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

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

Why Modernize a Data Warehouse?

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

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

The Need for Modernizing a Data Warehouse

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

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

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

Benefits of Modernizing a Data Warehouse

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

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

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

Wrapping Up

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

The post Driving Business Success with Real-Time Data: Modernizing Your Data Warehouse appeared first on Indium.

The integration of AI and NLP in data warehousing has opened new horizons for organizations, enabling them to unlock the hidden patterns, trends, and correlations within their data that were previously inaccessible. AI, with its cognitive computing capabilities, empowers data warehousing systems to learn from vast datasets, recognize complex patterns, and make predictions and recommendations with unprecedented accuracy. NLP, on the other hand, enables data warehousing systems to understand, analyze, and respond to human language, making it possible to derive insights from non-formatted data sources such as social media posts, customer reviews, and textual data.

The importance of AI and NLP in data warehousing cannot be overstated. These technologies are transforming the landscape of data warehousing in profound ways, offering organizations unparalleled opportunities to drive innovation, optimize operations, and gain a competitive edge in today’s data-driven business landscape.

Challenges Faced by C-Level Executives

Despite the immense potential of AI and NLP in data warehousing, C-level executives face unique challenges when it comes to implementing and leveraging these technologies. Some of the key challenges include:

• Data Complexity: The sheer volume, variety, and velocity of data generated by organizations pose a significant challenge in terms of data complexity. AI and NLP technologies need to be able to handle diverse data types, formats, and sources, and transform them into actionable insights.

• Data Quality and Accuracy: The accuracy and quality of data are critical to the success of AI and NLP in data warehousing. Ensuring data accuracy, consistency, and integrity across different data sources can be a daunting task, requiring robust data governance practices.

• Talent and Skills Gap: Organizations face a shortage of skilled professionals who possess the expertise in AI and NLP, making it challenging to implement and manage these technologies effectively. C-level executives need to invest in building a skilled workforce to leverage the full potential of AI and NLP in data warehousing.

• Ethical and Legal Considerations: The ethical and legal implications of using AI and NLP in data warehousing cannot be ignored. Organizations need to adhere to data privacy regulations, ensure transparency, and establish ethical guidelines for the use of AI and NLP to avoid potential risks and liabilities.

Also check out our Success Story on Product Categorization Using Machine Learning To Boost Conversion Rates.

The Current State of Data Warehousing

• Increasing Data Complexity: In today’s data-driven world, organizations are grappling with vast amounts of data coming from various sources such as social media, IoT devices, and customer interactions. This has led to data warehousing becoming more complex and challenging to manage.

• Manual Data Processing: Traditional data warehousing involves manual data processing, which is labor-intensive and time-consuming. Data analysts spend hours sifting through data, which can result in delays and increased chances of human error.

• Limited Insights: Conventional data warehousing provides limited insights, as it relies on predefined queries and reports, making it difficult to discover hidden patterns and insights buried in the data.

• Language Barriers: Data warehousing often faces language barriers, as data is generated in various languages, making it challenging to process and analyze non-English data.

The Future of Data Warehousing

• Augmented Data Management: AI and NLP are transforming data warehousing with augmented data management capabilities, including automated data integration, data profiling, data quality assessment, and data governance.
• Automation with AI & NLP: The future of data warehousing lies in leveraging the power of AI and NLP to automate data processing tasks. AI-powered algorithms can analyze data at scale, identify patterns, and provide real-time insights, reducing manual efforts and improving efficiency.
• Enhanced Data Insights: With AI and NLP, organizations can gain deeper insights from their data. These technologies can analyze unstructured data, such as social media posts or customer reviews, to uncover valuable insights and hidden patterns that can inform decision-making.
• Advanced Language Processing: NLP can overcome language barriers in data warehousing. It can process and analyze data in multiple languages, allowing organizations to tap into global markets and gain insights from multilingual data.
• Predictive Analytics: AI and NLP can enable predictive analytics in data warehousing, helping organizations forecast future trends, identify potential risks, and make data-driven decisions proactively. Example: By using predictive analytics through AI and NLP, a retail organization can forecast the demand for a particular product during a particular time and adjust their inventory levels accordingly, reducing the risk of stock outs and improving customer satisfaction.

Conclusion

In conclusion, AI and NLP are reshaping the landscape of data warehousing, enabling automation, enhancing data insights, overcoming language barriers, and facilitating predictive analytics. Organizations that embrace these technologies will be better positioned to leverage their data for competitive advantage in the digital era. At Indium Software, we are committed to harnessing the power of AI and NLP to unlock new possibilities in data warehousing and help businesses thrive in the data-driven world.

The post Revolutionizing Data Warehousing: The Role of AI & NLP 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.

While businesses have access to lots of data thanks to the growth in IoT-based devices, cloud, enterprise systems, and so on, they face two problems. One is that the data is in raw format and two, it is stored in multiple systems and multiple formats. As a result, businesses need a solution that can pull structured and unstructured data in one place and convert this data into a unified format to act as the single source of truth.

A Gartner survey for the data integration tools market, titled ‘Adopt Stream Data Integration to Meet Your Real-Time Data Integration and Analytics Requirements’ and published in March 2019, indicates that 47% of organizations require streaming data that can help them build a digital business platform. However, only 12% had an integrated streaming data solution for their data and analytics requirements.

Traditionally, businesses depended on the ETL model – Extract, Transform and Load – but this is run as batch jobs periodically, rendering the data outdated and of limited use for some use cases.

In these times when businesses have to take quick decisions and respond to changing external and internal in a timely manner to remain competitive, depending on the ETL can be limiting to growth.

Real-Time Data Movement with In-Flight Processing

The face of data has changed tremendously today. Data is not only that which is stored in tables but also textual and documents across different formats stored in document stores such as MongoDB, Amazon DynamoDB, Couchbase Server and Azure Cosmos DB. An ETL can transfer data from one database to another, but with unstructured documents stored in these data stores, businesses need in-flight processing and built-in delivery validation along with real-time data movement.

MongoDB, for instance, is a document store where many of the sources are relational, flat, or unstructured. It will require a real-time continuous data processing solution such as Striim to create the necessary document structure as required by the target database.

Striim Features for Data Movement

Striim, an end-to-end, in-memory platform, collects, filters, transforms, enriches, aggregates, analyzes, and delivers big data in real-time. Designed especially for stream data integration, it uses low-impact change data capture to extract real-time data from different sources such as IoT devices, document stores, cloud applications, log files, and message queues and deliver it in the format needed and can deliver to or extract from MongoDB (or equivalent) as required.

With CDC, it delivers to MongoDB one collection per table, inserting, deleting, and updating documents based on the CDC operation, the row tuple contents with metadata, and the fields containing data elements.

It facilitates filtering and transforming data using SQL. Data enrichment is made possible by coupling it with external data in caches. Query output or custom determine the JSON document structure.

Custom transformations are also possible for complex cases with custom processors. While it is possible to achieve granular document updates, moving data from master/detail-related tables into a document hierarchy is also possible.

Benefits of Striim

Some of the key features of Striim that enable businesses to improve operational efficiency and deliver from and to document stores in real-time with in-flight processing for data integrity include:

• Low-impact change data capture from enterprise databases that allows for continuous and non-intrusive ingestion of high-volume data. It can support data warehouses such as Oracle Exadata, Amazon Redshift and Teradata; and databases such as MongoDB, Oracle, SQL Server, HPE NonStop, MySQL, PostgreSQL, Amazon RDS for Oracle, and Amazon RDS for MySQL. It enables data collection in real-time a variety of sources such as logs, sensors, Hadoop and message queues to enable real-time analytics.

• Non-stop data processing and delivery are effected through an inline transformation using processes such as denormalization, filtering, aggregation, and enrichment. This facilitates storing only the relevant data in the required format. A hub and spoke architecture is supported using real-time data subsetting and optimized delivery is enabled in both streaming and batch modes.

• Built-in monitoring and validation allow for non-stop verification of the consistency of the source and target databases. In addition to interactive, live dashboards for streaming data pipelines, it also enables real-time alerts via web, text or email.

Striim makes it possible for businesses to upgrade from ETL solutions to streaming data integration at an extreme scale by providing a wide range of supported sources. Any data can be made available in platforms such as MongoDB in real-time, in the required format to leverage scalable document storage and analysis.

Some of the key benefits of Striim include continuous data movement from a variety of sources with sub-second latency in real-time; a non-intrusive collection of real-time data from production systems with least disruption; and in-flight denormalization and other transformations of data.

Indium – A Striim Partner

Indium Software is a strategic partner of Striim, empowering businesses to make data-driven decisions by leveraging the real-time Big Data Analytics platform. Indium offers innovative data pipeline solutions for the continuous ingestion of real-time data from different databases, cloud applications, etc., leveraging Striim’s highly scalable, reliable, and secure end-to-end architecture that enables the seamless integration of a variety of relational databases. Indium’s expertise in Big Data coupled with the capabilities on the Striim platform enables us to offer solutions that meet the transformation and in-flight processing needs of our customers.

To find out how Indium can help you with your efforts to replace your traditional ETL solutions with a next-gen Striim platform for real-time data movement with in-flight processing, contact us now:

The post Ready to Replace Your Traditional ETL Solutions? Indium can help you use Striim for Real-Time Data Movement with In-Flight Processing appeared first on Indium.

Security Concerns of Data Warehouse on Cloud

The agile and scalable data warehouse can be created within minutes, with real-time data to provide significant insights regarding new product development, detecting frauds, improving customer loyalty or optimal pricing. By taking it to the cloud, you can accelerate and simplify data warehouse development. While data management and integration are two critical aspects of cloud data warehousing, data governance and security are the two other aspects that need as much attention.

Your data warehouse is a wealth of organizational information right from financial to those related to customers and employees, credit card details and organizational trade secrets. This makes it vulnerable to cyberattacks from malicious outsiders and insiders. While cloud service providers may have security provisions, research shows that it is not completely safe and it is important for you to have data governance and security to protect your sensitive data on cloud as well.

Cloud data warehouse is vulnerable to four types of attacks:

• DDoS or Distributed Denial of Service where the servers are overwhelmed and unable to service genuine user request
• Data breaches due to unauthorized access
• Data loss due to deletion by accident, with malicious intent, or due to physical destruction of the infrastructure
• Visitors accessing your services through insecure points
• Timely alerts in case of potential threats

Data Governance for Greater Security

Insecure data not only poses a threat to data but also has the potential to tarnish your reputation, for customers to lose trust, compromises the safety of your employees and your business as well as a drop in revenues.

According to a Ponemon survey of more than 3,000 people in 507 companies, the data breach lifecycle in 2019 was longer at 279 days than that of 266 days in 2018. The malicious attacks lifecycle was longer by 12.5% at 314 days. While a lifecycle of more than 200 days cost companies $4.56 million, those shorter than that cost $3.34 million for a breach. The most common and costly causes of the breaches were malicious attacks at a per capita cost of $166.

This makes data governance and data security critical to protect data as well as ensure its integrity, availability and usability. Regulatory compliance is another reason why you need data governance for your cloud data warehouse. While protecting stakeholder interests, it also enables you to standardize your processes and procedures, improves your efficacy at a lower cost and enhances the quality of workflows and decision making.

Meeting Your Data Governance Needs

Indium Software, with expertise in Big Data technologies, understands that the key requirements of a good data governance are the clear definition of:

• Processes
• Roles
• Policies
• Standards
• Metrics

This enables establishing access rights, responsibilities and accountability to protect sensitive data and prevent it from falling into the wrong hands. It also ensures that the data is available consistently across the business with common terminology but is flexible enough to let individual business units retain it as per their needs. Most importantly, Indium ensures that all functions have access to a single version of truth that can provide them with deeper insights to develop strategies for their operations while aligning it with the organizational goals.

Indium’s endeavors to implement data governance includes ensuring greater data accuracy and completeness along with better data integration by creating a clear data map for data related to all key entities.

Indium also ensures that your data governance implementation complies with the guidelines provided by regulatory bodies such as the US HIPAA (Health Insurance Portability and Accountability Act) and the EU General Data Protection Regulation (GDPR) as well as industry requirements such as PCI DSS (Payment Card Industry Data Security Standards).

While having the policies that will govern the security and usage of your enterprise data is important, there is no silver bullet to protect your data. It will depend on your organizational needs and should be configured accordingly using the appropriate tools. It should be an integral part and an extension of your overall IT governance strategy for a smooth and seamless implementation.

Indium’s aim for implementing data governance, in short, is to establish a code of conduct and best practices for improved data management meeting legal, security, and compliance needs best suited to your organization and ensuring growth.

About Indium

Indium, a more-than-two-decades-old software company, has wide and deep experience in end-to-end delivery of Big Data solutions as well as in other cutting edge technologies such as AI, Blockchain, data analytics, application development and in the traditional platforms.

Our teams have cross-domain experience as well as expertise in the technology offerings. We offer high level services including consulting, implementation, on-going maintenance & managed services. To implement data governance and security in your cloud data warehouse, contact us now.

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Indium Software, a cutting edge solution provider with cross-domain expertise, proposed transforming the manufacturer into a data-driven organization by migrating the data from on-prem databases to a cloud-based, serverless data warehouse.

The cloud-based data warehouse has become the need of the hour to keep the total cost of ownership (TCO) low while leveraging the services provided by the public cloud providers such as Google BigQuery, Amazon Redshift or Azure Synapse Analytics (Formerly SQL DW). In the case of the pump manufacturer, Indium migrated the client’s data to Microsoft Azure and reduced the TCO by over 50 percent.

This is the direction in which the world is moving today. According to a MarketsandMarkets report, the global serverless architecture market size will touch USD 21.1 billion by 2025 from USD 7.6 billion in 2020, growing at a Compound Annual Growth Rate (CAGR) of 22.7 per cent. The three key factors spurring this growth are:

• The need to shift from CAPEX to OPEX
• Remove the need to manage servers
• Reduce the infrastructure cost

Easy Data Access and Management

Today, data generated from multiple sources can be made available to businesses to improve their decision making and devising business strategies across functions. However, traditional systems cannot handle the multiple formats they are available in and manual intervention is required to reconcile them all into one format. This can be time-consuming and prone to errors.

A cloud-based serverless data warehouse can automate the process of data management, making data easily available and accessible for advanced analytics and to gain meaningful insights for improving business processes and efficiencies. Some of the key benefits of opting for a serverless data warehouse would be:

• Being cloud-based, it can be accessed from anywhere, thereby allowing even the executives on the move to access data and reports that can speed up their decision-making process.
• Being fully managed by the providers, it reduces the burden on the internal IT team and lets them focus on innovation and improving their core business
• A solution like Azure also enables easily scalable computational storage at lower costs. Databases can be paused and resumed quickly which can save costs. Cloud providers have a cost management feature to keep a check.
• The level of optimization it offers cannot be matched by the traditional on-premise setup
• It provides columnar storage and parallel processing facilitating faster aggregate queries
• High availability and scalability ensure automatic data distribution and replication automatically across data regions (zones) on the cloud infrastructure
• Data Latency is milliseconds despite a highly distributed data set up
• Data security is assured through authentication and authorization managed within the cloud setup and data encrypted to comply with privacy regulations

Challenges in Migration

Yes, opting for a serverless data warehouse is not a walk in the park. Some of the factors you must keep in mind include:

• Selecting the right building blocks is important as not all of them are fully managed. For instance, Amazon Redshift requires you to choose the node type that is compute-optimized or storage-optimized. You will need to choose the number of compute nodes for the cluster and also manually size them.
• In some instances, you might need to integrate different serverless building blocks and also connect the entire solution using non-serverless blocks.
• You may opt for integrating individual building blocks instead of having one single solution. While improving configurability it will make the solution complex.
• Depending on the data model you opt for, costs can be a combination of upfront and variable.

Partnering with the Right Data Experts

Navigating these hidden complexities requires a deep understanding of data, data warehouses as well as the service providers. An experienced solution provider such as Indium can work closely with you to understand your needs and tailor the approach to suit your requirements.

We provide a simple, secure, cost-effective and scalable solution. We have expertise in Data Modelling, the most crucial stage in architecting the data warehouse. We derive the Technology Architecture by analyzing the process architecture, business rules, metadata management, tools, specific needs and security considerations. At this stage, the data integration tools, data processing tools, network protocols, middleware, database management and related technologies are also factored in.

For the serverless data warehouse architecture, a step by step order of data pipeline and transformation from one form to another is done. As a result, the entire cycle of storage, retrieval, processing data within the data warehouse is mapped. The architecture is designed to ensure that the workload is processed on time, performance is optimized and running costs are kept low.

Indium has a team with more than two decades of experience in the latest cutting edge technologies as well as domain expertise across different industries such as retail, e-commerce, manufacturing, banking, services or finance, among others. If you would like to leverage serverless data warehouse for improved analytics and lower cost of ownership, do reach out to us.

The post Serverless Data Warehouse: For Better Data Management at Lower Cost of Ownership appeared first on Indium.

Broadly, data analytics has influenced decision making at three levels: One, it has enhanced the ability of the healthcare professional to make better, informed decisions using data analysis. Two, insights from data have certainly helped key stakeholders from pharmaceutical companies and medical device players to insurance companies and even the government make systemic decisions. Three, healthcare enterprises make better business decisions on financial planning, marketing, operations, quality, and even risk management.

But there’s one thing that is a core process of any healthcare analytics project. And, that is data aggregation. The quality and robustness of the data aggregation process can make a major difference in the process of delivering insights.

While this may very well be the case in most sectors, in the healthcare sector, data aggregation often becomes even more important and challenging. The key reason for this is that – individual patient data must never be shared and cannot be compromised. Unless, of course, patient-specific data is used to deliver care to the same patient.

The data picked up from EHRs (Electronic Health Records) must be aggregated as a group, without compromising on any healthcare regulation, HIPPA rules, and even medical ethics.

Additionally, for true insights to be drawn, data must be captured from multiple EHRs. Once captured, this data must be aggregated and prepared for analysis. While Data Quality Validation (DQV) is certainly a critical aspect, even more important is the adherence to rules and regulations, while still running a seamless data preparation process for analytics.

In this post, we focus on how Indium Software can play an important role in helping various companies in the digital healthcare industry run a seamless, cohesive data aggregation process from multiple EHRs.

Specifically, we can support companies in the following areas within the Digital Healthcare Landscape

1. Digital Therapeutics
2. Pharmaceutical supply chain
3. Genomics
4. Consumer health & wellness
5. Administrative Departments of Care Providers
6. Specialty Care
7. Primary Care
8. Clinical Tools
9. Diagnostics
10. Drug discovery & development
11. Clinical trials
12. Real-world developments

Companies within each of these segments need data aggregation from multiple sources.

But, the quality of this data aggregation process must be top-notch and, more importantly, fit in with their current workflow.

Broadly, the following are the 4 pillars that make Data Aggregation extremely important:

1. Higher Quality Clinical Outcomes: This is probably the most important of all factors in the equation. Irrespective of business models, technology usage, and other key factors, access to the right data at the right time can certainly save lives.
2. Patient Safety: But one step even before the outcomes is to ensure that doctors and care professionals have access to specific patient’s medical records, in addition to broader data-driven insights from a large data set. This comes in handy, especially in cases of complicated illnesses. 
3. Interoperability the Key: The primary challenge often lies in building an interoperable system. We need to build a workflow so providers can leverage and access multiple data sets from various sources.
4. Tackling compatibility issues: Lack of compatibility between systems can often cause problems in the workflow. Indium’s data aggregation process will ensure that combining information from multiple sources will produce cohesive, shareable information.

Indium Software has served the healthcare and related industries for over a decade now, offering Data Analytics, AI, Digital Transformation and Software Testing services. We serve companies of all sizes – from mid-size growth companies to Fortune 500 companies.

As we further embrace Digital Health with renewed enthusiasm, now may be the time to leverage its full potential by deriving key insights from data.

If you have a specific query around our data aggregation service for the digital healthcare ecosystem, give us a shout.

For more information, visit: Healthcare And Life sciences

The post Data Aggregation from Multiple EHRs holds the key to effective Digital Healthcare appeared first on Indium.

Let’s start with what a data warehouse is – Integrated historical & current data in a central repository! This data repository is derived from external data sources and operational systems.

A data warehouse, being a central component of business intelligence allows enterprises to cover a rather wide range of business decisions.

These decisions may include – business expansion, production method improvements, product pricing so on and so forth.

Apart from the huge role that a data warehouse plays in analysis and reporting, a data warehouse provides the following benefits to an organization:

• It allows you to keep data analysis separate from production systems. Complex analytical queries cannot be run by operational databases used by organizations every day. This is where a data warehouse lets the organization to run such queries without there being any ramifications on the production systems.
• Data warehouses bring consistency to disparate data sources.
• Data warehouses have an optimized design for analytical queries.

The popularity of Data Warehouse-as-a-service has increased tremendously over the past five years. This is primarily because of the impact that cloud computing has had on big data architecture. Let’s now have a look at the major differences between cloud-based data warehouses and traditional data warehouses.

A Traditional Data Warehouse

The traditional on-premise data warehouse requires on-premise IT resources like software and servers for the functions of the data warehouse to be delivered. Infrastructure needs to be maintained effectively when organizations run their own on-premise data warehouse.

The 3 tier structure of a traditional data warehouse:

• The data warehouse server is what occupies the bottom tier. This contains data pulled from various sources and is integrated in a sole repository.
• The OLAP servers occupy the middle tier. This allows the data to be more accessible for the different types of queries that will be used on it.
• The front-end BI tools occupy the top layer. These tools are primarily used for querying, reporting and analytics.

In order to pull data into the data warehouse, ETL tools are usually used. These tools obtain data from various sources, process it and apply the relevant business rules to get the data into the right format based on the data model.

After this, the data is finally loaded into the data warehouse.

Bill Inmon and Ralph Kimball, two computer science pioneers have contrasting opinions when it comes to traditional warehouse design –

Bill Inmon suggested a top-down approach which meant that all enterprise data is stored in the data warehouse which is the central repository.

From this data warehouse, dimensional data marts which serve particular lines of business are created.

On the other hand, the bottom-up approach according to Ralph Kimball suggests that the result of the combining data marts is the data warehouse.

Cloud Data Warehouse

The concept of the cloud-based data warehouse approach stems from leveraging data warehouse services provided by the public cloud providers like Google BigQuery or Amazon Redshift or Azure SQL DW.

With data warehousing services accessible over the internet, public cloud providers allow companies to cut down heavily on their initial set up costs required for a traditional on-premise data warehouse.

Adding to that, the cloud data warehouses are fully managed by the providers. Hence, the service providers manage and assume entire responsibility of the required data warehouse functionalities. This includes updates and patches to the system.

In comparison – Traditional vs Cloud

The traditional data warehouse approaches differ from the cloud architectures. Cloud Architectures are somewhat different from traditional Data Warehouse approaches.

Take the case of Amazon Redshift – The operations of Redshift are designed where you are required to provision a cluster of cloud-based computing nodes.

A few of these nodes compile queries whereas a few of them execute these queries. Google on the other hand provides a serverless service.

This means that the allocation of machine resources is managed by Google dynamically.

These decisions are taken by Google thereby freeing up the user’s bandwidth. In the case of Azure, it is a solution that is relatively cheaper with an ability to scale and compute storage.

In Azure, you have the advantage of pausing and resuming your databases in minutes.

When it comes to cloud data warehouse, the level of optimization it offers is very tough to match by the traditional on-premise setup.

Another advantage of cloud over on-premise is columnar storage. This is when table values are stored by column and not rows.

This allows for faster aggregate queries in line with the type of queries you need to run in a data warehouse. Another feature that drastically improves query speeds is massively parallel processing.

This is done by using many machines to coordinate query processing for large datasets.

When it comes to scalability, in the cloud, it is just as simple as provisioning additional resources from the cloud provider.

On-premise scalability is expensive and time consuming as the need to purchase more hardware arises.

The tricky aspect of having a cloud data warehouse is security – transmitting terabytes of data over the internet brings about a security concern which includes compliance concerns as well.

This is because the data may carry sensitive information. An on-premise setup holds the edge here as these security concerns are totally avoided because the organization controls everything.

Summing it up

For medium and small-sized companies, the cloud makes data warehousing more accessible than before due to the low barriers to entry.

Cloud data warehouses entice even the biggest enterprise due to their lower costs – reduction in infrastructure management costs and easy scalability.

Putting things in perspective, the cloud does have its issues when it comes to security. However, the benefits clearly outweigh the negatives. Legacy on-premise setups are not entirely obsolete.

However, the volume and velocity of data is growing at the rate of knots today and cloud services are designed to handle this sort of data.

As it stands today, more and more workload is moving to the cloud and more and more companies have started providing cloud-based data warehousing services.

This trend tells us that the cloud is the future of data warehousing!

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This brings us to the part where data-driven decision making also faces a problem. The problem arises where collation of all the various sources of data in one repository is required. This is because all the data sources, systems and formats are disparate. What this means is that the need to organize all of this data in one repository for analysis is extremely important. This is where the data warehouse comes to our aid.

So, let’s dive into what a data warehouse is and why you need to invest in the best in class data warehousing services.

What is a Data Warehouse?

Any system that houses integrated data resulting from multiple data sources in an organization in a central repository is a data warehouse. Reporting, analysis and decision making is supported by a data warehouse by consolidating all the data at an aggregate level.

Subject-oriented, non-volatile and time variant were the terms used to describe a data warehouse by Bill Inmon who is regarded as the father of the data warehouse.

Subject-Oriented – This means analysts in their specified field of expertise like marketing can access relevant subject data in the data warehouse for analysis.

Non-volatile – This means that the data stored in the data warehouse should not and will not change.

Time-variant – Historical data is what the data warehouse contains and this is in contrast with transactional systems which consist of only recent data.

Modern Data Warehousing

All aspects of IT architecture have undergone a massive change since the advent of cloud computing. When it comes to data warehouses, enterprises have made a shift from on premise systems to cloud based warehousing services.

The answer to costly investments in hardware is cloud which allows computing access in a cost-effective and convenient way. When it comes to cloud, the organization only pays for the cloud –based services provided and sometimes for the delivery of those services by the computing resources.

More and more companies are shifting to cloud based data warehouse solutions from the traditional on premise data warehouses.

Data Warehouse Use Cases and Vendors

The common notion is that data warehouses help support business decision making, apart from this here are a few use cases that further illustrate this fact:

• Logistics and operations can be reviewed and analyzed, the results of this analysis can be optimized and improved to help run the business unit better.
• Customer relationships (current and prospective) can be improved.
• Performance/profitability can be tracked, analyzed and improved.
• Sales process analysis to be more efficient.

Microsoft, HP, IBM and SAP provide on premise data warehouse systems. These systems are database software optimized for database workloads and analytics. The organization still has to buy the necessary hardware in order to support the software.

On premise data warehouse packages inclusive of hardware and software are provided by companies like IBM, Oracle and Teradata.

When we talk about cloud offerings, these systems are offered as data warehouse as a service. There is no investment required except for the computer and an internet connection to access and analyze the data. In this space, the big players are Amazon Redshift, Panoply, Azure SQL Data Warehouse and Google BigQuery.

Below are a few tips for you to select the right data warehouse:

1. Network Latency is not the be all and end all

There is a lot of debate in the speeds of cloud based services vs on premise deployments.  Accessing your data warehouse via a network results in speed constraints. However, this does not have that much of an effect on performance as is popularly assumed.

Latency will be lesser of an issue with on premise systems than the cloud based servers. However, the difference in speed is negligible. In most cases, cloud based systems are better in performance than on premise data warehouses.

2. Cost transparency is the best

Key to choosing your data warehouse is considering the cost. Building up an on premise data warehouse will cost you tens and thousands of dollars. Over and above this will be the cost to maintain and administer the warehouse.

Cloud based data warehousing costs vary across vendors. This is primarily because different vendors offer varied pricing structures. Amazon Redshift charges you based on the type of computing instances need to house the data. On the other hand, BigQuery charges for storage and also for each query after.

It is best to opt for the most transparent pricing structure that fits your budget.

3. Compliance must be met

This is most relevant to cloud based data warehousing services. While choosing a data warehouse product, you need to ensure that the compliance standards of the data warehouse service providers and your company’s compliance policy are in sync and are mapped.

Healthcare information and patient data is strictly governed by HIPAA compliance laws. In the healthcare industry, any organization must ensure that the data warehouse is in compliance with HIPAA regulations.

4. Ensure High Availability

The prime focus while selecting a data warehouse should be on availability irrespective of whether it is on premise or on cloud. A higher level of availability is expected due to the necessity of data for decisions and the move to real-time analytics.

On cloud, products are offered with high uptime percentages and great availability. Outages are also known to occur and it is not like cloud services are not prone to downtime.

5. Ensure Scalability

Cloud-based data warehouse services excel tremendously when it comes to scalability. Along with the growth of organizations, the amount of data grows as well. This requires more computer power in order to analyze all the data effectively.

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