Back to Blogs

In today’s business landscape, data is the key which separates thriving companies from those falling by the wayside. Whether it is rapidly redesigning products & services to the new realities, closing talent supply-demand gaps, or resetting operational costs based on zero-based budgets – businesses & executives need the ability to make the right decisions at the right time with the right stakeholders.

Despite increasing investments in D&A capabilities to support such needs, a 2023 D&A Leadership Executive survey highlights the gap between the worlds of business & analytics where the overly optimistic assessment from leaders executing D&A initiatives (98% say D&A is providing value) does not match with the progress reported on longer-term initiatives such as driving business innovation with data (only 59.5% say yes), and, creating a data-driven organization (only 23.9% say yes).

So, if you are responsible for D&A capabilities, it can be difficult to separate what are the real, transformational initiatives capable of revolutionizing your business from the ‘noise’ of so many new technologies and trends promising ‘faster and more accurate access to insights.’ Using our experience supporting & guiding leaders at multiple levels, I help you cut through the noise to focus your limited time, effort & resources on the areas that will be most impactful to the goal of using D&A to drive business growth in 2023.

  1. Embrace managed AI to realize the promise of AI for your business.

    McKinsey – who expects a $10T+ return from AI – rightly identifies, success remains elusive for many businesses, with 72% not yet able to take advantage, while an astonishing 85% presently fail to realize any sort of business benefit from their attempts at integrating AI into existing operations! If majority of the AI projects fail, organizations must invest in failing fast. In other words, you must be able to identify the use cases that will achieve specific business outcomes & are feasible & do it in days not months. Rather than DIY, you should consider fully managed AI solutions for business such as Infocepts’ AI as a Service (AIaaS).

  2. Revisit resource allocation models for your D&A projects to get more done with less.

    Companies are facing financial crunch because of on-going economic uncertainty including the very real possibility of a recession in the next 12-18 months. Data shows that companies that come out strong after a recession – i.e., with twice the industry averages in revenue & profits – are proactive in preparing & executing against multiple scenarios backed by insights. They re-deploy employees against the highest value activities in their strategy, weigh trade-offs & take deliberate risks, but without cutting corners on critical projects. You should a) stop paying for legacy technology maintenance, b) fix your broken managed services model, & c) choose on-demand talent.

  3. Help your teams ‘actually’ use your D&A capabilities to increase adoption & realize business value.

    Many organizations have invested in specialized teams for delivering data. But the real value from data comes only when your employees use it. Data fluency is an organizational capability that enables every employee to understand, access, and apply data as fluently as one can speak in their language. With more fluent people in an organization, productivity increases, turnover reduces, and innovation thrives without relying only on specialized teams. Top roadblocks according to industry surveys include lack of trust in organizational data and lack of data skills in employees. You should a) assess your organizational fluency, b) establish a data concierge, & c) introduce interventions to strengthen access, trust, and adoption.

  4. Inverse your team’s thinking in 3 (counterintuitive) ways to be more responsive to your business needs.

    Large enterprises have multi-year roadmaps to advance their transformative D&A capabilities to include modernizing existing platforms, retiring legacy ones, or building new applications for business. A proven approach is centred around building a business-driven roadmap, using a modular foundational architecture, aligning cross-functional stakeholders, and allocating necessary talent for success. It sounds simple, but the execution is fraught with challenges.

    Patterns such as agile, data mesh, data fabric, and delta lake guide IT leaders for dealing with them, but there are 3 antipatterns in how work is managed that slow organizations down. You are likely experiencing them! If so, you should a) design work for outcomes, not functions, b) choose products over projects, & c) choose teams over staffing. These tactics will increase flexibility in your roadmap execution, responsiveness to your business needs, and effectiveness of your teams.

  5. Reimagine how you bring value to business – move fast with Solutions as a Service!

    Salesforce and Amazon pioneered commercially successful IaaS, PaaS, and SaaS models in cloud computing that gradually shifted significant portions of responsibility for bringing value from the client to the service provider. The benefits of agility, elasticity, economies of scale, and reach are well known. Since then, countless products have emerged in these 3 categories. But they are limiting & constrain clients from going one step further towards productized services, what we call, Solutions as a Service!

    You must combine products, problem solving & expertise together in one easy to use solution. This is inevitable given the sheer pace at which technology is evolving. This new category requires a shift in thinking and will give you a source of advantage like how the early cloud adopters received during the last decade. Infocepts offers many domain-driven solutions in this category to include data products such as e360 for people analytics and AutoMate for business automations.

Moving forward from ideas to actions

D&A leadership will be challenged like never before in 2023. You can either keep doing more of the same, or take a step back, reflect, and lean-in on new & better ways to move forward on your D&A initiatives. To learn more about the transformational D&A initiatives that you must get done, download our advisory note for the full analysis, examples, and recommendations.

Recent Blogs

58% of those companies who have implemented AI report increased efficiency and decision-making throughout their teams. Yet only 12% of companies are actually benefiting from this technology.

AI is rapidly reshaping the business landscape and is now poised to revolutionize decision-making processes. AI-supported decisions are changing how businesses operate and enhancing the effectiveness, speed and accuracy of decision-making processes. Many organizations have made great strides towards implementation but it’s a complicated, time consuming process that often leads to failure.

Organizations need to know what challenges they’re up against when attempting to leverage AI-assisted decision-making and the best practices that help them drive widespread value from their data.

Top challenges which organizations face in implementing AI-assisted decisions

Though AI-assisted decision making is a quickly growing field, many data and analytics executives struggle to successfully implement and scale these solutions within their organization. Here are the top challenges you’ll need to tackle:

  • Poor business case for AI
    Without a clear strategy and business case, organizations might not have a good understanding of what they hope to achieve with AI-assisted decision-making, making it difficult to determine if implementation is successful. Also, it can be difficult to integrate the solution into existing business processes, gain support of key stakeholders, measure ROI, and identify opportunities for ongoing improvement.
  • Data quality
    One of the biggest challenges in implementing AI-assisted decision-making is ensuring that there’s copious amounts of high quality data. Poor data quality can negatively impact the accuracy of AI algorithms and limit the ability to provide meaningful insights, leading to greater inefficiencies.

Best practices for implementing AI-assisted decision-making

Implementation of AI-assisted decision-making requires careful consideration of these challenges and a strategic approach to ensure efforts are delivering measurable business value and growth – and placing AI-powered insights into the hands of the organization. Below are a few best practices data and analytics executives should embrace:

  • Develop a comprehensive strategy
    A clear strategy helps align AI-assisted decision-making with overall business goals and ensure that resources are allocated effectively. High performing AI adopters tend to link their AI strategy to business outcomes. Executives need to define their business objectives and where AI can add the most value; assess their existing infrastructure to determine what must be in place to support the solution; conduct a feasibility study to understand the efficacy and cost of implementation; secure buy-in from key stakeholders; and develop a roadmap for implementation, including budget, milestones, and timeline.
  • Foster collaboration and communication
    Data and analytics teams, technology partners, and stakeholders from all levels of the organization should be involved in the design, development and implementation process to ensure all needs and concerns are taken into account. Establish regular communication channels and encourage cross-functional collaboration to facilitate open discourse about the status and progress of AI projects to increase buy-in and ensure alignment on the goals of AI-assisted decisions.

There are many other challenges which organizations face and best practices which will help you realize the full potential of AI-assisted decision-making. Read our ebook to learn more tips.

Check out Infocepts AI as a Service, an end-to-end solution providing 360° support to take care of all your woes and embed best practices in your implementation process easier than ever.

Recent Blogs

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.

Get your copy of key strategies for enterprises to ensure secure DevOps in the cloud.

Read Now

Recent Blogs

Post-pandemic, the increase in consumer demand presents once-in-a-generation opportunities to retail industry players. In 2021, retail sales1 increased 14.1%—the highest growth in the past two decades. US consumers spent more than $1 trillion on retail products as compared to 2020. And this increase seems to be continuing.

Capitalizing on Top Retail Trends – The D&A Scope

Leveraging post-pandemic changes in consumer behavior and expectations requires retailers to innovate data-powered product and processes. Here we present a roundup of retail industry trends and how data and analytics (D&A) initiatives can support them.

Invest in Retail AI Solutions to Improve your Digital Presence

A strong online presence is essential today, whether a business is service-based, brick-and-mortar, or strictly ecommerce. A 2020 eMarketer study2 reports that we spend nearly eight hours each day doing digital activities. Today out of the total retail sales, online retail and ecommerce accounts for about 19% while cutting-edge technology further accelerates online sales.

Retail AI solutions are redefining face-to-face communication and selling. The future for ecommerce looks increasingly more promising as multiple players enter the space and expand their global presence. And compared to traditional retail businesses, it’s easier for companies to scale online while realizing low associated costs.

From a consumer angle, 69% of new buyers came from Asia Pacific in 2020. One US fashion retailer sought to expand its business there, as well as in Europe and the Middle East. Infocepts helped it elevate eCommerce sales with a cloud-native, sales intelligence platform it then rolled out in 53 countries. Providing real-time insights to all its sales associates, the company’s new, data-driven solution helped it generate an additional revenue stream of $50 million plus.

Provide Omni-Channel Experience by Lowering Time-to-Insights

Customer behavior is changing, with their expectations being higher than ever. Easy access to information, multiple buying options, and rapid technological advancements provide them with more ways to shop online. An omni-channel approach focuses on improving the consumer experience while engaging them via more platform options. Infocepts has helped many companies worldwide to implement D&A solutions based on modern retail business, selling, and product strategies. These include:

  • BOPIS (buy online, pickup in store)
  • BOSS (buy online, ship to store)
  • View in your room (with the help of virtual reality apps)
  • Try before you buy

While consumers shop from the comfort of their homes, a unified, omnichannel experience can come as close as possible to that of a brick-and-mortar store. It all starts with your enterprise having a single source of data. Such coordinated handling of data, insights, and the market scenario enables it to quickly respond to changing customer behavior.

Wrestling with its legacy data system and lack of real-time analytics, one retailer was challenged while executing such a strategy. Upon engaging Infocepts, we rejuvenated its omnichannel operations with real-time data integration by way of our reusable Real-Time Data Streamer (RTDS) accelerator, which fast-tracks real-time intelligence for clients. Our solution quickly optimized inventory, shipping, order fulfilment, and workforce management for the client’s distribution center, reducing its costs by $3.6 million in three years.

Navigate Workforce Shortage Proactively with Best-in-Class Analytics

COVID-19 sent massive disruptions through global markets and workforces, with 87% of retailers reporting recruiting difficulties in 2021. The combination of rising customer expectations, heavier work demands on associates, and the new ways of working post-pandemic have changed how retailers are able to fulfill their staffing needs. Data and analytics can address such challenges by –

  1. Analyzing historical workforce data
  2. Predicting future needs to better match customer demand with real-time decisions

Another of our clients had issues with scheduling, staffing, and payroll management in its global retail locations due to limited visibility across workforce management data points. We helped the client improve efficiency and productivity by integrating its workforce management with store operations data. This helped optimize daily operations, and a variety of metrics and dashboards improved staffing, scheduling, and payroll management across its worldwide locations.

Optimise Inventory Planning with Real-Time Analytics

Post-pandemic, big retailers rushed to build up inventories amidst soaring consumer demand and supply chain bottlenecks—in some cases going so far as to rent their own cargo ships. Shortly thereafter companies tried selling off excess inventory. In the US, inventories rose $44.8 billion for companies on S&P consumer indexes; retailers were paying more for storage while prices of goods dropped.

Today, real-time, data-driven decisions enable companies to revamp their supply chain by achieving speed and accuracy in inventory management. During changing customer demand and other external trade factors, Infocepts helped another retailer exercise real-time decision making for inventory planning. Including multiple leading and lagging indicators, our solution integrated real-time data along with consumer behavior to predict demand.

Be Agile with Data-driven Intelligence at Every Step

Data-driven decision making is essential for growth and strategic planning in any organization. But simply using data is not enough. Agility—the ability to quickly react amidst constant change within your decision-making framework—is also critical for success.

For example, Amazon Go (still in beta), uses 3D camera technology and RFID3 to automatically detect which products customers are picking up, permitting them to leave the store without a formal checkout process.

The following technology innovations are already in use (or in beta):

  • Cameras and Wi-Fi measure the flow of instore traffic, thus helping retailers schedule their workforce.
  • Augmented reality (AR)/virtual reality (VR) coupled with eye tracking can predict how customers react to retail displays. This helps to position products better so as to increase sales.
  • AI for retail operations includes customer analysis, demand prediction, inventory optimization, and competitive research to help companies stay ahead through data-driven decision making.
  • Robots as sales assistants, drone deliveries, smart mirrors, devices with voice-based search, and digital beacons combine to provide cost savings while enhancing the shopping experience.

Armed with real-time data analytics, retailers will be able to act more quickly, test multiple concepts, and constantly improve sales and customer experience with agility.

Infocepts recently collaborated with Neumont College of Computer Science in Salt Lake City to implement Power BI reporting and its functionalities using Microsoft’s HoloLens 2. It lets users see product data in the 3D HoloLens world. Read about it here: Rethink Retail Analytics with Real Time Data.

Promote Interoperability with Evolving Industry Standards

For systems to be interoperable, they must be able to exchange data and present it in a way that is easily understood by users. Data interoperability is extremely important for growth in retail. The fusion of multiple data points offers huge value in operational insights and the ability to create collaborative workflows to improve process efficiencies. Organizations that use data to accelerate growth realize a faster ROI from multiple investment areas within their business.

A well-known global research firm offers a comprehensive view of consumers and the market through analytics to hundreds of its client retailers and packaged-goods manufacturers. But due to multiple legacy and out-of-the-box reporting tools within its infrastructure, its challenge was to streamline insights to provide a consistent user experience. Infocepts helped it create a unified portal to access 200+ analytical insights across 30+ applications.

The centralized portal enhanced data interoperability to improve overall effectiveness of its analytics ecosystem. After just three months, the company realized 50% higher engagements and interactions with data insights.

To remain competitive and excel, today’s retailers require help from strategic D&A experts. Infocepts helps them establish data interoperability, resulting in a single source of truth for organizational decision making.

Take Advantage of New-Age Retail Trends

The post-pandemic retail surge and rush to find innovative ways to better manage customer expectations will continue for many more years. Retailers who use data assets to transform their products, services, and business model will thrive. In order to accomplish this, you must promote a culture of data-driven decision making, prioritize D&A investments, and closely monitor your ROI. Partnering with Infocepts—a specialized D&A consultant that is highly rated by industry leaders, has deep technological expertise, and comes with a broad spectrum of reusable solutions—can prepare you to address your today’s challenges while preparing you for tomorrow’s.

Want to learn more? Talk to us to accelerate your retail transformation with a data-driven strategy and cutting edge solutions to improve your business outcomes and customer experience.


Recent Blogs

Data is a valuable asset in this hyperconnected digital age; the world is producing more of it than ever before. Each day, 2.5 quadrillion bytes of data is generated by internet users alone. Data-driven intelligent automation is essential in making sense of this enormous amount of information. It helps enterprises transform data analytics and unlock new growth opportunities to stay ahead. And it helps you draw more value from your data to make profitable decisions.

If used correctly, data-driven intelligent automation enables enterprises to achieve unprecedented efficiency, speed, and results. It also enables businesses to accelerate ROI from their data and analytics investments.

What is data-driven intelligent automation?

Data-driven automation differs from process-driven automation. The latter directs automation such that it can’t veer from a predetermined direction. Humans need to intervene whenever an exception (an assigned task that doesn’t fit the established pathway) is detected.

Often described as being an initial digital transformation step, a process-driven system is limited to automating repetitive tasks. Being easy to implement and manage, it lets organizations automate simple processes within a few weeks, such that they quickly realize time and cost savings, improvements in accuracy, and efficiency gains. Thus it is widely used in delivering physical products, streamlining customer services, and managing financial resources. But process-driven automation has significant limitations.

Evolving from process-driven systems, data-driven technology is a more complex form of intelligent automation. It’s more powerful in handling intricate processes because it’s guided by both data and context.

A data-driven system combines robotic process automation (RPA) and artificial intelligence (AI) technologies that includes deep learning (DL), natural language processing (NLP), machine learning (ML), decision-making engines, and deductive and prescriptive analytics. High-quality data is central to data-driven automation because the AI learns from it to enhance processes.

A data-driven approach automates processes much quicker and more precisely than process-driven systems. It can also span multiple systems and data silos. Unlike process-driven automation, advanced AI empowers it to process unstructured data and engage neurological judgement which uses human-like, judgment-based interactions.

More importantly, a data-driven approach can automate non-repetitive processes, thus increasing the scope of automatable processes across an enterprise. It can automate tasks that previously had to be performed by humans. And it makes RPA smarter, enabling it to engage in processes that don’t follow prearranged pathways.

Why should organizations embrace data-driven automation?

Adopting data-driven intelligent automation can help businesses accelerate their ROI from data and analytics investments. It can:

  • Reduce the incidence of manual errors
  • Cut down on human resource spending
  • Increase analytics speed
  • Improve big data analysis
  • Allow data scientists to generate new insights to guide business decisions

The data-driven approach simplifies and speeds up insight generation by automatically analyzing massive volumes of streaming data and quickly identifying patterns. It accelerates the data preparation process, automates report generation, and empowers users to make data-driven decisions.

Additionally, it makes it easier to share findings with multiple stakeholders across the organization. Quick access to reliable insights ensures key users can ultimately drive transformation projects and the business.

Data-driven intelligent automation offers high-value and unique use cases—from predictive analytics and customer engagement to product optimization. What follows are a few key benefits derived from its adoption.

Faster insights yields profitable decisions – Speed is a crucial differentiator in any competitive market. Real-time insights are essential for successfully launching new products or improving services. Data-driven intelligent automation can make sense of large amounts of variables and metrics pulled from multiple sources. Because the full data value chain is automated, users can access meaningful insights from raw data as it arrives, thus helping teams to take timely and profitable actions. For example, it’s particularly useful in managing marketing campaigns, insurance and financial contracts, fraud management, healthcare processes, and more.

Boost productivity – From data preparation to visualization, it can take a lot of time to manage data lifecycles. Automation can significantly reduce this effort, freeing up your data science teams to concentrate on vital issues and core business areas. It eliminates complexities related to monitoring ever-changing variables. And it makes it much easier for users to interpret data, find hidden patterns, spot minute anomalies, and uncover complex insights often missed by manual approaches.

Reduce costs – Because it reduces time spent on data preparation, analysis, and modeling, data-driven automation results in significant savings. SaaS-based platforms enable businesses to quickly scale data analytics without having to make large investments in building and managing AI capabilities in-house.

Intelligent automation by Infocepts

Data-driven systems are better at empowering businesses to take full advantage of intelligent automation. Hence new-age companies use data-driven automation to elevate customer experiences, improve processes, prevent waste and fraud in the industry.

While other modernization endeavors are ongoing, many organizations find it challenging to embrace data-driven intelligent automation within the limitations of their IT ecosystem. Infocepts can help. We design intelligent services to automate complex use cases and deliver autonomous insights to users having an immediate need. It leverages cutting-edge technologies—including AI, NLP, ML, natural language generation, computer vision, RPA, hyper automation, and low code—to deliver intelligent services that work perfectly un concert with user-driven demands.

Learn more about Infocepts data-driven intelligent automation solution and how it helps organizations reimagine their operating models and service delivery using AI and ML.

Recent Blogs

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

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

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

Recent Blogs

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

Recent Blogs