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.

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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.

The post Big Data’s Impact on IoT: Opportunities and Challenges in Analytics appeared first on Indium.

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

The post Big data: What Seemed Like Big Data a Couple of Years Back is Now Small Data! appeared first on Indium.

The fastest and the most efficient way to move large volumes of data is to have a standard pipeline. Big data pipelines let the data flow from the source to the destination whilst calculations and transformations are processed simultaneously. Let’s see how data migration can aid big data pipelines be more efficient:

Why Data Migration?

Data migration is a straightforward process where data is moved from one system to another. A typical data migration process includes Extract, Transform and Load (ETL). This simply means that any extracted data needs to go through a particular set of functions in preparation so it can be loaded onto a different, database or application.

There requires a proper vision and planning process before selecting the right data migration strategy. The plan should include the data sources and destinations, budget and security. Picking a data migration tool is integral to making sure that the strategy adopted is tailor-made to the organization’s business requirements or use case(s). Tracking and reporting on the quality of data is paramount to knowing exactly what tools to use to provide the right information.

Most of the times, SaaS tools do not have any kind of limitations on the operation system; hence vendors usually upgrade them to support more recent versions of both the source and destination automatically.

Having understood about data migration, let’s look at some of the desired characteristics of a big data pipeline-

Monitoring: There needs to be systemic and automatic alerts on the health of the data so potential business risks can be avoided.

Scalability: There needs to be an ability to scale up or down the amount of ingested data whilst keeping the costs low.

Efficiency: Data, human and machine learning results need to keep up with each other in terms of latency so as to effectively achieve the required business objectives

Accessibility: Data needs to be made easily understandable to data scientists through the use of query language.

Now let’s look at where data migration comes into the picture in a big data pipeline

The Different Stages of a Big Data Pipeline

A typical data pipeline comprises of five stages that is spread across the entire data engineering workflow. Those five stages in a big data pipeline are as follows:

Collection: Data sources like websites, applications, microservices and from IoT devices are used to collect the required and relevant data to be processed.

Ingestion: This step moves the streaming data and batched data from already existing repositories and data warehouses to a data lake.

Preparation: This step is where the significant part of the data migration occurs where the ETL operation takes place to shape and transform the data blobs and streams. The ready-to-be-ingested ML data is then sent to the data warehouse.

Computation: This is where most of the data science and analytics happen with the aid of machine learning. Insights and models both are stored in data warehouses after this step.

Presentation: The end results are delivered through a system of e-mails, SMSs, microservices and push notifications

Data migration in big data pipelines can take place in a couple ways depending on the business’ needs and requirements. There are two main categories of data migration strategies:

1. Big Bang Migration is done when the entire transfer is done in a limited window of time. Live systems usually go through a downtime whilst the ETL process happens. This is when the data is transitioned to a new database. There is a risk of compromised implementation, but as it is a time restricted event, it takes little time to complete.

2. Trickle Migration on the contrary, completes the migration process in different phases. During the implementation, the older and new the systems are run parallelly so as to ensure there in no downtime or operational breaks. Processes usually run in real-time that makes implementation a bit more complicated than the big bang method. But if this is done right, it reduces the risk of compromised implementation or results.

Best Practices for Data Migration

Listed down are some best practices that will help you migrate your data with desired results:

1. Backing Up Data

There are instances while migrating data that things will not always go according to plan. Things can go missing or potential data losses can occur if files get corrupted or are incomplete. Creating a backup helps to restore data to its primary state.

2. Verify Data Complexity and Standards

There arises a need to asses and check what kind of different data an organisation requires to be transferred. After finding out what the data format is and where it is stored, it can be easier to detect the quality of legacy data. This ultimately leads to being able to implement comprehensive firewalls to delineate useful data from duplicates.

3. Determine Data and Project Scope

The data migration strategy must be compliant with regulatory guidelines which means that there comes a need to specify the current and future business needs. These business rules must be cooperative with business and validation rules so as to make sure that the data is transferred consistently and efficiently.

4. Communicate and Create a Data Migration Strategy

The overall data migration process will most likely require hands-on engagement from multiple teams. Making sure there is a successful data migration strategy in check requires the team to be delegated with different tasks and responsibilities. This alongside of picking the right data migration strategy for your unique business requirements will give you the edge that you are looking for in an age of digital transformation.

Breeze through your Big Data Pipelines

Data pipelines as-a-service helps developers assembling an architecture that can help for easy upgrade of their data pipeline. There are a number of things such as being very meticulous with cataloguing that can help with bytes not being lost in transit.

Starting simple is the answer, alongside which there needs to be a careful evaluation of your business goals, the contributions to the business outcome and what kind of insights will actually turn out to be actionable.

The post Breezing through data migration for a Big Data Pipeline appeared first on Indium.

Data replication is an important part of disaster recovery (DR) plans because it ensures that a real-time copy of data is always available in the event of a system failure, cybersecurity breach, or other calamities, natural or caused by human mistakes. Copies of the replicated data can be kept in the same system, on-site or off-site servers, or across various clouds.

Benefits of Data Replication

While data replication is frequently used in disaster recovery (DR) plans, it is far from its only application. Data replication, when done correctly, provides enormous benefits to enterprises, end- users, and IT professionals alike.

Improving data availability: By storing data at several locations across the network, data replication improves system resilience and reliability. This ensures that data can still be accessed from a separate site in the event of a technical failure caused by a virus, software malfunction, hardware failure, or other interruptions. It’s a lifesaver for businesses with data stored in several locations since it ensures data access 24 hours a day, seven days a week, across all geographies.

Faster access to data: When accessing data from one place to another, there may be some lag. Users benefit from faster data access and query execution by storing replicas on local servers. For instance, employees from numerous branches of a firm can easily access data from their home or branch office.

Server performance: Data replication distributes the database across multiple sites in a distributed system, reducing the data load on the central server and improving network performance. For write operations, IT experts reduce the number of processing cycles on the primary server.

Disaster recovery: Due to data breaches or hardware failure, businesses are frequently vulnerable to data loss, deletion, or corruption. Data replication keeps backups of the primary data on a secondary appliance (hot copies) that may be recovered promptly.

How Does Data Replication Work?

Writing or copying data to different locations is referred to as replication. Copies are made on-demand, sent in bulk in batches according to a schedule, or replicated in real-time when data in the master source is written, updated, or deleted. There are various components, types, strategies, and schemes that go into successful data replication. Here are some of the components of the data replication process.

Publisher: It’s where the data comes from and where objects for replication articles are built. To reproduce data as a unit, these articles are grouped and published in single or numerous publications.

Distributor: This is where the publisher’s replicated databases are stored before being provided to the subscriber.

Subscribe: The recipient of duplicated data, which can simultaneously receive data from many publishers.

There are two types of data replication solutions in the business intelligence services market: synchronous replication and asynchronous replication.

Synchronous replication processes write data to both the primary and replica (target) storage systems at the same time. This ensures that the primary copy and duplicate are almost identical in real-time. However, due to its high cost, it will eat into your IT budget and may cause latency, slowing down your primary application (source). Synchronous replication is commonly used for high-end transactional applications that require immediate failover in the event that the primary fails.

When executing asynchronous replication, data is initially written to the primary source, then replicated to the target medium at specified intervals, depending on the parameters and implementation. Because replication can be scheduled at times of low network utilisation, there is more bandwidth available for production. Most network-based replication products support this strategy.

Data Replication Strategies

Strategy-1: Log-based

From the beginning, most database-based solutions maintain track of every modification in the database. It also creates a log file, sometimes known as a changelog, for the same. Each log file is a collection of log messages, each of which contains information such as the time, user, change, cascade effects, and change method. The database then allocates each of them a unique position Id and saves them in a chronological order depending on the Id.

Strategy-2: Statement-based

Statement-based replication records and stores all commands, queries, and activities that change the database and cause updates. The replicas are generated by re-running these statements in the sequence in which they occur in procedures that use the statement-based mechanism.

Strategy-3: Row-based

Row-based replication takes track of all new rows in the database and saves them in the log file as a record. Procedures using a row-based replication mechanism do replication by recapitulating over each log message in the sequence in which they were received. The location Id serves as a bookmark, in this case, allowing the database to easily continue the replication operation.

Data replication best practices

It’s critical to follow some good administrative practises after the replication network is set up:

• A strategy for regularly backing up a database should be in place. Regular backup restoration testing should also be performed.

• Since scripts can be easily stored and backed up, it is critical to script all replication components and repetitive operations as part of the disaster recovery strategy. The components can easily be re-scripted if the policies change.

• It is vital to identify the elements that influence replication performance. Hardware, database design, network settings, server configuration, and agent parameters are all part of this. All of these must be implemented and monitored for the application’s workload.

Parameters to be monitored during data replication:

• Replication time needed
• Replication that lasts for a long time
• The amount of replication actions that can happen at the same time is known as concurrency.
• Synchronization timeframe
• Consumption of resources

The post Data replication done right appeared first on Indium.

Of course, the pandemic accelerated the need for digital adoption across sectors and this trend is here to stay. The key tailwinds for driving this growth include the need for building new digital infrastructure in addition to the development and re-engineering of products and services. There are several sectors that are ripe for disruption by adopting and embracing next-generation digital engineering including manufacturing, retail, BFSI and healthcare.

From a business perspective, leadership teams are looking to drive improvements across both customer experience and operational excellence.

Indium Software is a leading digital engineering solutions provider, with the experience and expertise needed to transform businesses and help them leverage next-gen technologies for their growth. Satish Pala, who took over as the Chief Technology Officer at Indium recently, is a digital transformation leader with over 2 decades of experience in the IT industry, he has extensive knowledge in data & analytics solutions across domains. He has delivered many cutting-edge analytics solutions for some of the top Fortune 500 companies. Satish is a passionate technologist with a keen interest in analytics, cloud and next-gen business intelligence..

In this conversation, Satish shares his views on key digital engineering trends and Indium’s digital engineering practice

Excerpts from the interview:

In your new role as CTO, what would be some of your key focus areas? How will this impact Indium’s strategy and approach?

My key focus areas include strategy, technology, innovation, and people. The main objective is for Indium to exceed the business goals through revised strategy, delivery process excellence and digital engineering capabilities. This changes Indium’s approach to growth in multiple ways. Some include strategic items like partnerships, new packaged solution offerings, capability development in next-gen technologies, establishing centers of excellence, dedicated labs, etc. On the people front, we’re planning a series of initiatives to continue to build and enhance our digital talent pool through certifications and training.

Are projects in emerging technologies like AI and blockchain still “projects” by specific teams or are we seeing enterprise-wide adoption?

We are definitely seeing a trend in enterprise-scale adoption of AI, not so much in blockchain. Here at Indium, our AI-based solutions focus on use cases across the board. One example is our AI-based accelerator teX.ai, which is being leveraged at a real estate enterprise for their core business operations on text extraction from the documents like agreements, thereby making it a highly efficient process. There are several other examples of how teX.ai is being used by companies in BFSI and retail segments.

Based on your experience, what are some of the common challenges businesses face when it comes to digital engineering?

A few common challenges companies have to tackle when it comes to digital engineering include:

• Identifying the right set of technologies and tools for the businesses to be efficient and agile.
• A culture shift in moving from a legacy paper or physical process to a digitized process. If the transition is not handled well, there will be chaos and businesses getting stuck in between.
• Training and education to mitigate this challenge.
• Policies & controls around privacy, data, intellectual property, and security.
• Accumulation of Digital Assets and lack of information governance.

How can digital engineering help businesses accelerate change while operationalizing their business value?

Deriving value in terms of direct business outcomes is a critical requirement of any digital effort. It must move the needle in terms of driving growth, improving competitive advantages for the company or streamlining operational processes.

• Digitization makes the business processes quick and efficient, thereby enabling businesses to make better use of available resources
• The right use of tools, technologies and innovations help drive competitiveness in the market and reduce time-to-market
• Proper use of digital engineering solutions will help enhance end-user experience and increase retention

How do you see digital engineering technologies evolving and how can businesses keep pace? Can you share some key emerging trends?

Digital Engineering technologies are evolving at a rapid pace due to the vast increase in innovations by the technology community. There is a variety of options to choose, whether a business is starting the digital journey or already a digital business. These technologies include building applications for user interactions, for back-office operations, data exchanges, API gateways, data and AI-based for decision making, or supporting technology services like DevOps and cloud.

Adoption of low code platform development has increased rapidly as well. In the manufacturing industry, factories are embracing Industry 4.0 with renewed focus. For example, building a digital twin makes the process smarter, optimizes performance across the manufacturing lifecycle, and drives product enhancements due to AI-based insights.

Please tell us about Indium’s partnerships with AWS, Striim, Mendix and Denodo and how Indium will deliver a service layer on top of each of these?

Indium has several partnerships with some of the top players in the digital engineering space. We partner with them and take the solutions to our clients either in consulting, implementation and/or maintenance. Some key partnerships include:

• AWS – We are a Select Tier Partner focusing on cloud migrations, application modernization, data analytics and devops

• Striim – We are a professional services partner helping in build real-time data streaming solutions between heterogeneous data platforms, thereby enabling businesses to obtain value from data quickly

• Mendix – We are one of the top Mendix services players in the world with vast experience in building low code platform-based solutions and also possess a large community of highly qualified low code developers

• Denodo – We are a services partner for Denodo, which is the top data virtualization player in the world

The post Strategic Partnerships and New Product Offerings – Key Pillars of Indium’s Digital Engineering Practice appeared first on Indium.

Data warehouses form the basis of a data analytics program. They help enhance speed and efficiency of accessing various data sets, thereby making it easier for executives and decision-makers to derive insights that will guide their decision-making.

In addition, data warehouse platforms enable business leaders to rapidly access historical activities carried out by an organization and assess those that were successful or unsuccessful. This allows them to tweak their strategies to help reduce costs, improve sales, maximize efficiency and more.

AWS Redshift and Snowflake are among the powerful data warehouses which offer key options when it comes to managing data. The two have revolutionized quality, speed, and volume of business insights. Both are big data analytics databases capable of reading and analyzing large volumes of data. They also boast of similar performance characteristics and structured query language (SQL) operations, albeit with a few caveats.

Here we compare the two and outline the key considerations for businesses while choosing a data warehouse. (Remember, it is not so much about which one is superior, but about identifying the right solution, based on a data strategy.)

AWS Redshift

It offers lightning-quick performance along with scalable data processing without having to invest big in the infrastructure. In addition, it offers access to a wide range of data analytics tools, features pertaining to compliance and artificial intelligence (AI) and machine learning (ML) applications. It enables users to query and merge structured and semi-structured data across a data warehouse, data lake using traditional SQL and an operational database.

Redshift, though, varies from traditional data warehouses in several key areas. Its architecture has made it one of the powerful cloud data warehousing solutions. Agility and efficiency offered by Redshift is also not possible with any other type of data warehouse or infrastructure.

Essential and key features of Redshift

Several of Redshift’s architectural features help it stand out.

Column-oriented databases

Data can be organized into rows or columns and is dictated by the nature of the workload.

Redshift is a column-oriented database, enabling it to accomplish large data processing tasks quickly.

Parallel processing

It is a distributed design approach with several processors employing a divide-and-conquer strategy to massive data tasks. Those are organized into smaller tasks which are distributed amongst a cluster of compute nodes. They complete the computations simultaneously rather than in a sequential manner. The result is a massive reduction in the duration of time Redshift requires to accomplish a single, mammoth task.

Data encryption

No organization or business is exempt from security and data privacy regulations. One of the pillars of data protection is encryption, which is particularly true in terms of compliance with laws such as GDPR, California Privacy Act, HIPAA and others.

Redshift boasts of robust and customizable encryption options, giving users the flexibility to configure the encryption standards that best suits their requirements.

Concurrency limits

It determines the maximum number of clusters or nodes that can be provisioned at a given time.

Redshift preserves concurrency limits similar to other data warehousing solutions, albeit with flexibility. It also configures region-based limits instead of applying one limit to all users.

Snowflake

It is one of prominent tools for companies that are looking to upgrade to a modern data architecture. It offers a more nuanced approach in comparison to Redshift, which comprehensively addresses security and compliance.

Cloud-agnostic

Snowflake is cloud-agnostic and a managed data warehousing solution available on all three cloud providers: Amazon Web Services (AWS), Azure and GCP. Organizations can seamlessly fit Snowflake into the existing cloud architecture and be able to deploy in regions that best suit their business.

Scalability

Snowflake has a multi-cluster shared data architecture, which allows it to separate out compute and storage resources. This feature helps users with the ability to scale up their resources when they require large data volumes to load faster and scale down once the process is complete.

To help with minimal administration, auto-scaling and auto-suspend features have been implemented by Snowflake.

Virtual-zero administration

Delivered as a Data Warehouse-as-a-Service, Snowflake enables companies to set up and manage the solution without needing significant involvement from the IT teams.

Semi-structured data

The Snowflake architecture enables the storage of structured and semi-structured data in the same destination with the help of a schema on a read data type known as Variant, which can store structure and semi-structured data.

Redshift vs Snowflake: which is right for you?

Features: Redshift bundles storage and compute to offer instant potential to scale to enterprise-level data warehouse. Snowflake, on the other hand, splits computation and storage and provides tiered editions. It thus offers businesses flexibility to buy only the required features while maintaining scaling potential.

JSON: In terms of JSON storage, Snowflake’s support is clearly the more robust. Snowflake enables to store and query JSON with built-in and native functions. On the flip side, when JSON’s loaded into Redshift, it splits into strings – making it challenging to query and work with.

Security: While Redshift consists of a set of customizable encryption options, Snowflake offers compliance and security features geared to specific editions. It thus provides a level of protection most suitable for an enterprise’s data strategy.

Data tasks: A more hands-on maintenance is necessary with Redshift, particularly for those tasks that cannot be automated, like compression and data vacuuming. Snowflake has a benefit here: it automates many of such issues, helping save substantial time in diagnosis and resolving of those issues.

Final thoughts

Whether it is Redshift or Snowflake, when it comes to business intelligence (BI), both are very good options as cloud data warehouses. Irrespective of the choice of data warehouse, getting all the data to the destination as quickly as possible is essential to provide the background required for sound BI.

The post AWS Redshift vs Snowflake: Which One Is Right For You? appeared first on Indium.

A research report projects a big data security market growth at 17.1 per cent Compound Annual Growth Rate (CAGR) from USD 12.22 Billion in 2017 to USD 26.85 Billion by 2022.

Some of the key drivers of this growth include the ever evolving regulatory landscape, an increasing volume of business data generated from a variety of sources, and greater threat from cyber-attacks requiring scalable high security solutions.

Big Data is typically stored in the Hadoop Distributed File System, which provides a very basic form of security, not enough to protect business interests.

The name string level of authentication is not enough to deal with the nature of breaches that cloud and networking to the World Wide Web expose one to.

Security Challenges

The challenges to big data storage, retrieval and use come at multiple levels and can be broadly classified as:

• Generation of fake data
• Untrusted mappers accessing the system
• Insufficient cryptographic protection
• Mining of sensitive information by unauthorised sources
• Challenges to granular access control
• Difficulty in data provenance difficulties
• Lack of focus on security in high speed NoSQL databases
• Neglecting security audits

In 2021, Big Data challenges range from real-time compliance to wider data security changes across the board. In between, industry-specific data security changes, data encryption, job-critical data access and more assume tremendous significance too.

To counter these challenges, big data solutions for security can be ensured only through encryption, access control, security intelligence, data governance and data masking.

Protecting Data

The Big Data security world is still evolving and expected to mature as newer and newer challenges emerge.

In real world systems, organisations may have their entire solution developed on Big Data, or have apps talking to legacy systems.

This will decide the complexity of the security solution being developed and the levels of security that will have to be built in.

Designing entirely for the Big Data environment is relatively easier as a compact solution can take care of all possible internal and external breaches.

But, with diligence and proper assessment, a hybrid environment too can be effectively protected.

Encryption:

Customers who wanted to build a platform on top of a Big Data ecosystem encounter security concerns even though the architecture is successfully implemented.

Unfortunately, most of the applications that are built on top of Big Data ecosystem components weren’t designed to address this.

This means, many applications lack encryption enablement, policy enablement to address the user level ACL, compliance and risk management to handle in case of emergencies or breach.

To ensure that their data or environment is secured, organisations will have to build those features by themselves using Big Data security components.

Access Control:

One of Indium Software’s clients was developing a mobile app and needed security solution for their Big Data.

They also had legacy systems with which the HDFS had to communicate with.

The security solution needed to provide access controls for internal and external users, as well as assign privileges.

• As a first step, the security level of the existing legacy systems managed by the client had to be assessed, and the Big Data system protected appropriately from any possible breaches at the point where the two connected.
• Secondly, it had to ensure authenticated access to the internal teams based on their requirements.
• Thirdly, it had to protect the Big Data system from external threats.

Based on the evaluation of the security of the existing legacy systems, Indium Software developed a blue print to ensure robustness and designed the solution accordingly.

Second, it provided access control with an OpenSource tool called Kerberos to different teams based on their needs.

Through this, it was able to define privileges, thus ensuring authenticated and authorised user access of data at multiple levels.

This was based on the client list of users with privileges at the various levels, ensuring access of data to different teams based on their development goals.

Kerberos limits the assigning of privileges at the group level and not to individuals. Indium Software worked around this limitation by creating groups with single members to be able to grant appropriate authorisations.

Thirdly, it provided security for the web protocols using Knox to restrict external breaches.

This was especially important as the client had provided access to his customers, which needed to be allowed only on authentication.

Hadoop also provides auditing logs, which is being monitored and maintained as part of the SLAs.

Today, various rule based methods and different anomaly detection methods are already being used by many banks.

However, these have their own limitations and are not all that powerful. Fraud detection capabilities are enhanced with the influx of analytics and a whole new dimension to fraud detection techniques can be seen.

Along with this, performance measurement which helps standardize and maintain control for constant improvement is possible with fraud analytics.

The Evolving World

The advantage of being predominantly OpenSource is that there is a community of developers and as and when a patch is developed, it will become accessible to all.

This will take care of the limitations be it in the HDFS, Kerberos or any other security solution.

However, the threats are also expected to become just as sophisticated. Therefore, it is essential that businesses have a clear security strategy, define their goals and ensure the implementation of a good security solution to protect not just their data, but their business as well.

Investing in data integration tools is also a key part of reinforcing an organization’s Big Data security and ensuring it stays compliant with the data security protocols.

The post Big Data Security and its Evolution! appeared first on Indium.

As per a recent report, national spending on healthcare in the US reached US$3.81 trillion in 2019 before increasing further to reach US$4.01 trillion by 2020. The report also goes on to state that healthcare spending in the US would reach US$6.19 trillion by the year 2028, accounting for 19.7 percent of gross domestic product (GDP).

We are not even talking in billions here. The statistics I just stated are only for the US. Now imagine this on a worldwide scale.

Even if the other countries don’t spend as much as the US on healthcare, the size of the industry is massive! When an industry is this big and loaded with data, why not bring in analytics? Oh wait, even better! Why not introduce Big Data Analytics here?!

That is exactly what has happened and big data analytics is taking the healthcare industry by storm.

I am just going to point out a few ways as to how Big Data is changing the Healthcare industry:

To err is human, not with Big Data Analytics

We have come across various cases wherein wrong medications have been prescribed or dispatched to patients which has resulted in their condition becoming worse or even death.

Take the case of a hospital. With the number of records they have, keeping track of each and every patient requires a system and errors can happen just because of the sheer volume of data.

Leveraging Big Data Analytics to analyze user data and the prescription made to the particular patient can help reduce the errors.

Big Data Analytics can corroborate the data and rule out, out of place prescriptions which will reduce errors and save lives.

A tool or software for this would be ideal in a place which has many patients walking in on a daily basis.

Personalized medicines are the in thing

Personalized medicines are all the rage right now in the healthcare industry. Take a person’s genetic blueprint and lifestyle information and integrate it with thousands of other people.

This will help predict ailment and identify the best possible method of treatment. I just explained to you as to what personalized medicines are and how big data works in this space.

In case of an epidemic outbreak, big data can help track population movement with mobile geo location.

The actionable insights acquired from big data will give a fair idea as to where treatment centers should be placed or which areas need to be cordoned off.

The AT-RISK Factor

A combination of Big Data and Predictive Analytics can be used to classify people based on major health conditions.

The classification can be made based on the people who visit hospitals regularly and the high risk health conditions that are likely to affect a population in a particular geography.

In the case of the US, obesity and heart problems are prevalent. With the BMI records of people and their visits to hospitals clubbed with their past medical records, big data and predictive analytics can tell us who may be at risk of a cardiac arrest or any other health condition like high cholesterol.

This will help in providing customized care to the patients.

At all COSTS

One huge problem that all hospitals or medical facilities face is the problem of staffing.

They are either under staffed or over staffed. Predictive Analytics combined with Big Data is an extremely powerful tool.

It comes to the rescue with this issue as well. Predicting the admission rates along with the attrition rate will help with staff allocation.

In turn, Rate of Investment is drastically reduced and investment can be utilized to the maximum.

Care in Real Time

Providing proactive care to patients is the need of the hour in the healthcare industry.

Constantly monitoring vital signs of a patient brings us one step closer to that. This data can be analyzed in real-time and alerts can be sent when there is a substantial reading change in the vital signs. 

Machine Learning algorithms can trigger these alerts and intervention can be made the right time as we get to know instantly when there is a change in the patient’s vitals.

The use of wearable sensors or devices helps interact with the patient in a new and more meaningful way. This makes healthcare more convenient and persistent.

Supply Expenditure

With the increasing number of hospitals and patients, the supply for tools in the medical facilities has also increased.

A budget is prepared for the procurement of these tools prior to the purchase order. A common occurrence is over-stocking, where tools and supplies are bought in excess.

With the use of Big Data Analytics, predictions based on past year’s records can be made as to what the estimate for this year will be.

Predictive analytics enables hospitals to save a large chunk of their money by forecasting the demand for medical supplies accurately.

The saved amount can be reinvested to yield higher profits and or can be used as additional revenue.

Conclusion

Evidence based medicine is where the world is heading today. This requires making use of all available clinical data and factoring that into advanced analytics. 

Capturing and bringing together all the information about a particular patient give a more complete view to attain actionable insights.

This will help reduce expensive testing, wasting of resources and will aid in the right prescription of drugs and saving more lives.

Big Data and Predictive Analytics play a massive role in the healthcare industry as not only agents that save lives of patients.

It helps hospitals and medical facilities reduce their overheads and also contribute more to the economy by making use of their investments in a sensible way.

In this article I have mentioned a few ways as to how Big Data Analytics helps the healthcare industry.

The benefits are not limited to these points. The opportunities are endless when it comes to Big Data and newer ways to help out the healthcare industry are going to keep following.

The post How Big Data is Taking the Healthcare Industry by Storm appeared first on Indium.

Indeed, data is the most valuable, enduring asset of any organization, providing the foundation for digital transformation and strategy.

Effective data management is an essential part of today’s unpredictable business environment. Managing and understanding data better can help companies make informed and profitable business decisions.

The total volume of data that organizations across the world create, capture, and consume is forecast to reach 59 zettabytes in 2021, according to Statista. This data does not only comprise structured data in the form of documents, PDFs, and spreadsheets, it also includes tweets, videos, blog articles and more that make up unstructured data, which is essentially eclipsing the volume of structured data. Therefore, organizations not only face storage challenges but have a significant challenge in processing the wide-ranging data types.

Data Modernization

The process of migrating siloed data to modern cloud-based databases or lakes from legacy databases is known as data modernization. It enables organizations to be agile and eliminate bottlenecks, inefficiencies, and complexities of legacy systems.

A modernized data platform helps in efficient data migration, faster ingestion, self-service discovery, near real-time analytics and more key benefits.

For any modern business focused on building and updating the data architecture to spruce up their data core, data modernization is not only important but essential.

To gain optimal value, accelerate operations and minimize capital expenditure, companies must build and manage a modern, scalable data platform. Equally, it is vital to identify and deploy frameworks of data solutions along with data governance and privacy methodologies.

Data modernization is not without challenges as it requires creating a strategy and robust methods to access, integrate, clean, store, and prepare data.

Tips For Successful Data Modernization

Data modernization is critical for any modern business to stay ahead of the curve. With that said, let us find out how companies can be successful in their data modernization efforts.

Revise Current Data Management Strategy And Architecture

It is important to have an in-depth understanding of the organization’s business goals, data requirements and data analytics objectives when a company starts modernizing.

Thereafter, a data management architecture can be designed to integrate existing data management systems and tools, while innovative methods and models can be leveraged to accomplish the organization’s immediate objectives and adapt to future needs.

A well-designed architecture will enable data modernization to be approached systematically and holistically, thereby eliminating data silos and compatibility issues. It will also deliver consistent value and be flexible to integrate new capabilities and enhancements.

Inventory And Mapping Of Data Assets

If an organization cannot identify where the data assets are and what is protecting them, it will be tough to know if the access provided is suitably limited or widely available to the internet.

It is essential for organizations to first understand what data is being collected, what is being collected and what is being sent out. This helps identify the requirements and how a modern data management technology can help simplify the company’s data and analytics operating model.

The best way to begin a meaningful transformation is to simplify the problem statement. Hybrid cloud is also an integral part of any modern data management strategy.

Data Democratization A Core Objective

Until a few years ago, organizations had one major reason to modernize their data management ecosystems—which was to manage their rapidly growing data volumes.

Today the single, overriding reason is data democratization, which is about getting the right data at the right time to the right people.

It gives organizations wide-ranging abilities such as implementing self-service analytics, deploying large data science and data engineering teams, building data exchanges and zones for collaboration with trading partners and go after more data management activities.

Another key advantage of democratizing data is it helps companies achieve data trust and affords them more freedom to concentrate on transformative business outcomes and business value.

Robust governance is another focus area for organizations, who can thereby reduce data preparation time and give data scientists and other business issues the time to focus on analysis.

Technology Investment

Continuous investment in master governance and data management technologies is the best way to gain maximum control over organizational data.

Assuming ownership of data elements and processes, with leadership support, can often be ignored in data management programs but they are a key enabler in managing complex environments.

It is important for chief information officers (CIOs) to take stock of the legacy technologies present on-premises, the decision support system that is ageing and will be out of contract in a few months and more contribute to data modernization projects being successful.

Data Accountability

Establishing data accountability is a basic yet crucial step in reimagining data governance. Organizations that go beyond process and policy and prioritize insights and quality measures tend to be the most successful when it comes to data modernization.

In today’s rapidly changing world, almost everything is connected and digital. In this scenario, every bit of data about customers, transactions and internal processes are business assets that can be mined to enhance customer experience and improve the product.

Among the key issues facing IT leaders is while digital points continue to increase rapidly, many remain locked to monolithic legacy systems. A holistic look at solution development and delivery that leverage Agile, DevOps, Cloud and more such approaches are essential.

Summary

It is important for organizations to be aware of the evolving data management methods and practices. It could be said that data management is one of the most demanding issues IT leaders are likely to encounter in the year 2021 and beyond. For a company’s data modernization process to be successful, their data management approach should align with their overall business strategy.

The post 5 Tips For Successful Data Modernization appeared first on Indium.