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

Grab your copy to know the key 6 design principles to create effective data pipelines.

Our advisory note will help you plan a well-thought-through data pipeline strategy for improved business intelligence, data analytics, and insights at speed.

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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.

Check Our Advisory Note to Know More

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

Strengths

  • 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.

Weaknesses

  • 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.

Opportunities

  • 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.

Threats

  • 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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Data & Analytics is a critical function for today’s modern “data-driven” enterprise. Everyone wants happy business consumers, so it is quite common to focus a large amount of time and effort on front-end reporting & data visualization needs. This front-end focus can result in spending too little time implementing the vital rock-solid data delivery back-end. An unbalanced data delivery ecosystem like this can contribute to consumer frustration due to a lack of timely and useful data, trust issues with the data, and a general decrease in user adoption. It can quickly become the “beginning of the end” for any D&A project.

As the highest-rated Data & Analytics company in the world, we have seen what does and does not work and for the D&A space, we love the “Agile Data Delivery” methodology.

The Components of Agile Data Delivery

There are many components that make up agile data delivery, but for now, let’s focus on the key factors to a successful methodology. If you get these key components in place, you will be off to a great start with your journey towards agile data delivery and the benefits that will fall in place down the road.

1. Out with Waterfall; In with Agile.

To “deliver data with agility”, one needs to first forget about the traditional waterfall delivery method. It just does not work in today’s modern data-driven enterprise. Consumers can no longer wait months or years for data to be populated into the Data Warehouse/Data Lake. The modern Consumer needs data “now” to make timely data-driven decisions.

2. Data Delivery Team.

Get the right mix of folks in place. Even if this is an IT-driven data project, you will need a mix of IT and business personnel. In addition to IT, Data Architects, Data Integration and business intelligence developers, you need to have your key business stakeholders at the table.

3. Data Story Backlog

With your well-rounded team in place, meet at least once a week to discuss your data stories. What data does the business need to have access to? What is the priority for each data story? Do we already have this data? How does this data fall in line with Enterprise goals? The aim here is to identify as many data stories for our agile backlog aka the stuff we need to do.

Part of the process will also be to identify the individual data stories that will be worked on during the next sprint. Having both IT and the business involved in these discussions will ensure that everyone knows what the deliverables will be for the next sprint. And more importantly, there will be no surprises when delivery is completed.

4. Sprint.

The project manager will meet with all the IT teams and coordinate the delivery of the sprint’s data story. If this is new data, the data integration people will source the data and build ETL/ELT logic around the process. Business Intelligence (BI) developers will take this new data and build out the reporting deliverables as indicated by the data story “card”. Since all these tasks can take quite a while, a single large “epic” data story may be broken up into multiple stories—one for the data integration piece and one for the BI reporting piece. These “epic stories” can then be spread over multiple sprints depending on the complexity. But that is OK, as both IT and the business will already know the story delivery cadence, so expectations will already have been set.

5. Delivery.

To speed up the delivery of a data story, ensure that you slice and dice your deliverables into workable chunks of work. Individual stories/deliverables should fit in a single sprint (two-week sprints are relatively common for D&A projects).

Delivering quickly will allow the business to see tangible progress sooner. Based on what the business sees, the data story can be marked as completed as expected, or it will result in some rework, which will be prioritized and slotted into an upcoming sprint.

The magic of “delivering fast” is that you will also “fail faster”. This isn’t a bad thing—the sooner the business sees a problem, the sooner it can be adjusted. With the old waterfall delivery method, the business might not see issues for months, resulting in a lot more rework and backtracking.

6. Deployment.

Once a data story has been delivered and approved by the data delivery business liaisons, it is time to involve the greater business community. This is a vital step that will really help with overall adoption. The business liaison on the data delivery team will also be a key person to help with onboarding, education, and soliciting feedback from the community. It is critical that the community be educated as to what the new functionality can and cannot be used for. To help with this education, demos given by the business liaison and the BI developers are also vital. Weekly “office hours” can also be activated so that business Users can “drop-in” and ask any question and see focused demos with the data subject matter experts.

7. Monitoring.

Critical to user adoption is constantly monitoring and usage auditing of the new data feature. Are consumers using the new feature? How often do they access the new feature? These are just some of the metrics which are vital to understanding if our agile project has been successful or not. If a usage issue is identified, the data delivery team needs to reach out to the business team and curate a proactive plan to tackle the issue. This could include re-running the “onboarding sessions”, running some “lunch and learns”, or just dropping by the user’s desk and “checking n” to see how things are going.

Being proactive rather than reactive to user adoption issues can make all the difference
for the success of a data delivery project”

8. Repeat.

The final step is to return to your data story backlog. Reprioritize or adjust your stories, if necessary. Then, kick off the next data sprint.

After a few successfully delivered sprints, users will feel more confident about all the data stories being delivered. They will feel more empowered because they have a say in “what” and “when” data stories get delivered.

Agile Data Delivery can be magical, if done correctly

 

Are you ready to start your agile data delivery journey? Get started today with Infocepts.

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Organizations of any size are concerned about data privacy, and they have good reasons for it. Data breaches, security threats, and cybercrime can lead to negative and harmful consequences for anyone, so it becomes important to know and comply with data privacy regulations.

Organizations that want to comply with data privacy regulations have to ensure data integrity, confidentiality, and availability with physical, technical, and administrative controls. These controls must be effective enough in detecting and stopping unauthorized access to data.

Here are four tips that we recommend technology leaders implement in their organization to ensure compliance with data privacy regulations:

1. Understand the core of privacy regulations

Ensuring legal compliance should be a key part of every company’s strategy and objectives. Protecting your customers’ data and trust is not optional, but a must nowadays. It is not just the responsibility of the owner, known as the controller, of the data but the liability, risk, and responsibility is passed on to the supplier, vendor or any third party engaged. Luckily, there are privacy management platforms that incorporate legal guidance, like DataGuidance, OneTrust, or Nymity, that you can use to keep up-to-date and to consult on any necessary legal changes around data privacy.

2. Create a strong privacy foundation:

Organizations should create a strong privacy foundation and have a well-thought-out policy to stay ahead of the game. By institutionalizing data privacy as a core value, it will be easier to react to changing regulations or specific legal obligations because the infrastructure, personnel, and awareness are already in place. Stay transparent with your customers through consent management and clearly defined and stated privacy policies.

3. Appoint a Privacy Officer or a Privacy team.

Evaluate how your business handles data. Do you fall under the data controller, data processor, or a third-party category? Ask yourself the following questions:

  1. What type of data as an organization do you have to?
  2. What is the flow of data and where do we fall in that flow)?
  3. What is the origin of data or what data do you have access to?
  4. What contractual obligations if any (either coming directly from contracts or through DPA) do we have?
  5. What is the work is performed by your organization?

Once you understand your role as per required privacy laws, create a Data Privacy office or appoint a Data Protection Officer (DPO), depending on the organization size. In order to be and stay compliant, continual monitoring, and governance of data privacy legislation, policies, incidents is highly required and can be done so through your DPO.

4. Cultivate general awareness

In order to create a culture of privacy in an organization, it is important to educate both technical and non-technical members about their role in privacy, security, and respecting and protecting the personal information of the organization and customers. Creating an awareness campaign that is tailored to the organization is likely to have a profound effect on its success.

Ultimately, creating a culture of “privacy is everyone’s responsibility” will save an organization time, stress, and money.

Ready to get started? No matter where you are in your data privacy journey, we are here to help. Get started now!

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It is a well-known fact, thanks to the widely popular Latency Curve by Dr. Hackathorn, that the longer the time-difference between a business event and action (preventive or corrective), the lesser chance of potential impact of the action. To put it simply, data latency destroys the value of business insights. As a result, new architecture paradigms, technologies and tools promising to reduce data latency keep arising.

Lately, data streaming has emerged as a preferred choice to address latency challenges. Numerous tools, both open source and commercial, are available and accomplish the same. However they suffer from following shortcomings:

  • Restrictive end-points and data volume-based pricing models – Separate databases and schemas qualify as end-points rapidly adding to investments even for modest setups.
  • Additional workload on source systems – Running queries (at short time intervals) that poll source systems for changes is common practice.
  • Generic JDBC based target systems integration – Optimized tools for loading target systems such as Snowpipe for Snowflake are not leveraged.
  • Rigid design limiting out-of-the-box suitability – Specifics such as PII protection and in-flight analytics are excluded for user implementation. This requires custom user implementation leading to longer turnaround times.
  • High total cost of ownership (TCO) – End-point based licensing coupled with restrictions on data volumes leads to significant upfront capital expense. In addition, ongoing enterprise support by end-points contributes to high operational expenses. For example, a medium scale set-up with 10 endpoints could cost up to $1.6M USD in CAPEX and about $450K USD in OPEX.

The above challenges can prohibit most businesses from adopting commercial data streaming solutions. To address these concerns, Infocepts created its own proprietary data streaming solution – Infocepts Real Time Data Streamer (RTDS).

What is Real Time Data Streamer?

RTDS helps enterprise set-up high-performance real-time data pipeline with ease. It provides flexibility of a custom solution and the stability of a packaged solution at the same time. In addition, RTDS provides cost savings up to 4X compared to similar commercial tools, making it an affordable option for every size organization.

Case Study: US Fashion Retailer

A leading fashion retailer focused on selling clothes, shoes, watches, handbags, and other accessories leveraged Infocepts’ RTDS to set-up high-performance real-time data pipeline with ease. They were challenged with integrating all of their distribution center (DC) systems data, like their Warehouse Management (WMI) and Labor Management (LM) into a data warehouse for a high priority transformation project. Through this project the customer aimed to:

  • Optimize omni-channel operations by seamlessly integrating their website, POS and warehouse data
  • Implement digital walls enabling real-time insights into distribution center operations, promoting transparency and efficiency
  • Optimize Free Trade Zone (FTZ) reporting to prevent non-compliance costs
  • Integrate data from additional systems such as Labor Management (LM) to optimize distribution center operational planning

However, due to challenges like capacity restrictions plaguing source systems (IBM DB2 iSeries and DB2 LUW), data sensitivity (PII data) and velocity (sub-second latency) open source solutions could not help and leading packaged solutions proved to be very expensive thus not fitting the bill (literally).

At this point they turned to Infocepts. We helped develop and implement RTDS as a solution to help the customer see:

  • Estimated efficiency gains in distribution center operations of over 1.2 M USD over the course of 3 years
  • Significant savings in taxes and duties due to timely Free Trade Zone (FTZ) reporting
  • Intangible benefits such as improved transparency of digital walls and better productivity through self-service analytics
  • To top it all, lower TCO of RTDS lead to jaw-dropping cost savings of 1.4 M USD over 3 years

Summed up, Real Time Data Streamer did not just enable real-time it enabled real-value!

If you have identified data latency as issue to be fixed, trust Infocepts RTDS to take care of the rest. Schedule your demo with an Infocepts expert today and start saving money tomorrow!

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