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  • What Are the Benefits of Moving Windows Workloads to AWS?

    Running Windows workloads on-premises stops a company from swiftly adapting to shifting market needs. So, moving data and workloads to the cloud is vital for the digital transformation of a company. And resisting is like fighting gravity.

    Increased attention on the migration of workloads has echoed in multiple reports, with one claiming that 62% of organizations have a migration and modernization strategy in place.

    Your Windows workloads can be better in the cloud, specifically in Amazon Web Services (AWS). AWS is a broadly adopted cloud, offering over 200 fully featured services to businesses, improving their agility, efficiency, and innovation faster. Here in this blog, we’ll look at the primary advantages of hosting Windows workloads on AWS and why it’s a good idea for the fastest-growing startups, largest enterprises, and leading government agencies trying to improve their operations.

    Major Benefits of Moving Windows Workloads to AWS

    Business transformation is never easy. Ideally, migrating to the cloud is part of an organization’s adoption of a more modern, agile management strategy. Moving your Windows workloads to AWS makes your business operations more aligned. With modern infrastructure and cloud capabilities, your IT workforce can be freed up to focus on core tasks that are important for your company’s growth.

    Let’s take a look at the benefits of having Windows workloads on AWS and how easy it is for you to get there. 

    1. Cost Reduction

      One of the most evident benefits of transforming Windows workloads to AWS workloads is cost reduction. According to stats, running Windows workloads on AWS cuts the 5-year cost of operations by 56%. Businesses no longer have to worry about the price of developing and maintaining expensive infrastructure since AWS takes care of these costs.

    2. Reduction in Downtime

      Businesses that run Windows workloads on AWS notice a 98% reduction in downtime. Amazon provides a highly available and robust cloud infrastructure, as well as a variety of services and tools to minimize downtime. AWS-hosted apps can withstand traffic surges, disperse traffic over several instances, and remain operational in the case of an outage.

    3. Increased Productivity

      Various statistics prove that AWS increases business productivity. A Salesforce survey found that businesses that move to AWS cloud experience an average 26% improvement in their productivity. With the ability to access cloud-based software and services from anywhere, your business workforce can efficiently work remotely and collaborate more effectively. Additionally, AWS provides automated tools and services that help expedite processes and cut down on the time and labour required for manual tasks.

    4. Better Security

      Security is vital for a business, whether it has the in-house infrastructure or uses a managed Windows server. When it comes to delivering high security to your data, AWS ticks all the boxes by providing around 230 security, compliance, identity and access management, network security, and governance services, among many others. It also provides encryption across 116 distinct AWS services, five times more than other large cloud-based enterprise-level service providers.

    5. Higher Availability

      AWS cloud has 77 Availability Zones (AZ) spread across 24 locations. More than 350 Amazon EC2 instances are also present. Because of the high service availability, your AWS workloads are maintained continuously with minimal downtime. It was discovered in 2018 that AWS offers 7X higher uptime than the next-largest cloud provider. Businesses can ensure that their apps and services stay available to consumers without any risk of disruptions and possible revenue losses.

    6. Easy Migration Process

      AWS has helped thousands of businesses all over the world to adopt the cloud to move their Windows workloads. Migrating workloads to the AWS cloud platform is an easy process if done by experts. AWS Cloud Formation, which enables customers to build and manage AWS resources using code, and AWS Systems Manager, which streamlines hybrid cloud administration, are tools Amazon offers to assist businesses to optimize their Windows workloads on AWS without any challenge.

    Accelerate Innovation- Move to Cloud with R Systems

    Moving Windows workloads to the cloud will definitely accelerate your company’s innovation and growth. By hosting Windows workloads on AWS, businesses can achieve greater flexibility, scalability, and agility. R Systems has expert professionals to deliver bespoke cloud services to companies desiring to accelerate innovation with cloud-native technologies. We are an AWS Advanced Tier Services Partner offering solutions tailored to meet the specific needs of businesses of all sizes and industries.

  • Do our Banks Really Need Better Data Analytics?

    Highlights
    • This is What Banks Need Most to Be Transformational in the Digital Landscape
    • The Value of Advanced Analytics to Today’s Banking Industry can never be underestimated
    • So How Impactful is Advanced Analytics for Banks Worldwide?
    • Banks Can Claim Lost Revenue Avenues through their Improved Analytics Focus
    • Advanced Analytics is Imperative for Today’s Banking Success. Do You Agree?
    • What’s your perception of banking success?

    Banks must transform to fit in well with the Evolving Digital Ecosystem and Advanced Analytics will help them get to it with ease and precision… Or else … they will be losing out on their market share and profitability!

    This is What Banks Need Most to Be Transformational in the Digital Landscape

    Today’s banking systems are getting more complex than ever. To overcome this complexity, banks must stay abreast of the best way to mitigate risks, enhance security systems, ensure regulatory compliance and meet customer needs effectively.

    To launch the right products for the right customers in a secure, dynamic approach, banks must invest in certain frontiers that will pave their way towards success in the high-end digital future:

    • Make data work by enabling communication between disparate data formats that existed in the past and are the language of the future
    • Rely on people who possess the skills to derive insights from data. Empower them with the analytics and communication tools for collaborative decision making and meaningful information discovery
    • Form correlations between data and visualization of patterns and relations, as it is critical to advanced, transformational business planning

    The Value of Advanced Analytics to Today’s Banking Industry can never be underestimated

    In the end, it’s all about innovation and precision risk assessment, which will directly impact your financial bottom line. To expand your opportunities and be transformational while reducing costs, there is no better way to differentiate and charge through your competition rather than by driving decision making through analytics. Advanced analytics is an indispensable tool for generating sales leads, carrying out risk management or revenue management. Not only does analytics redefine core functions, but it an essential tool when it comes to marketing, budgeting and planning your business in general.

    So How Impactful is Advanced Analytics for Banks Worldwide?

    By the year 2020, close to 40 trillion gigabytes of data is expected to be generated, be it tweets, Skype calls, YouTube videos or emails.Sifting through this data and listening is imperative to realize important insights and come up with targeted strategies for customer acquisition and retention. It helps banks accomplish accurate reporting and ensure regulatory compliance and project their system as profitable and competitive.

    Clearly, this is not as easy as running queries on a database. It requires the use of advanced analytics – to address the variability and volume of available data.

    1. Precision Analytics can help calculate risks.

      Banks must find a way to manage risks, given the broad spectrum and depth of investments they engage in.Analytics in banking is hardly limited to the financial domain. Data pertaining to many areas, from their target market to the viability of their securities can be instrumental in determining, whether their investment would be worthwhile or not. Besides, it helps deliver better services to customers through their financial need analysis.

    2. Trends Can Unravel Important Data for Effective Future Planning:

      Analytics can be the source of determining key performance indicators and reporting can be an important source of responding to customer demand and strategic planning for the future.Visualization of critical data, customizability in extracting selective data sets and historical data analysis cannot be accomplished without analytics. Eventually, banks must remain competitive, and the two main factors that directly impact their market position – compliance to regulations and compliance to customer requirements. Both of which are entirely dependent on deep analytics.

    Precisely, 96% bankers acknowledge that the banking world is witnessing the organization of a digital ecosystem. However, the downside is that 87% of the surveyed banks admit that their systems are not smart enough to flow with the digital tide.

    Banks are losing out by maintaining a status quo and incrementally upgrading their analytics strategy to address a current need. Partnering and collaboration in conjunction with “agile, scalable systems” and “real-time data analytics” are the door to a successful, thriving banking business in the digital ecosystem.

    Banks Can Claim Lost Revenue Avenues through their Improved Analytics Focus

    Analytics directly impacts a bank’s market domination. It is rather critical for banks to change priorities and analytics approach and match their market position to currently prevailing trends.

    The Banking Top 10 Trends 2016 report sheds further light on this aspect. Charging optimally for every service delivery is critical and suboptimal or overpricing is commonplace without the use of advanced analytics.A pricing decision which is not based on analytics will create the means to give away appreciable portions of their revenue pie to players even outside of their domain. Eventually, banks become less informed about their customer expectations and therefore less profitable.

    In addition to becoming agile and adopting a service-oriented architecture (SOA), Advanced Analytics is one of the critical trends for banking success. It is a key factor that helps drive customer insights, curtail fraudulent activity and manage risks better.Banks need the intelligence that helps frame effective path-breaking strategies. Banks can take advantage of a number of analytics realms in prediction, visualization, simulation or optimization to address their specific business architecture needs and strategic requirements.

    Advanced Analytics is Imperative for Today’s Banking Success. Do You Agree?

    Banks must ensure that their digital strategy is not limiting to make the most out of data discovery from Advanced Analytics. Legacy infrastructure and the inability for effective data communication produce great obstacles.

    The inability to address this and other surrounding constraints prevents banks from successfully breaking into the digital.

    • Banks will be able to understand customers better, retain customers, acquire new customers and reduce attrition through their improved analytics focus.
    • Better analytics helps deliver targeted products and services, convert and serve customers better and market themselves better.
    • At the core, it helps drive better decisions and best in the market opportunities.

    All this translates into better profitability and a drastic upsurge in the financial bottom line.

    What’s your perception of banking success?

    Is Advanced Analytics the answer to profitability woes in the banking sector in today’s disruptive digital dimension?

    Share your views on social media and let other’s get a peek at the banking success factors!

  • Are You Leveraging The Power of Data Analytics Yet?

    Highlights
    • Why is Data Analysis Useful to Your Business?
    • Lingering around the start line- Deciding on what, when and how to use the Big Data?
    • The Big Difficulties of Big Data Analysis
    • Analysis of data and implementation of findings is what matters

    Data Analytics is the science of examining, concluding and implementing the useful data for organization’s growth. In today’s connected world, data is available everywhere. Travis Oliphant, CEO of data analytics firm Continuum Analytics, suggests data is more available now than ever, with “people connecting through the Internet, their mobiles, social media, business partnerships and personal friendships and associations.”

    Globally, 4.6 billion mobile subscriptions and around 1 to 2 billion people are accessing the Internet on a daily basis; therefore, the potential for data collection is enormous.

    The structured and unstructured data are enormously available, but they are seldom used by organizations to be benefitted in annual growth. The big data is continuously used by technology industry for strategizing annual goal.

    Why is Data Analysis Useful to Your Business?

    “Something is always better than nothing.” – To weave a strategy for growth of the business, the data availability is always a basic requirement. The voluminous data give the clear structure to carve-out the plan to cover the deficient areas in the business. Data Analysis can help give you not only an insight into your customer’s habits, preferences, and behaviors but can also be applied to help your business grow. For example, if launching a new product, analysis of current customer behaviors can help identify a need for your product, potential future customers, how to market to these customers and how to retain these customers.

    Already well established, with over 89% of US businesses saying they use data analytics, data analysis has been adopted by many industries across the globe including:

    • National Governments – In 2012, US Government announced the Big Data Research and development initiative to examine specific issues within government. At present, there are 84 programs.
    • Healthcare Sector – In the UK, data analysis of prescription drugs showed a significant discrepancy in the release of new drugs and the nationwide adoption of these treatments.
    • Elections – In India, the BJP winning campaign for the General elections in 2014, relied heavily on big data analysis.
    • Media – Relies completely on big data to fetch precise information, specifically where figures play a significant role. Media dominates the market by presenting the data as a secure and inevitable witness.
    • Science – Science and technology are correlated and share especial configuration. The huge amounts of data produced during experiments such as the Large Hadron Collider are analyzed using data analysis. The systematic data analysis cut shorts the risks engrossed.
    • Sports – Sports sensors are used to assess athletes and sportsmen’s condition, guide training and even predict injury. The sports related data analytics is required to be precise.

    Collecting data is not the issue, in their video, Big data what’s your plan? McKinsey suggests that companies struggle with data analysis in three key areas:

    1. Which data to use and where to source it?
    2. Analysis of the data, plus sourcing the right technology and people to carry out that analysis,
    3. Implementation of the analysis findings to change your business.

    So let’s start with number one…

    Lingering around the start line- Deciding on what, when and how to use the Big Data?

    Data is now more accessible than ever. To improve the efficiency and other services, every organization collects the related information; however, very few analyze this data to implement in the direction of improvement or change.

    Data trends can highlight success, identify problems and help provide alternative ways of working. And while most businesses know that data analysis can make them more efficient, productive and even help predict future market trends, it is scarcely used to its full potential. So why aren’t more people using data analysis?

    The Big Difficulties of Big Data Analysis

    Due to the large volume of structured and unstructured data, it often becomes difficult to manage and procure the relevant information from them. On the other hand, the traditional data analysis, which constitutes difficult methods become too wary to analyze. Traditionally, companies use to visualize datasets in programs such as Microsoft Excel which has a great capacity for simple datasets or employ a free tool such as Qlikview, but with Big Data things change.

    With over $15 billion spent solely on companies focusing on data management and analysis, companies are forced to employ data analyst or data scientist specifically for data analytics. In 2010, the industry was estimated to worth more than $100 billion and predicted to grow at approximately 10 % a year. So big data is big business.

    Analysis of data and implementation of findings is what matters

    To apply data analytics to your business first you need a plan or strategy. For example, if you want to improve your company’s effectiveness and efficiency, it is important to manage performance. To manage performance, you need to measure it. But the measures of performance you take need to be meaningful, and link to the desired outcome or goal.

    Therefore, the idea to employ a data analyst and specific software, to collate data and develop a plan of how to implement the required changes is quite synchronized.

    Ready to trap the Big Data?

    Using Data analytics provides potent information which can be used to achieve high merits of success and tangible solutions with great accuracy. It is not only great for your business, but data analysis can also identify customer preferences and behaviors, allowing you to personalize your products and business to your customers.

    In today’s connected world, data analytics is becoming vital for businesses who want to gain a competitive edge over others. And with the increasing amount of data available, never before have you had so much access to what your target market wants and needs.

    So get out there and see how data analysis can change and improve your business, you might just be wonder why you haven’t exploited data analytics potential before.

  • Process Mining and Robotic Process Automation: Made for Each Other

    Robotic Process Automation (RPA) has become a hot topic for organizations in the last few years. These days, many organizations are embracing RPA to automate their repetitive, high-volume tasks and cut headcounts. Though it serves as a useful tool to optimize business processes, but when used in isolation, it’s more likely to disrupt the processes than improve them.

    How to make RPA work for you

    One of the biggest barriers to RPA is identifying the right process to automate as automating the wrong process can magnify inefficiency. This is where Process Mining gets into the picture. It is an approach that aims to discover, analyze, monitor, and improve business processes by extracting valuable information from the data to remove bottlenecks and inefficiencies.

    While there is wide acceptance of the fact that Robotic Process Automation (RPA) and process mining augment each other, many companies have not been successful in putting both technologies to good use in their businesses very effectively.

    Challenges enterprises face scaling their automation program

    Companies struggle to scale their automation program at an enterprise level for various reasons. Keeping the organizational dynamics aside, many businesses find it overwhelming to analyze enterprise-wide processes and identify the right candidates for automation.

    The next challenge firms encounter comes with understanding the processes and estimating the associated benefits and costs to achieve the desired ROI by prioritizing high-value, low-effort opportunities. Studies have shown that 40-50% of the Bot Development Lifecycle is spent on identifying, prioritizing, and documenting the processes, with the rest of the time split among bot design, coding, review, unit testing, integrated testing, UAT, pre-deployment configuration, and deployment activities.

    The role of process mining in achieving process excellence

    Process mining gives a business a complete picture of their state of processes ‘as-is’, which in turn can be used by our RPA team to turn into actionable automation. Process mining helps you highlight the best automation candidates, enabling you to determine the extent to which RPA can be implemented in legacy processes and systems.

    Additionally, process mining tools often provide a capability of executing business-rule-driven automated actions, but they are generally limited in terms of the type of actions such as sending emails, pushing a report, or alerting business users for further actions. Using these process mining tool actions to kick off RPA bots gives you unlimited power of end-to-end automation.

    While RPA tools allow you to measure post-automation indicators of accuracy and productivity, process mining software provide pre-automation historical values, as well as the upstream and downstream impact of automation.

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    Maximizing benefits by using RPA and process mining together

    RPA bots generate detailed logs of each and every data element that they touch or use in decision making. Process mining software can benefit from such detailed logs to provide greater visibility into the process performance. Thus, these technologies truly complement each other to further your business goals. A recent Gartner report on Complemented RPA (CoRPA) even mentioned that: “A significantly improved version of the current RPA development tool known as the process recorder, that has UI interaction record and playback capabilities, will dynamically generate the RPA script based on lessons from process mining and process discovery.”

    Recognizing the multiplier effect of combining these two powerful concepts, one major RPA tool provider, UiPath, acquired process miner ProcessGold in 2019. Major process mining tool providers, like Celonis and Minit, also boast their capabilities to augment the power of automation. In addition, Nintex (Process Mapping and Analytics company) bought Foxtrot (RPA company) in 2019, and Appian (Process Management company) purchased Jidoka (RPA company) in 2020 to leverage the power of both technologies. Thus, it is becoming evident that Process Automation projects are more likely to succeed with the addition of process mining.

    Robotic process automation (or RPA) is a form of business process automation technology based on metaphorical software robots (bots) or digital workers. RPA systems uses application’s graphical user interface (GUI) to perform manual tasks directly in the GUI.

    Process mining is a family of techniques in the field of process management that support the analysis of business processes based on event logs. During process mining, specialized data mining algorithms are applied to event log data in order to identify trends, patterns and details contained in event logs recorded by an information system. Process mining aims to improve process efficiency and understanding of processes. The term Process Mining is used in a broader setting to refer not only to techniques for discovering process models, but also techniques for business process conformance and performance analysis based on event logs.

  • Are You Well Prepared for the Future of Banking & Financial Services?

    The BFSI industry is rapidly changing because of new regulations, digital economy, and millennial customers. This industry is under great pressure to cut costs while maintaining high levels of service and perfect regulatory compliance. However, it is becoming increasingly challenging as financial institutions have siloed systems and paper-intensive processes. Also, most of their employees are focused on repetitive and labor-intensive tasks, & as a result, are unable to focus on other high-value client-facing services. As they face intense competition in the market, they need to find ways to nurture cost-efficient growth.

    The solution to all these problems is Robotics Process Automation. RPA helps organizations to efficiently handle their operational tasks. The RPA robots (aka bots) are deployed to mimic the day-to-day and routine tasks that are performed by the employees following the same business rules. RPA bots can handle many repetitive manual tasks including copying, pasting, or entering data into forms and systems, or extracting, merging, formatting, as well as reporting the data. RPA has helped banks and financial companies reduce manual efforts (and associated costs), assure better compliance, increase processing speed & accuracy, as well as reduce risks while improving customer service. In the last few years, with cognitive automation, Artificial Intelligence, and Machine Learning, we are able to automate a wide variety of end-to-end processes in many operational areas, including loan processing, account opening/closing, and KYC.

    According to Forrester reports, the RPA market is set to reach $2.9 Billion by 2021 and the expectations of the BFSI industry deploying robots are high.


     

  • Streamlining Remote Patient Monitoring for Improved Physician and Patient Outcomes

    Our solution for integrating a Remote Monitoring Application into Electronic Health Record (EHR) workflows significantly reduced physicians’ administrative burdens while enhancing data portability. 

    We designed and implemented a Self-Measured Blood Pressure Monitoring (SMBP) system using FHIR and EHR integration tools for seamless connectivity. The solution features advanced algorithms to calculate daily and weekly scores and averages, providing actionable insights for physicians. 

    The application also includes Patient Health Coaching services and seamless device integration, improving hypertension management and patient outcomes. This comprehensive approach has enhanced physician satisfaction, improved patient outcomes, and streamlined hypertension management.

  • Unlock the Secrets to FMCG Success with Power BI

    Struggling with manual tracking and delayed insights? Learn how our tailored solutions helped a global beverage brand:

    • Boost User Adoption: Intuitive interface increased self-service BI usage by 50%.
    • Seamless Integration: Real-time insights from CRM, ERP, and social media analytics.
    • Cost Savings: Reduced licensing costs by 40%.
    • Faster Decision Making: Interactive dashboards for quicker insights and deeper analysis.
    • Scalability: Flexible cloud architecture to meet growing reporting needs.

    Ready to Transform Your Business? Fill out the form below to access the full case study and learn how Power BI can enhance your market performance.

  • Centralized Governance of Data Lake, Data Fabric with adopted Data Mesh Setup

    This article explains Data Governance perspective in connectivity with Data Mesh, Data Fabric and Data Lakehouse architectures.  

    Organizations across industries have multiple functional units and data governance is needed to oversee the data assets, data flows connected to these business units, its security and the processes governing the data products relevant to the business use-cases.  

    Let’s take a deep dive into data governance as the first step.  

    Data Governance

    Role of data governance also includes data democratization, tracks the data lineage, oversees the data quality and makes it compliant to the regional regulations.  

    Microsoft Purview has the differentiator on the 150+ compliance level regulations covered under Compliance Manager Portal:

    Data governance utilizes Artificial Intelligence to boost the quality level as per the data profiling results and the historical data set quality experience. ‍

    Master Data Management helps to store the common master data set in the organization across domain with the features of data de-duplication and maintaining the relationships across the entities giving 360-degree view. Having a unique dataset and Role based Access Control leads to add-on governance and supports business insights.  

    Data governance helps in creating a Data Marketplace for controlled golden quality data products exchange between the data sources and consumers, AWS Data Zone SaaS has a specialization on Data Marketplace capabilities:  

    Reference data set along with the Master data management helps to do the Data Standardization which is relevant in the data exchange between the organization, subsidiaries, partners as per the industry level on the Data Marketplace platform.

    Remember the data governance is feasible with the correspondence between the technical and the business users.  

    Technical users have the role to collect the data assets from the data sources, review the metadata and the data quality, do the data quality enrichment by building up the data quality rules as applicable before storing the data.  

    On the other hand, the business user has a role to guide on building the business glossary on data asset to Columnlevel, defining the Critical Data Elements (CDE), specifying the sensitive data fields which should be mask or excluded before data is shared to consumers and cooperating in the data quality enrichment request.

    Best practice is to follow bottom to top approach between the business and the technical users. After the data governance framework has been set up still the governance task always go through ahead which implies the business stakeholders should be well trained with the framework.  

    Process Automation is another stepping stone involved in the data governance, to give an example workflow need to be defined which notify the data custodians about the data set quality enrichment steps to be taken and when the data quality is revised the workflow forwards the data set again to the marketplace to be consumed by the data consumers.

    Data discovery is another automation step in which the workflow scans the data sources for the metadata details as per the defined schedule and loads in the incremental data to the inventory triggering tasks in defined data flow ahead.

    Data governance approach may change as per the data mesh, fabric, Lakehouse architecture. let’s get deep into this ahead.  

    Data Mesh vs Data Fabric vs Data Lake Architectures

    Talking about the dataflow in every organization there are multiple data sources which store the data in different format and medium, once connected to this data sources the integration layer extracts, loads and transforms (ELT) the data, saves it in the storage medium and it gets consumed ahead. These data resources and consumers can be internal or external to the organization depending on the extensibility and the use case involved in the business scenario.

    This lifecycle becomes heavy with the large piles of data set in the organization. The complexity increases when the data quality is poor, the apps connectors are not available, the data integration is not smooth, datasets are not discoverable.

    Rather than piling all the data sets into a single warehouse, organizations segregate the data products, apps, ELT, storage and related processes across business units which we term Data Mesh Architecture.  

    Data Mesh on domain level leads to de-centralized data management, clear data accountability, smooth data pipelines, and helps to discard any data silos which aren’t being used across domains.

    Most of the data pipelines flow within a particular domain data set but there are pipelines which also go across the domains. Data Fabric joins the data set and pipelines across the domains in the Integrated Architecture.  

    Data Virtualization and the DataOrchestration techniques help to reduce the technical landscape segregation but overall, it impacts the performance and increases the complexity.  

    There is another setup approach which companies are interested in as part of the digital transformation, migrating datasets from segregated storage mediums on different dimensions to a CentralizedData Lakehouse.  

    Data sets are loaded into a single DataLakehouse preferably in Medallion architecture starting with Bronzelayer having the raw data.  

    Further the data is segregated on the same storage medium but across individual domains after cleansing and transformation building up the Silver layer.  

    Ahead for the Analytics purpose the Goldlayer is prepared having the compatible dimensions-facts data model.  

    This Centralized storage is like Data Mesh adopted on Data Lakehouse setup.

    Different Clouds, Microsoft Fabric, Databricks provide capabilities for the same.

    Data Governance options

    As for the centralized and de-centralized implementation architecture the data governance also follows the same protocol.

    Federated Governance aligns with the Data Mesh and Centralized Governance fits to the DataFabric and Data Lakehouse architecture.  

    Federated governance is justified with thecomplex legacy setup where we are talking about a large organization having multiple branches across domains with individual Domain level local Governor officers.  

    These local Governor officers track thedata pipelines, govern the accessibility to involved individual storage mediums, the integration layers and apps such that as and when there’s any change in the data set the data catalog tool should be able to collect the metadata of those changes.  

    Centralized governance committee with data custodians handle the other two scenarios of the Data Fabric and Data Lake setup.

    To take an example of the data fabric where data is spread across different storage medium as say Databricks for machine learning, snowflake for visualization reports, database/files as a data sources, cloud services for the data processing, in such scenario start to end centralized Data Governance is feasible via Data Virtualization and the Data Orchestration services.  

    Similar central level governance applies where the complete implementation setup is on single platform as say AWS cloudplatform.  

    AWS Glue Data Catalog can be used for tracking the technical data assets and AWS DataZone for data exchange between the data sources and data consumers after tagging the business glossary to the technical assets.

    Azure cloud with Microsoft Purview,Microsoft Fabric with Purview, Snowflake with Horizon, Databricks with Unity Catalog,AWS with Glue Data Catalog and DataZone, these and other platforms provide the scalability needed to store big data set, build up the Medallion architecture and easily do the Centralized data governance.

    Conclusion

    Overall Data Governance is relevant framework which works hand in hand with Data Mesh, Data Fabric, Data Lakehouse, Data Quality, Integration with the data sources, consumers and apps, Data Storage,MDM, Data Modeling, Data Catalog, Security, Process Automation and the AI.  

    Along with these technologies Data Governance requires the support of Business Stakeholders, Stewards, Data Analyst, Data Custodians, Data Operations Engineers and Chief Data Officer, these profiles build up the DataGovernance Committee.  

    Deciding between the Data Mesh, Data Fabric, Data Lakehouse approach depends on the organization’s current setup, the business units involved, the data distribution across the business units and the business’ use cases.  

    Industry current trend is for the distributed Dataset, Process Migration to the Centralized Lakehouse as the preferred approach with the Workspace for the individual domains giving the support to the adopted Data Mesh too.  

    This gives an upper hand to Centralized Data Governance giving capability to track the data pipelines across domains, data synchronization across the domains, column level traceability from source to consumer via the data lineage, role-based access control on the domain level data set, quick and easy searching capabilities for the datasets being on the single platform.  

  • OTT Apps: Deep Link Automation Testing

    Overview:

    Discover how to optimize deep link automation testing for OTT apps with scalable frameworks, dynamic data, and real-time insights. This whitepaper unveils strategies to enhance app navigation, improve testing efficiency, and drive user satisfaction. Learn how you can stay competitive in the OTT landscape with deep link testing.

    Over-The-Top (OTT) applications have revolutionized content consumption by offering easy access to a wide array of content across multiple platforms. However, as the OTT landscape evolves, content fragmentation and churn rates increase. To address these, aggregators need to adopt deep linking to enhance user engagement and drive customer retention.  

    This whitepaper sheds light on the complexity of automating deep link testing for OTT applications, highlighting its transformative potential and detailing their role in user analytics, strategic partnerships, and precision marketing. 

    Key learnings from the whitepaper:
    • Deep Links and their relevance to OTT ecosystem
    • Challenges and standard solutions for automating deep link testing
    • Overview and features of a robust Deep Link Automation Testing Solution

  • Automating Document Analysis with AWS Bedrock

    Reduced document analysis time by 60% with AI-powered summarization

    Transformed manual document processing with AWS Bedrock’s Titan Text G1-Premier model

    Streamlined analysis of multiple document formats through unified S3 storage system

    Implemented secure, automated workflow from document ingestion to final report generation

    Enhanced consistency in blanket position request assessments through standardized outputs