Augmented Reality (AR) and Virtual Reality (VR) enabling technologies like Hololens are creating more valuable and immersive experiences for data analysts and users across industries. With AR & VR, data analysis is no longer an exercise of pouring over spreadsheets and reports but rather an immersive experience that allows users to “step into the data” by visualizing it in the context of the consumer’s world, making it more fun, engaging and collaborative.

Food security is threatened by several factors and plant diseases is one of them that reduces yield or even wipes out crops. Infocepts developed an AR and VR-enabled analytics application to detect plant leaf diseases and help implement early remediation to improve the yield rate in farming. The analytics app leverages Microsoft’s HoloLens 2, Azure Machine Learning (ML), and Power BI reporting. With the app, users can take an image of plant leaves in the field and the AI algorithm accurately predicts if the crop is diseased or healthy. Insights are visually displayed on the Power BI dashboard with detailed analysis of plant diseases and recommended remediation to protect the crop.

This innovative solution allows early disease detection and adoption of sustainable practices to reduce reliance on non-renewable energy and chemical use. The solution helps agricultural clients improve price forecasting of their farming yield through yield rate analysis. It has a lasting impact on economic factors like the overall food prices in countries. Additionally, farm owners can use this solution to view a 3D model of their field within the HoloLens environment, allowing them to visualize the layout of their farms and the health of their crops in real-time.

As seen on PRWEB


Infocepts, a global leader in end-to-end data analytics solutions, announced today that it is highest rated on 2022 Gartner Peer Insights Voice of Customer Report for Data and Analytics Service providers. Gartner Peer Insights is a free peer review and ratings platform designed for enterprise software and services decision makers. The “Voice of the Customer” is a document that applies a methodology to aggregated Gartner Peer Insights’ reviews in a market to provide an overall perspective for IT decision makers.

With an overall rating of 4.8 out of 5, Infocepts is the highest rated for the third consecutive year on the 2022 Gartner Peer Insights’ Voice of Customer’ report for Data & Analytics Service Providers.

Here are a few snippets of feedback shared by Infocepts clients:

“Infocepts has implemented critical value add projects with great quality by utilizing the business knowledge”

“Infocepts has been a great partner of ours and a pleasure to work with. They bring a high level of professionalism and expertise to our project.”

“Too many consulting companies are happy to be order takers/direction followers and this is what in my mind makes Infocepts stand out in the crowd”

“My experience with Infocepts has been excellent. I’m surprised by the way their associates partner with me and my organization to enable us to meet our goals.”

Read all our verified reviews here

In addition, Infocepts is one of the only two data and analytics firms to acquire 100% score on the ‘willingness to recommend’ parameter. Infocepts rating is also the highest among all other firms for Service Capabilities, Sales Experience, and Execution Experience.

“We are thrilled to be recognized in the report for three consecutive years in a row. This underscores our commitment to deliver consistent outstanding customer experience using our solution-led approach” said Shashank Garg, CEO, and Co-founder of Infocepts. “We will continue to innovate to meet the needs of our customers and are grateful for the feedback they share with us on Gartner Peer Insights”, he added.

About Infocepts

Infocepts is a global leader in end-to-end data and analytics that enables customers to become data-driven and stay modern. We bring people, processes, and technologies together the Infocepts way to deliver predictable outcomes with guaranteed ROI. Working in partnership with you, we help businesses modernize data platforms, advance data-driven capabilities, support systems, and build augmented business applications and data products.

Founded in 2004, Infocepts is headquartered in Tysons Corner, VA, with offices throughout North America, Europe, and Asia. Every day more than 250,000 users use solutions powered by Infocepts to make smarter decisions and businesses achieve better outcomes. For more information, please visit  or follow @Infocepts on Twitter.

Gartner Disclaimer

Gartner® and Peer Insights™ are trademarks of Gartner, Inc. and/or its affiliates. All rights reserved. Gartner® Peer Insights™ content consists of the opinions of individual end users based on their own experiences, and should not be construed as statements of fact, nor do they represent the views of Gartner or its affiliates. Gartner does not endorse any vendor, product or service depicted in this content nor makes any warranties, expressed or implied, with respect to this content, about its accuracy or completeness, including any warranties of merchantability or fitness for a particular purpose.

Our data universe has witnessed a colossal year-on-year growth of 42.2% worldwide1. IDC predicts that the Global Datasphere will grow from 80 Zettabytes in 2022 to 175 Zettabytes by 20252. With such growth also comes the responsibility of making information easily accessible and interpretable across a range of users that includes customers, business partners, and employees.

Business leaders often speak about the lack of value realization from data analytics projects. They understand that technology enabled innovation opens a world of opportunities, but business benefits are elusive owing to poor enterprise data awareness and lack of adoption.

While this problem is common, businesses are yet to focus on data literacy as a fundamental skill at their workplaces. In the absence of data literacy, enterprises often encounter challenges like miscommunication between departments owing to different data definitions, risk of data breaches, and untapped business improvement opportunities – despite having technical capabilities in their portfolio.

You must recognize that not only data and analytics skills, but also access to the right tools and quality data along with a supportive culture will together help build a data-first mindset. Finally, to permeate the culture across all levels in the organization, it is essential to provide the right change management interventions to nourish data literacy initiatives over sustained periods of time.

With our experience in helping our clients improve data literacy within their enterprises, we have identified six essential factors that organizations should consider as they go about data literacy initiatives:

  1. Assess your data literacy score – Poor understanding of data is most often attributed to knowledge gaps. So, in defining your business case to launch a data literacy campaign, get a baseline of the current knowledge levels and skill gaps in your workforce. In addition, quantifying data literacy levels helps construct your program design aligned to your organization culture.

    Not every style of training is suitable across age groups or varying functional needs. While working with an APAC luxury retail client, Infocepts was able to identify the training needs within each department, translated that to a pilot program and then expanded the model to a multi-country rollout.

  2. Ensure executive management sponsorship – Driving change management isn’t easy; it’s no different in ushering culture change pertaining the usage of data. To orchestrate full support from C-suite management at the start of your program it is essential to articulate the following, rather than focusing on platform capabilities alone:

    • The business case – a clear visibility of value expected to be delivered and against which organization wide investments are required
    • Roles and responsibilities – it is imperative to establish the bandwidth required to launch and sustain the program, for both key roles and other stakeholders
    • Benefits realization – we need to communicate well on how the performance metrics will shift across departments (e.g., turnaround time improvement for customer service)
    • Committed timelines – the program should be designed to deliver quick wins, rather than accrue value at the end of long-time intervals.

    It’s also critical to convey that data literacy won’t thrive simply by purchasing new software or a having a large team of specialized data scientists address all the needs of a workforce numbering in the thousands.

    While engaging with one of our client’s, a global bank, Infocepts identified poor data quality as one of the limiting factors for adoption and literacy among users due to lack of trust in data. Fixing this problem required executive interventions to prioritize data quality improvement initiatives across functions in addition to helping users adopt self-service through focused data literacy programs.

  3. Data visualizations empower literacy – One size doesn’t fit all when it comes to evangelizing the language of data across a large user base. A quick win is to teach users the power of data visualizations, enabling them to graduate from static spreadsheets to their own “passion projects” now available to them through enhanced analytical tools. Given such empowerment, the usual result is that—on their own—users now discover white spaces and data patterns that were hitherto unknown.

    But ease of access to relevant datasets is imperative for this to occur.

    Across multiple engagements, one of the lessons we learned is that while job function does matter, at times even the most junior employees are able to churn out Sankey diagrams that explain cash flows—while middle levels are satisfied with the bare minimum aggregate information.

    Both risks and opportunities are often hidden in the details. A powerful data visualization platform connects users, provides options for various user roles, and facilitates ease of communication with charts, graphs, and rollups.

    Read the Infocepts Data Storytelling Guide to learn more about how to communicate information tailored to an audience using compelling visuals and narratives.

  4. Elevate the surrounding systems and infrastructure – Data literacy projects yield the desired value when they’re not treated on a standalone basis. To drive change in their company culture, our clients have realized maximum benefits when adopting a long-range view. It includes data governance office oversight, self-service analytics team support, and a focus on both data quality and data discovery.

    While this might seem like a large intervention, within each department you could encourage users to identify the top three problems where they invest significant time in data collation or arguing about data veracity. Piloting such a use case helps propagate the desired messages, as well as establish the systems and behavioral tweaks required to sustain a larger program.

    Without the right enabling factors there is often dilution of goals. Poor data quality datasets dampen user interest, while a trusted data dictionary enables even new workforce entrants to find desired reports on built-in, Google-like search portals. This cuts down on manual dependence while also creating a self-sustaining cycle of interest. In turn that fosters more inquiries, a governed cadence of process changes, and emphasis on the best use of data.

    For one of the global retailers Infocepts initially implemented a data cataloguing solution that increased users’ understanding of data by 5X and boosted adoption. At the same time, it reduced IT helpdesk costs by more than half. The data literacy program with focus on education and engagement, provided an overarching layer on top of the data projects.

  5. Involve data scientists within the mainstream – The business environment has moved beyond historical and diagnostic analytics. Featuring the highest levels of automation, it now involves machine learning of data patterns to predict future scenarios and model the best responses based on user behavior. To retain competitive positioning, you must embrace data science approaches. Only in this way can you make a razor-sharp pitch to predict future defaults or the next best option to offer your customers.

    Gone are the days when e-commerce, point of sale (POS), and enterprise resource planning (ERP) teams operated in silos. With data being the connecting glue, data scientists can easily model customer behavior from the data stored in these systems to determine the right pricing and product positioning to fuel greater sales. Your workforce excels when it has deeper knowledge of—and access to—scenarios and customer data; all the more in a fast-moving environment.

    For one of Infocepts’ health informatics clients, we boosted its data science capability to aid new product development ideas—initially by developing various statistical models on its analytics platform. Tailored data science trainings for the staff nourished these product ideas and enhanced organization performance—yielding a 150% rise in new data science use cases.

  6. Adopt a continuous cycle of improvements – Managing the motivation of your workforce and engaging them in continuous cycles of innovation is critical in data literacy engagements. It also helps if the shift in performance metrics of your workforce is visible across cross-functional business teams, and key contributors are also recognized.

    While data literacy programs focus primarily on training, we must acknowledge how data communities and informal hackathons, or contests can crowd-source new ideas that lift morale and improve user adoption. You will benefit from a program that stitches together a continuous cycle of learning, reinforced with incentivization to make your organization data literate.

    Infocepts has helped a leading global market research firm boost user adoption by implementing a continuous improvement strategy. Apart from skill development, we worked closely with the client to bring a significant shift in speed of data delivery, establish a more data savvy workforce, and improve operational excellence.

Get started with Infocepts’ comprehensive data literacy program to help accelerate ROI from your data-driven investments.


Recent Blogs

Data engineering jobs are often highly competitive as they are one of the most sought-after careers globally. The range of technical skill sets needed for the job is high, often leaving candidates confused while preparing for a job interview. While some aspirants for this role focus on learning newer tools and platforms, some develop a sound business foundation. So how does one prepare for these interviews for data engineering jobs? This article focuses on this topic and offers essential tips to help you better prepare for the interview:

Before the interview

#1: Take time to Understand the Job Profile

To begin with, while applying for the job, understand the job description to figure out what the job entails. Then, think through which courses, projects, and scenarios are relevant to the responsibilities mentioned in the job description. It is natural that you may forget something from your past, especially things that happened a while back. But if you have mentioned it in your resume, be prepared to answer questions about it.

#2: Learn About the Company You Have Applied For

Understand more about the company you are interviewing for – their website is a great place to start. Put yourself in the interviewer’s chair and think about what questions they might ask you. Job search websites like Glassdoor are valuable resources for finding interview questions for specific companies. In addition, it would help to talk to friends and colleagues who are data experts to understand what their job profile looks like and what are some of the common challenges they face at work.

#3: Revise Your Core Skills

As a data engineer, you may be required to know one or more programming languages like Java, Python, SQL, Unix/Linux, and R. Understand the job description and revise the expected technical skills needed for the profile. For instance, if the job focuses on a backend-centric system, you may want to prepare on Scala or Python. Also, review and highlight the technical concepts like distributed systems & computing engines, MPP (massively parallel processing) databases, and event-driven systems that may be required for the job.

Review data pipeline systems and new tools and features across big data platforms, especially in the Hadoop ecosystem. Apache Spark is popular amongst the data engineering community and the next big thing to learn for any data engineer.

#4: Know about the nice-to-have skills:

As a data engineer, it is an added advantage to know the basics of one or more of the following :

  • Modern data architectures
  • Real-time data processing using tools like Apache Kafka
  • Workflow tools such as Apache Airflow
  • No-SQL databases like Cassandra, HBase, MongoDB
  • Cloud platforms like Microsoft Azure or AWS, or GCP
  • Modern DBaaS (Database-as-a-service) platforms like Databricks and Snowflake
  • Code repository and version control using tools like Git, Bitbucket
  • Data pipeline automation using Machine learning and Artificial Intelligence techniques

While this is an elaborate list, focus on the ones mentioned in your job description.

#5: Prepare for Scenario-based Questions

To make the discussion effective, identify an end-to-end data flow scenario from your experience and prepare to speak about it. Make sure to state the goal clearly and how you handled data lineage, duplication, loading data, scaling, testing, and end-user access patterns. Talk about how the pipeline made data accessible to multiple data-consuming applications through well-maintained and reliable endpoints. You should be able to talk fluently about different phases of a data pipeline, such as data ingestion, data processing, and data visualization. You should also explain how different frameworks work together in one data pipeline. At the same time, highlight points such as data quality, security, and how you improved the availability, scalability, and security of the data pipelines for on-prem or cloud-based applications. This will give a holistic picture to the panelists.

#6: Communication is Key

Learn how to explain your past projects in technical and business terms. Aside from being able to code and assemble data, you must also be able to describe your approach and methodology to the interviewers. Also, practice speaking about your choices and why you chose a particular approach or tool over another.

Interviewers will always look for how well you represent any business scenario and how confidently you can speak about the projects you have worked on. A good way to practice is to do a mock-up session with a friend unfamiliar with big data.

During the interview

#7: Provide Contextual Answers – This is the best way to showcase your analytical and problem-solving skills. Having the ability to quickly produce a viable solution to any problem shows the recruiter that you can handle tough situations. Backing this with experience will help you stand out from the competition. For example, an interviewer might ask:

When did you last face a problem managing unstructured data, and how did you resolve it?

They want to know your way of dealing with problems and how you use your strengths to solve data engineering issues. First, give them a brief background about the problem and how it came to be, then briefly talk about what processes and technologies you used to disentangle it—and why you chose them.

#8: Demonstrate your Problem-Solving and Technical Skills

If you are asked a scenario-based question, first understand the question well before you answer it. Scenario-based questions can be tricky, and the panelists may want to evaluate your analytical abilities by posing questions that do not provide complete clarity. In such a scenario, asking the panelists additional questions if needed is the best strategy to be clear on the question before you choose to answer. Sometimes there is no right or wrong answer to such questions. The interviewer is most likely testing your approach rather than the solution itself.

While answering a scenario-based question, try to demonstrate your technical skills wherever applicable.

#9: Be Ready to Code

Some interviewers may ask you to quickly write a function to modify the input data and generate the desired output data. You will be expected to employ the most effective data structures and algorithms and handle all potential data concerns nimbly and efficiently. Even if you cannot write the code by maintaining the proper syntax, pseudo-code also works in most cases. Interviewers would look at the logic you have used to build the code.

In the real world, data engineers do not just utilize the Company’s built-in libraries but often use open-source libraries too. You may be asked to design solutions utilizing well-known open-source libraries like Pandas and Apache Spark in your coding interview. You will probably be given the option of looking up resources as needed. If the position demands expertise in specific technologies, be prepared to use them during your coding interview.

#10 Finally Relax!

It is natural to get caught up in the questions and feel intimidated by the person across the table. But do not lose sight of the fact that your interviewer wants you to do well. They want to hire someone exceptional for the position—and they hope you are that someone. Go into the interview with the right mindset and prepare a few questions to ask the interviewer when you get a chance.

Interested in working on complex data engineering projects? Apply to Infocepts today

Recent Blogs

McLean, Virginia—Jul 08, 2022— CDO Magazine has featured our Senior Director, Ben Dooley in the Leading Data Consultants for North America List 2022. The award recognizes the instrumental role consultants like Ben play in enabling organizations to become data-driven.

It is an honour to be recognized as a Leading Data Consultant in North America by CDO Magazine. Solving business challenges with advanced data solutions is my passion! “, said Ben Dooley on this recognition.

CDO magazine is a global publication with focus on data and analytics content desired by C-suite executives. The magazine connects CDOs throughout the globe and has created a platform where the best ideas, innovations, companies, and leaders are celebrated.

McLean, Virginia—June 28, 2022— Infocepts’ Aparna Prabhudesai has been named the 2022 ‘Diversity Leader of the Year’ as part of the ‘Women in IT Summit & Awards series – Asia Edition’ organized by DiversityQ in association with Information Age. With this award, Aparna who heads OD & Leadership Development at Infocepts, joins the advocates and role models who are working towards a more inclusive tech sector. The award panel recognized her as a tech leader who goes above and beyond with her work, expanding far beyond just gender diversity.

Diversity & inclusion is not just about women in the workplace, it is also about taking cognizance of two other major contributing groups that have very minimal representation, the LGBTQ community and persons with disability” commented Aparna after she was announced as a winner. She also took the opportunity to thank Infocepts for giving her complete autonomy to drive various diversity and inclusion initiatives in the company.

The complete list of all award winners can be viewed here.

Data and analytics has become the bedrock of business strategies helping companies understand their customers, build better products, save costs, provide higher-quality services and transform their businesses. With the explosion in user-generated data and businesses wanting to deliver the right products at the right time, the data analytics industry has exploded as a career path. Job roles such as data engineers, analytics cloud professionals, data scientists, AI and machine learning engineers are in high demand today.

According to Nasscom, the demand for digital talent jobs in India is around eight times bigger than the size of its fresh talent pool and skills such as big data, analytics, cloud computing, mobility, machine learning and cyber security are in great demand.

To keep up, businesses will seek full-stack engineers who will be able to create the data integration layer, standardize the data consumption layer, and enable prescriptive and descriptive reporting with embedded AI and ML models. They will also need multi-skilled roles to handle the end-to-end data-to-insights journey. Due to the demand-supply gap, businesses will need to constantly reskill and upskill in addition to hiring experienced talent from the market.

Tips to succeed as a data analytics professional

With business cycles becoming shorter, data and analytics have become all about speed, innovation, and delivering business value. You need to think about how long it takes for end users to unleash value from the reports or insights you are generating. If it’s not clear or if it’s taking too long, you may be doing something wrong.

Whether you are an aspiring data analytics professional or have been in this field for quite some time, you have the opportunity to not only learn new skills, but also help shape the future.

Here are some tips to guide your long-term success in this area:

Develop expert-level competencies

Data and analytics companies don’t just hire professionals to access specific tools. Any end-to-end D&A projects now typically leverages upto eight technologies and companies are not willing to invest in multiple tool experts anymore. When it comes to cloud, companies use an average of 20+ cloud services and many customers are already using a multi-cloud setup. Building expertise in one or two tools is not going to take you far and building expertise in all is not going to be practical as new services are added every day.

Today, companies are hiring for experts in competencies such as cloud, data engineering, analytics, data management, advisory and service management. These competency experts are individuals who are able to work on multiple technologies in a competency and have the capability to create an end-to-end solution to a real-life data problem.

You should develop a T-shaped or Pi-shaped profile with depth in one (or more) competency in formative years and then diversify to build breadth across competencies for long-term success in your career.

Invest in learning essential technical skills

Data Modeling, Dimensional modeling, and SQL are some of the basic skills a data analytics professional absolutely needs to have. But they are not enough. Go further and consider learning Java, R, or Python programming. Python is among the most common coding languages required in data science and data engineering roles, along with Java.

A data analyst or engineer should be capable of working with unstructured data as well. Seek opportunities to develop your skills on predictive analytics, machine learning, and artificial intelligence to stay relevant. The key is to keep acquiring new skills and tools to stay up-to-date with the latest developments, technologies, and methods that will enable you to deliver the most effective solutions.

Develop your Data Storytelling skills

As a data analytics professional, you should be good at data storytelling. The most important aspect of a data analyst’s job is communicating insights effectively to non-technical audiences, such as the marketing or sales departments. You need to be creative with data to help answer questions or solve problems. You must apply the appropriate data visualization techniques to get your point across and enable your audience to understand the information easily. Whether you are data engineer, data analyst or data scientist, you should develop your ability to present insights in the form of intuitive, information-rich dashboards.

Pursue continuing education

Having a bachelor’s degree in computer science, information technology, or statistics will provide you with the ability to handle and analyze data. However, you may need to pursue post-graduate education to advance your career.

According to KDnuggets, a leading industry resource on data analytics and machine learning, data scientists tend to be well-educated; 88% have at least a Master’s degree and 46% have doctorates. While there are outliers, most data scientists have a sound educational background that is necessary to cope with the demands of this profession. A management degree can also come in handy to help you reach leadership positions faster and excel in it. If you plan to pursue post-graduate education, be sure to work in a company that supports this and will allow you to take classes after work or on weekends.

Focus on your problem-solving and soft skills

Problem solving and collaboration are among the most important soft skills a data analytics professional needs. Problem-solving is an essential aspect of data analysis – it is vital to know what questions to ask. You will get the answers you need if the queries you ask are based on your knowledge of the firm’s business, product, and industry.

A data analytics professional must also know how to collaborate with colleagues and clients. Careful listening skills are essential to understanding what type of data and analyses a client or stakeholder requires. The ability to communicate in a direct, easy-to-understand, and clear manner also goes a long way in advancing your career. In addition, these soft skills can make you more effective at convincing people to act on the findings and help you resolve problems or conflicts.

Data and Analytics Careers at Infocepts

Interested in pursuing a data analytics career in a global company? Apply to Infocepts now

Infocepts was recently named as Great Place to Work and as one of the best firms for data scientists to work for by Analytics India Magazine, alongside some of the biggest names in analytics. We exclusively focus on data and analytics and are known for investing in helping our associates become the best versions of themselves.

Recent Blogs