In the grand scheme of things, it’s becoming increasingly evident that data is the new black gold. Industries across the board are awakening to the realization that data is no longer just an afterthought or an add-on; it’s an essential component of success. In the 19th century, oil was the lifeblood of the global economy and politics. In the 21st century, data is controlled to take on the same critical role.

Of course, data in its raw and unrefined form is essentially useless. It’s only when data is skillfully gathered, integrated, and analyzed that it starts to unlock its actual value. This value can manifest in many ways, from enhancing decision-making capabilities to enabling entirely new business models. In the healthcare industry, data is playing a particularly pivotal role. Refined data is helping professionals make better-informed decisions, improve patient outcomes, and unlock new frontiers of medical research. The future of healthcare is all about data, and those who know how to wield it will undoubtedly emerge as leaders in the field.

However, healthcare providers’ timely access to real-time or just-in-time information can significantly enhance patient care, optimize clinician efficiency, streamline workflows, and reduce healthcare costs.

Investing in robust electronic health record (EHR) systems encompassing all clinical data is crucial for healthcare organizations to understand patient conditions and comprehensively predict patient outcomes.

Is Data a Real Game Changer in the Healthcare Industry?

The answer to whether the analytical application of existing data will shape the future of healthcare is a resounding “yes.” With advances in data-collecting tools and healthcare technology, we’re witnessing a new era of healthcare delivery that will revolutionize the industry.

Imagine a world where wearable medical devices warn you of potential health risks or medical advice apps offer personalized guidance based on your unique DNA profile. These are just a few examples of how cutting-edge technology is making its way into the healthcare space, enabling data-driven decisions that improve patient outcomes and drive down costs.

Real-time data is a game-changer for case review and clinical time management, allowing healthcare professionals to understand patient situations and forecast outcomes more effectively. To fully realize the potential of data-driven healthcare, healthcare organizations must implement robust data management systems that can store all clinical data and provide the necessary tools for data analysis. By doing so, healthcare professionals will be empowered to make informed decisions that enhance patient care, improve outcomes, and ultimately transform the healthcare landscape.

Also, read the best approach to testing digital healthcare.

How do you use data for a better future?

When it comes to healthcare, data is everything. However, with the massive amounts of data that healthcare professionals must contend with, the sheer volume of information can be overwhelming.
As the industry has shifted toward electronic record keeping, healthcare organizations have had to allocate more resources to purchasing servers and computing power to handle the influx of data. This has led to a significant surge in spending across the sector.

Despite the clear advantages of data-driven healthcare, managing such large amounts of information presents unique challenges. Sorting through and making sense of the data requires robust data management systems and advanced analytical tools. However, with the right approach, healthcare professionals can leverage this data to make informed decisions that improve patient outcomes and transform the industry.

How does data analytics benefit the healthcare industry?

A small diagnostic error can have devastating consequences in the healthcare industry, potentially costing lives. The difference between an actual positive malignant tumor and a benign one can be the difference between life and death. This is where data analytics comes into play, helping to eliminate the potential for error by identifying the most relevant patterns in the available data and predicting the best possible outcome.

Beyond improving patient care, data analytics can also assist hospital administration in evaluating the effectiveness of their medical personnel and treatment processes. As the industry continues to shift toward providing high-quality and reasonable care, the insights derived from data analysis can help organizations stay on the cutting edge of patient care.

With data analytics, healthcare professionals can harness the power of big data to identify patterns and trends, predict patient outcomes, and improve the overall quality of care. Healthcare organizations can optimize their processes by leveraging data-driven insights, minimizing errors, and ultimately delivering better patient outcomes.

Approaches of Data Analytics

Data analytics is a complex process involving various approaches, E.g., predictive analysis, descriptive analysis, and prescriptive analysis, including feature understanding, selection, cleaning, wrangling, and transformation. These techniques are applied depending on the type of data being analyzed.

Analysts must first understand the features and variables relevant to the analysis to derive insights from the data. From there, they can select the most relevant features and begin cleaning and wrangling the data to ensure accuracy and completeness.

Once the data has been prepared, analysts can apply various transformation techniques to derive insights and patterns. The specific methods used will depend on the nature of the data being analyzed but may include methods such as regression analysis, clustering, and decision trees.

Predictive Analysis

Analysts leverage sophisticated techniques such as relational, dimensional, and entity-relationship analysis methodologies to forecast outcomes. By applying these powerful analytical methods, they can extract insights from large and complex datasets, identifying patterns and relationships that might otherwise be obscured.

Whether analyzing patient data to forecast disease progression or studying market trends to predict demand for new medical products, these advanced analytical techniques are essential for making informed decisions in today’s data-driven world. By leveraging the latest tools and techniques, healthcare professionals can stay ahead of the curve, improving patient outcomes and driving innovation in the industry.

Descriptive Analysis

In the data analytics process, descriptive analysis is a powerful technique that can be used to identify trends and patterns in large datasets. Unlike more complex analytical methods, descriptive analysis relies on simple arithmetic and statistics to extract insights from the data.

Analysts can gain a deeper understanding of data distribution by analyzing descriptive statistics such as mean, median, and mode, helping to identify common trends and patterns. This information is invaluable during the data mining phase, assisting analysts to uncover hidden insights and identify opportunities for further analysis.

Prescriptive Analysis

In data analytics, prescriptive analysis represents the pinnacle of analytical techniques. Beyond simple descriptive or predictive analysis, prescriptive analysis offers recommendations for proceeding based on insights gleaned from the data.

This highly advanced analysis is the key to unlocking new opportunities in the healthcare industry, enabling professionals to make more informed decisions about everything from treatment protocols to resource allocation. By leveraging sophisticated algorithms and machine learning techniques, prescriptive analysis can identify the optimal path forward for any situation, helping organizations optimize processes, maximize efficiency, and drive better patient outcomes.

Gathering Real-time Data in Healthcare

Real-time data refers to data that is immediately obtained upon its creation and can be collected using various methods, including:

• Health Records
• Prescriptions
• Diagnostics Data
• Apps and IoTs

Real-time data is crucial for managing the healthcare industry’s patient care, operations, and staffing routines. By leveraging real-time data, the industry can optimize its entire IT infrastructure, gaining greater insight and understanding of its complex networks.

Examples of Real-time Data Technologies in Healthcare

Role of AI/ML in healthcare

Regarding medical diagnostics, the power of data analytics cannot be overstated. Thanks to cutting-edge machine learning and deep learning methods, it’s now possible to analyze medical records and predict future outcomes with unprecedented precision.

Take machine learning, for example. By leveraging this technology, medical practitioners can reduce the risk of human error in the diagnosis process while also gaining new insights into graphic and picture data that could help improve accuracy. Additionally, analyzing healthcare consumption data using machine learning algorithms makes it possible to allocate resources more effectively and reduce waste.

But that’s not all. Deep learning is also a game-changer in the fight against cancer. Researchers have achieved remarkable results by training a model to recognize cancer cells using deep neural networks. By feeding the model a wealth of cancer cell images, it could “memorize” their appearance and use that knowledge to detect cancerous cells in future images accurately. The potential for this technology to save lives is truly staggering.

RPA (Robotic process automation) in healthcare

The potential for RPA in healthcare is fascinating. By scanning incoming data and scheduling appointments based on a range of criteria like symptoms, suspected diagnosis, doctor availability, and location, RPA can dramatically boost efficiency. This would relieve the burden of time-consuming scheduling tasks from the healthcare staff and probably improve patient satisfaction.

In addition to appointment scheduling, RPA can also be used to speed up health payment settlements. By consolidating charges for different services, including testing, medications, food, and doctor fees, into a single, more straightforward payment, healthcare practitioners can save time and avoid billing errors. Plus, if there are any issues with cost or delays, RPA can be set up to email patients with customized reminders.

But perhaps the most exciting use of RPA in healthcare is data analysis. By leveraging this technology to produce insightful analytics tailored to each patient’s needs, healthcare providers can deliver more precise diagnoses and treatment plans. Ultimately, this can lead to better outcomes and an enhanced patient care experience.

Role of Big Data in Healthcare

In today’s world, the healthcare industry needs an innovation that can empower medical practitioners to make informed decisions and ultimately enhance patient outcomes. Big data is the transformative force that can revolutionize how we approach healthcare. With the ability to analyze massive amounts of data from various sources, big data can provide medical practitioners with the insights they need to understand better and treat diseases. By leveraging this data, doctors can develop more targeted treatments and therapies that have the potential to improve patient outcomes drastically.

Beyond the immediate benefits of improved treatment options, big data also plays a vital role in driving new drug development. Through advanced clinical research analysis, big data can predict the efficacy of potential new drugs, making it easier for scientists to identify the most promising candidates for further development. This is just one example of how big data is revolutionizing the way we approach healthcare, and the benefits will only continue to grow as we explore more ways to harness its power.

Finally, big data is helping healthcare practitioners to create focused treatments that are tailored to improve population health. By analyzing population health data, big data can detect patterns and trends that would be impossible to identify through other means. With this information, medical professionals can develop targeted treatments that can be applied on a large scale, ultimately improving health outcomes for entire populations. This is just one of the many ways that big data is changing the way we approach healthcare, and it’s clear that the possibilities are endless. As we continue to explore this transformative technology, there’s no doubt that we’ll discover even more innovative ways to leverage big data to improve health outcomes for patients around the world.

Wrapping Up

In conclusion, real-time data analysis is a transformative force in the healthcare industry that has the potential to revolutionize the way we approach patient care. With the ability to analyze vast amounts of data in real-time, medical practitioners can make faster and more informed decisions, resulting in improved patient outcomes and ultimately saving lives.

From predicting potential health risks to identifying disease outbreaks and monitoring patient progress, real-time data analysis is driving innovation in healthcare and changing the way medical professionals approach treatment. By leveraging cutting-edge technologies and advanced analytics tools, healthcare organizations can collect and analyze data from various sources, including wearable devices, electronic health records, and social media, to better understand patient needs and provide personalized care.

As the healthcare industry continues to evolve, it’s clear that real-time data analysis will play an increasingly important role in delivering better health outcomes for patients worldwide. Real-time data analysis can improve patient care, reduce costs, and save lives by giving medical practitioners the insights they need to make more informed decisions. The possibilities for the future of healthcare services are endless, and I’m excited to see the continued innovations that will arise from this transformative technology.

The post Real-Time Data Analysis and its Impact on Healthcare appeared first on Indium.

AI advancements are occurring more frequently now than ever before. ChatGPT, a fine-tuned version of GPT 3.5, is one of the hottest topics right now. One of the challenges of GPT is hallucination, which means It may generate a nonfactual response. In this blog, I will take you through a question-and-answer system (Q&A) based on our custom data, where we will try to overcome the hallucination problem using retrieval mechanisms.

Before building a Q&A system, let’s understand the theoretical aspects of “GPT and Hallucination”.

GPT is a deep neural network model based on the transformer architecture, a kind of attention-based model that makes use of self-attention to process sequential data, like text. Recently, GPT-4, a multimodal programme that can process text, images, and videos, was released. The transformer decoder block is the foundation of the GPT architecture.

The general concept behind GPT was to pre-train the model on unlabeled text from the internet before fine-tuning it with labelled data for tasks.

Let’s examine pre-training on unsupervised data in more detail. Maximizing the log conditional probability of a token given previous tokens is the goal here. According to the GPT paper [1]

We can fine-tune it for different tasks on supervised data after pre-training.

Hallucination

The term “hallucination” refers to an LLM response that, despite having a good syntactical appearance, contains incorrect information based on the available data. Hallucination simply means that it is not a factual response. Because of this significant issue with these LLMs, we cannot completely rely on the generated response.

Let’s use an example to better understand this.

Here, when I ask ChatGPT a question about Dolly, it gives me a hallucinatory answer. Why? Since it was trained on a sizeable body of data, it does its best to mimic the response.

Below is the appropriate response to Dolly from the DatabricksLab GitHub page.

Also Read:   Generative AI: Scope, Risks, and Future Potential

Reduce Hallucinations by: 

• Taking low temperature parameter values
• Chain of thought prompting
• Agents for sub task (can use lang chain library)
• Use context injection and prompt engineering

Use Case

Using GPT and BERT (Bidirectional Encoder Representations from Transformers), let’s build a Q&A on a custom dataset.  To find the context for each user’s question semantically, I’m using BERT in this situation. You can query custom data found in various documents, and the model will respond.

GPT can be used in two different ways to meet specific requirements:

1. Context Injection

2. GPT’s fine-tuning

Let’s take them in turn.

Context Injection

Here, the plan is to send context along with the text completion query to GPT, who will then use that information to generate a response. Using BERT and the corresponding document text corpus, we will locate the context for each question.

Architecture

Now let’s examine the architecture

• Read each PDF file individually first, then break up the content into smaller sections.
• Locate and save the embeddings for each chunk. (Vector databases can be used for quick querying.).
• Accept the user’s question and ID as input.
• Using the input ID, choose the correct PDF. Locate the question’s embedding and semantically extract pertinent text from the PDF.
• Use the input question and pertinent text passages to create the prompt.
• Get the response by sending the prompt to GPT.

Now let’s proceed step by step with the code:

There are many methods for embedding, such as the open-source, pre-trained BERT family model and the paid OpenAI embedding api. I’ll be using hugging face’s open source embedding here.

Code

Import necessary libraries:

Here, I’m storing the embedding and metadata using a pinecone vector database. You can also use any other vector databases (some of open-source vector database are Weaviate < https://weaviate.io/ >, Milvus < https://milvus.io/ >) 

Let’s get all the api keys:

Let’s now set up the database and the pre-trained embedding model:

Let’s read the PDF/TXT document now. In order to find the embedding for each chunk, we will first chunk the content.

Read the file:

Save the embedding with metadata:

Now that we have embeddings for every document, let’s find out the context for a particular user question that was prompted by the GPT.

Get context:

Finally, let’s proceed. To get the response, create a prompt and send it to the openAI completion api.

GPT response:

Voila…

GPT fine-tuning

 In this use-case, fine-tuning is not recommended. Even so, there are numerous use cases where fine tuning will work fantastically, such as text classification and email pattern.

To fine-tune, first create the data in the format listed below.

Here, “prompt” refers to your query and context. The ideal response to the relevant question is considered complete. Make a few hundred data points, then execute the command below.

Use the below command for Data preparation.

Fine tune a particular model using the command below.

*If you are getting api_key error then add –api-key <’your api key’> after openai in the above command.

Python code that utilizes your refined model:

Check more on fine tuning by Open ai – https://platform.openai.com/docs/guides/fine-tuning  

Conclusion

A potent large language model with the potential to revolutionise NLP is the GPT family. We’ve seen a Q&A use-case based on our unique dataset where I used the context of the prompt to get around the GPT response’s hallucination issue.

We can use GPT to save time and money in a variety of use-cases. Additionally, the enhanced version of GPT (ChatGPT) has a wide range of applications, including the ability to create various plugins using various datasets and create chatbots using our own dataset. Continue looking into the various use-cases.

The post The Art of Answering Questions: How GPT is Changing the Game appeared first on Indium.

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/

The post Getting Data Preparation Right for Advanced Analytics appeared first on Indium.

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.

The stress on improvement in process and quality led to the development of methodologies such as Lean and Six Sigma to increase throughput but was still driven by humans with the technology used only for metrics and advanced analysis.

Though manufacturing companies did derive much benefit from these methodologies, the advent of Industry 4.0 technologies such as cloud, artificial intelligence, and Industrial Internet of Things (IIoT) devices has magnified the benefits manifold.

IIoT has made it possible for manufacturers to create smart factories and integrate systems. This has provided them with a unified data source that enables advanced analytics to identify patterns and trends and facilitate informed decision making.

End-to-end connection of machines right from production to delivery provides manufacturers with visibility improving the formulation of strategies and policies for accelerating growth.

Fast-Paced Adoption of IIoT

The integration of systems enables manufacturing companies to have better control of their inventory and supply chain as well as improve energy management. This naturally leads to cost reduction, resource optimization, increased profitability and overall enhanced operational efficiency due to industrial automation, centralized monitoring and predictive maintenance of assets.

No wonder then that the market for IoT in manufacturing industries is expected to grow at a CAGR of 10.1%, from USD 33.2 billion in 2020 to USD 53.8 billion by 2025, according to a ResearchAndMarkets.com report.

A PwC survey of around 1,000 industrial manufacturers revealed that 71% were already building or testing IoT-related solutions in both active and in-development projects and 68% intended to increase their investment in the next couple of years.

The surveyed companies were investing in better technology infrastructure, data management, workforce culture and change management to reap the benefits of digital transformation.

Benefits of Smart Factories

A smart factory with interconnected systems can automate workflows across functions and manage complex processes with greater visibility and traceability. Some of the key areas where they can see the advantages of IIoT devices include:

1. Predictive Maintenance: The breakdown of machinery and the resulting disruption to production is one of the biggest challenges manufacturing companies face. This causes unexpected delays in addition to the cost of repair. In smart factories, sensors embedded in the machinery provide data that can help analyze machine performance as well as receive alerts in case of any issues or deviations from preset specifications for preventive maintenance. This improves the longevity of the machinery, effects cost savings as well as enables scheduling maintenance in a more planned manner.

2. Product Quality: A piece of faulty equipment can also affect product quality. Embedded technologies can help manufacturers keep their machines well-calibrated to ensure that the machinery is as per specifications and can produce the desired product.

3. Supply Chain Management: The IoT devices can be connected to the ERP or SCM system to track inventory and draw real-time insights about product movement from raw materials to finished goods for a smooth supply chain management. It enables the different departments to have a view of the production process and also removes the need for manual documentation, thereby reducing manual errors and the resultant costs.

4. Safety and Security: Worker safety and security in the plant are becoming important due to regulatory requirements as well as to reassure employees and improve their engagement with the business. IoT systems can make it easier for safety leaders to be alerted in case of any potential hazards and risks and monitor Key Performance Indicators (KPIs) of health and security to not only improve compliance but also make the shop floor safe.

5. Energy Efficiency: Not only is energy one of the highest areas of expenditure for manufacturing companies, but it is also one of the most important areas where conservation is the most needed. IoT devices can help identify inefficiencies at the device level to enable businesses to address them effectively. This can help reduce waste and also meet regulatory standards more efficiently and effectively.

The integration of systems also ensures access to enterprise-wide data that facilitates better visibility into operations and more informed decisions. This provides a competitive advantage in addressing potential challenges before they become a problem and helps managers take a proactive approach rather than a reactive one.

At Indium Software, serving the manufacturing sector has been one of our key focus areas and, over the last decade, we’ve picked up immense expertise in serving fast-growing manufacturing companies in industrial, energy, automotive, and diversified segments.

The core of Industry 4.0 revolves around data. And, Indium’s experience in data management and data engineering are key assets while serving this segment.

Challenges to IoT

IoT comes with its own challenges too: Cost, Security, and Lack of Standards, to specifically name a few points.

Manufacturing companies with legacy equipment may find that customizing their existing machinery to scale up to become an embedded device comes at a cost. However, this can be more cost-effective than investing in new equipment and provide the flexibility they require.

Therefore, identifying the right partner who understands their business and can develop bespoke solutions that enable digital transformation at a reasonable cost would be a prime requirement.

The second is security. As more and more devices get added, the security environment becomes that much more complex. Ensuring encryption and other protection to safeguard data would be the second criterion that a partner should be able to ensure.

Using open frameworks and modern software development tools to write IoT firmware can help overcome the limitations of the lack of standards.

A partner such as Indium Software, with more than two decades of experience in cutting edge technologies, can help manufacturing companies experience painless digital transformation.

Our team of experts has experience in Industry 4.0 technologies, IoT, open frameworks, data engineering, security and testing, which is combined with cross-domain expertise to deliver best-fit solutions meeting the unique needs of our customers.

If you would like to know how we can help you improve your operational efficiency with IoT on your shop floor, contact us now.

https://www.indiumsoftware.com/inquire-now/

The post Enhance Efficiency in Manufacturing and Production with IoT & Advanced Analytics appeared first on Indium.

The field of Bioinformatics has always been one where the level of technological advancements has been pathbreaking. Being a subdiscipline of biology and computer science, Bioinformatics deals with acquiring, storing, analyzing and disseminating biological data, more often than not, amino acids sequences and DNA.

Within this discipline comes RNA splicing. RNA splicing falls within gene transcription and in fact is a stage in gene transcription. It is the process by which information from a strand of DNA is duplicated into a new molecule of messenger RNA (mRNA).

Genetic material is safely and stably stored in the nuclei of cells as a template by DNA. Transfer of code from DNA to proteins is done by mRNA which is built in couple of stages.

Translation to pre-mRNA is done for every gene in the initial stage. The gene is split into 2 sections called exons (code sections) and introns (non-coding sections). The pre-mRNAs contain exons which are then joined via splicing.

The importance of RNA splicing is to eliminate the intervening introns (non-coding sequences) of genes from pre-mRNA and joining the exons (protein-coding sequences) in order for easy translation of mRNA into a protein.

The Use Case

In this context, Indium Software worked on a project for a client in this space. The client is a thriving genetic engineering company that drives research and innovation on RNA splicing errors. To extend their existing RNA research and therapeutics focus, the client intended to create a solution for RNA splicing errors leveraging analytics predictions.

To detect, catalog and interpret the pattern of RNA, the client had an existing application. However, they were facing performance issues while generating reports for the results of the experiments and was looking at significant wastage of time which they wanted to address.

Business Requirement and Implementation Strategy

In order to provide the ideal solution, we had to understand create a business case and understand the requirements which were as follows:

• Integrate R programming with the application, for report generation.
• Generate the report for the experiment within a minimum time frame.
• Deploy the application in Microsoft Azure cloud platform.

The thought process in order to have a successful implementation was that report generation be implemented within the existing Ruby on Rails (RoR) application by integrating an R engine, which is triggered internally with dynamic parameters. The reports will then be generated as HTML in R and then rendered in RoR.

Further to this, the application architecture had to be improved to achieve reduced complexity & improved file processing to quickly load and generate the reports. To achieve availability and maintainability of the application, deployment using dockers on cloud had to be done.

Solution Time!

Indium’s best minds in data science and application development put their heads together to deploy the following solution to generate reports for the RNA sequence experiments:

• With the new requirements that came to light, we updated the report generation application built on RoR.
• For report generation, we incorporated an R programing engine. This would be triggered by the RoR application with dynamic parameters.
• The job the R engine performed was to read the input .txt file and create an HTML report. This would then be rendered using RoR in the app.
• We then Leveraged Ruby on Rails’ innate capabilities to split multiple tabs into individual rails, thereby reducing the wait time and facilitating the user to view the results faster.
• While running the report, a .txt file was dynamically generated and saved in the Rails repository. The same file would be overwritten when the user adds new input that eventually saves disk space.
• The .R data file was generated on the first run. This was done so that on the next consecutive run, the reports will load faster rather than reading through all files.
• The Rails application was then deployed using dockers on the Microsoft Azure Cloud.

Ensuring Customer Delight

Through our implementation, not only was it a project completed, but our client was amazed with the results we achieved. Some of the highlights of how our client benefitted from this are:

• The Report generation time was reduced to just 15 seconds (one fifth of what it was) by splitting multiple tabs into individual rails.
• The complexity of the Ruby on Rails code in the application was reduced significantly, improving ease of use and efficiency.
• The generation of .R Data file reduced the loading time of the Reports by nearly 30%.
• Dockerized Cloud deployment improved the availability and maintainability of the application.

The post RNA Splicing Error Report Generation using Ruby on Rails for a Genetic Engineering Company appeared first on Indium.

Businesses spend tons of money on marketing to acquire new customers and increase customer base. However, businesses can save a lot by just retaining existing customers. Yes, according to customer retention stats , 5% boost in customer retention increases the businesses profit to 25% to 95%

Some of the industries that are affected by churn rate are insurance companies, online streaming services, ecommerce, subscription services (Gyms) etc. In this blog, we are going to consider the example of Insurance companies. This blog will showcase the struggle insurance companies face to retain their customers and how analytics can help address the issue.

Who is a lapsed customer?

A lapsed customer is who has not come back to buy in an expected amount of time. In this case a customer who does not renew his/her insurance policy. This happens due to many reasons. The customer might not be happy with the service, they might get a better service for the same price or they do not like a particular product.

No matter what the reason is, it is your responsibility to bring them back, otherwise, you are leaving money in the table.

Luckily, with advanced analytics, you can target certain customers and create specific marketing campaigns to encourage them to buy from you again.

Analytics Case for Lapsers

How to methodically attack the lapsers problem?

As an Insurance company, you will collect a massive amount of data from your customers. Not just customer data, but also the transaction data with multiple product lines of general insurance, life insurance, health insurance & medical insurance etc.

The problem here is that the data sits in the computer and most businesses are not capitalizing on the benefits it can bring to the business.

Lapsed customer is one of the big problems the insurance industry faces. With the help of advanced analytics, insurance companies can not only solve the lapsers problem but also target the right products to the right customers, thus bringing more revenue.

To demonstrate, let us divide the customer base into 3 segments and Strategize accordingly

1. Lapsers – Customers who have moved out of the system. We have to strategize a plan to win back the lapsers
2. Likely to Lapse – Customers who are most likely to move. We have to retain them.
3. Loyal Customers – Customers who have a low likelihood to move. Cross-sell other products to these customers

Lapsers – Win-back Strategy

The best strategy to convert a lapsed customer into a loyal customer is by reaching out to them and persuade them with similar products. You must figure out what will be the “next best product” to sell to the lapsers.

Fortunately, analytics can help in finding the right products for lapsed customers.

Find the next best product with the help of a collaborative filtering recommendation technique. This technique will help you by filtering out items that a customer like based on reactions by similar customers.

Keep in mind, use a limited set of 3-4 recommended products rather than all products to win back the customer. Suffocating your customers with all your products will not help you achieve your goal.

Likely to Lapse – Retention Strategy

The next step would be to identify the customers who are likely to lapse and make sure they are not leaving you. To identify that,

• Build a lapse model to find the likely lapsers.
• Take the top 30-40% high probability lapsers and try retaining
• Retain using the same product or Sell another product

Loyal Customers – Cross-sell Strategy

Loyal customers are the ones who are most likely to stay with you. These customers like your products and will be ambassadors for your brand. The best strategy for them is to cross-sell your products.

What product to cross-sell to loyal customers?

Use association rules to increase cross-sell conversion. The Association rules method will be apt for uncovering the relationship between variables in large databases. This rule-based machine learning method will help in the cross-selling strategy.

Once you have the data and plan, all you have to do is make sure that cross-selling campaign is run successfully.

Customer who buys ‘a’ GI and ‘b’ TI also buys ‘c’ Insurance

In a Nutshell

This blog explained the use case of Insurance industry alone. However, analytics can be used across multiple industries. In the future, more and more businesses will use predictive analytics to forecast events and gain actionable insights that will help them in making the business better.

By using analytics, you are not only getting a competitive advantage but also save time, resource and money in the long run. After all, data is only a strategic asset when you can put it to work. Analytics will only help in identifying whom to target with what, it is the organization’s responsibility to make sure the best products and services are delivered to their customers.

The post How Insurance Industry can benefit from Advanced analytics? appeared first on Indium.

The format revolves around 8 teams who go into an all-out bidding war where they buy players in an auction prior to the start of the tournament. With the team needing to comprise of the perfect balance of batsman, bowlers, all-rounders and a wicket keeper, buying the right players is extremely important. This is where player performance analytics plays a huge role. Teams are required to purchase the right player for the right position from a large talent pool of Indian and foreign players. With the same standard auction budgets in place for every team, each and every player needs to be analyzed based on their strengths and weaknesses.

The need for player performance analytics!

Traditionally, team owners would bid for players based on a combination of the player’s reputation and the coach’s personal opinions. This led to all teams bidding exorbitant sums for a small group of famous players who were in many cases not ideally suited for the teams bidding for them. Additionally, there was no bidding consultant capable of advising on the performance or playing style of each of the hundreds of relatively unknown and overlooked but potentially talented players.

In order to help a team with a successful auction, Indium Software helped an IPL team by predicting which players to pick for which position. The foray of data analytics into sports has been rapid over the years and has paved its way into cricket as well.

The client who reached out to Indium is a technology-centric Sports Consultant who advises professional teams across different sports on strategies that lead to performance enhancement.

The requirement given by the client was rather straightforward. They wanted to tap into the pool of players who were unknown yet supremely talented. They wanted to build their team by spending optimally but getting the most talented roster in the league. Hence, the below points illustrate what they were looking for from Indium:

• Recommendations on which players to bid for and the analytical reasoning via statistical evidence.
• A ranking list of the most promising players by their playing position using CPIs (Composite Performance Indicators) which were to be developed in conjunction with domain knowledge.
• The rankings should leverage years of highly specific player & game statistics and be objective, comprehensive (50+ criteria) and account for players’ ‘form’.
• Coaches should be able to scan the rankings and infer which of the players best fit their teams’ needs by digging deep into the accompanying analytical metrics.

Bowling over the client with our solution!

In order to achieve this, Indium had to analyze tons of data and come up with a solution that would bowl over the client. Indium implemented the following solution:

The solution pertained to two cases – Ranking bowlers and batsmen separately using di­fferent criteria for each. For both cases, the preliminary steps of data cleaning and data aggregation were performed.

• Data Cleansing – The data was cleansed and formatted by combining unrelated data sets across games, tournaments and country leagues to form a unified, structured database.
• Data Aggregation – In a sport like cricket where multiple data points for a playing medium like batting can be collected, the aggregate statistics for each player can be highly complex. The preliminary set of relevant aggregates were chosen after brainstorming with the client.
• Index creation – To rank the list of players, the team created formulae and algorithms to evaluate player performance using analytics.
  1. Compiled broad aggregate statistics for each individual player.
  2. Ascertained the relevant metrics which drove good player performance for each department role (bowling/ batting) using statistics and domain research.
  3. Advanced analytics techniques were leveraged to generate relevant, dependable and detailed statistics which exposed the players’ strengths and weaknesses.
• Two methods were used for calculating a Composite Performance Index.
  1. A Descriptive method – using formulae to derive the bowling and batsmen strength.
  2. A Predictive method – using ML methods on historical data to determine the index.

Indium’s Impact on Auction Day!

The impact that this had on the team selection process was mind boggling. Indium’s solution gave the team a huge competitive advantage. The results from Indium’s solution are as below:

• Most of the top 10 most bid bowlers and batsmen figured in our recommendations.
• The recommendations narrowed the pool of players from 350 to 20 permitting the coach to target his focus.
• An objective and comprehensive ranking of each available player (indicating performance) was presented alongside revealing statistics (indicating team fit).
• The team was able to plan its bidding strategy which led to it utilizing only 70% of its bidding budget.
• Indium discovered high performing and good-fit players who were not on the team captain, coach or team owner’s radar.
• Indium provided precise statistics of the selected players’ strengths and weaknesses to leverage during team training.

This led to the IPL team being very successful in the auction and having a stunning roster. This further allowed the unknown players to come into the spotlight due to their performances. As always, we were delighted to see a happy client and our work spoke for itself during the auction. Are you looking to derive actionable insights through performance analytics to improve team performance? Reach out to us, we would be glad to work with you.

The post IPL Player Prediction using Player Performance Analytics appeared first on Indium.

The phrase augmented analytics refers to the use of machine learning (ML) and natural language processing (NLP) to enhance data analytics, data sharing and business intelligence.

According to the report, businesses will increasingly implement augmented analytics where automation will help them find the most important insights – both opportunities and risks. It’ll optimize the entire process of decision making, bringing in efficiency into several key processes that may have been manual in the past.

Advantage Indium: Expertise + Experience

Indium Software is a cutting-edge Advanced Analytics solutions provider leveraging machine learning and artificial intelligence to automate data-centric applications. It offers clustering, regression and classification services not just extracting insights from numeric data but also from text, audio-visual and image inputs.

Indium also has cross-domain experience, having served clients across sectors including banking and financial services, retail, e-commerce, FMCG and next-generation technology companies. Indium’s current clients include a Fortune 500 FMCG company and one of the world’s leading online cab operators.

The company has built AI/ML and NLP-based solutions for different functional teams including those in marketing, finance and operations.

The team uses Open Source tool sets such as Python, R, or proprietary tools such as STSS for data management and dashboarding tools such as Qlikview, Tableau, Power BI and R Shiny. The team’s expertise in machine text analytics has also led to the development of a proprietary, patent-pending tool teX.ai.

Moreover, Indium provides end-to-end services including Big Data Analytics, Pricing Analytics, Customer Analytics and Social Media analytics. The company is well-positioned to serve as a pure-play analytics partner or an end-to-end solutions provider delivering a range of analytics and digital services including product development.

Considering most modern applications (apps) incorporate data engineering and analytical models, it makes sense to work with a single partner, who can integrate big data solutions with product development.

In the last 20 years, Indium has worked on 350+ client engagements, serving a range of customers from startups to mid-size growth companies to Fortune 500 clients. From undertaking complete projects to augmenting resources, Indium Software has had the opportunity to display its capabilities and provide customer delight on every occasion.

Customer Success Story #1: Working with a leading price comparison platform for e-commerce

A leading online price comparison platform  helps consumers with quick comparative information on various products from a large number of third-party online shopping players. The platform combines data collection from 1500+ online retailers. The key challenge in the workflow was to enable end-users with real time visibility of products (including availability, description, etc.) and their dynamic changing prices.

However, each site would have its own breadcrumb trail, making it difficult to identify and process all products that belonged to the same sub-category. Indium was able to use text analytics to process data in real time with minimal lag and effectively crawl dozens of e-commerce sites to return only the most relevant information with the correct taxonomy.

Using batch processing, the team was able to:

–         Reduce the process time from 25 hours to 7 hours

–         Reduce the data refresh and response time from 2 seconds to 1 second providing real time price information

–         Reduced cost of operations (including the need for manual tasks) by 35 percent

Indium’s text analytics capabilities delivered value for money for a tough problem with limited resources and in a short duration of time.

Subsequently the client has engaged Indium for two of its other projects: one in the areas of web analytics and another in data-driven campaign management.

Customer Success Story #2: Working with a fintech company specializing in payday advances

A Singapore-based financial services company offering a streamlined mobile app that gives payday advances was embarking on a technology modernization program to improve business workflow, increase revenue and decrease costs.

Apart from improving the performance of several business operations and legacy systems, the company was looking to derive insights from advanced analytics in two specific areas: Churn Analysis and User Profiling.

Using tool sets such as Elasticsearch, R, Python, Logistic Regression, XGBoost and K-means Clustering for Descriptive and Predictive Analytics, Indium was able to increase customer retention, lower costs through loan losses and increase ‘tips’, which was its main source of revenue.

·          Effective user profiling led to an increase in the user acquisition rate by 10 per cent

·          Identifying and addressing the underlying reasons led to a churn reduction of more than 20 per cent

·          The combined power of an operationally and intuitive interface resulted in the surge in tip collections in the range of 6.5 per cent

·          Potentially bad clients were weeded out

Our approach to a client engagement

Indium is highly process-driven and follows a three-step process:

·          Step 1 revolves around Data Preparation.

·          Step 2: Data exploratory analysis is like a preliminary investigation to assess the data and correlate it with the final output.

·          Step 3: Models are built and fine-tuned following testing and training of datasets. Once it has been tuned to the optimum extent, it is then deployed and integrated with the client’s system.

In some projects involving text analytics, multiple models are explored before arriving at the optimal solution keeping in mind the client’s context.

As mentioned before, Indium Software is a trusted partner for fast growing organizations around the world. We bring to the fore our deep expertise in advanced analytics, to formulate data-driven solutions for a multitude of business situations.

The post Why Indium is the Right Partner for Advanced Analytics appeared first on Indium.

The USP of the product was to suggest personalized marketing ideas based on individual user profiles. 

The project was a terrific experience for Indium’s Digital Team, that was gearing up to solve a big data problem that involved multiple tools. 

But there was a complex problem right at Step No. 1! Well before building out our data models and deriving insights!

The problem revolved what is now called Data Preparation.

Soon we realized, we were not alone. Forrester Research conducted a study in 2017 and found that 80% of the time spent on data projects revolved around data preparation.

TDWI, an expert in education and research on all things data, conducted a survey with leading CIOs to find that more than 37 percent of the survey participants indicated dissatisfaction with their ability to easily find relevant data for business intelligence (BI) and analytics.

The survey participants recommended that “a self-service, automated approach to data preparation” was probably the only way forward. 

In our case – wherein we were building data models for marketers based on real-time data to take real-time action – it was an even greater challenge. We not only had to prepare data for BI but also do so in record time. We had to process 500 million messages per day.

Step 1: Process, Process, Process

CRISP-DM, which stands for Cross-Industry Standard Process for Data Mining, is an industry-proven way to guide your data mining efforts. The first step is to transform your data so it can be used for analytics.

If you’re using Hadoop, for example, the MapReduce style is the most common approach to split your data and process it in parallel.

The Map refers to filtering and sorting, while the Reduce refers to a summary operation.

In today’s world, Data Chaos is a reality. You data sets will be filled with outliers and nulls. In the case of our project involving this SaaS product, our first effort was to reduce the time taken for data transformation using the ETL process.

We reduced the process time for this from 11 hours to 2 hours. The data required for real-time reporting was generated using Hive tables.

Today, there are emerging methodologies using tools like Trifacta, which can be used for visual data wrangling. The benefits of using such a tool is that you can wrangle data both visually and statistically.

The added benefit of such a tool is that the business owner of the data can contribute in a hands-on manner to the data preparation process.

Step 2: Ensure there is data security and transparent data lineage

The benefits of data quality cannot be emphasized enough. The data preparation process must ensure that metadata from multiple sources are defined, the process of blending and cleansing data is not done on an excel sheet which can often induce manual errors. 

Step 3: Standardize & Repeatable

A key part of the data preparation process is to ensure it is standardized. Data teams will do well to automate the process as much as possible, driving efficiency and reducing processing time.

It may also be a good idea to make it a “repeatable” process, so it becomes easier to deliver real-time analytics.

Step 4: Collaboration between Business & Tech

At Indium Software, we believe the future of data preparation will revolve around collaboration between business and technology teams.

A standard workflow with repeatable methods will go a long way in reducing complexity of data preparation.

In the real-time analytics project we did for the B2B SaaS client, we used the following tools to deliver predictive models:

• Hadoop
• Oozie
• Solr
• Hive
• HDFS
• HBase
• Phoenix

A combination of Hadoop Distributed File System (HDFS) and Phoenix Implementation loaded real-time data into HBase.

HBase, which is modelled after Google’s Bigtable, delivered real-time reports.

But the key to the entire process was our Data Prep Process. We reduced the ETL from 11 hours to 2, which laid the foundation for the entire project.

The post A Structured Approach to Data Preparation for Advanced Analytics appeared first on Indium.