In our progressively digital world, safeguarding personal data has become crucial. Governments worldwide have taken action by enacting data privacy laws to protect the rights of individuals. This article analyzes the distinctions among the European Union’s General Data Protection Regulation (GDPR), the California Consumer Privacy Act (CCPA) in the United States, Brazil’s General Data Protection Law (LGPD), Singapore’s Personal Data Protection Act (PDPA), and Australia’s Privacy Act. Additionally, it delves into the measures that companies should adopt to ensure compliance with these frameworks.

European Union’s General Data Protection Regulation (GDPR)

Enacted in 2018, the GDPR stands out as a groundbreaking data privacy law applicable to all EU member states. It governs the protection, processing, and transfer of personal data. Key components of the GDPR encompass the user’s entitlement to access and control their data, the imperative for explicit consent, notification of data breaches, and the concept of a Data Protection Officer (DPO). Notably, the GDPR possesses global extraterritorial reach, impacting companies worldwide that handle data belonging to EU citizens.

California Consumer Privacy Act (CCPA)

The CCPA, established in 2018, represents the United States’ most comprehensive data privacy legislation to date. While it shares certain similarities with the GDPR, significant disparities exist. The CCPA confers upon California residents the right to be informed about the personal data collected about them, the right to request the deletion of their data, and the ability to opt out of data sales. Furthermore, the law obligates businesses to disclose their data collection practices and provides clear opt-out mechanisms. Unlike the GDPR, the CCPA focuses on commercial activities rather than individual residency.

Brazil’s General Data Protection Law (LGPD)

Passed in 2018, the LGPD is Brazil’s response to the escalating significance of data privacy. It draws substantial inspiration from the GDPR, sharing numerous fundamental principles. Analogous to the GDPR, the LGPD grants individuals control over their personal data, necessitates explicit consent, and enforces data breach notifications. However, the LGPD introduces unique elements, including establishing a National Data Protection Authority (ANPD) responsible for enforcement and requiring a legal basis for processing sensitive data. The LGPD’s global reach is narrower compared to the GDPR.

Singapore’s Personal Data Protection Act (PDPA)

Implemented in 2012, Singapore’s PDPA establishes the legal framework for collecting, utilizing, and disclosing personal data. It resembles the GDPR and CCPA, focusing on consent, data accuracy, transparency, and individual rights. The PDPA applies across private and public sectors and includes a Do Not Call (DNC) registry, enabling individuals to opt out of telemarketing communications. Additionally, organizations engaging in significant data processing activities must designate a Data Protection Officer (DPO).

Australia’s Privacy Act

The Privacy Act stands as Australia’s principal legislation safeguarding personal information. It applies to governmental bodies and select private sectors such as healthcare and telecommunications. The Act addresses various facets of data privacy, encompassing the collection, usage, and disclosure of personal information. It also affords individuals the right to access and correct their data, along with mechanisms for filing privacy-related complaints. Unlike the GDPR and CCPA, the Privacy Act does not impose a mandatory data breach notification requirement.

Steps Companies Should Take to Ensure Compliance

  1. Understand the Applicable Laws: Businesses must acquaint themselves with the precise requisites of each pertinent data privacy law pertaining to their operations. This entails understanding the scope of applicability, pivotal clauses, and potential penalties for non-compliance.
  2. Conduct a Data Audit: Conduct a comprehensive evaluation of the personal data gathered, processed, and stored by the company. Determine the legal grounds for processing personal data and guarantee the acquisition of explicit consent whenever obligatory.
  3. Implement Appropriate Security Measures: Businesses should establish robust security protocols to safeguard personal data against unauthorized access, disclosure, manipulation, or destruction. This involves employing encryption, access controls, regular vulnerability assessments, and incident response protocols.
  4. Develop a Privacy Policy: Create a clear, concise, and transparent privacy policy that outlines how the company collects, uses, and protects personal data. Additionally, elucidate individuals’ rights, including the rights to access, rectify, and erase personal data.
  5. Establish Data Breach Response Procedures: Devise and implement effective procedures for detecting, investigating, and responding to potential data breaches. This could encompass crafting an incident response plan, designating a data protection officer, and establishing notification procedures for affected individuals and pertinent authorities, as mandated by applicable law.
  6. Provide Ongoing Employee Training: Ensure all personnel receive adequate training concerning data privacy laws, corporate policies, and their responsibilities in safeguarding personal data. Companies should provide periodic training updates to help them stay abreast of regulatory changes.

In Summary…

Adherence to data privacy regulations is crucial for enterprises navigating the digital landscape. The GDPR, CCPA, LGPD, PDPA, and Privacy Act represent pivotal frameworks designed to protect individuals’ personal data and confer control over their information. Acquiring an understanding of the nuances and differences inherent in these data privacy laws is imperative for organizations to tailor their compliance efforts accordingly. Businesses can ensure compliance with these frameworks by executing comprehensive data audits, robust security implementations, transparent policies, and continual training. Ultimately, prioritizing data protection cultivates client trust, fosters conscientious data management practices, and contributes to a digital ecosystem that values privacy.


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In today’s fast-paced business landscape, data analytics has emerged as a cornerstone for informed decision-making and driving growth. However, several challenges can impede the scaling of data analytics initiatives within an enterprise. From grappling with legacy systems that lack compatibility to establishing robust data governance frameworks, and from facing cultural resistance to ensuring data quality and measuring the return on investment (ROI) of data analytics projects, organizations often encounter roadblocks on their path to success.

In this article, I identify top obstacles and offer practical and effective solutions to help enterprises overcome them. By taking a proactive approach to confront these challenges, you can unlock the true potential of your data and analytics programs, enabling smarter, data-driven decision-making, and propelling your business towards unprecedented growth and success.

Here are the five most significant challenges when it comes to scaling data analytics with an Enterprise:

  1. Outdated Systems – Scaling data and analytics in numerous enterprises is hindered by obsolete legacy systems. These systems present inflexibility, high maintenance costs, and an inability to support modern analytics tools. Consequently, data engineers encounter significant challenges when attempting to derive insights from the data.

    The remedy lies in modernizing the legacy systems. Enterprises should consider migrating their data to cloud-based systems or embracing agile applications that seamlessly integrate with modern analytics tools. This approach streamlines data extraction, enhances scalability, and empowers data engineers to extract insights from the data with greater efficiency.

  2. Ineffective Data Governance – In any enterprise, data governance plays a vital role in scaling data and analytics. It encompasses the establishment of policies, procedures, and standards to ensure data integrity, availability, and security. Proper implementation of data governance is paramount, as it safeguards against storing and utilizing incorrect data, which could result in flawed analyses.

    To achieve an effective data governance framework, clear communication of governance policies and procedures to all stakeholders is essential. Additionally, these policies and procedures should be customized to suit the unique needs of the enterprise, while defining the roles and responsibilities of various departments.

  3. Cultural Resistance – Enterprises may encounter employee resistance while attempting to scale data and analytics, as some employees view it as a threat to their job security. Additionally, resistance to change can emerge due to a lack of buy-in from senior management.

    To foster employee buy-in, it is important to educate them about the advantages of embracing data and analytics solutions within the enterprise. Providing training and education on the latest technology and techniques can alleviate concerns and reinforce the benefits of the initiative. Furthermore, demonstrating leadership through a top-down approach, where senior management leads by example and showcases effective data and analytics utilization, can inspire confidence and acceptance among the employees.

  4. ROI Measurement Challenges – Measuring the return on investment (ROI) for expanding data and analytics initiatives poses a considerable hurdle, particularly when the initial investment is perceived as a fixed cost. This perception can make it challenging to obtain funding or allocate resources for future scaling efforts.

    To gauge ROI efficiently, businesses must prioritize the assessment of how data and analytics initiatives directly influence their overall business outcomes. Measuring metrics like operational efficiencies, revenue growth, and cost savings can demonstrate the ROI of data and analytics initiatives. Furthermore, by conducting regular assessments, organizations can pinpoint areas requiring improvement and use this insight to guide their future investments in D&A initiatives.

  5. Poor Data Quality – Ensuring data quality is essential for the success of data and analytics initiatives. Insufficient data quality can result in misleading analyses, inaccurate insights, and potentially lead to legal or financial consequences for the enterprise. These data quality issues may arise due to inconsistent data, inaccuracies, or other data quality control challenges.

    To guarantee data quality, the enterprise must implement data quality control procedures and conduct regular checks to ensure that the data meets rigorous standards. Additionally, investing in data quality management technologies can enhance the efficiency of data quality control procedures, further bolstering the overall data quality.

In Summary…

Scaling data and analytics within the enterprise presents its share of obstacles, but with focused efforts, it is attainable. Enterprises must make strategic investments in modern technologies, create well-defined governance policies, offer comprehensive training and education, accurately measure ROI, and implement effective data quality control procedures to achieve successful scaling. Addressing the needs of all stakeholders and departments is crucial throughout this process. By following these steps and ensuring alignment with various stakeholders, data and analytics can become a powerful tool that facilitates business growth.

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I had the privilege of speaking and actively participating in thought-provoking discussions in the recently concluded Data Engineering Summit 2023. In this article, I share key insights from my own talk, as well as my takeaways from the keynotes and engaging conversations I had with fellow data enthusiasts at the summit.

  1. Smart Data Engineering is flipping traditional approaches – Intelligent systems, techniques, and methodologies are being employed to improve Data Engineering processes and provide clients with added value. Organizations are dedicating resources to implementing cutting-edge AI technologies that can enhance various Data Engineering tasks, from initial ingestion to end consumption. The emergence of Generative AI is transforming the way data is analyzed and utilized in organizations. While it is currently revolutionizing the consumption side of the industry, the pace of developments indicate that it will soon have a significant impact on Data Analytics workloads. This shift towards Generative AI will pave the way for new approaches to Data Engineering projects in the upcoming quarters, resulting in increased efficiency and effectiveness.

  2. FinOps will be a game changer – As companies move their Data and Analytics workloads to cloud-based platforms, they are discovering the potential for costs to go out of control without careful management. Though various solutions exist, few provide a sufficient return on investment, leaving customers in search of fresh methods to manage expenses across cloud infrastructure. FinOps provides monitoring teams with tools they need for cloud cost screening and control while promoting a culture of cost optimization by increasing financial accountability throughout the organization. CFOs are especially pleased with this development and are keen on spreading this cost-conscious approach.

  3. Data Observability is not a buzzword anymore – Mature organizations are proactively utilizing observability to intelligently monitor their data pipelines. Unforeseen cloud charges can arise from occurrences such as repetitive invocation with Lambda or the execution of faulty SQL code, which can persist unnoticed for prolonged periods. The implementation of observability equips operations teams with the ability to better comprehend the pipeline’s behavior and performance, resulting in the effective management of costs associated with cloud computing and data infrastructure.

  4. Consumption-based D&A chargeback is the way to go – Shared services teams are encountering challenges when it comes to accurately charging their internal clients for their utilization of D&A services. The root of this problem is attributed to the lack of transparent cost allocation mechanisms for data consumption, which makes it difficult to determine the genuine value of a D&A service. The solution lies in implementing consumption-based cost chargeback, which not only addresses the current challenges but also prompts businesses to adopt more intelligent FinOps models.

In summary, the summit provided valuable insights into the latest trends, challenges, and opportunities in the field, highlighting the importance of collaboration, innovation, and upskilling. There are many exciting developments that promise to revolutionize the industry. As we move towards a data-driven world, it is clear that data engineers will play a crucial role in shaping our future, and it is essential that they stay informed, adaptable, and agile to keep up with the rapidly evolving landscape.

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In traditional software development, enterprise teams tackle security of applications and mitigation of risks towards the end of the application development lifecycle. Due to this, security and compliance issues almost always lead to delayed product releases or worse, release of applications with some security weak points. Adopting cloud and data platforms further add new security complexities and the need for thorough infrastructure assessment. DevOps has changed the way we look at software development and has made us rethink security. It helps teams with faster application development and deployment while new features of cloud and data platforms now form the basis of DevOps. Reducing vulnerability and securing all cloud applications should be part of your DevOps best practices and strategy.

Security Essentials for Integrating DevOps with Cloud

Below are a few top strategies to help you integrate DevOps practices and computing features to improve security of D&A applications on cloud.

  1. Secure DevOps Development Practices

    DevOps principles with well-defined security criteria and design specifications help enterprises define a secure architectural framework for current and future applications or services. Multi-factor authentication (MFA), securing data in transit, and continuous threat monitoring are essential. Teams who implement threat modeling within DevOps are well equipped with insight into behaviors, actions, and situations that can cause security breaches. This helps to analyze potential threats in advance and plan for mitigation by creating a secure architecture. For security testing, teams can include vulnerabilities assessment and penetration testing (VAPT) as systems are created, as well as when they go live.

    With respect to DevOps, Infocepts’ best practices include the most up-to-date security features, security testing, and continuous threat and vulnerability monitoring. Exercising these practices, we’ve helped global clients transform their data infrastructure and security.

  2. Choose a Secure Cloud Infrastructure

    Secure deployment is crucial for enterprise data systems, pipelines, and performance indicators. Consulting with a data analytics and cloud specialist is important to help you select the right infrastructure. Your cloud platform and its architecture should include built-in security vulnerabilities and patch management to streamline team workflows. Post platform selection, the cloud infrastructure should be regularly analyzed to detect security threats and readiness criteria. Your DevOps strategy should include assessment of all cloud services and related security sections. Active security monitoring must assess programs or software before they are implemented.

    The Infocepts cloud migration solution has helped multiple clients implement cloud-native security and compliance for their technology stacks. We have helped them get full visibility into cloud misconfigurations, discover cloud resources and sensitive data, and to identify cloud threats.

  3. Go Serverless

    Large, serverless applications amount to a collection of smaller functions located in stores. As they are smaller and cloud based, risk from long-term security threats or attacks is reduced — as are network threats in yesterdays’ data centers, virtual servers, databases, and overall network configuration. Serverless computing development lets DevOps concentrate on code creation and deployment rather than taking care of security vulnerabilities within applications.

    Infocepts’ cloud migration solution has helped a US media company go serverless, thereby resulting in improved application security. Serverless cloud technology has provided our client with reduced operational, and infrastructure overhead costs coupled with overall improved performance.

There are other important factors and best practices which should be considered by DevOps teams for improving security of their applications and infrastructure. Secure application development delivers improved automation across the product delivery chain, prevents errors, minimizes risk and downtime, and enhances further security integration in the cloud environment. Cloud migration is essential to incorporating security protocols into day-to-day operations; thus companies become increasingly more secure by design. Infocepts’ solutions—embracing modern DevOps practices—can help you implement a robust cloud infrastructure.

Interested to Know More? Check our Advisory Note

Our advisory note helps DevOps and cloud professionals understand important things to consider while integrating DevOps practices with cloud features in order to improve overall security, cloud operations, process automation, auto-provisioning of cloud services and more.

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Data is everywhere, enabling unprecedented levels of insights within all businesses and industries for decision-making. Data pipelines serve as the backbone to enable organizations to refine, verify, and make reliable data available for analytics and insights. They take care of data consolidation from various sources, data transformation, and data movement across multiple platforms to serve organizational analytics needs. If not designed and managed well, data pipelines could quickly become a maintenance nightmare having a significant impact on business outcomes.

Top Two Reasons for a Poorly Designed Data Pipeline:

Designing a data pipeline from scratch is complex and poorly designed data can impact data scalability, business decisions, and transformation initiatives across the organization. Below are the top two reasons amongst many which lead to a poorly designed data pipeline.

  1. Monolithic pipeline – Monolithic pipelines lack scalability, modularity, and automation feasibility. Minor changes in the data landscape needs huge integration and engineering efforts.
  2. Incorrect tool choices – Data pipelines in an organization grow from one tool to multiple quickly. The correct tool to be deployed depends on what use case it is supporting, and a single tool cannot be used for all business scenarios.

Creating an Effective Data Pipeline

Looking at the criticality of data pipelines, it is particularly important for organizations to spend a good amount of time in understanding the business requirements, the data and IT landscape, and then designing the pipeline. The below steps should be part of any data pipeline strategy planned by organizations –

Modularity – A single responsibility approach should be followed while designing the data pipeline components so that it can be broken into small modules. By this approach, each pipeline module can be developed, changed, implemented, and executed independent of each other.

Reliability – Data pipelines should be set up to support all downstream (Service Level Agreements) SLA requirements of consuming applications. Any pipeline should support re-runs in case of failures and executions should be automated with the help of triggers and events.

There are many other factors and principles that impact data pipelines and should be part of its design strategy. Infocepts Foundational Data Platform Solution enables you to adopt the right-fit data pipeline strategy early and avoids any future complexities, migration needs, or additional investments. A well-thought-through data pipeline strategy helps improve business intelligence and comprehensive analysis by delivering only the required data to end users and applications.

Check Our Advisory Note to Know More

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Many organizations make inefficient data choices because they are unsure of the purpose and use of popular data architectures such as data warehouses, data lakes, data hubs, lakehouse, data fabric, and data mesh. A comparative view based on technology and business requirements is necessary when selecting a suitable architecture. Selecting the wrong one can result in future complications and uncoordinated not-so-successful investment decisions.

The evolution of data architectures

Data architecture is a big umbrella term that encapsulates everything from data storage to computing resources and everything in between. The architecture includes all the technology that facilitates data collecting, processing, dashboarding, and also operational aspects like usage and compliance. Data architectures evolved from the requirements of consolidating and integrating data from various distinct transactional systems. Modern architectures like Data Mesh and Data Lakehouse help integrate both transactional (data origins) and analytical (convert data to insights) aspects seamlessly across platforms. The evolution of data architecture can be summarised using the below diagram –

Modern data architectures

Let’s go through a few of these architectures, their top benefits, and shortfalls:

  1. Data Warehouse:Data Warehouse design aims to move data from operational systems to business intelligence systems, which have historically assisted management with operations and planning. A data warehouse is where you store data from multiple data sources to be used for historical and trend analysis reporting. The biggest benefit of a data warehouse is that it provides a consolidated point of access to all data in a single database and data model. One of the limitations of data warehouse is reported mostly when there is a requirement to modify data during ingestion, and this modification causes system instability.
  2. Data Lake The Data Lake architecture is the extension of the good old warehouse architecture. With the explosion of unstructured and semi-structured data, there was a greater need to extract insights from them to make effective decisions. It is well known to be an inexpensive choice to store unlimited data and allows for faster transformations due to multiple running instances. Limitations include the possibility of multiple data copies in various layers thus increases the cost of ownership and maintenance.
  3. Data Mesh Data Mesh is distributed architecture paradigm based on domain-oriented ownership, data as a product, self-serve data infrastructures, and federated data governance. Its decentralized data operations and self-serve infrastructure enable teams to be more flexible and independent, improving time-to-market and lowering IT backlog. However, domain-specific LOBs are needed for managing skills to enable the data pipeline. This turns out to be an added responsibility for business stakeholders and not for IT.

There are many other types of data architectures and pros & cons to each one of them with some appealing characteristics which make them unique.

Which modern data architecture model makes the most sense for you?

It is a difficult choice since each framework has its advantages and disadvantages, but you do have to choose if you want to make the most of your data. Defining the correct data architecture model for your needs and a future-proof strategy is extremely necessary in the digital age. It is not practical to continuously redefine architecture from scratch, nor does a quick-fix approach work. We need to be able to fit new concepts and components neatly into existing architecture for adapting to changes without disruption.

Infocepts foundational data platform solution helps assess your current ecosystem, design a target state consistent with your strategy, select the best-fit modern data architecture, and implementation using capacity-based roadmaps. Our approach supported by automation enables the creation of modern data platforms using data architectures as per the business case in weeks, not months.

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Our advisory note can be used by data and analytics professionals to understand the foundations of the many modern data architecture patterns, their pros, and cons as well as the recommendations and considerations for choosing the ones that fits them the best.

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Built using newer technologies such as decentralized blockchains, Web 3.0 is the next big step for the internet and everything it controls. It uses artificial intelligence to enhance user experience. Web 3.0, being the basic structure used by cryptocurrencies such as Bitcoin and Ethereum, blockchain approach enables the service to be supported by a decentralized network. This will be a revolutionary step and can have a huge impact on organizations, users, and the way businesses operate. For example, site owners won’t have to rely on bigger companies such as Amazon (AWS) and Google to obtain server space.

Conceptually, Web 1.0 was created to retrieve data from servers, e.g., searching for something on Google in 2004. Web 2.0 introduced more interactive sites such as social media platforms where data is read and written back and forth. That is, someone posts on Twitter, Facebook, or LinkedIn, you retrieve it from the server by viewing it in a browser, then send data back when you like the post and/or add a comment. Web 3.0 has wider applications in IoT, Edge computing, live streaming, behavioral engagement, semantic search and so on.

Possible use-cases implemented using Web 3.0 (Courtesy – Single Grain)

Gaining access to any site or application often requires you to log in with your user ID, email address, password, and sometimes biometrics such as fingerprint. There are many credential keepers online; some are store data locally while others live in the cloud. For example, for some time Google has prompted you to optionally save your password in a digital wallet if you login through its service. With Web 3.0 you’ll have a private key created using blockchain; it could be kept in a secure digital location or in a third-party wallet.

Some tech giants have already started to implement ideas based on the Web 3.0 concept. Last year Twitter announced Bluesky, a project intended to be a decentralized social media platform. Using blockchain concepts outside of the realm of cryptocurrency, it’s a big steppingstone for any organization to learn if this new method of building platforms is truly viable.

A few companies claiming to be working on implementing Web 3.0 styles include:

  • GameStop has been hiring non-fungible token (NFT) software engineers and marketing directors for an NFT platform as well as Web 3.0 game leads to accelerate the gaming scene and related commerce. It frequently states that “blockchains will power the commerce underneath” of the new platforms it’s creating.
  • Reddit is looking to lure 500 Mil. new crypto users onto its platform by adding new features and changing the way its website is built. It has moved the subreddit “r/cryptocurrency” to the Arbitrum network, which will reportedly help with transactions on the site. It also states that it is working toward forking blockchains through community-made decisions. And it seeks to move its current 500 Mil. Web 2.0 users into its scalable Web 3.0 environment.
  • Incorporating these ideas, Meta seeks to provide user self-sufficiency on its new Web 3.0 Metaverse platform.

We’ll surely see many Web 3.0 branching ideas and innovations. And it’ll be interesting to see if platforms such as Twitch, YouTube, or even some of Microsoft’s services are exploring similar concepts. Seeing their implementation in non-cryptocurrency markets could open the door to yet more possibilities.

Organizations embracing Web 3.0 can use AI to filter data not needed by clients, such as PII (personally identifiable information). They’ll be able to quickly filter huge amounts of data, increase application response times, and diagnose problems faster. Another AI example is the ability to forecast ways for users to improve customer service and implement that across applications and portals.

Web 3.0 SWOT Analysis


  • Higher efficiency while searching – Search engines can use better algorithms to provide more relevant results rather than simply the popular, most-often visited pages. Enhanced AI would also provide more recent and accurate information.
  • Faster loading and uninterrupted services – A big advantage of Web 3.0 is its ability to load data in each node rather than on a central server somewhere. This would avoid technical difficulties companies often face, as well as reduce problems of server overloads on smaller sites and applications.


  • CPU intensive – Algorithms running across many layers, along with applications creating nodes of data, means there will likely be some performance issues due to intensive CPU requirements. People using outdated machines might experience pages loading more slowly, thereby resulting in poor user experience. Those with newer devices should realize overall better performance.
  • Expensive and time consuming – The process is on a large scale and is a newer concept, so it’s expected to take some time to change major industry components. This might impact costs.


  • Higher data personalization for users – Today Google is likely to show you a related ad as you look something up. Web 3.0 is expected to be heavily AI-focused; with its large-scale adoption, you’ll likely want to take a more calculated approach as you construct your user profiles. This should equate to your exposure to less repetitive, more accurate content due to its being highly tailored to your specific interests.


  • Security – While Web 3.0 will be faster and more advanced, it also creates an intranet amongst all users. This might be seen as an efficiency advantage, but you also risk exposure and breach of information. Certain data such as ad information or devices in use wouldn’t be shared, but name, zip code, or age information might be easier to publicly access. Data protection and individual privacy will need to be properly structured and enforced by each organization.

Web 3.0 will continue being integrated into more applications as it gains additional popularity, although the process is difficult to implement and can be expensive. That said, it does have the potential to change the way users interact behind the scenes. Blockchain and Web 3.0 ideas do have some limitations, but we could see a massive increase in mobile accessibility if more companies work toward a better online environment. Quicker logins, shared accounts between platforms, and user-owned data could be the future of the internet.

Talk to us to learn how we can help in analyzing and interpreting data, as well as in creating data products and services to enable your web 3.0 adoption.

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Most analytics projects fail because operationalization is only addressed as an afterthought. The top barrier to scaling analytics implementations is complexity around integrating the solution within existing enterprise application and integrating the practices across disparate teams supporting them.

In addition, a number of Ops terms are springing up every day, which is leaving the D&A business & IT leaders more confused than ever. This article attempts to define some of the Ops terms relevant for Data and Analytics applications and talks about common enablers and guiding principles to successfully implement the ones relevant for you.

Let’s look at the multiple Ops models below:

Fig 1: D&A Ops Terms

ITOps – The most traditional way of doing the IT operations in any company is “ITOps”. In this, an IT department caters to the infrastructure needs, networking needs and has a Service Desk to serve its internal customers. The department will cover most of the operations like provisioning, maintenance, governance, deployments, audit and security in above three areas. This department will not be responsible for any application-related support. The application development team relies heavily on this team when it comes to any infrastructure-related requirement.

DevOps – With some of the obvious challenges with ITOps , the preferred way of working is “DevOps”. The project teams need to adapt to the processes where there is less dependency on IT team around infrastructure requirements, and the project teams can do the bulk of ops work themselves using a number of tools and technologies. This mainly includes automation of CI-CD pipeline including test validation automation.

BizDevOps – This is a variant of the DevOps model with business representation in DevOps team for closer collaboration and accountability to drive better products, higher efficiency, and early feedbacks.

DevSecOps – This includes adding the security dimension to your DevOps process to ensure the system security and compliance as required for your business. This ensures that security is not an afterthought and it is a responsibility shared by development team as well. This includes infra security, network security and application-level security considerations.

DataOps – It focuses on cultivating data management practices and processes that improve the speed and accuracy of analytics, including data access, quality control, automation, integration, and ultimately, model deployment and management.

CloudOps – With increasing cloud adoption, CloudOps is considered a necessity in an organization. CloudOps mainly covers infrastructure management, platform monitoring and taking predefined corrective actions in an automated way. Key benefits of CloudOps are high availability, agility and scalability.

AIOps – Next level of Ops where AI is used for monitoring and analysing the data within multiple environments and platforms. This combines data points from multiple systems, defines the corelation and generates analytics for further actions, rather than just providing the raw data to Ops team.

NoOps – This is the extreme case of ITOps where there is no dependency on the IT personnel and entire system is automated. Good example of this is serverless computing in cloud platform.

Let us now look at the common guiding principles and enablers which are relevant for all these models as well for any new Ops model which may be defined in the future.

Guiding principles:

  1. Agility – The adopted model should help increase the agility of the system to respond to the changes with speed and high quality.
  2. Continuous improvement – The model should be able to take into consideration the feedback early and learn from the failures to improve the end product.
  3. Automation – The biggest contributor is the automation of every possible task that is done manually to reduce time, improve quality and increase repeatability.
  4. Collaboration – The model is successful only when various parts of the organization are working as a singular team towards one goal, and are able to share all knowledge, learnings and feedbacks.

Enablers – There are multiple dimensions on how any model can be enabled using the principles mentioned above.

  1. People – There is a need to have a team with the right skills and culture, and which is ready to take on the responsibility and accountability to make this work.
  2. Process – Existing processes need to be optimized as required or new processes should be introduced to improve the overall efficiency of the team, and to improve the quality of the end product.
  3. Technology – With the focus on automation, technology plays a key role where it enables the team for continuous development and release pipeline. This will cover various aspects of the pipeline like core development, testing, build, release, deployment etc.

Amongst the ones you see above, which Ops model works best for you will depend on the business requirements, application platform and skills availability. It is clear that the Ops model is not optional going forward and one or more DevOps models are required to improve agility, automation, operational excellence and productivity. It requires proper planning, vision, understanding, investments and stakeholders buy-in to achieve desired success with any of the chosen Ops models.

References –

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Data is now the soul of every organization. Placing data at the center of your business strategy gives you a competitive advantage in today’s digital age. According to Gartner, D&A is shifting to become a core business function, rather than it being a secondary activity done by IT to support business outcomes. Business leaders now think of D&A as a key business capability to drive business results.

You must now concentrate your digital transformation efforts on adopting new data-driven technologies and processes for more valuable insights from data so that you can use them to address future needs.

The Need for a Robust Data Architecture

Data management architecture defines the way organizations gather, store, protect, organize, integrate, and utilize data. A robust data management architecture defines every element of data and makes data available easily with the right governance and speed. A bad data management architecture, on the other hand, results in inconsistent datasets, incompatible data silos, and data quality issues, rendering data useless or limiting an organization’s ability to perform data analytics, data warehousing (DW), and business intelligence (BI) activities at scale, particularly with Big Data.

The Journey and the Challenges You Will Likely Encounter

Most organizations start their journey with a centralized data team and a monolithic data management architecture like a data lake, in which all data activities are performed from and to a single, centralized data platform. While a monolithic data architecture is simple to set up and can manage small-scale data analytics and storage without sacrificing speed, it quickly becomes overwhelming. Furthermore, as data volume and demand grow, the central data management team becomes a bottleneck. Consequently, there is a longer delay to insight and a loss of opportunity.

To enhance and improve your capability to extract value out of data, you should embrace a new approach, like data mesh, for handling data at scale. Although previous technical advances addressed data volume, data processing and storages, they could not handle scale in additional dimensions such as the increase of data sources, changes in the data landscape, speed of reaction to change, and variety of data consumers and use cases. These aspects are addressed by a data mesh architecture, which promotes a novel logical perspective of organizational structures and technological design.

What is Data Mesh?

To harness the real potential of data, Data Mesh uses current software engineering techniques and lessons learned from developing resilient, internet-scale applications. As described by Zhamak Dehghani, Data mesh is a decentralized socio-technical approach to managing analytical data at scale. It is a method to reconcile and hopefully solve issues that have troubled initial data designs, which are often hampered by data standards issues between data consumers and producers. The data mesh pushes us toward domain-driven architecture and empowered, agile, and smaller multi-function teams. It combines the most acceptable data management methods while maintaining a data-as-a-product perspective, self-service user access, domain knowledge, and governance.

Some principles must be followed to achieve an effective data mesh. These principles demand maturity of the organization’s culture and data management.

  1. Domain-oriented data ownership and architecture: As domain ownership has changed in modern digital organizations, where product teams are aligned with the business domain. A data mesh approach empowers product teams to own, govern, and share the data they generate in a regulated and consistent manner. This method combines data understanding with data delivery to accelerate value delivery.
  2. Data as a product: Rather than considering data as an asset to be accumulated by establishing responsibility with the data product owner, a shift to product thinking for data allows higher data quality. Data products should be coherent and self-contained.
  3. Self-serve data infrastructure: The goal of building a self-serve infrastructure is to give tools and user-friendly interfaces so that developers can create analytical data products faster and better. This method assures compliance and security while also reducing the time it takes to gain insights from data.
  4. Federated computational governance: Traditional data platforms are prone to centralized data governance by default. A federated computational governance architecture is required for data mesh, which preserves global controls while improving local flexibility. The platform manages semantic standards, security policies, and compliance from a central location, while the responsibility for compliance is delegated to data product owners.

Benefits of Adopting a Data Mesh Design

Organizations benefit from adopting an effective data mesh design for several reasons, including:

  1. Decentralized data operations and self-serve infrastructure enable teams to be more flexible and operate independently, improving time-to-market and lowering IT backlog
  2. Global data governance rules encourage teams to generate and distribute high-quality data in a standardized, easy-to-access manner
  3. Data mesh empowers domain experts and product owners to manage data while also encouraging greater collaboration between business and IT teams
  4. Data mesh’s self-serve data architecture takes care of complexity like identity administration, data storage, and monitoring, allowing teams to concentrate on providing data more quickly

At the same time, data-driven improvements like these may help cut operational expenses, drastically reduce lead times, and allow business domains to prioritize and make timely choices that are relevant to them. Also, it makes data accessible across the business while also allowing for technical flexibility.

Is Data Mesh Right For You?

It is essential to keep in mind that a data mesh is one of many data architecture approaches. One must first determine, if your objective and goals are compatible with this new paradigm or whether a different one would be more appropriate for your organization. Ask yourself these quick questions:

  • What is the level of collaboration between your data engineers, data owners, and data consumers?
  • Is it difficult for these parties to communicate with one another?
  • Is your data engineers’ lack of business domain expertise a major productivity bottleneck?
  • Do your data users have productivity challenges as a result of this?
  • Are you dealing with unavoidable domain-specific business variations in data across business units?

If you responded yes to these questions, particularly the last one, a data mesh may be a good match for your needs. If that is the case, you should begin by gaining executive backing, establishing a budget, identifying domains, and assembling your data mesh team.

Are you still wondering whether or not data mesh is the right choice for you?

Our data specialists can assist you in defining your data strategy, reach out to our data architecture experts.

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With the increase in data and a rapidly changing technology landscape, business leaders today face challenges controlling costs, fulfilling skill gaps for employees, supporting systems and users, evaluating future strategies, and focusing on modernization projects.

Here we discuss six reasons why organizations are embracing managed analytic solutions that rely on experts to build, operate, and manage their data and analytics services. These are based on the recurring themes which we have observed and experienced while working with our customers.

  1. Keep costs low: Total cost of ownership for running and maintaining D&A systems has several cost elements like staff costs, operational costs, software + infrastructure costs, and (intangible) opportunity costs like technical debt and avoidable heavy lifting. While cutting costs in the short term may lead to some immediate gains, cost effectiveness in the long term and on a sustainable basis is the end goal. The right way to approach and achieve guaranteed, predictable cost savings is through a potent combination of automation, talent, and process improvements.
  2. Improve system stability and reliability: Missing SLAs, performance issues, frequent and persistent downtimes, and an inability to comply with regulatory requirements are the usual suspects when it comes to areas giving sleepless nights to leaders navigating enterprise data and analytics (D&A) systems. Improving system stability and reliability requires long term planning and investments in areas like modernization of D&A systems, data quality initiatives under a larger data governance program, RCA with feedback, 360-degree monitoring and pro-active alerting.
  3. Intelligent D&A operations: You may want to drive operational efficiency by reducing the piling automation debt, bringing in data-driven intelligence (and human ingenuity) to achieve AI-driven autonomous and real-time decision making, better customer experience and as a result superior business outcomes. An example would be on demand elasticity (auto scaling) to scale-up the processing power of your D&A systems, based on forecasted demand due to seasonality in the business based on past trends.
  4. Focus on core business objectives: You may need to focus on your core business objectives and not get stuck in the daily hassles of incident management and fire-fighting production issues. We have seen that reducing avoidable intervention from your side becomes difficult, especially when you are managing it in-house or using a managed services vendor operating with rigid SLAs. A recommended approach would be to engage with a trusted advisor to figure out the right operating model for managed services with shared accountability and define service level outcomes. This will enable you to devote attention to more innovation focused and value-added activities which drive business results.
  5. Get the expertise you need: Given multiple moving parts involved in successfully running D&A systems, and the sheer flux of technological changes, your business needs the ability to tap into a talent pool easily, and on-demand. If executed well, this does wonders to your capabilities in managing D&A systems and achieving desired business outcomes.
  6. Improve user experience: This is the most important and yet often the most neglected aspect in a lot of cases. In the context of managed services, an elevated user experience entails data literacy, ability to leverage tools to the fullest, clarity on SLAs and processes, trust in data quality, ability to derive value from analytic systems and hence adoption.

Infocepts Managed Services solution helps organizations achieve one or more of these motivations. We help drive digital transformation, handle legacy systems, reduce costs, enhance innovation through operational excellence, and support scaling of business platforms and applications to meet growing business needs. You can rely on our D&A experience and expertise to build, operate, and run analytic systems which help to drive outcomes quickly, flexibly, and with reduced risk.

Get in touch to know more!

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