Hear Akhil Agrawal speak about his 15 years with Infocepts – learning, leading and growing as an outcome-focused D&A expert!

In a video interview with our Marketing Manager, Sundeep Dawale, Akhil talks about his first interaction with Infocepts, initial days, first colleagues, memorable achievements, culture, and advise for those in the early stages of their careers.

The conversation brings to light several interesting aspects about Akhil’s career. He was working in London for a large software services company when he was first approached by an acquaintance to join Infocepts, a start-up back then. And as he puts it, “there has been no looking back since then.”

Watch the video to learn more about a career at Infocepts

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COVID-19 has forever changed how business is done and what customers expect from modern businesses. To stay relevant in today’s digital era, organizations are using intelligent automation across most of their business processes to revamp operations, service delivery, and achieve desired business outcomes with zero or minimal human intervention.

The potential of robotic process automation (RPA) — bolstered by data-driven autonomous insights based on artificial intelligence and machine learning — can completely change how products and services are delivered and how they are perceived by consumers. Here are some data-driven intelligent automation use cases where we have solved real-world problems and addressed different client goals.

  1. Global market research firm saves millions with automated data-driven insights

    Infocepts helped a global market research company automate its repeatable, high volume, and time-consuming report generation process, saving over $1M annually through the right intelligent automation solutions and the needed skills. Our client is a leading research firm and helps CPG manufacturers and global retailers make key decisions by providing in-depth market demographic insight.

    It offers various business intelligence reports helping over 15,000 users understand what is happening in their target markets, why it is happening, and decide what to do next.

    Its 65+ dashboards comprised of 10K+ elements which were manually customized by developers to meet the respective end-user requirements of each new client. The process needed manual modification of report elements which was error-prone, provided inconsistent user experience, and led to lost opportunities due to tough competition.

    Our innovative solution automated all repeatable high-volume report generation tasks and saved our client over $1M per year. The solution automated complex business processes or workflows which generate and deliver autonomous insights. It leveraged multiple cutting-edge technologies (AI, ML, NLP, Computer Vision, Low Code, RPA, and Hyper Automation) and can now smartly deliver insights based on user-driven demand through continuous monitoring and intelligent automation.

  2. Omni-channel retailer cuts operating costs with custom-built automation suite

    A leading US retailer operating over 300 stores in North and South America partnered with Infocepts to overhaul its complex data pipeline, which consisted of over 130 workflows, more than 800 mappings, and over 600 tables. Any delays in data loading had a direct impact on the timeliness of enterprise BI report delivery, especially the sales reports that served as the basis of inventory planning, marketing, and sales target decisions. The system also relied on manual monitoring, which was prone to errors that directly caused higher ticket volumes and a lack of confidence in the reports. With increasing operational costs and frequently needed cleanup activities, modernization was the need of the hour.

    Infocepts intelligent automation solution helped the client significantly declutter databases with a custom-built suite capable of automatically providing real-time notifications when failures, environment changes, and unusual jobs are detected. The solution used reusable components and pre-packaged scripts which provided flexibility and scalability. Intelligent automation helped achieve a total of 100k USD savings for the first year and the savings gradually increased as the same sized operations team was able to monitor the growing number of data processing jobs.

  3. Managed services automation saves 5,000 manhours annually

    Intelligent automation enabled Infocepts to revamp a 24×7 managed services program for our client – a leading technology company that provides “customer experience software as a service” utilizing speech analytics and AI-powered text. The clients’ services involved extracting actionable insights from diverse customer interaction modes to propel sales growth while ensuring compliance and increasing operational efficiency. The client relied on discrete proprietary applications that ran on different servers, creating diverse environments that became increasingly complex to manage manually.

    Our customized intelligent automation solution helps provide near real-time alerts and updates on server health, eliminating manual monitoring efforts and reducing the time it takes to resolve issues. It reduced the time spent on bug fixing and elevated customer service levels drastically. Infocepts solution provides reliable, round-the-clock monitoring and scalable support—along with a 30% reduction of effort in recurring manual activities.

  4. Pharmaceutical company uses AI-powered segmentation solution to identify key opinion leaders

    The opinion-leader segmentation process of an American pharmaceutical company did not meet the current market standards and lacked reliability. It lacked modern features like the ability to analyze digital activities, popularity, and relationship matrix of healthcare influencers and professionals. Also, data was manually updated which was error-prone thus affecting classification and risking incorrect segmentation.

    Infocepts automated the client’s key opinion leader identification and segmentation process using an AI/ML-powered intelligent automation solution. Machine learning algorithms made it much easier for the client to identify top-performing, established, and rising key opinion leader segments for different countries. It also provided meaningful and actionable insights on professional credentials, influence circle, interaction metrics, network, and growth variables. It accelerated our client’s medical science decision-making process and enabled our client to formulate effective personalized engagement strategies focused on healthcare thought leaders.

Get started with Infocepts today to learn about our intelligent automation solution that automates data & analytic capabilities using innovations in Data Science, AI, ML or Robotic Process Automation

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Cloud computing is one of the most significant technology transformations since the introduction of the Internet in the early 1990s. Growth in cloud-computing traffic, mobile data traffic, and the continuous development and adoption of AI, IoT, and related technologies also contribute to the rapidly increasing data volume and complexity. After all, data gets created every swipe, click, stream, search, or share. So it is not surprising that there are more cloud engineer jobs than ever.

If you are a cloud professional seeking employment in an increasingly competitive industry, you will want to stand out in a prospective data and analytics firm. Here are five resume writing tips to keep in mind:

  1. Effectively summarize your cloud skills

    Emphasize your accomplishments in cloud-related projects and provide a comprehensive overview of the roles you fulfilled. Prospective employers want to know your daily job responsibilities and how these impacted your company or department’s overall development.

    Below is a good example:

    ‘Highly experienced Cloud Architect with extensive expertise in performing Cloud Readiness Assessments, generating cloud service maps, and overseeing security concerns as part of the wider Cloud Adoption Framework. Strong organizational skills, ability to manage large teams from start to finish, from requirements through design, implementation, and deployment.’

    You can also include your professional aspirations to indicate what platforms and services you are looking to build your skills and expertise on.

  2. Emphasize your technical skills

    Highlight your technical skills in cloud computing and data analytics to demonstrate that you are a suitable candidate with the necessary skills for a cloud professional position.

    Do you have a solid grasp of the Cloud Service Provider (CSP) market? Highlight the cloud platform(s) you are familiar with, such as AWS, Microsoft Azure, or GCP—the three major public cloud computing platforms. Then, list other cloud platforms you know how to use, such as IBM, Oracle Cloud Infrastructure, or Alibaba Cloud.

    Many cloud-based development projects demand experience with industry-standard programming languages and the ability to write code to create, deploy, and administer apps. Mention your experience with popular programming languages for cloud-based software, such as Java, JavaScript, and Python. If you have a strong command of SQL and data structure principles for developing database-driven apps, highlight it.

    Also, be sure to list any experience you may have in:

    • Working with various cloud-native services
    • Data pipeline design, configuration, implementation, testing, and monitoring
    • Database modeling in distributed cloud computing environments
    • DevOps and Containerization
    • Scripting and programming languages
    • Migrating non-cloud native apps into cloud-native architectures and cloud environments
    • Processing, managing, and extracting value from big datasets
  3. Be honest

    When describing abilities on your cloud data architect resume, be truthful about your level of expertise. For example, indicate if you are a beginner or if you have mastery of a specific skill.

    Understand that cloud skills are still nascent and growing as companies adopt the cloud. Your hiring manager will not expect you to have many years of experience on a particular cloud platform or service. What’s more important is – your ability to demonstrate knowledge and expertise in the skills you claim to have. It would be best if you also showcased the flexibility and confidence to apply your learnings to understand new cloud platforms and services faster.

  4. Make education a focal point

    Your education is as important as your work experience in the data analytics industry. To work as a cloud engineer, you generally need a bachelor’s-level degree. A computer science degree might also improve your employment prospects, with its wide breadth and emphasis on theory.

    In addition to your educational qualifications, be sure to list all your training, self-learning, and certifications related to cloud services, infrastructure, or applications. If you have participated in hackathons or innovation challenges hosted by your current organizations or external bodies, list them in your profile – those will make interesting conversations during your interview.

  5. Don’t make it longer than it needs to be

    Your resume needs to impress an employer within 15 seconds. Most recruiters only go through resumes halfway down the page before deciding whether they want to keep reading or not, so you must demonstrate your competence right away. Highlight your:

    • Expertise in building well-architected cloud solutions
    • Technical skills in cloud services, infrastructure and administration
    • Experience in developing cloud systems
    • Skills to support cloud operations

Looking for a cloud engineer position? Apply to Infocepts today. Visit our Careers page for more information.

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Labelled as the sexiest job of the 21st century by Harvard Business Review back in 2012, data science continues to be an in-demand career choice for many even today. As a leading Data & Analytics firm, our ability to enable our clients to gain disruptive advantage using the power of data is largely attributed to our home-grown talent. This is true for data science as well.

We asked Sanket Ninawe, our data science CoE lead, a few questions to understand his approach to identifying, mentoring and nurturing data scientists at Infocepts. Read on to know more.

What’s your selection criteria for fresh graduates to be part of your data science team?

Many students want to become data scientists fresh out of college and when they come to us, our job is to assess how well they understand data structures, algorithms and programming. As part of the interview process, we review the courses and trainings undertaken by them. We consider students who have undertaken courses like computer science algorithms, statistics, or machine learning in college. We then proceed to test their knowledge on computer science concepts, algorithms, databases and Python.

What happens after selection? Do you conduct any training or orientation for them?

Yes, we put them through an intensive two-month boot camp where our instructors introduce them to various data science concepts and algorithms necessary for building machine learning models. It is an exhaustive training program designed to prepare them for handling a capstone project. As a part of this project, they are required to work on a use case and present their solution to the data science CoE.

Are your trainees project-ready at the end of your bootcamp?

No, they must complete some more add-on trainings such as natural language programming, deep learning, and more concepts. While we train them on these concepts, we also help them understand cloud technology because data science models are typically hosted on the cloud. AWS, Azure, or Google Cloud being some of the popular choices. So, we make them familiar with these platforms over the next few months.

Once we are confident about the progress they have made, we make them a part of our data science CoE. We further evaluate their progress when they become a part of the CoE and provide any additional support they may require. They may then be aligned to a POC or use case we may be working on. It gives the new data science members a chance to gain hands on experience to use their newly gained skills.

What next after their trainings and hands-on PoC engagements?

They begin working as junior data scientists on our client projects, where they will be guided by a senior data scientist to build data models with live data. We closely monitor their performance on the project and when they reach sufficient threshold, we recognize them as qualified data scientists.

After a period of about 3 to 4 years, they become eligible to be regarded as senior data scientists. While on their way up there, they are also expected to help us grow our team. We involve them while conducting interviews including technical rounds. This gives our data scientists an opportunity to explore how data scientists from other organizations work.

How do you ensure that your data science team stays sharp?

We encourage our data scientists to build point of views on latest technologies or new concepts that might be trending. They can spend some time in between projects to learn any new technologies that we may require in the future. The team has the freedom to choose the area of expertise they are most interested in like machine learning, NLP, image recognition, and so on.

In addition, we ensure that our data scientists are confident in communicating externally because our clients are often not well versed with data science concepts. Our team should be able to clearly explain how they have or will build the required data science model. To that effect, we assist them in creating presentations and we also have a monthly forum where they can showcase their work. The forum enables the team to learn from each other while evaluating the rationale for the choices they make to build their data science models. Everything put together helps them to excel in their roles.

Any closing thoughts you would like to share?

Infocepts was recently recognized by Analytics India Magazine as one of the top data science companies to work for in 2022. This was a proud moment for all of us. The average age of my team is 25 years, mainly comprising of fresh graduates and some senior, experienced professionals we have hired. Also, I can proudly say that our retention rate is around 99%.

We are looking to hire both fresh and experienced talent to grow our data science team. You can visit our Careers page to apply.

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One thing businesses learned from COVID-19 was to rapidly adapt to the changing situation in real-time. It’s more important than ever for businesses to have a truly data-driven culture versus having unplanned, isolated pockets of insights. It also becomes critical to equip frontline users with data-driven insights, thereby empowering such decision-makers with modern, self-service analytics to help your business grow.

Challenge with traditional analytics – Since the earliest days of BI reporting, IT teams and analysts curated data and reports for business users to consume. Self-service reporting became popular later, but it required days-long training and handholding to use visualization tools and for users to get used to newer platforms. In most cases, data was not catalogued. Or it was so poorly cataloged that it resulted in even more data and knowledge silos limited to certain sets of users only. Such dependency still lies with those technical teams charged with building reports for non-technical consumers.

Search-driven analytics for speed and scale – Search features are powerful; improves user experience; and you can find them inside most modern apps and websites. With Search-driven analytics, users are empowered to search for information in a much simpler way, engage with the data, and make faster business decisions (a long, complex process with the traditional analytics tools).

Four Reasons for Adopting Search-Driven Analytics – the Next Big Thing in BI

Improved user adoption with intelligent search – In a generic sense, search has transformed everyone’s lives—there is no training required to quickly access information anyone needs. For fast and easy access, users perform a simple Google search or ask Siri, et al.

As for search-driven business analytics, minimal training is required for users to independently get insights from retrieved data on the fly. Built-in, intelligent search eliminates data silos and provides users with faster results and insights—despite having bigger data workloads.

Democratized data across the enterprise – In the simplest terms, data democratization means everybody has access to the same information; there are no gatekeepers repeatedly creating bottlenecks. Puzzled by data-related questions, built-in search capabilities enable everyone to effortlessly seek and analyze data to 1) expedite decision-making and 2) identify opportunities for the enterprise.

Faster time to insights – Using yesterday’s models, anytime a question entered a business user’s mind (and with limited reports or dashboards not readily providing the answers), they had to contact multiple teams and stakeholders, getting tied up with data stewards, analysts, and IT teams. This tedious process takes a few days until an answer is finally delivered in the form of a new report.

But with search-driven analytics, the ability for users to take action far more quickly is greatly enhanced. Every user is equipped with data, and ease of using search-driven analytics establishes a true data-driven culture. Now armed with these faster insights, businesses can focus on critical growth opportunities, fine-tune operations, and make faster decisions for improving customer and user experiences.

Improved productivity and lower costs – Today, many enterprise data teams are spending significant time in collecting data to answer questions coming from users. Modern search-driven analytics eliminates this resource challenge.

One tangible benefit is lower operational cost (OPEX), lower per-user cost, and a significant reduction in the request queue for IT teams and others. Their improved productivity means they can cater to more critical business tasks and customer needs.

In addition, you eliminate business opportunity costs by empowering users to generate actionable insights by themselves. Discovering they’re able to quickly and easily answer day-to-day data questions on their own, this new-found ability enhances application adoption rates.

Bringing Modern Search Capabilities to All Businesses

Modern self-service is not just for data analysts—it’s for all users to engage directly with your data and get their questions answered quickly and easily. In an easy-to-use manner, search-driven analytics makes it possible for everyone to get the data they seek and draw insights by simply asking questions. By ‘talking to the data,’ they can use their own voice to instantly comb through sources and access insights—whenever and wherever they’re needed.

Leveraging AI, machine learning, natural language processing and natural language generation, we help our clients build augmented business and conversational apps with best-in-class accelerators. Talk to us to learn how—by leveraging search-driven tools or by integration with the likes of Alexa, Siri, and Google-like search interfaces—Infocepts can bring conversational capability into your enterprise analytics and platforms—making exploratory insights available to all users.

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