InfoCepts

  • 4 Challenges faced By Data & Analytics Leaders

    The data and analytics space has grown rapidly in recent years, creating new problems for D&A leaders. Data volume and sources have increased and business leaders want insights to be more readily available at a lower cost. In addition, enterprises also need to maintain trust in their data; after all, each stage of the business chain—from day-to-day operations to long-term objectives—requires confidence.

    According to an eBook by InfoCepts, today’s data and analytics leaders face four recurring issues:

    1. Shortening time-to-insight 

    2. Optimizing BI investments 

    3. Creating trust in data 

    4. Managing exponential growth in data volume and variety 

    This blog post discusses each of these challenges and how to address them.

    Challenge #1: Shortening time-to-insight

    Time-to-insight is critical because a delay in decision-making may result in missed sales possibilities and customer dissatisfaction.

    • Streamline your data modeling process by cutting unnecessary steps and using modern tools as per your use cases to accelerate insights. Front-end BI tools can process multiple data sources in real time and rapidly create transformations. Agility in the modeling process frequently leads to additional capabilities and reduced lead time.
    • To reduce wasted ETL, rationalize data requirements in your enterprise data warehouse. Perhaps some sources are not needed at all. Correctly categorizing your data can help shorten implementation cycles and save you 20-40% on ETL costs. 
    • Allow your employees to access their data using self-service BI. Too often, people have to wait for IT staff availability when they want reports or dashboards completed. This is why it is so important for self-service to be a priority for leaders.

    Challenge #2: Optimizing BI investments

    The data explosion in the last few years has created a demand for analytics, but budget allocations have not kept pace with this trend. As a result, D&A leaders are constantly challenged to do more with fewer resources. Here are some suggestions for boosting BI efficiency and reducing costs.

    • Use automation. The operational costs of BI can balloon because of the amount of time and money spent on repetitive tasks like regression testing, monitoring, and deployment activities. Automation can make these processes cheaper and less prone to errors.

      To decrease manual touchpoints during upgrades by 50–75%, InfoCepts developed a plug-and-play Analytics Platform Automation Kit (APAK) accelerator and UI path code. Learn more about it here.
    • Consider a global execution model to reduce implementation and operations costs. Some companies are hesitant to consider onsite/offshore models for various reasons, including data security, time difference, and cross-team collaboration. But there are methods to overcome these challenges. For example, some enterprises use implementation partners (their offshore development centers) within their private, secure networks to guarantee the security of their data. Furthermore, COVID-19 proved that teams could function effectively without being co-located.

     Challenge #3: Creating trust in data

    Trust is essential to the successful use of data. People have to be confident that the information is accurate and reliable. But many enterprises struggle to integrate their data and maintain one accurate version of the truth. Perhaps data comprehension is inconsistent among departments, or there are data quality issues. 

    • Automated quality checks – Data reporting passes through a variety of transformation layers in a complicated multiple-source setup. Although regular ETL process enhancements can improve flexibility, the constant pressure to meet commitments often means reexamining test cases becomes less of a priority. If this continues, it could result in decreased data quality.

      A good defense is to build a list of important metrics that must be validated. Every time the reporting data changes, an automated quality check process should be triggered and a good process should be in place to address the data quality issues quickly.  
    • Data certification – This is especially important for companies that have implemented self-service BI. Duplication and discrepancy can arise in ungoverned environments where users generate and share analytics with other users. Having a group of data stewards who can certify data sources and reports before making them available to more users can help establish trust.

    Challenge #4: Managing exponential growth in data volume and variety 

    According to Domo’s “Data Never Sleeps 7” (2019), 188 million emails, 500,000 Twitter posts, 200,000 Skype calls, and 18 million text messages are sent per minute. Aside from the surge in the quantity of data, there has also been a tremendous increase in semi-structured and unstructured data (social media shares and likes, videos, scientific data, satellite images, etc.). All these make it increasingly difficult for businesses to keep up with the growing storage and computing capacity demands. Here are some tips for addressing the problem:

    • Assess whether moving to the cloud makes sense for your business goals. The initial investment for on-premise applications, including servers, networking, and security, is expensive. Cloud is a fantastic option for managing enormous and unpredictable data volume. It allows companies to get started with minimal infrastructure while still providing flexibility in terms of capacity expansion. 

    Another benefit is the ability to instantly increase or decrease storage and/or processing power on demand. There is no denying that cloud infrastructure vendors and data analytics companies have many competitive offerings. However, it is prudent to have experts review and recommend the best solutions. 

    • Consider building a data lake to manage and store both semi-structured and unstructured data. Traditional data warehouses cannot deal with unstructured and semi-structured data. A data lake may be beneficial. It is essentially a centralized repository that offers the advantage of storing unstructured, semi-structured, and structured data at any scale.

      InfoCepts has designed and built data lakes for several finance and marketing enterprises.

    The four recommendations listed above can assist you in overcoming time-to-insight, increasing cost, data trust, and data volume issues. They can help you build a solid and future-ready analytics system.

  • Effective Data Visualization Strategies to Bridge the Gap between Data and Users

    The McKinsey Global Institute says that data-driven organizations are not only 23 times more likely to gain customers. They are also 6 times more likely to retain them—and 19 times more profitable. There is no doubt that effective data visualization bridges the gap between end users and data. But many companies have yet to harness its power.

    Did you know that…

    “90 percent of enterprise data is never analyzed”

    “61 percent of business users depend on IT teams to analyze data “

    “50 percent of information is in silos”

    “The adoption rate for business intelligence (BI) and modern analytics is 35 percent”

    “The data literacy rate of enterprise decision-makers is only 24 percent”

    The importance of user adoption

    In a fast-changing world, trends are unlikely to last. They tend to change within a few weeks, which is why agility is more crucial than strength. Anyone slow to react misses out on significant opportunities.

    With faster decision-making becoming critical, it is important to have instant access to data and understand it quickly to ensure speedy and informed decision-making. Proper data visualization with accurate data interpretation empowers executives to act decisively and reduce risk.

    But many enterprises are failing at user adoption. Gartner* states that organizational BI and analytics adoption is only at 30 percent, whereas a Harvard Business Review Analytic Services survey found the rate at 27 percent. Low user adoption rates remain widespread and affect IT executives negatively.

    Reasons for poor user adoption

    The most common challenges to user adoption include poor user experience, poor report performance, unavailability of data, and the inability to understand data. These reasons are valid, but they are highly contextual, too.

    InfoCepts surveyed analytics practitioners within its customer base to identify the top reasons that are negatively impacting user adoption:

    1. IT dependence and lack of self-service

    There is a lack of self-service BI capabilities and coordination between teams. The development process is rigid, and there is too much dependency on the IT team.

    2. Data access issues

    Data is in silos, the required information is not handy, data security is rigid, and there is no access to relevant reports.

    3. Data literacy issues

    Users lack data literacy and are unaware of design best practices in data visualization. Moreover, they may not access capability development and training in data analysis tools.

    4. Lack of insights and usability

    Poor interface design and user experience, lack of descriptive and predictive features, and using multiple BI tools lead to inconsistent experiences. As a result, users are unable to dive deeper into data.

    5. Process and methodology issues

    There is a flawed development process and a lack of a change management. There may be too many tools involved, and the data is not trustworthy.

    InfoCepts also found that user adoption gets impacted by the lack of business value and issues within an organization’s people and culture.

    Strategies to drive user adoption with data visualization

    Bridging the gap between data and users requires the proper use of data visualization. InfoCepts recommends the five initiatives that can improve user adoption for analytics applications:

    1. Enterprise information portals

    Integrate all analytical assets in a centralized platform to eliminate data silos and simplify data access to non-technical and technical users. An information portal is an interactive application with a centralized repository of dashboards, data, enterprise-level analytics applications, and reports. The secure platform must enable multiple users to quickly access the data relevant to their business functions without relying on third parties.

    Information portals must be personalized and enable secure access to be effective. Moreover, they have a modern interface design, provide monitoring and alerts, and allow collaboration.

    2. Address data literacy issues

    Users who are more data-aware will value analytics. Encouraging a data-driven culture requires prioritizing data literacy. Organizations must also help users understand how to use data and interpret numbers with effective data visualization. Driving data literacy may involve a cultural change along with a technology-driven initiative.

    Organizations can implement data literacy with an assessment program, starting with categorizing data users and their roles and encouraging or providing workshops, courses, self-paced learning, and opportunities for online certification. A ‘train the trainer’ program and a 30-60-90-day plan can also help.

    3. Enable self-service analytics

    As soon as the organization is empowered with data, implement self-service analytics for business users to establish a data-driven culture.

    Self-service analytics provides insights on-demand without relying on an IT team. Implementing it calls for a structured and methodological approach. InfoCepts recommends a three-phase self-service enablement approach to address governance strategy, data management, data quality, data preparation, and data security & privacy:

    • Phase 1: Focus on the system, use case, and process while defining KPIs, data preparation, and business rules. Create basic reports.
    • Phase 2: Focus on defining frameworks and templates.

    Phase 3: Focus on governance, ensuring that all the frameworks and processes are followed to ensure self-service implementation.

    Successful implementation of self-service analytics can reduce dependence on IT teams, encourage collaboration, enable access to multiple data sources, and simplify change management.

    4. Narrate stories from data

    Effective data storytelling communicates actionable insights through narrative and visual stories. It is an essential skill that helps users gain and broadcast quick insights from data.

    Even when a user has access to data, it is challenging to convey business insights meaningfully. Data storytelling turns data into insights. However, most organizations do not have sufficient skills for effective data visualization to build meaningful reports or dashboards. As a result, users lack actionable insight, take too long to gain insights, or fail to become flexible and interactive.

    Operationalizing data storytelling can reduce time to insight while delivering meaningful outcomes. These factors lead to enhanced user adoption. Creating useful dashboards requires knowledge of business and data, understanding how data is represented using the best practices in data visualization and design, and optimizing the use of technology.

    5. Implement augmented analytics

    Soon more than 50 percent of analytics queries will be generated through search using natural-language processing or voice, or will be automatically generated.

    Augmented analytics is a new data analysis approach to automate insights with natural-language generation (NLG) and machine learning (ML). It transforms how analytical content is consumed, developed, and shared while accelerating the time for insights in a business. These insights let users act fast on data and make critical decisions.

    Augmented analytics impacts user adoption by simplifying analytics and making them easier to use. It is a crucial feature of modern BI and analytics, self-service, data science, and data preparation platforms. Automated insights can be embedded into conversational analytics and enterprise applications to mainstream their usage.

    InfoCepts can operationalize next-generation BI capabilities using commercial out of the box tools or a custom development approach that seamlessly integrates with your preferred enterprise BI tools. It also applies a persona-based implementation strategy to focus on the user during development, ensuring that specific requirements get addressed.

    Learn more about how InfoCepts improves analytics adoption in this eBook.

  • The Future of Data Analytics Careers and How to Succeed in this Field

    Data analytics (D&A) plays a crucial role in solving the toughest challenges in society, such as pandemic response, climate change, designing clinical trials, response to wildlife, predicting the needs of healthcare resources, making policy recommendations, and many more.

    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 it’s expected to be 20 times larger by 2024. The estimated open positions are at 620,000, but this could increase to 1.6M over the next four years.

    Data continues to grow exponentially for businesses, too, and there is an ever more pressing need to turn this data into insights that can improve the customer experience. D&A platforms are keeping up with the evolving demand for data insights, resulting in new platforms, APIs, open-source tools, and algorithms to meet the needs and solve gaps faster.

    To keep up, businesses will be seeking full-stack engineers who can create the data integration layer, standardize the data consumption layer, and enable prescriptive and descriptive reporting with embedded AI and ML models.

    Likewise, they will need multi-skilled roles to handle the end-to-end data-to-insights journey. Businesses will need to reskill and upskill if they cannot fill the skill gap due to the supply and demand.

    As analytics continues to be mainstream, the future of data and analytics careers seems brighter than ever. A modern data scientist should also carry data engineering and data storytelling skills to generate and communicate predictive and prescriptive insights to business stakeholders.

    Tips on succeeding as a data analytics professional

    Whether you are an aspiring data analytics professional or have been in this field for quite some time, you must continuously learn new skills to succeed. Here are some tips on setting yourself up for long-term success in this field:


    Develop expert-level competencies

    With business cycles becoming shorter, data and analytics have become all about speed, innovation, and continuous improvements in quality. Data science professionals need to think about how long it takes to recognize and understand the value they are generating for end-users. If it’s not clear or if it’s taking too long, they may be doing something wrong.

    Data and analytics companies don’t just hire professionals to access specific tools. They are hiring for expert-level competency in cloud and data engineering, analytics and data management, business consulting, and service management. To succeed in this field, it’s important to develop a T-shaped profile at the minimum, though Pi-shaped is much better. It’s best to develop one or more competencies in the formative years, 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 studying Java or R or Python for statistical programming. Python is one of the most common coding languages required in data science and data engineering roles, along with Perl, C/C++, or Java. A data analyst or engineer should also be capable of working with unstructured data.  

    Seek opportunities to learn 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 the data analyst’s job is communicating results from a technical perspective to non-technical audiences, such as the marketing or sales departments. To handle data appropriately, a data analyst must understand the demands of their non-technical team members and also empower them with quantified insights.

    You need to be creative with data to help answer questions or solve problems. You must also apply the appropriate data visualization techniques to get your point across and enable your audience to understand the information easily. You also need presentation skills to keep your audience engaged.

    Pursue continuing education

    Having a Bachelor’s degree in computer science, sociology, physics, or statistics will provide you with the ability to handle and analyze massive 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.

    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.

    Do not forget your soft skills

    Problem solving and collaboration are among the most important soft skills a data analyst needs.

    Problem-solving is an essential aspect of data analysis. The bulk of analytics, about 90%, is focused on critical thinking. Thus it is vital to know what questions to ask. You will get the answers you need if the queries you ask are based on a firm’s knowledge of 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. These soft skills can make you more effective at convincing people to act on the findings and help you resolve problems or conflicts.


    Working at InfoCepts

    If you are a driven, dedicated, and curious individual that is always ready to tackle data-driven problems, then a data analytics careeris a good fit. You must work in a company willing to support your professional and personal growth.

    InfoCepts was recently named one of the best places to work for data scientists by Analytics India Magazine, alongside some of the biggest names in analytics. The company is known for nurturing a culture of development and investing in helping its associates become the best versions of themselves. If you are interested in pursuing a data analytics careerin a global company, explore the Careers section of the InfoCepts website.

  • IA vs RPA – How Does Intelligent Automation Differ from Robotic Process Automation?

    Robotic process automation (RPA) andintelligent automation (IA)remove the burden of repetitive and tedious tasks that draw out organizational productivity.These technologies free up resources so they can focus on high-value tasks. However,RPA and IA are different from each other. In this blog post, we define each concept and outline itsunique benefits.

    What is intelligent automation?

    Intelligent automation (sometimes called intelligent process automation) is a digital solution that combines AI, machine learning, intelligent document processing, natural language processing, and robotic process automation (RPA). In simplest terms, using IA means deploying a machine or technology to perform mundane tasks intelligently. IA can process higher-functioning tasks requiring some analysis, reasoning, decision, and .

    Businesses need to implement a self-evolving IA strategy toconstantly improve process efficiencies, cope with digital disruptions, and keep up with competitors.

    Find out how IA can increase enterprise agility.

    What is robotic process automation?

    Gartner defines RPA as a noninvasive integration technology that automates repetitive, routine, and predictable tasks via an orchestrated user interface interactions that emulate human actions. It refers to scripts, software, or applications that automate rule-based, repetitive, and straightforward tasks that are typically time-consuming when manually performed. Thus, it helps reduce labor costs while preventing human error.

    RPA can either be unassisted or assisted. Unassisted RPA deploys bots on a centralized server for manual control, making it ideal for automating workflow scheduling and end-to-end tasks from a central aspect of control. Assisted RPA deploys bots on individual desktops, so a human resource can efficiently perform some tasks while relying on the bot to perform more technically complex or cumbersome tasks.

    Learn how InfoCepts automated a high-volume, time-consuming, and repeatable report generation process using RPA.

    How do RPA and IA work together?

    Intelligent automation is often mistaken for RPA. They’re related, but they’re not exactly similar to each other. IA platforms may have RPA capabilities, but RPA does not require IA capabilities to work.RPA systems are programmed to follow a strict set of rules, which can be problematic in some cases. Following are some use cases where IA is :

    • Financial services – IA helps banks and other financial institutions speed up customer response times while complying with stringent regulations.For example, an IA platform can replace legacysolutions to ensure that loans are closed faster while improving the overall client experience.
    • Government – IA can introduce efficiencies for processes that involve manual form-filling, such as applications for passports, birth,and marriage certificates, driving licenses, etc. This eliminates mundane tasks to allow employees to focus on serving constituents.
    • Manufacturing – Using IA in supplychain management allows all paperwork to be done in a centralized digital location.This saves time by reducing manual input while reducing human errors. For example, automation can connect existing ERP with other systems to create a central point of reference for all orders and thus reduce lead time.

    Intelligent automation uses RPA to automate repetitive and routine tasks. It simulates human intelligence using artificial intelligence technologies and provides the techniques and tools to perform high-value tasks that require decision-making. IA can increase process efficiency, optimize back-office operations, improve the customer experience, reduce costs and risks, optimize workforce productivity, and ensure effective monitoring and fraud detection.

    Get in touch with InfoCepts to learn how data-driven IA and RPA can improve your business.

    Additional sources: https://www.nitcoinc.com/blog/intelligent-process-automation-vs-robotic-process-automation/

    https://www.infocepts.com/webinars-and-videos/increase-enterprise-agility-with-intelligent-automation/

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