Data and analytics tool migration can help growing businesses deal with evolving requirements for data-driven insights. Modernizing your data and analytics platform can also reduce the security risks and costs, associated with using multiple analytics tools. However, migration is a complex process. Whether you are looking to consolidate redundant tools or upgrade from legacy systems, it is necessary to be aware of potential pitfalls that lead to failures.

In this article, we have discussed some of the common pitfalls that can lead to budget overruns, extended deadlines, user confusion and change resistance. We have also highlighted, how we help you to avoid these pitfalls and ensure success in your journey.

  1. Wrong Migration Choices

    Wrong choice of tool, infrastructure, capacity or migration approach can derail your project. A properly designed tool comparison exercise helps in identifying the best-fit tool for your organization in the context of your requirements. Also, ensure that you choose the right infrastructure and sizing for your analytics tool to avoid performance bottlenecks and SLA breaches later on.

    Infocepts brings depth and breadth of experience across all modern analytics platforms to help you make the right choice of tools, infrastructure, capacity and approach to minimize the design gaps and risks early in the process.

  2. Failure to Align Key Stakeholders

    Successful analytics tool migration requires stakeholders to understand what success looks like and agree on structured approaches to execute migration initiatives. Before starting, it is vital to create a comprehensive roadmap and an optimized implementation plan to align all key stakeholders. Clear prioritization and readiness assessment is crucial to avoid any surprises.

    Infocepts brings in experienced teams to help you align your stakeholders and manage the change effectively.

  3. Lack of Necessary Expertise

    A BI tool migration project does not just require technical skills in the source and target BI platforms. Most platform migration projects may involve moving to the cloud, rationalizing your BI inventory and taking advantage of new capabilities to reap the full benefits of the new tool. To do this – in addition to tool skills, you need to factor in skills such as advisory, cloud, data modelling, data storytelling, change management, automation, optimization and more.

    Infocepts brings end-to-end data analytics capabilities and specialized migration teams to help you succeed in your migration journey.

  4. Taking Adoption for Granted

    Your users have likely been using your current analytics tool for many years. They are well aware of its shortcomings and have adapted to it, forming bad habits from having to make do with the current system for such a long time. These habits can be hard to break, and some users may even initially reject the new platform.

    This is why training is critical to the success of an analytics tool migration process. It is not enough to teach your users how to use the new platform step by step. Rather, training should be viewed as an ongoing effort to make your key users proficient in the new tools. This then creates cost savings and long-term process efficiencies.

    Infocepts offers training combined with user adoption support to ensure post-migration success.

  5. Losing Control of Schedule and Budget

    It is expensive to manage multiple platforms or maintain a legacy system. The costs of support and upgrades, license fees, hardware, administration, training, and maintenance can quickly spiral as time goes by. But while analytics tool migration and modernization offer potential for savings, it is also easy for companies to lose financial control of a migration project because of inefficiencies during execution.

    Infocepts offers accelerators that can be combined with pre-built migration toolkits to migrate analytics ecosystems efficiently. We can help your company get the most out of your investments in new platforms while minimizing costs associated with change.

Talk to us about our holistic platform migration approaches that accelerate the process and reduce the risk of failure.

Get Started with Infocepts today!

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New opportunities due to rapid cloud adoption amongst teams and proliferation of data has opened new challenges for organizations in today’s digital age. Optimizing not just businesses processes but also cloud platforms, its operations, and associated costs is important for improving ROI. This results in improved resource management, transaction volume, better user experience and adoption, resulting in increased business value. Improper cloud cost governance will lead to ballooned inefficiencies, inflated cloud bills, and deaccelerated innovation, with the dangers of organizations becoming less competitive and losing their edge.

Cloud cost optimization deals with financial management through spend analysis and control strategies, to maximize return on investments on cloud endeavors. It is not a one-time cost-cutting measure but is a continuous cycle (monitor-analyze-optimize) for realizing value on every dollar spent.

We have listed below a few of the many strategies to optimize cloud costs –

Centralized Cloud FinOps team enabling decentralized decision making

With the advent of cloud platforms, finance teams no longer control the spending while IT engineers are empowered to spin up resources with a click of a button without any budget considerations. Hence a leadership-approved, centralized, cross-functional FinOps team is needed to forecast budgets, monitor cloud usage, and govern billing rates. This FinOps team comprising of cloud experts responsible for evangelizing best practices is pivotal to the success of any cloud cost optimization journey.

Centralized Cost Monitoring

Organizations continue to struggle to understand cloud expenditures and the granularity of the cloud bills. The cost tools provided by cloud vendors are not enough to provide spend analysis tied to business value or handle multi-cloud or hybrid cloud complications. Hence it is important for teams to create visualizations and dashboards on top of the cloud bills to abstract its complexity and intricacies, and to make it accessible to all stakeholders in real-time.

Efficient Development and Release Cycles

Companies that employ manual deployment and provisioning approaches on the cloud has an inefficient and costly release cycle. Employ DevOps, standardize, automate, containerize, and accelerate risk-free deployments on the cloud, thereby reducing effort and improving efficiency.

Automated Monitoring and Configuration of Resources

Managing delinquent resources is not easy without monitoring and automation. Teams need to optimize resources and plan for shutting down idle resources, spinning down scaled-up resources, removing unused resources, etc. to cut on cloud wastage. Employing Infrastructure as Code (IaaC) with Policy as Code (PaC) helps with seamless and consistent deployment of infrastructure and services on the cloud.

There are many other important strategies which help organizations optimize cloud costs and improve platform efficiency and operations. Organizations should not target to surmount all cost challenges with one strike but consider it as a journey.

Looking to learn more? Our advisory note provides six important strategies to efficiently manage your data and analytics cloud costs.

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As an organization’s D&A landscape grows with time in terms of size and complexity, redundancy is inevitable. This makes it imperative to rationalize users, data, analytic reports and applications from time to time. In addition, there could other business drivers such as mergers and acquisitions often calling for consolidation of tools and migration to more modern platforms. The other key driver is the need to stay abreast with the new capabilities made possible by tectonic technological shifts and evolving customer expectations within the constraints of stringent regulations.

Rationalization Approach:

Rationalization of any D&A landscape should be built upon the key pillars of automation, governance, and communication – with an agile mindset to execution. We have listed below four key steps which provide a perspective on how you should plan and execute such a rationalization program:

  1. Analyze Objectives and Need: Figure out business objectives and drivers for the need to rationalize. Also understanding the key stakeholders involved and their willingness to help with the impending bigger change (e.g.: cloud migration), which rationalization may be a part of.
  2. Create a Holistic Plan: Create an inventory of key parameters like data volume and number of users. Outline an initial project plan which includes associated timelines, costs, and resource planning. This is where you need to decide whether to outsource the task to a trusted partner or if it makes sense to do it internally by developing, nurturing, and sustaining the needed skills in the organization.
  3. Formulate a Roadmap: There is no silver bullet when it comes to creating a roadmap as every enterprise, its drivers, and constraints are different. However, leveraging some building blocks can help you accelerate the process without re-inventing the wheel.
  4. Automate as Many Steps as Possible: Automate the Identification of patterns from data to determine the business rules for rationalization (e.g.: “ignore all reports not run in last 6 months”). Modern accelerators like Infocepts BI Converter can help with automated metadata analysis and identify candidates for rationalization.

Critical Success Factors:

There are several critical success factors do help enterprises ensure a successful D&A application rationalization. Listing the top two below –

  1. Well-Planned Timeline: Planning an analytics rationalization project involves working backwards from the larger deadline, whether its cloud migration cut-over date or closure of acquisition. Putting in adequate buffers for business users for seasonal and annual peak workloads (like budgeting, Black Friday campaigns) also helps in ensuring a smooth sailing.
  2. Stakeholder Alignment: It is important to ensure the availability and alignment of the concerned stakeholders, especially the business users. Their involvement as well as inputs are vital for the program success and often needs context-setting and discussions prior to kickstarting such programs.

Infocepts helps customers rationalize and migrate their databases, data pipelines, or analytics tools using a repeatable approach to achieve the desired state quickly and efficiently. We apply best practices, solution accelerators, and cross-functional experts to deliver predictable results and operational flexibility—all while avoiding unnecessary costs and hassles.

Check out our Advisory Note to learn more about all the essential steps and critical success factors to guide your success in D&A platform modernization initiatives

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The biggest and most successful consumer companies have taken a lead in delivering exceptional customer experiences (CX) across different stages of the buying journey. They have honed their CX prowess by building customer data platforms to identify salient customer pain points and resolve those with the biggest pay offs. A customer data platform plays a pivotal role in aggregating data from multiple customer touchpoints in real-time and structuring them to build customer profiles including their most pressing concerns.

The results produced by customer data platforms (CDP) are only as good as the data flowing into them. A growing number of companies are therefore sparing no efforts to aggregate data through Voice of Customer (VoC) programs in addition to aggregating data from traditional sources. These platforms play a pivotal role in the evolution of customer experience design and our understanding of digital journeys.

According to research from Gartner, 75% of large B2B organizations and 65% of large B2C organizations are in the beginning stages of CX maturity. Our experience with consumer enterprises tells us that organizations looking to transform their CX have requirements ranging from reducing everyday customer engagement friction to completely overhauling their strategy.

Regardless of the CX maturity stage at which companies may find themselves in, building a customer data platform is subject to the following considerations.

  1. Invest Judiciously

    As a customer focused company, you may have already invested in a CRM and systems that help you identify your customers, their buying behavior, and the way they engage with you. However, the increasing influence of the digital medium renders them inadequate to track their sentiments. No amount of brand positioning and smart pricing strategies can make up for the inability to identify and resolve customer concerns.

    Due attention should be given to the existing technology stack to build a data platform that complements it and maximizes your investments. For instance, you may have a variety of structured and unstructured data that needs to be aggregated and processed to derive insights. We handled one such situation where our client, a global media company, wanted a data and analytics (D&A) solution to improve ad placements. Their existing solution could not accurately correlate viewer behavior with their ad placement and targeting parameters. Our D&A solution captures viewer behavior in real time and provides timely insights on viewer consumption across channels. It has enabled their marketing department to effectively deliver ads and grow their ad revenue by millions of dollars.

  2. Focus on Experience

    While it is good to have a customer data platform that ingests data from various sources to give you a complete picture of your customers including drilldown capabilities, it is better if it can take further action. You should aim to build a customer data and experience platform that features AI-driven automation, real-time analytics, and UX optimization. Such a platform enables marketers to create compelling customer experiences with simplified workflows for better productivity. It provides meaningful analysis of marketing initiatives across channels.

    A customer data and experience platform is especially useful for creating a seamless omnichannel experience while conforming to regulations like CCPA and GDPR. It secures personally identifiable data and frees customers from adding their personal details thereby reducing cart abandonment.

    We helped a global publishing and event management company to build a customer data platform to collate, curate and manage audience data, B2B products and events data. With all this data in one place, the company successfully doubled its user base by enhancing digital audience experiences with personalized product search and recommendations.

  3. Ensure Personalization

    Technology giants like Netflix and Amazon are leading the way in personalizing their CX with high quality recommendations. Ensure that your data platform has a powerful recommendation engine that identifies patterns in your user data to personalize CX. It will go a long way towards attracting high quality traffic, improving customer satisfaction, and pushing the average order value higher.

    A luxury retail company that approached us to revamp their legacy e-commerce platform benefited from the recommendation engine we built for them. Our AI and ML based platform that analyzes clicks, impressions and historic purchases and features photos of recommended offers based on real-time preferences, increased our client’s sales by 18% in one year.

    Infocepts Foundational data platform solution helps to assess the need for customer data platform, assist in selecting the right tools, define and implement a modern customer data platform using proven methodologies and accelerators.

Get Started with us to build a robust customer data platform tailored to your needs.

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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.

Grab your copy to know leading practices and tips to select your best-fit data architecture.

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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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Nagpur, India—April 17, 2022— The year 2022 marks the first year of the annual Infocepts Foundation scholarship program that includes a grant of INR 50,000 for each recipient and a 360-degree career guidance program. The guidance program includes regular mentoring sessions, internship opportunities, projects, and assignments. The goal of the scholarship program is to achieve a seamless campus to corporate transition and advance career opportunities for the recipients.

The Infocepts Foundation received more than 500 applications. After a rigorous evaluation process, 24 engineering students specializing in Computer Science, Information Technology, Artificial Intelligence, Machine Learning, and Data Science & Data Analytics were selected for the scholarship program. The selected students are enrolled in some of the best engineering colleges such as Visvesvaraya National Institute of Technology (VNIT), Yeshwantrao Chavan College of Engineering (YCCE), Ramdeobaba College of Engineering, G H Raisoni College of Engineering and many more.

The scholarship ceremony was organized at Infocepts’ MIHAN, Nagpur campus and presided by Shashank Garg, Co-Founder & Director and Smrita Dubey, Chief People Officer. The event was also attended by top representatives from the educational institutes, Infocepts leaders, members of the press, and the recipients’ parents.

During his address, Shashank Garg shared his experience on what career opportunities data and analytics offers. He appreciated the students for their achievement. Smrita Dubey shared more insights about Infocepts’ social initiatives. Dr Micah Aiyub, Head Social Initiatives, Infocepts, shared that the scholarship program goes beyond covering fees to secure a promising future with a special focus on all-round development.

The recipients’ parents expressed their heartfelt gratitude to Infocepts. Some of them explicitly mentioned the difference Infocepts Foundation’s scholarship program had made to their lives considering the hardships they were facing since the Covid-19 pandemic.

The event concluded with felicitations for college dignitaries and Hi-Tea.

Nagpur, India—April 17, 2022— Infocepts, a global leader of end-to-end data & analytics solutions has been named the ‘Best Exporter of the Region” in the 4th edition of VIA & SOLAR Vidarbha Udyog Gaurav Awards 2021. The awards program honored entrepreneurs and industrialists for their efforts and untiring work in bringing development and providing employment in the region.

The Best Exporter of the Region award was presented to Infocepts in recognition of our socio-economic contributions to Vidarbha, our market position, innovativeness, delivery of end-to-end data & analytics solutions to leading global organizations and our CSR focus.

This award is yet another milestone that is bringing us closer to our vision of being recognized as a global leader in D&A solutions and seen as an aspirational career destination for current and future associates.

Here are some more of our recent recognitions:

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