The Internet of Things (IoT) is a network of linked devices that interact with one another to carry out specific functions. Everything from smart home appliances to industrial machinery may be part of this network. The IoT has the potential to revolutionize industries and open up new business opportunities by utilizing the power of big data. As with any new technology, there are substantial obstacles that need to be overcome.

One of the biggest opportunities that big data and the IoT present is the ability to make data-driven decisions in real-time. For example, in the manufacturing industry, sensors on machinery can provide real-time data on performance, allowing for predictive maintenance and reducing downtime. Similarly, in healthcare, IoT devices can monitor patients and provide data to healthcare professionals, allowing for more personalized care.

However, with the amount of data generated by the IoT, there are also significant challenges in terms of managing, processing, and analyzing this data. Traditional data management tools and techniques are often not sufficient to handle the sheer volume of data generated by the IoT. Additionally, there are concerns around data privacy and security, as the IoT often involves sensitive data being transmitted over networks.

Here are few insights from Gartner or Forrester

According to a Gartner report, the combination of big data and the IoT presents significant opportunities for businesses, particularly in areas such as supply chain management, predictive maintenance, and customer engagement. However, the report also highlights the challenges associated with managing and analyzing the large volume of data generated by the IoT, as well as the need for businesses to ensure data security and privacy.

Similarly, a Forrester report emphasizes the potential of the IoT and big data to drive digital transformation in various industries. The report notes that businesses that effectively leverage these technologies can gain a competitive advantage by improving operational efficiency, reducing costs, and delivering better customer experiences. However, the report also warns that businesses must address challenges such as data management and security to realize the full potential of the IoT and big data.

Here are a few challenges and opportunities we should be aware of.

Opportunities:

Real-time data-driven decisions: The ability to collect and analyze real-time data from IoT devices can enable businesses to make data-driven decisions quickly and efficiently.

Increased efficiency and productivity: By using IoT devices to monitor and optimize processes, businesses can increase efficiency and productivity, leading to cost savings and increased revenue.

Improved customer experience: The IoT can be used to collect data on customer behavior and preferences, allowing businesses to offer personalized experiences and improve customer satisfaction.

New revenue streams: The IoT can open up new revenue streams for businesses by enabling them to offer new products and services, such as subscription-based models or pay-per-use models.

Challenges:

Data management: The sheer volume of data generated by IoT devices can be overwhelming for businesses, and traditional data management techniques may not be sufficient to handle it.

Data security and privacy: The IoT involves the transmission of sensitive data over networks, raising concerns around data security and privacy.

Interoperability: As the IoT involves devices from different manufacturers, there can be challenges in ensuring that these devices can communicate and work together seamlessly.

Skill gaps: As the IoT is a relatively new technology, there may be skill gaps in the workforce, making it challenging for businesses to effectively leverage it.

Use Cases:

One use case for big data and the IoT is in the transportation industry. By using IoT devices to collect data on traffic patterns and road conditions, transportation companies can optimize routes and reduce congestion. In agriculture, IoT devices can monitor soil conditions and weather patterns to optimize crop yields. In the energy industry, IoT devices can monitor power usage and detect inefficiencies, leading to cost savings and reduced carbon emissions.

How Indium Software can address

Indium Software has extensive experience in developing and implementing solutions for big data and IoT use cases. For example, our team can develop customized algorithms and machine learning models to analyze IoT data and provide real-time insights. We can also help ensure data privacy and security by implementing robust encryption and access control measures. In addition, our team can develop and deploy custom dashboards and visualizations to make it easy for businesses to understand and act on IoT data.

Here are a few real-time scenarios that illustrate how the combination of big data and the IoT is being used to drive innovation and growth across various industries:

Smart Manufacturing: A manufacturing company has implemented an IoT system to monitor and optimize its production processes in real-time. The system collects data from sensors embedded in manufacturing equipment and uses big data analytics to identify patterns and optimize production. By leveraging this technology, the company has been able to reduce downtime, increase productivity, and improve product quality.

Predictive Maintenance: A transportation company has deployed IoT sensors on its fleet of vehicles to monitor their performance and detect potential maintenance issues before they become major problems. The system collects data on factors such as engine performance, fuel consumption, and tire pressure, and uses big data analytics to identify patterns and predict maintenance needs. By leveraging this technology, the company has been able to reduce maintenance costs, increase vehicle uptime, and improve customer satisfaction.

Smart Agriculture: A farming company has implemented an IoT system to monitor and optimize its crop production processes. The system collects data from sensors embedded in soil and crop fields, as well as weather data and other environmental factors, and uses big data analytics to identify patterns and optimize crop production. By leveraging this technology, the company has been able to increase crop yields, reduce water and fertilizer usage, and improve overall farm productivity.

Wrapping Up

The potential of big data and the IoT is enormous, and businesses that can effectively leverage these technologies will have a significant advantage in the marketplace. However, it is crucial to address the challenges associated with managing and analyzing the data generated by the IoT. Indium Software has the expertise and experience to help businesses overcome these challenges and unlock the full potential of big data and the IoT.

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Let’s look at a few reasons why modernizing a data warehouse is essential and highlight the benefits it brings.

Traditional data warehouses have served organizations well for many years. These systems typically involve batch processing, where data is extracted from various sources, transformed, and loaded into the warehouse periodically. While this approach has been effective for historical analysis and reporting, it falls short when it comes to real-time decision-making. With the rise of technologies like the Internet of Things (IoT), social media, and streaming data, organizations require access to up-to-the-minute insights to gain a competitive edge.

Why Modernize a Data Warehouse?

Modernizing a data warehouse is crucial for several reasons. First and foremost, it enables organizations to harness the power of real-time data. By integrating data from multiple sources in real-time, businesses can gain immediate visibility into their operations, customer behavior, market trends, and more. This empowers decision-makers to respond quickly to changing circumstances and make data-driven decisions that drive growth and efficiency.

Moreover, modernizing a data warehouse enhances scalability and agility. Traditional data warehouses often struggle to handle the increasing volumes and varieties of data generated today. However, by adopting modern technologies like cloud computing and distributed processing, organizations can scale their data warehousing infrastructure as needed, accommodating growing data volumes seamlessly. This flexibility allows businesses to adapt to evolving data requirements and stay ahead of the competition.

The Need for Modernizing a Data Warehouse

Evolving Business Landscape: The business landscape is experiencing a significant shift, with organizations relying more than ever on real-time insights for strategic decision-making. Modernizing your data warehouse enables you to harness the power of real-time data, empowering stakeholders with up-to-the-minute information and giving your business a competitive edge.

Enhanced Agility and Scalability: Traditional data warehouses often struggle to accommodate the growing volume, velocity, and variety of data. By modernizing, organizations can leverage scalable cloud-based solutions that offer unparalleled flexibility, allowing for the seamless integration of diverse data sources, accommodating fluctuations in demand, and enabling faster time-to-insight.

Accelerated Decision-Making: Making informed decisions swiftly can mean the difference between seizing opportunities and missing them. A modernized data warehouse empowers organizations with real-time analytics capabilities; enabling stakeholders to access and analyze data in near real-time. This empowers them to make quick decisions swiftly, leading to better outcomes and increased operational efficiency.

Benefits of Modernizing a Data Warehouse

Real-Time Decision-Making: Modernizing a data warehouse enables organizations to make timely decisions based on the most up-to-date information. For example, an e-commerce company can leverage real-time data on customer browsing behavior and purchasing patterns to personalize recommendations and optimize marketing campaigns in the moment.

Enhanced Customer Experience: By analyzing real-time data from various touchpoints, organizations can gain deeper insights into customer preferences and behaviors. This knowledge can drive personalized interactions, targeted promotions, and improved customer satisfaction. For instance, a retail chain can use real-time data to optimize inventory levels and ensure products are available when and where customers need them.

Operational Efficiency: Real-time data management allows organizations to monitor key performance indicators (KPIs) and operational metrics in real-time. This enables proactive decision-making, rapid issue identification, and effective resource allocation. For example, a logistics company can leverage real-time data to optimize route planning, reduce delivery times, and minimize fuel consumption.

Wrapping Up

Modernizing a data warehouse is no longer an option but a necessity in today’s data-driven landscape. By adopting real-time data management, organizations can unlock the power of timely insights, enabling faster and more informed decision-making. The benefits extend beyond operational efficiency to include improved customer experience, enhanced competitiveness, and the ability to seize new opportunities as they arise. As technology continues to advance, organizations must prioritize data warehouse modernization to stay agile, remain relevant, and  flourish in a world that is increasingly centered around data.

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L.L. Bean turned to Google Cloud to fulfill its cloud requirements. By modernizing data on, it experienced faster page loads and it was able to access transaction histories more easily. It also focused on value addition instead of infrastructure management. And, it reduced release cycles and rapidly delivered cross-channel services. These collectively improved its overall delivery of agile, cutting-edge customer experience.

Data Modernization with Google Cloud for Success

Many businesses that rely on siloed data find it challenging to make fully informed business decisions, and in turn accelerate growth. They need a unified view of data to be able to draw actionable, meaningful insights that can help them make fact-based decisions that improve operational efficiency, deliver improved services, and identify growth opportunities. In fact, businesses don’t just need unified data. They need quality data that can be stored, managed, scaled and accessed easily.

Google Cloud Platform empowers businesses with flexible and scalable data storage solutions. Some of its tools and features that enable this include:

BigQuery

This is a cost-effective, serverless, and highly scalable multi-cloud data warehouse that provides businesses with agility.

Vertex AI

This enables businesses to build, deploy, and scale ML models on a unified AI platform using pre-trained and custom tooling.

Why should businesses modernize with Google Cloud?

It provides faster time to value with serverless analytics, it lowers TCO (Total Cost of Ownership) by up to 52%, and it ensures data is secure and compliant.

Read this informative post on Cloud Cost Optimization for Better ROI.

Google Cloud Features

Improved Data Management

BigQuery, the serverless data warehouse from Google Cloud Platform (GCP), makes managing, provisioning, and dimensioning infrastructure easier. This frees up resources to focus on the quality of decision-making, operations, products, and services.

Improved Scalability

Storage and computing are decoupled in BigQuery, which improves availability and scalability, and makes it cost-efficient.

Analytics and BI

GCP also improves website analytics by integrating with other GCP and Google products. This helps businesses get a better understanding of the customer’s behavior and journey. The BI Engine packaged with BigQuery provides users with several data visualization tools, speeds up responses to queries, simplifies architecture, and enables smart tuning.

Data Lakes and Data Marts

GCP’s enables ingestion of batch and stream/real-time data, change data capture, landing zone, and raw data to meet other data needs of businesses.

Data Pipelines

GCP tools such as Dataflow, Dataform, BigQuery Engine, Dataproc, DataFusion, and Dataprep help create and manage even complex data pipelines.

Discover how Indium assisted a manufacturing company with data migration and ERP data pipeline automation using Pyspark.

Data Orchestration

For data orchestration too, GCP’s managed or serverless tools minimize infrastructure, configuration, and operational overheads. Workflows is a popular tool for simple workloads while Cloud Composer can be used for more complex workloads.

Data Governance

Google enables data governance, security, and compliance with tools such as Data Catalog, that facilitates data discoverability, metadata management, and data class-level controls. This helps separate sensitive and other data within containers. Data Loss Prevention and Identity Access Management are some of the other trusted tools.

Data Visualization

Google Cloud Platform provides two fully managed tools for data visualization, Data Studio and Looker. Data Studio is free and transforms data into easy-to-read and share, informative, and customizable dashboards and reports. Looker is flexible and scalable and can handle large data and query volumes.

ML/AI

Google Cloud Platform leverages Google’s expertise in ML/AI and provides Managed APIs, BigQuery ML, and Vertex AI. Managed APIs enable solving common ML problems without having to train a new model or even having technical skills. Using BigQuery, models can be built and deployed based on SQL language. Vertex AI, as already seen, enables the management of the ML product lifecycle.

Indium to Modernize Your Data Platform With GCP

Indium Software is a recognized data and cloud solution provider with cross domain expertise and experience. Our range of services includes data and app modernization, data analytics, and digital transformation across the various cloud platforms such as Amazon Web Server, Azure, Google Cloud. We work closely with our customers to understand their modernization needs and align them with business goals to improve the outcomes for faster growth, better insights, and enhanced operational efficiency.

FAQs

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Automating Intelligent ETL with Data Live Tables

Given the fast-paced changes in the market environment and the need to retain competitive advantage, businesses must address the challenges, improve efficiencies, and deliver high-quality data reliably and on time. This is possible only by automating ETL processes.

The Databricks Lakehouse Platform offers Delta Live Tables (DLT), a new cloud-native managed service that facilitates the development, testing, and operationalization of data pipelines at scale, using a reliable ETL framework. DLT simplifies the development and management of ETL with:

• Declarative pipeline development
• Automatic data testing
• Monitoring and recovery with deep visibility

With Delta Live Tables, end-to-end data pipelines can be defined easily by specifying the source of the data, the logic used for transformation, and the target state of the data. It can eliminate the manual integration of siloed data processing tasks. Data engineers can also ensure data dependencies are maintained across the pipeline automatically and apply data management for reusing ETL pipelines. Incremental or complete computation for each table during batch or streaming run can be specified based on need.

Benefits of DLT

The DLT framework can help build data processing pipelines that are reliable, testable, and maintainable. Once the data engineers provide the transformation logic, DLT can orchestrate the task, manage clusters, monitor the process and data quality, and handle errors. The benefits of DLT include;

Assured Data Quality

Delta Live Tables can prevent bad data from reaching the tables by validating and checking the integrity of the data. Using predefined policies on errors such as fail, alert, drop, or quarantining data, Delta Live Tables can ensure the quality of the data to improve the outcomes of BI, machine learning, and data science. It can also provide visibility into data quality trends to understand how the data is evolving and what changes are necessary.

Improved Pipeline Visibility

DLT can monitor pipeline operations by providing tools that enable visual tracking of operational stats and data lineage. Automatic error handling and easy replay can reduce downtime and accelerate maintenance with deployment and upgrades at the click of a button.

Improve Regulatory Compliance

The event log can automatically capture information related to the table for analysis and auditing. DLT can provide visibility into the flow of data in the organization and improve regulatory compliance.

Simplify Deployment and Testing of Data Pipeline

DLT can enable data to be updated and lineage information to be captured for different copies of data using a single code base. It can also enable the same set of query definitions to be run through the development, staging, and production stages.

Simplify Operations with Unified Batch and Streaming

Build and run of batch and streaming pipelines can be centralized, and the operational complexity can be effectively minimized with controllable and automated refresh settings.

Concepts Associated with Delta Live Tables

The concepts used in DLT include:

Pipeline: A Directed Acyclic Graph that can link data sources with destination datasets

Pipeline Setting: Pipeline settings can define configurations such as;

• Notebook
• Target DB
• Running mode
• Cluster config
• Configurations (Key-Value Pairs).

Dataset: The two types of datasets DLT supports include Views and Table, which, in turn, are of two types: Live and Streaming.

Pipeline Modes: Delta Live provides two modes for development:

Development Mode: The cluster is reused to prevent restarts and disable pipeline retries for detecting and fixing errors.

Production Mode: Cluster restart for recoverable errors such as stale credentials or memory leak and execution is retried for specific errors.

Editions: DLT comes in various editions to suit the different needs of the customers such as:

• Core for streaming ingest workload
• Pro for core features + CDC, streaming ingest, and table updation based on changes to the source data
• Advanced where in addition to core and pro features, data quality constraints are also available

Delta Live Event Monitoring: Delta Live Table Pipeline event log is stored under the storage location in /system/events.

Indium for Building Reliable Data Pipelines Using DLT

Indium is a recognized data engineering company with an established practice in Databricks. We offer ibriX, an Indium Databricks AI Platform, that helps businesses become agile, improve performance, and obtain business insights efficiently and effectively.

Our team of Databricks experts works closely with customers across domains to understand their business objectives and deploy the best practices to accelerate growth and achieve the goals. With DLT, Indium can help businesses leverage data at scale to gain deeper and meaningful insights to improve decision-making.

FAQs

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Small data consumes less data but is just as insightful because it leverages techniques such as;

• Time-series analysis techniques
• Few-shot learning
• Synthetic data
• Self-supervised learning

Wide refers to the use of unstructured and structured data sources to draw insights. Together, small and wide data can be used across industries for predicting consumer behavior, improving customer service, and extracting behavioral and emotional intelligence in real-time. This facilitates hyper-personalization and provides customers with an improved customer experience. It can also be used to improve security, detect fraud, and develop adaptive autonomous systems such as robots that use machine learning algorithms to continuously improve performance.

Why is big data not relevant anymore?

First being the large volumes of data being produced everyday from nearly 4.9 billion people browsing the internet for an average of seven hours a day. Further, embedded sensors are also continuously generating stream data throughout the day, making big data even bigger.

Secondly, big data processing tools are unable to keep pace and pull data on demand. Big data can be complex and difficult to manage due to the various intricacies involved, right from ingesting the raw data to making it ready for analytics. Despite storing millions or even billions of records, it may still not be big data unless it is usable and of good quality. Moreover, for data to be truly meaningful in providing a holistic view, it will have to be aggregated from different sources, and be in structured and unstructured formats. Proper organization of data is essential to keep it stable and access it when needed. This can be difficult in the case of big data.

Thirdly, there is a dearth of skilled big data technology experts. Analyzing big data requires data scientists to clean and organize the data stored in data lakes and warehouses before integrating and running analytics pipelines. The quality of insights is determined by the size of the IT infrastructure, which, in turn, is restricted by the investment capabilities of the enterprises.

What is small data?

Small data can be understood as structured or unstructured data collected over a period of time in key functional areas. Small data is less than a terabyte in size. It includes;

• Sales information
• Operational performance data
• Purchasing data

It is decentralized and can fit data packets securely and with interoperable wrappers. It can facilitate the development of effective AI models, provide meaningful insights, and help capture trends. Prior to adding larger and more semi-or unstructured data, the integrity, accessibility, and usefulness of the core data should be ascertained.

Benefits of Small Data

Having a separate small data initiative can prove beneficial for the enterprise in many ways. It can address core strategic problems about the business and improve the application of big data and advanced analytics. Business leaders can gain insights even in the absence of substantial big data. Managing small data efficiently can improve overall data management.

Some of the advantages of small data are:

• It is present everywhere: Anybody with a smartphone or a computer can generate small data every time they use social media or an app. Social media is a mine of information on buyer preferences and decisions.
• Gain quick insights:  Small data is easy to understand and can provide quick actionable insights for making strategic decisions to remain competitive and innovative.
• It is end-user focused: When choosing the cheapest ticket or the best deals, customers are actually using small data. So, small data can help businesses understand what their customers are looking for and customize their solutions accordingly.
• Enable self-service: Small data can be used by business users and other stakeholders without needing expert interpretation. This can accelerate the speed of decision making for timely response to events in real-time.

For small data to be useful, it has to be verifiable and have integrity. It must be self-describing and interoperable.

Indium can help small data work for you

Indium Software, a cutting-edge software development firm, has a team of dedicated data scientists who can help with data management, both small and big. Recognized by ISG as a strong contender for data science, data engineering, and data lifecycle management services, the company works closely with customers to identify their business needs and organize data for optimum results.

Indium can design the data architecture to meet customers’ small and large data needs. They also work with a variety of tools and technologies based on the cost and needs of customers. Their vast experience and deep expertise in open source and commercial tools enable them to help customers meet their unique data engineering and analytics goals.

FAQs

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However, while data and advanced analytics are inseparable, the quality of data determines the quality of analytics as well. Raw data can be incomplete, inaccurate, or filled with errors. Data could be in multiple formats, and structured and unstructured. To ensure that the insights gained from the data are meaningful and help deliver the desired outcomes, the data needs to be processed to make it usable for further analysis. This process is called data preparation and it is an essential step before running advanced analytics.

What is Data Preparation?

Data preparation or data wrangling, as it is also called, is a complex process that encompasses several steps including:

• Finding relevant data
• Collecting it from different systems, both internal and external
• Combining data from different sources
• Structuring
• Imputation of missing data
• Removal of Outliers

Further, this data needs to be processed, profiled, cleansed, validated, and transformed to ensure the accuracy and consistency of BI and analytics results.

Enriching and optimizing data can by integrating internal and external data or from across systems enhance its usefulness and provide greater value and insights. Data preparation also enables curating data sets for further analysis.

Making Data Preparation Efficient

While data preparation is an integral part of advanced analytics, data science is a complex field and often businesses find that this takes up a large portion of their time. It requires the involvement of specialists along with specific tools and technologies to achieve the desired results. The ideal is for businesses to be able to catch errors and correct them quickly, create top-quality data fast for timely decision making.

This requires automation and machine learning to accelerate the preparation process and ensure scalability, future-proofing, and accelerated use of data and collaboration. Automation plays a crucial part in ensuring speed and efficiency, but even to train algorithms, data preparation remains a crucial step.

Some of the best practices in data preparation include:

• Data Governance: Data governance, not a part of the preparation, provides the framework based on which businesses can lay down their advanced analytics goals, define processes and establish the standards for data preparation.
• Ensure Reliability of Source: Establish the reliability of the source and the relevance of data by defining the data needed for the task, identifying the sources, and ensuring the span of time you will need it.
• Start Small: Begging by creating a random sample for validating your data preparation rules before taking on larger volumes.
• Try Different Cleansing Strategies: Find out which strategy works for your stated need by trying them on the small data set before making it operational.
• Cleansing is Iterative: When you run the data preparation processes on your entire data set after clearing the proof-of-concept, there could still be some exceptions. This requires you to constantly finetune your data preparation process to improve the quality of your analytics
• Automate and Augment Data Processes: Creating AI and machine learning-based tools to help with collecting relevant data, scanning it, and transforming it based on organizational goals for repeatable tasks can speed up your data preparation process.
• Enable Self-Service: Normally, data science teams are required for data preparation and this can be a bottleneck as different functions need different data sets. Relying solely on one set of data scientists can delay getting the data for analytics. By enabling self-service using AI-based data preparation tools can allow business users to get the data sets they need for their analytics faster and in real-time.
• Create Collaborative Workflows: Enabling sharing can improve the reusability of authenticated data pipelines and accelerate data preparation.

Indium Approach

Indium Software has a large team of data scientists and data engineers who can help businesses with their data preparation. For instance, one of our customers in the US, a real estate and infrastructure

consulting services provider, helps their customers make informed decisions that can improve the cost-efficiency of their infrastructure projects. They wanted to create a solution that could detect the different types of wires that were present in the thousands of images that they and needed them to be accurately annotated before using them for wire detection models.

The challenges included acquiring good quality annotated data for training the model, ensuring the quality of data with consistency and accuracy, and controlling the cost of labeling data.

Data Annotation was a key part of the engagement and was a precursor to the supervised ML training.

• Labelme software was used to annotate the wires in the photos.
• Two different types of wires – transmission (red) and communication (green) were tagged.
• A total of 3000+ documents were annotated and converted to VOC and COCO formats, which can then be directly consumed by the AI models.

Indium’s streamlined process approach significantly reduced the effort taken to identify the different types

of wires by 40% and reduced the time taken for the entire data pre-processing activity by 45%. A high level of accuracy was achieved by employing effective quality control mechanisms, thereby minimizing human errors.

Indium can also help with automation and enabling self-service to free the IT teams of our clients. Our cross-domain experts understand the different needs of different industries as well as cross-pollinate ideas for developing innovative approaches to solve complex problems.

To know more about how Indium can help you with your data preparation needs to facilitate advanced analytics in your business, contact us now. To know more, click here: https://www.indiumsoftware.com/advanced-analytics/

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This has been one topic that I have been dying to write about and have finally gotten around to doing it. 

Advanced analytics services being as advanced as it is today, has touched upon almost every industry in more ways than one.

For the love of the Michael Schumacher’s of the world, I chose to write on how advanced analytics has impacted the motorsports universe, Formula 1 in particular.

Before analytics became a big part of the sport, a team’s success or failure in a weekend race was almost entirely decided by the split-second decisions made by the driver.

Fast forward to today and a variety of factors— from pre-race simulations to post-race analysis and broadcast experience—are influenced by real-time data streams.

How often have we seen teams that were down in the dumps and roar back to life in the forthcoming seasons? Intense periods of testing during the off-season are not the only reason.

The Red Bull racing F1 team was down in the dumps until 2009 and all of a sudden 4 straight constructor’s titles from 2010-2013.

Bringing Sebastian Vettel on-board was a huge factor but the other factor was big data analytics.

To make my case, let me explain using the Mercedes-AMG Petronas team.

Lewis Hamilton sitting in his silver arrow has been unbeatable for the last 5 years. From 2014-till date, Mercedes have wreaked all kinds of havoc on the main stage.

Constructor championships and driver championships have been won with ease. It was only in this season that Ferrari made significant improvements and mounted a massive challenge to the silver arrows of Mercedes.

The lead up to Race Day

Data analytics is a very critical factor when it comes to grand prix racing. A grand prix race is a 3 day weekend.

On Friday, the car is set up with many sensors so that the team can monitor what is working and what is not.

During qualifying on Saturday, only the most critical of sensors stay on and the rest are removed. On race day, the car setup must be identical to the car setup on Saturday.

Prepping for a race is a very painstakingly difficult process which needs to take into account the many variables like:

• The type of track and number of corners
• What was the car setup like last year and how did it perform?
• What down-force should you be gunning for?
• How quick should the transmission shifting be?
• Results of the wind tunnel tests
• How other teams are configuring their cars

Matt Harris who is the head of IT for the Mercedes-AMG Petronas team further attests to the fact that big data analytics plays a massive part in the team’s betterment.

When there are many configurations while setting up the car, changing one will offset another and you obviously understand how the domino effect works.

It is Harris’ job to determine which combination of configurations work best. There will be different settings, like one when fuel is low and the car needs to pit, one when tires are worn out, so on and so forth.

Using data processing and analytics techniques to figure out which setting works best and what the contingency setting needs to be is an extremely difficult task.

Harris says pre and post mortem of the event are the most key points in time for him and his team.

Visualizing the Win!

On the Friday of the grand prix, a silver arrow is loaded with close to 300 sensors which measure every tiny aspect of the car.

These sensors measure things from transmission liquid temperature to car’s ride height. The sensors are removed from Saturday and Sunday cars to reduce weight.

Conditions from a 1000 times per second to 1 second intervals are sampled by these sensors. The Mercedes servers handle nearly 18000 channels of data.

This amounts to about 500 GB of data per race and totals up to nearly 10 TB of data per season.

Baseline data adds up to 30 TB as data from the previous year and the year before that is carried as well.

To carry around this data securely, Mercedes stores it on solid-state PureStorage arrays.

The team employs 30 members to analyze the data on track and there are 30-200 pairs of eyes analyzing this data back at headquarters.

Harris says that the amount of data generated is so much that the number of eyes to sift through this data is not enough.

To confirm race-day strategy, the team needs to weed out any abnormalities or problems if any.

Mercedes uses TIBCO’s Spotfire visualization tool to analyze the race car. This further allows pre race configuration changes and strategy changes on race-day.

Harris states – “Having people look at all 500 GB of data is pointless. Just try to look at the anomalies and differences alone.

Data Visualization enables us to make intelligent decisions quicker and faster. Sifting through tons of data to find out what may seems interesting is not required.”

The Analytical High Gear

Having the fastest car on track is how you win championships. To achieve this, transmission is extremely crucial.

Whenever a driver shifts gears, a few 100 data points are received. Around 100 gear changes are made by a driver per lap and this easily translates into millions of data points per race.

Mercedes analyzes each gear shift across a variety of variables. A few of these are:

• Speed of the shift
• Speed of the engine
• Amount of wear on the clutch
• Oil temperature

This will give the team an idea of how much damage the transmission system is absorbing. If the damage is too much, the system will seize before the chequered flag.

If the driver ends the race with the transmission system in pristine condition, that is not a favourable outcome as well.

Balancing the damage and performance is the end game and that is where analytics provides the solution to this tricky conundrum.

What is the conundrum you may ask! Should the gear change be smoother or quicker? Smoother means less wear and tear, but if it is smooth, it isn’t fast.

Analytics allows the team to ensure fast gear changes with damage enough to last the race.

With a faster gear change 50 milliseconds per lap can be gained says Harris. 50 milliseconds in an F1 race is a lot.

We have seen winners being decided with a difference of one thousandth of a second. Therefore, the tiny 50 millisecond advantage is a big advantage.

Making the Silver Arrow What It Is!

Along with Spotfire, various other data analysis tools and techniques are used by the Mercedes team.

One of these is the Computational Fluid Dynamic (CFD) simulations which bring out the optimal balance between drag and downforce.

Downforce is key to holding the center of gravity and keeping the cars glued to the track. However, this causes more drag and puts a limit on how quick the car can be on the straights.

In the latter stages of 2017, Harris’ team merged the capabilities of Stream base and Spotfire to achieve a closer to real time view.

Working on more technological advancements like machine learning and deep learning frameworks, Harris says, his team and him have looked at a few of these frameworks and picking one seems to be a problem right now

Mercedes winning the Analytics game

At the beginning of the 2018 season the Mercedes-AMG Petronas Motorsport team struggled a bit and Hamilton didn’t win a race in the first few grand prix’s.

We all saw how the season turned out with Mercedes taking home its 5^th straight constructor’s championship.

It seems like Harris made all the right calls even though they were a little slow out of the gates.

The analytics team at Mercedes AMA Petronas is one of the best teams out there and the results are there for us to see.

All teams want to achieve faster straight line speed, holding corners at high speeds and the best aerodynamics.

Mercedes seem to have cracked it and Ferrari have followed suit in 2018 by giving the silver arrow a run for its money.

Redbull is never far behind. The 2019 season is going to be extremely interesting with the Redbull team shifting to Honda engines from the Renault engines.

Advanced analytics is the definitely the way for all teams to gain the competitive advantage that Mercedes has given the Silver Arrow.

Will it be the season of the prancing horse, the silver arrow or the bull? Whoever wields the analytics sword best, will be the last team standing!

The post Advanced Analytics: Mercedes’ Weapon of Choice! appeared first on Indium.

Sports as we know it today has come a long way. There were times when watching sport on television was considered a massive step forward in terms of technology.

Fast forward 60 years, watching sport on television has become the most basic thing. Today we watch sport on the go on our mobile phones or any device with a screen and internet connectivity.

Proud of how far we’ve come, aren’t we? Hopefully I can change your opinion on that by the end of this article.

What is sport all about? Sport is a bunch of people getting together to play a game with pre defined rules and a referee to ensure that these rules are adhered to during the passage of play.

I am a sport lover and play sports all time. My love for tennis and football in particular cannot be defined.

My issue when it came to technology and advanced analytics was with the game of football in particular. Football is such a beautiful game.

The strategies that the coaching staff come up with and the way it is executed on field by the players, it actually is a thing of beauty.

I was a football player myself (just an average one at that) and have been part of various teams. I know firsthand how strategies are built, how much thought goes into one single run of play.

Analytics in Football :

Most of you would’ve seen the movie Moneyball. The movie was based on the book Michael Lewis wrote in 2003.

It talks about how a jock turned luminary uses advanced statistics to gain a competitive edge over his better funded opponents.

This book brought about a revolution in sports. Fans and boards of football clubs didn’t want to settle for subpar statistics or analytics anymore.

What Moneyball did is that it took an old cliché – “sports are businesses” and made us move on to the next logical question – “how do we do things smarter?”

Analytics is also a powerful scouting tool for at least three reasons.

Firstly, it helps scouts and football clubs save time and money by being able to search for players and profiles of the ideal player from large and detailed databases. While databases cannot be a replacement for scouts, they can certainly complement their scouts’ eye for talent.

Secondly, the computer has all the actions that took place in a match in its memory, enabling players, scouts and team analysts revisit the moment that potentially pivoted the contest in favor of either team. From a scouts’ perspective, this is particularly valuable for scouts who can often be misled by performance of players in specific matches.

Databases also help limit pre-conceptions, providing clarity of player performances when there’s the possibility of a false-positive or a jaw-dropping moment that probably went unnoticed during the match.

Now let’s talk about advanced analytics. Advanced analytics in today’s world plays a massive role in every business sector.

Advanced analytics has been a boon for us. Moving from descriptive analytics to prescriptive analytics, we actually have come a long way.

In various businesses, where the requirement is demanding, advanced analytics are of utmost importance.

The Debate :

When we look at football, it’s a game that does not require too much machine intelligence.

Instead, it is a game that needs skills and instincts of humans playing in it.

When you bring in analytics and technology and try to reduce the human element in the sport, it simply just crushes the spirit of the game.

Relying on analytics heavily killed the Premier Leagues long ball game and brought in the pressing, continual passing tiki-taka.

Each league for that matter had its own style of play. The Premier League had the brash and brazen style of football that was termed “The way real men play football”.

There were beautiful long balls, harsh tackles but all the players just sucked it up, walked it off and it was all up to the referee on the pitch to penalize the offender or not.

There were arguments and fights, the passion from the fans was crazy, that was the football that screamed of passion, when players got in the face of other players not fearing punishment.

The Eric Cantona’s, the Ivan Genaro Gattuso’s, the Jaap Stam’s of the football world went missing soon enough and the diving and the biting began.

Then there was the tiki-taka style of football that was played in the Spanish La Liga, the silky style of play that caught everyone off guard.

The legendary Pep Guardiola and his army at Barcelona were the masters of the tiki-taka.

There was Real Madrid who were always a star studded line-up with excessive parts of their play relying on lightning quick counters which most often than not left the opponents stunned.

There was Manchester United who had their own brand of football being managed by the legendary Sir Alex Ferguson.

That United team was a team of sheer grit and character. Each of these leagues had their own beauty and the teams had their own style of play.

When you bring in excessive technology and analytics, there emerge sorry technologies like VAR (Video Assistant Referees).

There are 3 stages as to how the VAR works :

  Step 1

Incident occurs

The referee informs the VAR, or the VAR recommends to the referee that a decision/incident should be reviewed.

  Step 2

Review and advice by the VAR

The video footage is reviewed by the VAR, who advises the referee via headset what the video shows.

  Step 3

Decision or action is taken

The referee decides to review the video footage on the side of the field of play before taking the appropriate action/decision, or the referee accepts the information from the VAR and takes the appropriate action/decision.

Now the referee can consult with VAR for basically any doubts he wants clarified. What does this do?

• Removes the human element from the game.
• Takes up excess time and brings too many stoppages within the game, a game that was previously free flowing and continuous.

This makes it similar to Formula 1 racing. The analytics which brought about the fuel weight management systems and the numerous pit stops took the continuity out of the race and viewership reduced with the increase in technology.

A pretty similar trend might occur in football if this implementation becomes mandatory.

The Positive Side of Advanced Analytics in Football:

Analytics are not all that bad in football. Let’s take the case of when Simon Wilson joined Manchester City in 2006.

Simon Wilson was a consultant for an analytics startup called Prozone initially. He joined City to start a department of analytics and hired the best data analysts under him. He wanted to change the way how data was used by football teams.

He saw that, after a defeat there was no introspection as to why they had lost and what needed to be done next time.

City were a mid table club at that time.  In September 2008, when the club was acquired by the Abu Dhabi United Group for Development and Investment, a private-equity outfit owned by a member of the Abu Dhabi royal family, the team suddenly found itself with the resources necessary to mount a challenge for the Premier League.

Today, Wilson is Manchester City’s manager of strategic performance analysis.

He has five departments under him, including the team of performance analysis, which is now led by a sports scientist named Ed Sulley.

After each match, the team’s performance data would be examined. The list is extensive. Line breaks (a rugby term), ball possession, pass success rates, ball win/loss time ratio were what used to be analyzed.

“Instead of looking at a list of 50 variables we want to find five, say, that really matter for our style of play,” says Pedro Marques, a match analyst at Manchester City.

“With the right data-feeds, the algorithms will output the statistics that have a strong relationship with winning and losing.”

Wilson recalls one particular period when Manchester City hadn’t scored from corners in over 22 games, so his team decided to analyze over 400 goals that were scored from corners.

It was noticed that about 75 percent resulted from in-swinging corners, the type where the ball curves towards the goal.

The next 12 games of the next season saw City score nine goals from corner.

In Today’s Context :

Teams are investing heavily in analytics today and it is working in their favor. Look at where Manchester City are today, sitting atop the Premier League table and not being threatened at all.

Look at Manchester United this season, their game has been such where their possession percentages are low but their goal conversions are high.

The Manchester Derby on 7^th April 2018 saw United have only 35% of the possession but they managed to trump City 3-2. Each team has their set of analysts who provide inputs as per the strength of the team.

Conclusion

Advanced analytics is like the coin Two Face in Batman has, “Heads you die, Tails you survive!”

It can reap crazy rewards from a team’s point of view but at the same time can disrupt the lovely game by bringing in unnecessary stoppages, replays and by taking the human element out of it.

The numerous replays and the different angles, show the fans if the referee has made an error or not.

Let the error happen, after all to err is human. Refereeing in football is not an exact science and it’s all real time.

Let there be arguments about a decision, let the passion in the argument come through.

Do you want to watch a football match like the El Classico or the Manchester Derby and sit with your bunch of friends and say “it was a very clean game, the best team won!” Hell NO! Don’t drive the passion out of football with technology and analytics. Let football be football and let technology stay away!

The post Analytics in Football – A Double Edged Sword appeared first on Indium.

According to research and analyst firm Gartner, end-user spending on public cloud services is set to reach USD 304.9 billion in 2021, growing at a compound annual growth rate (CAGR) of 18.4 percent since last year. It is also worth mentioning that a vast majority of organizations using cloud services plan to increase their spending on cloud (further!) to overcome the disruption of the global health crisis.

The rise in cloud spending is likely to be sustained for a few years, Gartner says, with organizations increasing their investments in remote-working technologies, mobility, collaboration and more.

In return, what advantages are organizations aiming to gain from spending on cloud engineering services, which include analytics, business intelligence, databases, networking, software, et cetera? Let us find out.

Centralized data access

When data is siloed across your content management systems, marketing automation, ERPs and numerous other systems, it’s challenging to get a comprehensive view of your business and decision-making is harder too.

Cloud-based data analytics platforms help organizations by integrating all the data into a single point of access for every user to make business decisions.

Among the key advantages of having centralized data access is you can eliminate duplicate entries of data, provide executives and decision-makers with the right data at the right time, reduce the time spent to identify which gathered data is right, and enhance data intelligence capabilities of the organization.

Security and governance

Governance may be challenging but security is built into cloud analytics platforms, helping you configure user permissions to make sure everyone in your organization has access to the data they need while also ensuring accountability and transparency.

With the increasing volume of customer data being collected and used in 2021, organizations must ensure that privacy and governance requirements are met.

Today’s customers want to know what details of them are being collected and where they are stored. It is possible they will lose faith in an organization if their personal details are widely accessible.

Gartner predicts that, until 2025, 90 percent of organizations which fail to control public cloud use will inadvertently share sensitive data, which also means identity and access management (IAM) will be a key challenge for individual and machine users in 2021 and beyond.

Scalability

Instead of purchasing new hardware as data requirements change, cloud analytics platforms provide organizations with the option to turn on or turn off their services as required. For example, you can scale up your services quickly if you have a spike in data and, when the activity is normal, scale back down.

The ability to scale up or scale down cloud resources helps organizations to significantly reduce the risk that comes with rapid growth. Most applications can be managed cost-effectively on the cloud and be easily migrated using lift-and-shift strategies.

This flexibility further helps organizations to provide innovative products and services and not be constrained by infrastructure that does not suit their current requirements.

Data sharing and availability

The best cloud-based analytics platforms and solutions provide business users easy access to data, enabling them to analyze and explore in every possible context.

From laptops to smartphones, cloud analytics services also give users a comprehensive, unified experience irrespective of the device, including being able to analyze and share data and applications anywhere.

Cost savings

According to a McKinsey study, legacy systems account for 74 percent of a company’s IT expenses while affecting agility too.

With cloud services, no upfront costs are involved as the cloud service provider fulfills all infrastructure needs. Less power consumption and the lack of need for in-house expertise for server and software maintenance result in even more cost savings with cloud platforms.

Cloud service providers also have multiple data centers and offer resilience with data replication, which is particularly useful during a system crash or natural disasters such as flooding.

In addition, updates and upgrades happen automatically on cloud-based data analytics platforms. This saves organizations significant costs as they don’t have to worry about ongoing maintenance which is typically part of on-premise servers.

Summary

From providing centralized data access to reducing costs, cloud-based analytics solutions are helping companies get maximum value from their data. As the volume of data grows exponentially, cloud is very much the future of data analytics as it provides agility and fosters company-wide use of analytics for data-driven business decisions.

The post 5 Key Advantages Of Cloud-based Data Analytics Platforms appeared first on Indium.

It can help e-marketplaces with product classification. It can help to improve product design, devise focused marketing strategies, increase operational efficiency, and much more.

Recommended: What Text Analytics Tells Us about a Customer’s e-commerce Shopping Experience

While the list of possibilities is long, the success rate is not as high. According to a Gartner study, 80% of AI projects will be unable to scale in 2020 and by 2022, only 20% will deliver business outcomes.

One of the key reasons for this failure could be not selecting the right text analytics tool that can meet business goals and scale up.

A Buyer’s Guide for choosing a Text Analytics Solution

When scouting the market for the right text analytics solution, the 5 points to keep in mind include:

1. Customization: Each organization has a different business goal and a different set of data mix to work with. Most advanced platforms use a variety of methods such as machine learning, natural language processing, business rules and topic identification to analyze data. However, these tools tend to have a fixed, black-box model approach which may generate results fast but may be ineffective in working with small data sets. Being able to see the combination of algorithms and modify them to suit your specific and unique needs is necessary for you to benefit from the tool. 
2. Accuracy of Sentiment Analysis: Human beings communicate in complex ways. Words in themselves may sometimes mean the exact opposite in a particular context. Sarcasm is a tool that conveys much but requires reading between the lines to understand it. Emojis and exclamations contribute to the meaning. So tools that merely group words to identify the sentiment as positive or negative can be widely off the mark and be misleading. Training the tool with enough data sets to be able to accurately assess the tone becomes very important for the tool to be successful. A solution such as teX.ai from Indium Software is built on a strong foundation of semantics where the tone and the other components of communication are also factored in to arrive at the meaning accurately.
3. Use of Metadata: Aiding the semantics capabilities of any good tool like teX.ai is metadata that is often ignored by many tools. This can enhance the understanding of the sentiment better and get clarity in the face of ambiguity.
4. Multi-Lingual Support: This is the age of globalization where businesses can reach out to international markets. The Internet supports people to express themselves in the language they are most comfortable with and this makes it essential for businesses to be able to tap into chatter in those languages. Text analytics focused on English alone is no longer enough and the tool should be just as proficient in the semantics of that language to be able to unearth hidden meanings.
5. Dashboards and Visualization: Are you getting only basic charts or does the tool empower you to customize reports for a better understanding of the results is a clincher. Tools with better analytics models and enhanced dashboards along with multiple visualization options can help you get a better view from your slicking and dicing of data.

Indium Software’s teX.ai is a comprehensive tool with several visualization options, an intuitive user interface, customizable algorithms providing semantics-based sentiment analysis with multi-linguistic support that can fit the text analytics needs of organizations of any size.

Relevant read: Text Analytics of Social Media Comments Using Sentiment Analysis

Are You Ready for Text Analytics?

While the tool capabilities are very important for the success of the text analytics project, it is also essential to assess your internal preparedness to derive greater success from your text analytics initiative.

• Having the Right Data sets: Your organization must have enough documentation with textual data and of the right kind to get meaningful insights.
• The Right Team: While the text analytics software can help with analytics, your team should have the capability to benefit from the data to gather actionable insights.
• Company-wide Buy-In: Any analytics initiative can provide holistic insights only with an enterprise-wide commitment to implement the changes needed to enhance customer delight.
• Speed of Implementation: The speed of transformation as well as implementing the changes based on insights will have an impact on the success of the project.

Indium – End-to-End Solution Provider

teX.ai is a SaaS product solution from Indium Software, a technology solutions company. Incepted in 1999, Indium is a ISO 27001 certified company with 1000+ team members, servicing 350+ clients across several domains. It provides customer-centric, high quality technology solutions that deliver business value for Fortune 500 and Global Enterprises.

The teX.ai solution and the experienced team with cross-domain expertise can empower you to leverage your unstructured data for insights that can accelerate growth.  teX.ai helps produce structured data, metadata & insights by extracting data from text, summarizing information and classifying content.

If you wish to implement a scalable text analytics project to transform your business, contact us now.

The post Picking the Right Text Analytics Product: A 5-Step Guide appeared first on Indium.