination of digital twin and generative AI. Seamlessly connect the contextual insights from your digital twin to the creative power of generative AI. Gain a competitive advantage by leveraging real-time data and predictive capabilities from your digital twin, amplified by the innovative possibilities of generative AI.

Make informed decisions, streamline operations, and unlock untapped growth opportunities with unparalleled efficiency and optimization. Experience the future of business transformation today with our cutting-edge digital twin integrated with generative AI.

The need of Artificial Intelligence (AI) in Analytics

What is AI Analytics

The age of Artificial intelligence has arrived and is causing a lot of excitement. AI for data analytics is not to be left behind. AI Analytics involves the extensive use of machine learning and natural language processing for data analysis. Complex algorithms are used to learn from data patterns, identify the trends, anomalies, and data set correlations. AI analytics is the ideal tool to get data-driven insights for making informed business decisions. It is widely different when compared to traditional analytics. While traditional analytics rely on statistical methods to analyze data, AI analytics uses machine learning to learn and adapt over time, ensuring the insights derived are more relevant and reliable to present needs. Traditional analytics use fixed statistical models and might miss certain insights which might be decisive. Custom AI solutions are capable of processing unstructured data (text/images), which traditional analytics might find difficult to analyze. Custom AI solutions can automate data analysis by classifying and tagging data enabling faster identification of trends and patterns in data. To start using AI analytics, know the business problem you are trying to solve and the data needed to resolve it. Select the right custom AI solutions, tools, techniques, and algorithms for data analysis. Use custom AI solutions to derive insights and have a plan to implement and integrate them into the business process. Ensure the data is high quality and chosen algorithms are appropriate for the task.

How AI Analytics benefits your business

The best benefit of AI based analytics is the automation of data analysis tasks, which will invariably reduce time and the need to invest in resources. This also results in a faster turnaround of tasks helping businesses to make quicker decisions, resulting in increased efficiency. It also enables more accurate predictions than traditional analytics solutions. The insights can be used to create better products for customers in specific target segments or to drive personalized marketing campaigns. There is a reduced risk of cyber threats as AI based analytics can identify them before they occur, ensuring enhanced compliance and risk management. Operational inefficiencies can be fixed, new revenue streams and growth opportunities can be identified for driving profitability and sustaining a competitive edge. Customer service can be personalized and made more responsive by using insights from customer data and preferences. Effective pricing strategies can be developed using insights to know market trends and customer behaviors. Supply chain operations can be optimized, including logistics and transportation to reduce costs, marketing channels that can enable the best possible outcomes can be identified, and the type of messaging needed to influence the target audience. In manufacturing, AI analytics can optimize the use of equipment and machines through predictive maintenance for improved productivity. Defects or any quality issues can be identified by analyzing data from production lines, enabling the improvement of quality control processes. Accurate forecasts on sales or customer demands can be made, helping businesses to remain agile to changing market needs. Factors leading to customer churn can be identified, and steps can be taken quickly to retain them.

How AI is used in Data Analysis

Here are a few answers on how to use AI in data analytics. AI has transformed data analysis with its ability to analyze complex datasets to identify patterns and trends. Analytical artificial intelligence is used for data analysis. Structured and unstructured data can be collected and processed in an automated manner. Automation is also applied for data classification based on data attributes such as customer demographics, purchase history, etc., and predictive modeling to forecast futuristic trends and patterns and derive insights to draft business strategies. Natural language processing techniques can be used to analyze data from social media posts and customer reviews. Customer feedback can be used for sentiment analysis to understand their attitudes and preferences. Insights from analytical artificial intelligence can be used for personalizing products or content recommendations, especially in self-service digital platforms. Similar data points can be grouped to do a cluster analysis and gain insights into specific customer segments or product categories. Stock prices or web traffic time series data can be analyzed using AI algorithms to find any patterns of trends, including identifying anomalies such as fraudulent behaviors. Financial companies can use AI to simulate complex financial market behaviors or supply chains to test multiple scenarios. Such simulations can be used to optimize fleet transportation networks and manufacturing processes. One key highlight of using AI for data analysis is that it can auto-clean and prepare data for analysis, select the important features in a dataset to reduce the complexity of analysis, and identify relationships between variables to predict outcomes with a high level of efficiency.

AI Analytics: Top use cases

Here are common use cases of AI and analytics. The most important is predictive maintenance, where equipment or machinery downtime can be predicted, and proactive steps can be taken to avoid downtime. In financial services, fraudulent activities can be identified quickly to mitigate risks of any breach or compromise. Credit risks for loans or any financial products can be assessed, helping banks make more informed decisions on lending. In healthcare, patient data and medical records can be analyzed to improve diagnoses, find health risks and enhance treatment outcomes. In customer service, it can power up chatbots and virtual assistants and reduce dependencies on support staff. Customer behaviors and preferences can be analyzed to develop custom products and services. Customer voice data can be analyzed to enhance speech recognition accuracy for analyzing customer feedback and sentiments. Customer segmentation is another area of application where AI can be used to categorize customer demographics and preferences. Social media data can be analyzed to understand customer views and preferences. Sales trends and seasonal buying trends of customers can be identified to forecast sales revenue and develop sales strategies. Inventories can be optimized based on customer demand patterns, and supply chain efficiencies can be improved. Logistics operations can be optimized to reduce costs. In this digital landscape, cyber threats cannot be ignored. AI analytics can identify insider threats or external threat vectors to proactively mitigate these risks and prevent a breach.

Top AI tools for Data Analytics

IBM Watson Studio: A data analytics AI platform best used for prescriptive analytics. It is good for preparing data, building ML models and deploying them. Ideal for use in operations, finance, and sales.

Amazon SageMaker: It has pre-built algorithms, AutoML, Jupyter notebooks, managed infrastructure, and model hosting. ML models can be built, trained, and deployed to scale. This tool is used for its ease of use and scalability.

DataRobot: Good for automating end-to-end ML workflows. ML models can be built and deployed at scale. Features include automated machine learning, model deployment, and monitoring. It has an intuitive UI and an ideal for any business.

Microsoft Azure Machine Learning: It has a range of AI tools for data analysis and building and deploying ML models. With features such as automated machine learning, model management, and deployment options, it can easily be integrated with Azure Cognitive Services to build end-to-end AI solutions.

H2O.ai: A good open-source platform to build, deploy and manage ML models. It can be used with Python and R, Hadoop, and Spark. Its UI is intuitive, making it easy for data scientists and developers to work together.

Google Cloud AI Platform: This AI analytics platform offers many AI tools for data analysis and building and deploying ML models. It can be integrated with other Google Cloud services, such as BigQuery and Cloud Storage, for building AI solutions.

RapidMiner: Widely used in finance, healthcare, and marketing sectors, it is used along with Python and R, Hadoop, and Spark for quickly developing and deploying ML models at scale.

How Ness Can help you Get Started with your AI Analytics journey

Ness data & analytics services can rapidly transform your business by helping you realize value from machine learning initiatives faster. Our customized data & analytics services can convert your data into intelligence to enable risk management, preventive maintenance, portfolio analytics, fraud detection, personalized promotions, portfolio analytics, data intelligence, churn analysis, fraud detection, inventory optimization, and predictive maintenance. Our data & analytics services cover end-to-end MLOps and DataOps process design, workflow, and implementation capabilities. We also design and deliver AI governance through Responsible AI and Ethical AI lenses to ensure your AI investments are free of bias, auditable, traceable, and explainable. We can offer expertise in AWS and Azure. Our data scientists and engineers are trained and certified in Sagemaker, Azure ML, Databricks, mlflow, Kubeflow, and open-source Python libraries like TensorFlow, Keras, PyTorch, scikit-learn and Theano. Ness services can evolve any organization’s AI analytics initiatives by enabling more AI powered insights. With Ness AI driven analytics expertise, businesses can gain deeper insights into their data, reveal hidden patterns, and make better decisions. Ness AI powered analytics solutions can empower businesses to respond to changing customer needs or market trends. Our AI analytics services can reduce operational costs by automating data processing and data analysis. By leveraging our AI powered analytics solutions, companies can enhance forecasting accuracies and sustain their competitive edge. Ness AI driven analytics offerings can unlock the full potential of organizational data, which was previously not achievable by using legacy analytics tools and methods.

FAQs

How is AI used in data analytics?

AI is mainly used to automate data analysis. That tasks can be data preprocessing, pattern recognition, predictive analytics, data visualization and predictive analytics to name a few.

Is data analytics related to AI?

Data analytics and AI are related. AI in data analytics help in analyzing data to get useful insights for making decisions. There are many AI techniques to analyze data and get valuable insights from it.

Can a data analyst work with AI?

Yes, they can work together to develop AI models and deploy them in production environments to get valuable insights.

Is AI a branch of data analytics?

They are different fields; however, AI is applied in data analytics to analyze data and extract insights.

How to build Data Infrastructure? Tips and Best Practices

What is Data Infrastructure

Many firms have a wealth of data. However, most of them are scattered across systems and in silos. Business functions have a tough time in the access and analysis of this data to make any decisions. Even with teams of data analysts, they are often hindered by slow processing times and inconsistent data quality. What companies must realize is that leveraging data is key to business growth and gaining a competitive edge. The answer is to have a reliable data infrastructure. Data infrastructure definition can be understood as follows – It is the foundational technology and architecture which enable the storage, management, and processing of data. It can consist of hardware, software, servers, storage devices, database infrastructure, analytical & business intelligence tools and networking resources to support data storage, data processing and data analysis. The infrastructure can ensure a centralized view of data making it easy for every business function to collaborate and share insights. The data will remain secure and compliant while remaining available for business use. Decisions can be made quickly and more accurately due to up-to-date information. There is less time spent of data acquisition and analysis, including proper data governance. Data is available on-demand to analyze customer behavior and market trends, which can help in sustaining competitive advantage. Big data infrastructure is critical in today’s data-driven world and must be custom-built based on the needs and requirements of the company. It is an enabler for innovation and customer experiences and requires maintenance, monitoring, and optimization on a regular basis.

What does Data Infrastructure Include

A comprehensive, well integrated big data infrastructure is critical for a company’s growth in the digital age. It should have the data infrastructure tools and processes to integrate data from various business functions. By integrating data, stakeholders can get a unified, comprehensive view of data for identifying opportunities for improvement or growth. It should have processes to improve data quality, such as data cleaning, validation, and enrichment. Data infrastructure also ensures data governance by establishing data access controls and compliance. Stakeholders will be able to derive insights from data using analytics and reporting tools to know the market trends, optimize operations and enhance decision making. Just in case of a breach or system outage, the infrastructure will ensure the data is restored, and there is business continuity. It is also recommended to have a cloud-based data infrastructure as it can be scaled up or down based on business needs, while handling large amounts of data, it has strong data governance and data security protocols reducing data breach risks, and more importantly, it almost eliminates the need for investments in hardware and software to manage database infrastructure, data warehouses, and data management systems.

Why do we need Data Infrastructure

Data infrastructure establishes data quality, data accuracy and data consistency. This helps in making informed decisions. Data can be integrated from various sources, ensuring there are no silos of data. There is better data security mitigating risks of a malicious breach or unauthorized access. A cloud-based data infrastructure will bring scalability to meet any growing volumes of data as the business grows. The crucial aspect of having data infrastructure is that due to the availability of AI-enabled data analytics and reporting tools, businesses can get insight into customer behaviors and preferences, identify market trends and patterns and optimize their operations accordingly to drive growth. There is improved compliance with regulations such as GDPR and CCPA, including better flexibility and cost savings due to the cloud infrastructure. Moreover, it offers a single unified source of truth for the data, and there is a low risk of any data errors leading to increased productivity, innovation, and better decisions. ROIs can be tracked, ensuring the spending is optimized for marketing campaigns and while developing products and services, enabling companies to track and measure investment success through the financial year. As data infrastructure forms the foundation of any data-driven organization, a data infrastructure engineer is equally responsible for the data infrastructure design, deployment, and maintenance of a secure and reliable data ecosystem. A few data infrastructure examples include Amazon Web Services (AWS) Elastic MapReduce (EMR), Microsoft Azure Synapse Analytics, Google Cloud Bigtable, Snowflake Data Cloud, Apache Kafka, and Cloudera Data Platform.

Challenges in Building a Data Infrastructure

The complexities of building a robust data infrastructure. Here are some challenges that need to be surmounted to realize the full potential of data.

  • Ensuring data accuracy, consistency, and completeness is a common challenge
  • Another complex undertaking is establishing processes and policies for data management
  • Integrating data from multiple sources and systems can be time consuming, especially with legacy systems
  • Data storage and management of large data volumes are expensive
  • Data security needs advanced technologies to ensure resilient security protocols & to provide the right data to the right people at the right time
  • Meet data privacy regulations such as GDPR and CCPA is not an easy task for companies dealing with customer data
  • Scalable data infrastructure is only possible through a cloud infrastructure which requires specialized technical expertise for data infrastructure design and deployment
  • Cloud infrastructure integration with legacy systems is challenging and needs diligent planning and execution
  • Institutionalizing a data-driven culture in the organization needs requires a paradigm shift in employee mindset
  • Implementing a data infrastructure need changes in existing processes, and employee roles and responsibilities
  • The cost of building a data infrastructure is high
  • Overcoming data silos is not easy for organizations having a fragmented data landscape
  • Ensuring the efficiency of a data infrastructure can be difficult if the KPIs and success metrics are not properly defined

How to Build Modern Data Infrastructure: Tips and Best Practices

Here are tips and practices which can be used while designing and building a reliable data infrastructure. Have clarity on your goals, objectives, and the KPIs you intend to achieve, and make sure they are aligned with business needs. Choose the data resources, both internal and external, that you need. Establish a data governance model with the required processes and policies to meet access and security needs. Ensure data is of high quality by maintaining accuracy, completeness, and consistency. Pick the right technology portfolio which fits your needs and budget. Always opt for cloud-based data infrastructure to leverage cloud features such as scale, flexibility, and reduced costs. Have a centralized data warehouse capable of unifying data from several sources. Utilize data modeling approaches to structure data. Adopt tools and methods that can enable data analytics and data visualization. Automate data processing to reduce mistakes. Encourage a data-driven culture to motivate employees to use and value data for decision making. The impact of your data infrastructure must be measured through clear success metrics and KPIs to ensure it is meeting your business objectives.

Best Tools for Modern Data Infrastructure Automation

A modern data analytics infrastructure can be automated to make it more efficient, reliable, and scalable. Here are some of the data infrastructure tools for data infrastructure automation. Apache Airflow is a platform to author, schedule, and monitor workflows programmatically. Jenkins is an automation service to automate tasks such as building, testing, and deploying software. Ansible, an open-source tool, can be used for the automation of configuration management and application deployment. Puppet is to automate the deployment and management of infrastructure and applications. For automating, deploying, scaling and managing containerized applications, Kubernetes can be used. Docker can be used for packaging and deployment of applications as containers, and Terraform, to build, change, and versioning infrastructure. For the cloud infrastructure AWS CloudFormation, Google Cloud Deployment Manager or Microsoft Azure Resource Manager can be used. Grafana can be used for data analysis and visualization, and Elk Stack for data collation, process, and analysis of log data. Chef is used to automate infrastructure configurations, and Nagios is suitable for monitoring the health of data infrastructure and applications.

How to get started with data infrastructure

The business objectives must be clear and ensure you know how data should be used to achieve them. Involve the key stakeholders in the planning process. Do a critical analysis of the existing state of your data infrastructure and find the gaps for improvement. Design a roadmap on how to deploy the data infrastructure. Find a data management system that fits your company’s needs. A data catalog will be of help to organize data assets. Establish a data ingestion process to capture data and implement a data processing framework to make data fit for analysis. The storage and data retrieval architecture must be designed in such a way that it meets the performance and scalability needs. Design the architecture with security in mind – this is best done by implementing data governance policies and procedures to meet data privacy and security needs. Also, choose the data visualization tools that enable users to comprehend data easily. Monitor the effectiveness of your data infrastructure and ensure it is meeting your business needs. It is also recommended to leverage the data infrastructure engineering services vendor such as Ness, who has a data infrastructure engineer talent pool to design and implement data architectures that enable organizations to store, manage and analyze vast volumes of data.

FAQs

What are examples of data infrastructure?

Data storage systems, data processing frameworks, data integration tools and data streaming platforms are some of the examples of data infrastructure.

What is a good data infrastructure?

A good data infrastructure should enable and support a company’s data management and analytics requirements.

Why build a data infrastructure?

A data infrastructure is critical for an organization’s growth. It helps to manage data, do data analysis, make decisions, and drive operational efficiencies and innovation.

Why is big data infrastructure important?

A big data infrastructure is needed to manage large data volumes & different data types, and drive data processing and analytics.

What are the three elements of data automation?

Three elements of data automation include data collection, data transformation, and data analytics and reporting.

Firmy v ČR se připravují k přechodu na novou generaci řešení SAP

Praha, 31. května 2023: České podniky se začínají ve zvýšené míře zajímat o možnosti přechodu na nejnovější generaci podnikového informačního systému SAP S/4HANA. Důvodem je blížící se konec oficiální podpory stávající generace ERP řešení spolu s očekáváním, že ceny konzultačních služeb během té doby dále porostou a nedostatek kvalifikovaných a zkušených odborníků se bude prohlubovat. V neposlední řadě firmy motivuje i fakt, že chtějí lépe řídit chod svého podnikání a kvalitněji se rozhodovat.

„SAP již v roce 2020 oznámil, že bude podporovat předchozí generaci řešení, SAP Business Suite 7, která je mezi zákazníky nejrozšířenější, do konce roku 2027 a za příplatek do roku 2030. Firmy mají nejvyšší čas začít se na přechod chystat, protože na základě našich zkušeností může podnik potřebovat i více než rok, aby nový systém SAP S/4HANA řádně naimplementoval. Jednou z možností, jak zkrátit dobu nasazení až o 50 % je konverze do cloudového prostředí, kterou Ness svým zákazníkům také nabízí,“ uvádí Tomáš Foltýn, Senior Local Sales Manager ve společnosti Ness Czech, která patří mezi přední partnery SAP na českém trhu.

Řada zdejších organizací uvažuje o přechodu na novou verzi SAP ERP, nyní však mnoho z nich netuší, kde začít. „Proto v naší nabídce služeb máme produkt, který pomáhá naším zákazníkům maximálně využít možnosti, které aktuálně SAP nabízí a nastavit tak správný směr a kvalitní základ samotné implementaci. Přeci jen je potřeba k projektu takovéhoto rozsahu přistupovat zodpovědně – nový ERP systém si firmy pořizují na 15 a více let,“ popisuje Markéta Eggerthová, ředitelka divize SAP v Ness Czech.

Hlavní motivací přechodu na systém SAP S/4HANA je pro většinu firem a organizací možnost rychlejšího čerpání byznysových přínosů, které systém nabízí. Dosluhující systém jim totiž obvykle v plnění jejich byznysových cílů již nestačí. Řeší se také rostoucí cena lidské práce, která u SAP konzultantů meziročně stoupá zhruba o desetinu. Kromě inflace svoji roli hraje i rostoucí poptávka. Podle údajů personální agentury Grafton Recruitment si SAP konzultant v Praze může vydělat 75-150 tisíc hrubého měsíčně, v regionech nejčastěji 70-100 tisíc korun.

„Po službách spojených s implementací systému SAP S/4HANA a rozšiřujících aplikacích je na trhu značná poptávka. Za jejím růstem stojí zejména zvýšený zájem podniků digitalizovat své podnikání. V závislosti na složitosti stávajícího systému a nových požadavcích vyžaduje zavedení systému SAP S/4HANA až 20 konzultantů, často po dobu více než jednoho roku. Takovýto počet lidí obvykle firmy ve svých týmech nemají a chybí jim i potřebné znalosti a zkušenosti. Proto oslovují certifikované SAP partnery,“ říká Tomáš Foltýn.

Experti ze společnosti Ness Czech doporučují, aby se firmy primárně poohlížely po partnerovi SAP, který nabízí komplexní metodologie pro přechod do SAP S/4HANA. „Přechod na SAP S/4HANA není triviální a jen propracovaná metodologie přechodu umožní podnikům plně využít všech možností, které nový systém nabízí. Součástí metodiky, kterou SAP pro implementace poskytuje, jsou tzv. Best Practices pro celou řadu odvětví, jejichž využití dokáže projekt a jeho následnou údržbu velmi zefektivnit,“ upozorňuje Tomáš Foltýn.

Mezi klíčové přednosti systému SAP S/4HANA patří mnohem větší rychlost zpracování dat, díky níž podniky získají přehled o stavu svého podnikání v reálném čase. Datový model SAP S/4HANA je efektivnější, ale byť s vyššími nároky na HW infrastrukturu. SAP S/4HANA dále podnikům umožňuje kvalitnější a rychlejší rozhodování právě díky optimalizovanému datovému modelu a novým analytickým funkcím. Moderní uživatelské prostředí SAP Fiori zase dělá uživatelské prostředí přívětivější a umožňuje přístup do SAP i z mobilních zařízení.

O společnostech Ness Czech a Ness Digital Engineering
Ness Czech, přední český systémový integrátor a součást mezinárodní skupiny Ness Digital Engineering, patří mezi největší poskytovatele IT služeb v České republice. Od roku 1993 se profiluje jako průkopník v zavádění nových technologií a softwarových produktů. Mezi nejvýznamnější zákazníky patří například O2 Czech Republic, Komerční Banka a ČÚZK. Ness Czech má pobočky v Praze, Brně a Ostravě, zaměstnává přes 260 lidí a její roční obrat činí přes 835 milionů korun.

Ness Digital Engineering je globální poskytovatel komplexních řešení a služeb v oblasti informačních technologií. NDE působí v Severní Americe, Evropě, na Blízkém východě a v Asii. Zaměstnává přes 4 500 lidí a spravuje 11 inovačních center.

Kontakt pro média
Kamil Pittner, Media Consultant, PRCOM, +420 604 241 482, kamil.pittner@prcom.cz

A Leading Equities and Derivatives Clearing Firm Transforms and Modernizes their Risk and Margining Platform

Case Study

A Leading Equities and Derivatives Clearing Firm Transforms and Modernizes their Risk and Margining Platform to Provide Increased Transparency

The solution delivers a modern cloud-based streaming architecture, resulting in a scalable, high-performance data and computing fabric.​

Overview

The client clears billions of options contracts per year across 16 exchanges worldwide and is the largest clearing corporation for listed equities options worldwide. The client is the buyer to every seller and the seller to every buyer in the U.S. listed options markets, responsible for maintaining liquidity and the efficient trade flow in these markets. In the last few years, the explosion of volume in the equities market and listed equity derivatives has put a tremendous strain on existing technology such that legacy systems could curtail future growth. By rebuilding key systems in AWS, the client has ensured a smooth and unrestricted path to continuing to fill their key mandate.

Challenge

The trading volume of equities derivatives has increased exponentially since the onset of options trading in the early 1970s. Even before the Covid-19 pandemic, the client had embarked on a multi-year technology modernization initiative (Project Renaissance) to strengthen its foundational capabilities and better serve market participants. During the Covid-19 pandemic, both trading volume and volatility increased sharply across listed equities and equities derivatives models worldwide, requiring systems that scale to levels uncontemplated even a few years ago. The client selected Ness to help it rebuild its market risk and margining platform to provide an environment for intra-day risk management, intra-day computations, pricing, and re-valuation. The new system enhances the efficiency and speed of margin, stress-testing, and back-testing calculations. The new system also increases transparency and insight for clearing members into exposures, allowing ad hoc queries and real-time processing.

Solution

To implement these new capabilities, the client partnered with Ness to transition their Risk and Margining system from a batch-based overnight process to a near-time, event-based system. The Risk and Margining system is an ideal candidate to leverage a modern cloud-based streaming architecture resulting in a scalable, high-performance data and computing fabric.

Ness led the architectural design phase and recommended AWS as an infrastructure solution to scale and deliver quickly utilizing a computational platform based on Kafka and Flink. The combination of these two technologies – Kafka as a scalable messaging platform that is ideal for market and trading data and Flink as a stateful processing engine that can efficiently scale to manage massive, parallel processing streams – provided a foundation for the client’s needs in the future.

Result

The application built by Ness enables the client to:

  • Leverage Infrastructure as Code to allow each developer to spin up and test variable configurations of a Kafka and Flink application in AWS using self-service tools
  • Provide massive and near-perfect scaling to allow “overnight” batches every 20 minutes
  • Provide the capability to execute “micro-batches” of calculations, allowing for the calculation of intra-day risk
  • Demonstrate control order, aggregation, and impact on performance
  • Monitor and track drift to the source and between any two consumers
  • Manage capacity and costs in AWS

Revolutionizing Commercial Vehicles through Predictive Maintenance

Introduction

The transportation industry drives the world’s economies by moving people and goods most efficiently. Be it trains, airplanes, trucks, or ships, the optimal performance of these assets are crucial to ensure safety, lower downtime, and maximize operational efficiency. This is possible only through automotive predictive maintenance. Automotive predictive maintenance has changed the way how maintenance is conducted in the transportation sector.

Understanding Predictive Maintenance

Definition of Predictive Maintenance

Automotive predictive maintenance is a proactive approach to maintaining vehicles by leveraging AI-ML and data analytics to predict any failure or degradation of performance in equipment before it can even occur.

Importance of Predictive Maintenance in the Commercial Vehicles Industry

Here is how it works. By continuous monitoring and analysis of real-time data from sensors and systems fitted onto the equipment, companies can get deep insights into the actual condition of the equipment. These insights can help take the right actions to prevent any breakdown or disruption. Vehicle predictive maintenance offers many benefits, be it safety, efficiency, cost savings or extending equipment performance, making it the game changer in the transportation industry. Vehicle predictive maintenance is a proactive way of meeting vehicle maintenance needs and enhancing customer satisfaction.

Suggested Read: Predictive Maintenance on Commercial Vehicle Fleets

Comparison with Traditional Maintenance Approaches

The traditional maintenance approach is a reactive and popular approach to maintaining commercial vehicles. They rely on time-based schedules. Vehicles go through routine maintenance activities at predetermined intervals. It is based on certain generalized assumptions. However, predictive maintenance is a condition-based approach. Maintenance is scheduled based on the equipment condition and real-time data from sensors, IoT devices, and monitoring systems. This type of maintenance reduces unnecessary maintenance tasks. Using analytics and ML algorithms, real-time data can be analyzed for any sign or chance of equipment failure. Preventive measures can be quickly taken to prevent any failure before it occurs. It also reduces repairs, minimizes costs, and increases asset lifespan.

Predictive Maintenance in the Automotive Industry

Benefits of Predictive Maintenance for Vehicles

Here is a list of the benefits of predictive maintenance for vehicles and how it helps ensure the optimal performance and reliability of vehicles in the automotive industry.

Vehicle reliability: Through continuous monitoring of engines, transmission, brakes, and electrical systems, predictive maintenance can detect any variation from normal operating conditions. Such timely interventions help in mitigating breakdown risks and improve vehicle reliability.

Enhanced Safety: Safety is paramount in the automotive sector. By monitoring critical running components on time, predictive maintenance can find any need for repair or replacement early, which might compromise vehicle safety if left unattended. This also reduces accidents and ensures better performance on the road.

Cost Savings: Most of the traditional maintenance tasks are based on fixed schedules, whatever may be the condition of the vehicle. This can lead to unnecessary costs and wastage. Predictive maintenance is needed only based on the vehicle’s condition. All issues are detected early, and only targeted repairs or replacements are done. This results in reduced costs.

Better Operational Efficiency: Predictive maintenance helps in reducing sudden breakdowns by proactively addressing any part failure before it can occur. This ensures maximum vehicle uptime, resulting in improved productivity, better customer satisfaction and revenue.

Data-Driven Decision Making: By analyzing real-time data, maintenance teams can get deep insights into vehicle health and performance. Teams can make more informed decisions, optimize resources and ensure operational excellence.

Regulatory Compliance: Meeting environmental and safety regulations is very important. Predictive maintenance can help meet these requirements by monitoring vehicle performance to specific standards. By proactively detecting issues, fleet companies can maintain compliance with regulatory bodies.

Scalability and Adaptability: Predictive maintenance systems can be scaled and adapted to any type of vehicle or fleet size. Be it a small or diverse fleet of vehicles, predictive maintenance can be applied to all. Due to the scalability of these systems, fleet owners can expand their operations and implement efficient maintenance practices to ensure consistency in performance across the fleet.

Examples of Predictive Maintenance in Automotive Industry

Predictive maintenance in automotive industry has many applications. As predictive maintenance in automotive industry is proactive, it has many benefits.

  • Engine Health Monitoring: By using sensors and data analytics technologies, the engine component performances can be monitored to detect any anomalies or failures. These components can be pistons, cylinders, and valves. The data from sensors or monitoring systems can be analyzed to enable proactive actions and mitigate the risks of a breakdown.

  • Transmission System Maintenance: The condition of transmission components such as gear, clutches, or bearings can be monitored by analyzing real-time data and through diagnostic checks to find such problems as gear slippage or wear and tear in parts. The timely repair and maintenance mitigate failure risks and downtime.

  • Brake system monitoring: Brakes are critical for the safety of vehicles. With sensors embedded within brakes, various parameters can be monitored, such as brake pad wear, hydraulic pressure, and rotor condition. Data from the sensors can be analyzed and used to predict the wear limits and schedule maintenance or replacements based on these predictions.

  • Electrical System Diagnostics: Performance of electrical parts like batteries, alternators and wiring can be monitored to predict any failures, such as voltage irregularities or excessive resistance. This helps in timely repairs and replacement of components to reduce risks of any malfunction of electrical systems.

  • Tire Health Monitoring: Tires are one of the critical components for vehicle safety. Tire conditions such as tread wear, tire pressure, and tire temperature can be monitored through sensor data. The data can be analyzed to detect any sign of tire degradation to enable maintenance such as tire rotation, tire alignment adjustments or replacements. This helps in improving tire lifespan, enhancing fuel efficiency and ensuring safer driving.

Market size and Growth Potential of the Predictive Maintenance Industry

The global automotive market size was estimated to be around $23 billion in 2022. Automotive market size will reach $28.7 billion by 2023. The automotive market size growth is due to the rise in low emission vehicles and demand for premium vehicles and electric mobility vehicles. It is also important to know the size of the automotive industry can vary globally. The size of the automotive industry is high in developed economies. The size of the automotive industry in emerging markets like India, Brazil and Mexico is also growing with expansion in their manufacturing capabilities. In this context, how much is the automotive industry worth? The market is projected to reach more than 25 billion USD by 2025. The predictive maintenance market size was valued at USD 8.31 billion in 2022. The predictive maintenance market size is supposed to surpass USD 67.21 billion by 2030, at a CAGR of 29.36% during the period 2022-30. On the same note, how much is the car industry worth? The market size of the car industry by 2030 is expected to be around 40 billion USD. Predictive maintenance market is growing due to several factors. First and foremost is the increased adoption of IoT and big data analytics. The rise of IoT devices and the advancements in big data analytics to collect, analyze and interpret real-time data from these devices is enabling proactive maintenance. Fleet companies are experiencing reduced downtimes and are able to optimize their maintenance schedules. It has helped them to improve the life of their vehicles and their productivity. There is also an increasing priority for asset performance to ensure operational excellence. Predictive maintenance has helped maximize asset uptime and customer satisfaction through proactive monitoring of vehicles.

There is increased adoption of IoT and big data analytics. The rise of IoT devices and the advancements in big data analytics to collect, analyze and interpret real-time data from these devices is enabling proactive maintenance. Fleet companies are experiencing reduced downtimes and are able to optimize their maintenance schedules. It has helped them to improve the life of their vehicles and their productivity. There is also an increasing priority for asset performance to ensure operational excellence. Predictive maintenance automotive has helped maximize asset uptime and customer satisfaction through proactive monitoring of vehicles. The intervention by AI-ML is largely responsible for the enhancement of the capabilities of predictive systems. They have ensured better predictions, anomaly detection and improved recommendations for maintenance. Predictive maintenance industry is proving its value in sectors such as manufacturing, energy, transportation and health care. Predictive maintenance solutions are getting customized to these sectors to meet their specific requirements through new use cases and applications. Predictive maintenance industry has a huge impact through its contributions to environmental sustainability efforts. Be it reducing energy waste or optimizing resource utilization, the predictive maintenance industry is playing a big role in making green initiatives a success.

Fleet Predictive Maintenance

Explanation of Fleet Predictive Maintenance

Fleet predictive maintenance in transportation is a focused approach in maintenance management that specializes in optimizing end-to-end fleet maintenance of commercial vehicles. Predictive maintenance in transportation includes using analytics and predictive modeling techniques for monitoring, analyzing, and predicting the health and performance of commercial vehicles in a fleet. Fleet predictive maintenance aims to reduce downtime, costs, and enhance the efficiency of the fleet by finding and fixing any technical issues with the vehicles before they can lead to failure. Fleet operators get access to real-time data from sources like sensors, telematics systems and past maintenance records and use analytics to gain deep insights to understand the vehicle’s condition.

Advantages and Challenges of Fleet Predictive Maintenance

The key advantage of fleet predictive maintenance is its ability to offer proactive and condition-based strategies. The fleet can be monitored for the occurrence of any potential issues and can be addressed before they can occur, reducing downtime and reliability of vehicles. It ensures better planning and allocation of resources. By continuously analyzing vehicle data, fleet managers can ensure their technicians, spare parts and equipment are utilized in the most efficient manner. Maintenance costs can be reduced as fleet operators can conduct maintenance activities based on the condition of the part and usage. Driver and passenger safety can be enhanced by reducing accident risks and equipment failures. Fleet managers will also be able to quickly meet customer demands and improve service outcomes.

The key challenge to fleet predictive maintenance includes the availability of accurate and quality data while dealing with legacy systems or varied data formats. Data integration from various systems and sources can get complex as it requires strong connectivity and interoperability between hardware and software platforms for creating a centralized view of vehicle health. With the increase in fleet size, managing and processing data in real-time can get demanding. A reliable and scalable data infrastructure and analytics solutions can resolve this challenge and accommodate the growing fleet size. Predictive maintenance requires skilled manpower in ML, data analytics, and asset diagnostics. Hiring good talent can be challenging as the demand for data scientists and analytics professional is on the rise.

Best Practices for Implementing Fleet Predictive Maintenance

While implementing fleet predictive maintenance, it is necessary to plan and execute it correctly for effectiveness and success. The objectives must be clear. For example, it can either be reducing downtime, enhancing asset reliability or reducing costs in maintenance. Evaluate the readiness of the data infrastructure. Make sure the data is accurate and reliable. The quality and completeness of data must be assessed to ensure it is suitable for predictive analytics. Select the right technology that can handle real-time data processing to derive actionable insights. Consider factors like scale and integration capabilities in the technology solution. Establish data integration processes to collate, clean and integrate data by connecting onboard sensors and maintenance databases to get a complete view of vehicle health. Collaborate with maintenance teams to find the thresholds and conditions which indicate the need for maintenance. Ensure the KPIs, workflows and actions for maintenance are in place for a timely response. Train and educate maintenance staff by familiarizing them with predictive maintenance and encourage collaboration between engineers, technicians, data analysts, and fleet management. Ensure strong security measures through data governance policies and secure fleet data and maintain data privacy by aligning with data protection regulations. Keep monitoring the results and ROI. Keep track of KPIs and use the insights to showcase the value of predictive maintenance to stakeholders.

Predictive Maintenance Analytics

Role of Data Analytics in Predictive Maintenance

Data analytics drives predictive maintenance with the power of data. Data is collected and integrated into a unified platform for analysis. Technologies such as Apache Hadoop and Spark are used to manage and process data more effectively. With the help of distributed computing and parallel processing, these technologies can handle the data demands such as volume, variety, and velocity. Data analytics is used to find trends, patterns, and any anomalies in the data. Through the application of ML and statistical algorithms, predictive maintenance systems find any unwanted behaviors or deviations from required performance. Predictive models are also used to find any chances of equipment failures and estimate the life span of a part. Data analytics can monitor equipment performance in real-time. With the help of streaming analytics, real-time alerts and notifications can be triggered to ensure proactive maintenance. Data analytics platforms also offer visualization and reporting capabilities. This helps to represent complex data in an actionable format through visualizations, dashboards and reports to enable decision making and collaboration in maintenance teams. Predictive analytics in automotive industry is therefore necessary to offer deep insights for making decisions. Predictive analytics in automotive industry helps in optimizing operations and enabling innovation. Predictive analytics in automotive industry helps companies gain a competitive edge in the market.

Tools and Technologies Used in Predictive Maintenance Analytics

Some of the commonly used tools and technologies to process and analyze data include:

ML algorithms: Many types of ML algorithms like regression, decision trees, random forests, and neural networks, can help in building predictive models by using historical data. ML algorithms can learn from data patterns and relationships to make predictions on equipment behaviors.

Data Visualization Tools: These tools ensure data is presented in a visually appealing manner. It helps in comprehending trends, patterns and anomalies through interactive charts, graphs, and dashboards.

Big Data Platforms: Apache Hadoop and Sparks are used to handle large data volumes and do distributed computing for facilitating data storage, data processing, and data analysis.

IoT Sensors and Devices: Internet of Things devices and sensors collect real-time data to monitor parameters like temperature, vibration, pressure, and energy consumption. These devices can also enable continuous data streaming for real-time predictive analytics.

Cloud Computing: Cloud brings in scale, flexibility and cost-effectiveness to predictive maintenance. It also provides computational power and storage to analyze large datasets. Cloud also enables collaboration and access to analytics tools from anywhere.

Artificial Intelligence: AI techniques like NLP (natural language processing) and image recognition is also used to derive insights from unstructured data sources such as maintenance records.

Preventive Maintenance vs Predictive Maintenance

Definition of Preventive Maintenance and How It Differs from Predictive Maintenance

To maintain commercial vehicles, two distinct approaches can be followed, Preventive maintenance and Predictive maintenance. Each of them has its specific purpose. Preventive maintenance in automobile industry is a way of scheduled maintenance that includes routine inspections, servicing, and repairs at preset intervals. The objective of preventive maintenance in automobile industry is to ensure the prevention of equipment failures and breakdowns by identifying issues before it can occur. This is done based on the recommendations of the manufacturer or historical maintenance records. The maintenance is based on the time, usage or threshold of the equipment. Preventive maintenance in automobile industry includes activities like lubrication, filter replacements, calibration, and visual inspections. Predictive maintenance is more proactive in nature. By using AI-ML technologies and data, it can monitor equipment in real-time and analyze the data to predict any failure before it occurs. It uses conditional monitoring methodologies to get data on equipment, its performance, and usage. This data is analyzed to find any patterns, anomalies or deviations from expected operating conditions. By using predictive models, teams are able to figure out when the equipment might fail or remain useful.

Advantages of Predictive Maintenance over Preventive Maintenance

The advantages are many. Here are a few. Predictive maintenance ensure a higher levels of equipment reliability by analyzing data and proactively address any issues for maximizing uptime of the equipment. When compared to preventive maintenance, predictive maintenance can lead to lesser cost savings. Vehicle maintenance needs to be done only based on equipment condition and not otherwise. This helps to avoid unnecessary costs and better utilization of labor resources and parts. Equipment lifespan is also increased by addressing issues early on, preventing further deterioration of parts and the need for any major repairs or replacements. Predictive maintenance ensures the maintenance activity is scheduled during a planned downtime or on a low-demand period, hence reducing any impact on operations. As predictive maintenance depends on data analytics, fleet managers can predict safety risks and ensure the vehicle is in optimum working condition. Data can also be used to optimize equipment utilization, plan upgrade or replacement in parts or to assess the health of the vehicle. Based on the changes in operating conditions and equipment requirements, maintenance schedules can be adjusted and prioritized.

Transitioning from Preventive Maintenance to Predictive Maintenance

The transition from preventive to predictive maintenance must be carefully planned and implemented. Evaluate the existing preventive maintenance program and understand the activities, be it the frequency of inspections and servicing and the costs. Find their limitations and challenges, including the areas were predictive maintenance interventions are required. Define the reasons and objectives to transition towards predictive maintenance. Set measurable goals to align with business objectives. Collate data and monitor them. This can be through sensors, data loggers, or utilizing data sources such as SCADA systems or IoT devices. The data must be accurate and relevant. Implement data analytics to analyze the data. This can be done through ML algorithms and statistical models to gain insights for predicting failures and schedule maintenance. Define the conditions such as vibration analysis, oil analysis, thermography, acoustic monitoring, or any other relevant techniques which are based on the industry or equipment type. The frequency and method of collecting data must also be determined. Condition triggers must be set up, which indicates the need for maintenance. Define the signal criteria, be it inspections, repairs, or replacement of components. Design a predictive maintenance schedule based on conditional monitoring triggers. Optimize the schedule to reduce downtime of equipment and operational disruptions. Monitor continuously the effectiveness of the program and refine it based on feedback and insights. The transition to predictive maintenance requires a paradigm shift in mindset, processes, and technology. It is a journey that needs commitment and investment.

Conclusion

Predictive maintenance has immense potential to revolutionize the commercial fleet vehicle sector. With its array of benefits offered through next-gen technology, data analytics, and connectivity, it is becoming a preferred way to maintain commercial vehicles while maximizing vehicle availability and uptime. Fleet owners can reduce the occurrence of costly breakdowns and ensure more precise maintenance planning and scheduling. They can also reduce unnecessary maintenance and ensure optimal utilization of resources. With improved integration with telematics and IoT, there is a diligent collection of data to ensure real-time monitoring, remote diagnostics and predictive analytics for proactive maintenance. The promise of predictive analytics automotive industry is immense and will continue to grow. This industry will also see the emergence of collaborative ecosystems involving vehicle manufacturers, fleet operators, maintenance providers, and technology partners to foster innovation and evolution of industry-wide improvements in predictive maintenance strategies.

Click here to watch our webinar on Predictive Maintenance and Commercial Vehicles Industry.

FAQs

What is automotive predictive maintenance?

Automotive predictive maintenance is the use of analytics and next-gen technologies to predict failures or a breakdown in vehicles.

What is a predictive maintenance system?

A predictive maintenance system is an approach to predict when a vehicle might fail to conduct maintenance activities in a proactive manner.

What are the three types of predictive maintenance?

The three types of predictive maintenance include condition-based maintenance, predictive analytics, and machine learning based maintenance.

What is the purpose of fleet maintenance?

The purpose of fleet maintenance is to ensure the vehicles in the fleet are safe and reliable for effective operation and are in good running condition.

What industries use predictive maintenance?

Industries which use predictive maintenance include manufacturing, healthcare, transportation, energy and utilities, oil and gas.

What is predictive maintenance automotive?

Predictive maintenance automotive is the use of analytics, ML algorithms and sensors to prevent and mitigate the risk of failure in vehicles or automotive systems.

How much is the automotive industry worth?

The global automotive industry is approximately 2.9 trillion USD as per the current estimates.

What are predictive analytics automotive industry?

Predictive analytics automotive industry involves the use of data analysis algorithms to make predictions or forecasts for the automotive industry.

How much is the car industry worth?

As per the current research statistics, the global car industry is worth more than 3 trillion USD in 2023.

What is the car industry worth?

The global passenger car market size is expected to touch 2,675 billion by 2030.

DTCC – FRTB data service to optimize Balance Sheet Capital

DTCC – FRTB data service to optimize Balance Sheet Capital

About the Case Study

The client selected Ness to co-develop a front-to-back solution using scalable aggregation and an agile DevOps-first approach.​ The solution provided a scalable SaaS platform on AWS for real-price observations of derivatives and cash instruments as part of the Fundamental Review of the Trading Book (FRTB) regulation.

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