Tag: Big Data Analytics

Why Data Engineering Is the Backbone of Digital Transformation

January 20, 2026

And why transformation fails when it is treated as a support function Many digital transformation programs fail quietly. Systems are implemented. Tools are adopted. Dashboards proliferate. On paper, progress appears steady. Yet decision-making remains slow, insights feel fragile, and the organization struggles to convert data into sustained advantage. When this happens, attention often turns to adoption, skills, or culture. Rarely does leadership question the structural layer underneath it all: data engineering. This is a costly blind spot. Because while digital transformation is discussed in terms of customer experience, automation, and analytics, it is data engineering that determines whether any of those capabilities can scale reliably. Why Data Engineering Is Commonly Undervalued At a leadership level, data engineering is often viewed as technical groundwork—important, but secondary. It is associated with pipelines, integrations, and infrastructure rather than outcomes. This perception is understandable. Data engineering operates mostly out of sight. When it works, nothing appears remarkable. When it fails, problems surface elsewhere: in dashboards, reports, or AI models. As a result, organizations tend to overinvest in visible layers of transformation while underinvesting in the discipline that makes them sustainable. Digital Transformation Is Not About Tools — It Is About Flow At its core, digital transformation is about changing how information flows through the organization. Automation replaces manual steps. Analytics informs decisions earlier. Systems respond faster to changing conditions. None of this is possible if data moves slowly, inconsistently, or unreliably. Data engineering is the function that designs and maintains this flow. It determines: When these foundations are weak, transformation becomes episodic rather than systemic. Why Analytics and AI Fail Without Engineering Discipline Many organizations invest heavily in analytics and AI, only to see limited impact. Models are built, proofs of concept succeed, but scaling stalls. The reason is rarely algorithmic sophistication. It is almost always engineering fragility. Without robust pipelines, models depend on manual data preparation. Without stable data structures, logic must be rewritten repeatedly. Without disciplined change management, every update risks breaking downstream consumers. For CXOs, this manifests as analytics that feel impressive but unreliable. Over time, leadership confidence erodes—not because insights are wrong, but because they are brittle. Data Engineering as Business Infrastructure A useful shift for senior leaders is to think of data engineering the way they think of core business infrastructure. Just as logistics enables supply chains and financial systems enable control, data engineering enables decision infrastructure. It ensures that: When this infrastructure is strong, analytics scales quietly. When it is weak, every new initiative feels like starting over. The Hidden Link Between Engineering and Agility Organizations often speak about agility as a cultural trait. In reality, agility is heavily constrained by structure. When data pipelines are fragile, teams avoid change. When data logic is scattered, improvements take longer than expected. When fixes require coordination across too many components, momentum slows. This is why many organizations feel agile in pockets but rigid at scale. Strong data engineering reduces the cost of change. It allows experimentation without fear. It makes iteration safer. In that sense, engineering discipline is not opposed to agility—it enables it. Why Treating Data Engineering as “Plumbing” Backfires When data engineering is treated as a support activity, several patterns emerge. First, it is under-resourced relative to its impact. Skilled engineers spend time firefighting rather than building resilience. Second, short-term fixes are rewarded over long-term stability. Pipelines are patched instead of redesigned. Complexity accumulates silently. Third, accountability blurs. When issues arise, responsibility shifts between teams, reinforcing the perception that data problems are inevitable. Over time, transformation initiatives slow not because ambition fades, but because the system resists further change. The CXO’s Role in Elevating Data Engineering Data engineering cannot elevate itself. It requires leadership recognition. When leadership frames data engineering as core infrastructure rather than background activity, priorities shift naturally. A Practical Signal to Watch CXOs can gauge the health of their data engineering backbone with a simple observation: Do analytics initiatives feel easier or harder to deliver over time? If each new use case requires similar effort to the last, engineering foundations are weak. If effort decreases and reuse increases, foundations are strengthening. Transformation accelerates only when the system learns from itself. Explore our latest blog post, authored by Dipak Singh: The True Cost of Poor Data Architecture The Core Takeaway For senior leaders, the key insight is this: Organizations that recognize data engineering as the backbone of transformation invest differently, sequence initiatives more thoughtfully, and experience less fatigue over time. Transformation does not fail because leaders lack vision. It fails when the infrastructure beneath that vision cannot carry the load. Get in touch with Dipak Singh Frequently Asked Questions 1. How is data engineering different from analytics or BI?Data engineering builds and maintains the pipelines, structures, and systems that make analytics possible. Analytics and BI consume data; data engineering ensures that data is reliable, scalable, and reusable. 2. Can digital transformation succeed without modern data engineering?Only in limited, short-term cases. Without strong data engineering, initiatives may succeed in isolation but fail to scale across the organization. 3. Why do AI initiatives stall after successful pilots?Most stalls occur due to fragile data pipelines, inconsistent data definitions, or lack of change management—not model quality. These are data engineering issues. 4. How can executives assess data engineering maturity without technical depth?Look for signals such as reuse, delivery speed over time, incident frequency, and whether new initiatives feel easier or harder than past ones. 5. When should organizations invest in strengthening data engineering?Ideally before scaling analytics, AI, or automation. In practice, the right time is when delivery effort plateaus or increases despite growing investment.

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Illustration of data cubes connected to a server, with text

Why Companies Collect Data but Still Fail to Use It

January 7, 2026

The quiet breakdown between information and action Most organizations do not suffer from a lack of data. They suffer from a lack of movement. Data is collected relentlessly—transactions, operations, customers, systems, sensors. Storage expands. Dashboards multiply. Analytics teams grow. And yet, when decisions are actually made, the influence of data often feels marginal. This paradox is rarely addressed head-on. Leaders sense it but struggle to explain why data usage remains stubbornly low despite years of investment. The issue is not availability; the issue is that using data forces choices—and most organizations are not designed to absorb those choices comfortably. Data Collection Is Passive. Data Usage Is Confrontational. Collecting data is easy because it is passive. Systems generate data automatically. Little judgment is required. No one has to agree on what it means. Using data is different. It is active—and confrontational. It forces interpretation, prioritization, and accountability. It exposes trade-offs. It surfaces disagreements that might otherwise remain hidden. This is why organizations unconsciously optimize for accumulation rather than application. Data can exist in abundance without disturbing existing power structures. Using it cannot. The First Breakdown: Decisions Are Vague In many organizations, decisions are framed broadly—improve performance, drive efficiency, optimize growth. These statements sound decisive but are analytically empty. When decisions are vague, data has nowhere to attach itself. Analytics teams produce insights, but no one can say with confidence whether those insights should change anything. Data usage rises only when decisions are explicit. Until then, data remains informational rather than operational. Here’s our latest blog on: Business vs IT in Data Initiatives—Bridging the Gap That Never Seems to Close The Second Breakdown: Incentives Are Misaligned Even when insights are clear, they are often inconvenient. Data may suggest reallocating resources, changing priorities, or acknowledging underperformance. These implications rarely align with individual incentives or established narratives. When incentives reward stability over adaptation, data becomes threatening. It is reviewed, acknowledged, and quietly ignored. This is not resistance to data—it is rational behavior within the system. Until incentives and expectations align with evidence-based decisions, data-driven decision-making remains aspirational. Ready to clarify this for your organization? Contact us today. The Third Breakdown: Accountability Is Diffused In organizations with low data maturity, insights are everyone’s responsibility and no one’s accountability. Analytics teams generate reports. Business leaders consume them. Outcomes drift. When results disappoint, blame disperses. Using data requires ownership. Someone must be accountable not just for producing insight but for acting on it—or explicitly choosing not to. Without this clarity, data remains commentary, not a driver. Why More Data Often Makes Things Worse When leaders notice low data usage, the instinctive response is to collect more data or build more dashboards. This usually backfires. More data introduces more interpretations, more caveats, and more ways to delay decisions. Instead of clarity, leaders face cognitive overload. Instead of alignment, teams debate nuances. Abundance without focus leads to paralysis. This is why organizations with modest data but strong discipline often outperform those with vast, underutilized data estates. How Leadership Behavior Shapes Data Usage Whether data is used or ignored is ultimately a leadership signal. When senior leaders ask for data but decide based on instinct, teams learn that analytics is decorative. When leaders tolerate inconsistent metrics, alignment erodes. When data contradicts a preferred narrative and is quietly set aside, a message is sent. Culture follows behavior, not intent. Organizations that truly use data make expectations visible. They ask not just, “What does the data say?” But what are we going to do differently because of it? The Role of Timing Timing is an often-overlooked factor. Data frequently arrives after decisions are already mentally made. When insights come too late, they become explanations rather than inputs. This reinforces a damaging loop: analytics is seen as backward-looking, which justifies ignoring it for forward-looking decisions. Breaking this cycle requires integrating data earlier into decision workflows—not adding more analysis afterward. What Actually Changes Data Usage Organizations that close the gap between data and action do not start with tools. They start by clarifying decisions. They reduce metrics aggressively. They assign explicit ownership. They close the loop between insight and outcome. Most importantly, leaders notice when data is not used—and ask why. Usage increases not because data improves, but because expectations do. The Executive Reality For CXOs, the most important realization is this: Data does not create value by existing Data creates value by forcing choices If choices are uncomfortable, data will be sidelined Organizations that accept this reality stop chasing volume and start building discipline. They recognize that unused data is not a technical failure but a leadership one. Once that shift occurs, analytics stops being a background activity and becomes an engine for action. Most organizations are not short on data. They are short on decision clarity, accountability, and reinforcement. Until those conditions exist, data will remain visible in meetings but absent in outcomes. The organizations that move beyond this trap are not those with the most data but those willing to let evidence challenge comfort. That is when data finally earns its place at the table. Start by redesigning decisions—not dashboards. Talk with us about aligning data, authority, and accountability at the leadership level. Get in touch with Dipak Singh: LinkedIn | Email Frequently Asked Questions 1. Why do organizations with strong data infrastructure still struggle to use data? Because infrastructure solves collection, not decision-making. The real barriers are unclear decisions, misaligned incentives, and lack of accountability. 2. Is the problem more cultural or technical? Primarily cultural and structural. Technical limitations are rarely the main constraint once basic analytics capabilities exist. 3. How can leaders tell if data is actually influencing decisions? By asking what changed because of the data. If decisions would have been the same without it, data is not being used—only referenced. 4. Why does adding more dashboards often reduce data usage? Because it increases cognitive load and interpretation ambiguity, giving teams more reasons to delay or debate decisions. 5. What is the fastest way to improve data

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5 levels of data maturity: where most companies actually stand.

5 Levels of Data Maturity: Where Most Companies Actually Stand

December 29, 2025

Most leadership teams would describe their organizations as reasonably data-driven. Reports are circulated before review meetings. Dashboards exist for finance, operations, and business teams. Decisions are at least expected to be supported by numbers. Yet when critical choices need to be made—whether it is approving a capital investment, responding to a margin decline, or committing to a growth initiative—confidence often drops. Meetings slow down. Numbers are questioned. This gap between having data and using data for decisions is where data maturity truly reveals itself. And it is also where most companies overestimate their position. Why Data Maturity Is So Often Misjudged In many organizations, data maturity frameworks are interpreted as technology ladders: spreadsheets to BI tools, BI tools to data platforms, and platforms to AI. While tools matter, this framing misses the executive reality. From a CXO perspective, maturity is not about how modern the stack looks. It is about whether data consistently: Creates shared understanding across functions Reduces ambiguity at decision points Accelerates action instead of delaying it In practice, data maturity is an operating characteristic, not a technical one. It shows up in how decisions are debated, how quickly teams align, and how confidently leaders act. With that lens, most organizations fall into one of the following five levels. Level 1: Data Exists, but Is Fragmented At the first level of data maturity, data is plentiful but disconnected. Finance maintains its own spreadsheets, operations tracks performance in parallel systems, and business teams rely on locally created reports. Over time, individuals—not roles—become custodians of critical data logic. Reviews depend heavily on who prepared the numbers rather than on the numbers themselves. Leadership meetings focus on understanding the data instead of discussing outcomes. For CXOs, this stage feels chaotic. Decisions are often postponed because acting on untrusted information feels riskier than waiting. While this level is common in growing organizations, many underestimate how long remnants of this fragmentation persist. Level 2: Reporting Without Alignment As organizations invest in business intelligence and dashboarding, reporting becomes more structured. Metrics are tracked regularly. Review calendars are established. On the surface, this looks like progress—and it is. However, this stage introduces a more subtle problem: misalignment disguised as visibility. Different teams interpret the same KPI in different ways. Definitions vary slightly but meaningfully. One function optimizes for growth, another for efficiency, and a third for risk, all while referencing the same metric. Meetings begin to revolve around reconciling perspectives rather than deciding actions. At this level, CXOs often experience frustration. Data is available, but it does not converge the organization. Instead of enabling decisions, it fuels debate. Many companies stall here, believing the solution lies in better tools or more dashboards. If these first two levels sound uncomfortably familiar, it may be time for a structured reality check. A short, decision-focused data maturity assessment can help leadership teams: Clarify which decisions are being slowed down by data friction. This is not about adding dashboards—it is about restoring momentum at critical decision points. Level 3: Operational Visibility—The False Peak With time and discipline, reporting stabilizes. Definitions settle. Numbers are broadly accepted. Organizations can reliably explain what happened last month or last quarter. This is an important milestone—and also a dangerous one. At this stage, leaders have visibility but not necessarily control. Data explains outcomes after they occur, not while decisions are still adjustable. Root-cause analysis remains manual and retrospective. Forecasts rely more on assumptions than on analytical insight. For many CXOs, this feels “good enough.” Performance reviews run smoothly. The organization appears data-driven. As a result, ambition fades. This is the most common ceiling in enterprise data maturity. Level 4: Decision-Centric Analytics True maturity begins when analytics is explicitly designed around business decisions. not reports. At this level, the organization becomes deliberate about which decisions matter most and what data is required to support them. KPIs have clear ownership. Metrics are tied to business levers. Finance, operations, and business leaders work from the same underlying logic. The shift is subtle but powerful. Discussions move away from questioning numbers toward evaluating trade-offs. Scenario analysis becomes practical rather than theoretical. Decisions are made faster, with greater confidence. Reaching this stage is less about advanced analytics and more about governance. accountability, and leadership behavior. Tools support the transition, but they do not drive it. Level 5: Embedded Intelligence Very few organizations reach the highest level of data maturity, and fewer still need to. Here, analytics is embedded into everyday workflows. Predictive insights inform planning cycles. Prescriptive recommendations guide specific actions. Manual reporting Effort is minimal because insight delivery is largely automated. For CXOs, the experience changes dramatically. Less time is spent reviewing data, and more time is spent acting on it. Decisions feel calmer, not more complex. Data operates quietly in the background as a trusted partner rather than a focal point. Where Most Companies Actually Stand Despite years of investment in data platforms, analytics teams, and AI initiatives, Most organizations operate somewhere between Level 2 and Level 3. They have visibility but lack: Consistent metric ownership, Cross-functional alignment, and Decision-oriented analytics. The most common mistake is attempting to leap forward by adding new technology before addressing these fundamentals. This rarely works. Data maturity does not scale upward unless it is anchored downward. A Practical Reality Check for CXOs If leadership meetings frequently debate numbers instead of decisions, maturity is lower than it appears. If finance spends more time reconciling data than analyzing it, maturity is constrained. If analytics initiatives restart every few years under new labels, the issue is structural, not technical. These patterns are not signs of failure. They are signals of where the organization truly stands. Ownership beats automation. Clear accountability for data and decisions matters more than advanced pipelines. Consistency creates confidence. Stable definitions and repeatable logic drive adoption more than novelty. Context turns data into insight. Metrics without narrative invite misinterpretation and inaction. Speed matters—but only after clarity. Faster reporting amplifies value only when questions are well framed. Governance should guide, not gate.

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The Ultimate Guide to Data Engineering & Architecture

December 26, 2025

The Modern Data Stack Explained Simply Data engineering and data architecture are no longer back-office technical functions. They sit at the heart of how modern organizations generate insights, power analytics, and deploy machine learning at scale. The modern data stack has emerged as a response to legacy data warehouses, brittle ETL pipelines, and siloed analytics tools. For data engineers, data architects, BI leaders, and C-level technology executives, understanding how modern data platforms work—and how data engineering fits into them—is now a strategic requirement. This guide breaks down the modern data stack in simple, practical terms and explains how data engineering tools, architectures, and operating models come together. The Modern Data Stack Explained The modern data stack is a cloud-native, modular approach to data engineering and analytics. Data engineering sits at the core, enabling reliable data ingestion, transformation, and modeling. Modern data platforms prioritize scalability, flexibility, and analytics-ready data. The right data engineering tools reduce operational complexity and accelerate business insights. What Is the Modern Data Stack? The modern data stack is a collection of cloud-based data engineering tools that work together to ingest, store, transform, and analyze data efficiently. Unlike traditional monolithic systems, modern data platforms are: Cloud-native Loosely coupled Best-of-breed Core Layers of the Modern Data Stack At a high level, the modern data stack includes: Data Sources SaaS tools (CRM, ERP, Marketing platforms) Applications and product databases IoT and event data Data Ingestion ELT-based pipelines Batch and real-time ingestion Cloud Data Warehouse or Lakehouse Centralized analytics storage Elastic compute and storage Data Transformation SQL-based modeling Analytics engineering practices BI, Analytics & ML Dashboards, reports, and data science workflows What is the difference between a traditional data stack and a modern data stack?Traditional stacks rely on tightly coupled, on-prem systems, while modern data stacks use cloud-based, modular tools optimized for analytics and scalability. How Data Engineering Fits into the Modern Data Stack Data engineering is the connective tissue of modern data platforms. A data engineer is responsible for: Designing scalable data pipelines Ensuring data quality and reliability Optimizing performance and cost Enabling analytics and machine learning teams Without strong data engineering, even the best modern data stack will fail to deliver value. Key Responsibilities of Data Engineers Today Modern data engineers focus less on maintaining infrastructure and more on: Building resilient ELT pipelines Applying software engineering best practices Collaborating with analytics engineers and data scientists Supporting self-service analytics This evolution has reshaped data architecture itself. The Architecture Behind Modern Data Platforms Modern data architecture emphasizes separation of concerns. Key Architectural Principles Decoupled storage and compute ELT instead of ETL Schema-on-read Analytics-first modeling These principles allow data engineering teams to scale without rewriting pipelines every time the business changes. Is data engineering part of data architecture?Yes. Data engineering implements data architecture by building and maintaining pipelines, models, and data platforms based on architectural design principles. Modern Data Stack Tools Explained Data Ingestion Tools Modern data engineering tools prioritize reliability and ease of use: Managed connectors for SaaS data Change data capture (CDC) Event-driven ingestion Examples include Fivetran, Airbyte, and Kafka-based systems. Cloud Data Warehouses & Lakehouses These platforms form the foundation of modern data platforms: Snowflake BigQuery Amazon Redshift Databricks They provide elastic scaling and support both BI and ML workloads. Data Transformation & Analytics Engineering Transformation has shifted closer to analytics: SQL-based transformations Version-controlled data models Testing and documentation Tools like dbt enable data engineers and analytics engineers to collaborate effectively. What tools are part of the modern data stack?Common modern data stack tools include ingestion platforms, cloud data warehouses, transformation tools like dbt, BI tools, and orchestration frameworks. Why Organizations Are Moving to the Modern Data Stack Business Benefits Faster time to insight Lower infrastructure overhead Improved data reliability Better collaboration across teams Technical Benefits Simplified data engineering workflows Reduced pipeline brittleness Easier scalability For CIOs, CDOs, and CTOs, modern data platforms align technology investments with business agility. Common Modern Data Stack Use Cases Analytics & BI Self-service dashboards Operational reporting KPI tracking Data Science & Machine Learning Feature engineering Model training at scale Real-time predictions Product & Growth Analytics User behavior analysis Funnel optimization Experimentation platforms Can the modern data stack support real-time analytics?Yes. With streaming ingestion and real-time processing layers, modern data stacks can support near real-time analytics and ML use cases. Looking to modernize your data engineering architecture? Talk to our data engineering experts to assess your current data platform and design a scalable modern data stack. How to Choose the Right Modern Data Stack Key Evaluation Criteria Data volume and velocity Analytics and ML requirements Team skill sets Cost and governance needs Build vs Buy Considerations Modern data engineering teams must balance: Managed services vs custom pipelines Vendor lock-in risks Long-term scalability There is no one-size-fits-all modern data stack. The Future of Data Engineering & Modern Data Platforms Trends shaping the future include: Lakehouse architectures Data observability and quality automation AI-assisted data engineering Metadata-driven pipelines Data engineers will increasingly act as platform builders rather than pipeline maintainers. Will the modern data stack replace traditional data warehouses?In many organizations, yes. However, some legacy systems will coexist with modern data platforms for years. Frequently Asked Questions What is the modern data stack in simple terms? The modern data stack is a cloud-based set of data engineering tools that ingest, store, transform, and analyze data efficiently. How does data engineering differ from analytics engineering? Data engineering focuses on pipelines and infrastructure, while analytics engineering focuses on transforming data for analytics and BI. What skills does a modern data engineer need? SQL, cloud platforms, data modeling, orchestration tools, and software engineering best practices. Is the modern data stack only for large enterprises? No. Startups and mid-sized companies often adopt modern data stacks earlier due to flexibility and lower upfront costs. What are the best data engineering tools today? Popular tools include Snowflake, BigQuery, dbt, Airbyte, Fivetran, and Databricks. Ready to build a future-proof data platform? Explore our data engineering services or schedule a consultation to design and

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InsureTech Insights: Leveraging Alternate Data for Risk Assessment

InsureTech Insights: Leveraging Alternate Data for Risk Assessment

July 12, 2023

InsureTech is the latest buzzword that is making the headlines in the insurance sector and with good reason. From suitable risk assessment using alternate data to tapping big data analytics and artificial intelligence in insurance for better outcomes in diverse arenas, insurers are expected to step on the gas further across the next couple of years in this domain. Here is a brief glimpse into the same. 1.What are the key drivers of InsureTech? These are some of the major driving forces behind the InsureTech revolution that is sweeping the world today. Let us now learn a little more about the deployment of artificial intelligence in insurance. 2. How is AI used in insurtech?  Artificial intelligence in insurance and InsureTech are symbiotically linked due to the multifarious applications and use cases that have transformed the industry in recent years. Here are a few aspects worth noting in this regard: AI is beneficial for the entire InsureTech ecosystem in multiple ways, as is mentioned above. A closer look is also necessary at the various sources or types of alternate data that insurance companies can use for better risk assessment. 3.What kind of alternate data can help towards solving the credit risk? FAQs 1.What are the privacy and ethical considerations associated with using alternate data in risk assessment for InsureTech? InsureTech players must address privacy, ethics, and data validity when using alternate data. Key considerations include responsible data collection and usage, obtaining consent, ensuring analytical tool validity, fairness, and unbiased systems, data quality, regulatory compliance, and full disclosure principles. 2.Are there any successful case studies or real-world examples of InsureTech companies leveraging alternate data for risk assessment? There are many examples of InsureTech entities making use of alternate data for risk assessments. ZestFinance, for instance, deploys AI for evaluating both traditional and non-traditional information to gauge risks while automating its underwriting procedure for lower risks. Nauto has already been using AI for forecasting purposes. The aim here is to avoid collisions of commercial fleets (driverless) by lowering distracted driving. The AI system uses data from the vehicle, camera, and other sources to predict risky behavior. 3. What future trends do you foresee in the use of alternate data for risk assessment in the InsureTech industry? There will be greater emphasis on leveraging telematics and usage data garnered through connected vehicles and IoT devices along with smart home devices. At the same time, more machine learning models will be used for algorithm-based risk assessments. The Metaverse will be another channel for insurers to combine their AI-backed Chatbots with sales pitches, internal training, data gathering, and even NFTs for personal document verification. 4. Are there any challenges or limitations in leveraging alternate data for risk assessment in InsureTech? There are a few limitations/challenges in using alternate data for assessing risks in the InsureTech space. The quality of the data and whether it tells the whole story is one challenge along with the fact that there are ethical and privacy-related considerations, regulatory aspects, and the issues related to disclosures, user consent, and the methods of gathering data.

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Business Intelligence & Data Analysis – The Next Big Thing

August 31, 2021

33.3 billion dollars is what global business intelligence (BI) is targeting in the next five years. The report suggests in 2021 itself recorded a jump from 21% to 26% of the adoption rate of BI. Therefore business intelligence is going to be the next big thing in the business space. But it is not BI only that is taking over the world, and it has become imperative to extract insights from the data. Thus, refocussing on data analysis is also one of the big things we will see in the near future. Business Intelligence is a technology-driven process to collect data from different sources, analyze them & finally deliver an ‘Actionable Information‘ that helps the company to make important predictive business decisions. This is possible by using various BI tools such as Power BI, Tableau and many more. Some of the important features of BI tools are : Reporting Analytics and Interactive Dashboard Development Data mining and Process Mining Complex Event Processing Benchmarking Predictive and Perspective Analytics Data gaining popularity in 2022 For businesses to reach the strategic endpoint, data analysis plays a vital role. Here are a few ways by which we know why Data Analysis is so popular in 2021 No-Code Process: BI tools are so easy to use & require no coding knowledge, thus attracting both technical & non-technical individuals. Anyone can pull data from various sources, modify & create visualizations – all without writing a single line of code. This encourages everyone to be data-driven and more interested in pursuing a career in Data Analysis. Easy Collaboration: One of the main reasons for data analysis using BI tools getting popular in 2021 is because of its ‘Collaborative’ nature. The process is called ‘Collaborative BI’, which merges the BI tools with other collaboration tools. This allows the data visualizations/ reports to be shared with co-workers in the same organization so that they can understand. This method allows everyone in the team (even the non-technical ones) to be on the same page & help them make wise decisions about the business. Collaborative BI promotes : Knowledge sharing Faster Decision-Making Better Teamwork More transparency & Visibility Wide range of Data Sources:  Data Source, in BI, refers to the location from where the information or raw data is originated. Our modern BI tools are designed so that they can pull data from various sources, such as Excel Workbook, SharePoint folder, Pdf, XML, JSON and even from the databases (SQL, Oracle & a lot more). Power BI, as a BI tool, has the ability to be connected with a MySQL database, and one can run SQL queries for more refined analytics. This ability to connect with more platforms makes Data Analysis more reachable for today’s professionals.  Top 5 Benefits of Business Intelligence (BI) : Today, businesses can collect data along with every point of the customer journey. This data may include different attributes, like system usage, no. of clicks, interactions with other platforms and a lot more. The organizations have the ability to pull this data from various sources & transform it into a meaningful insight that is easily understandable by everyone in the team. Following are some of the key benefits of adopting Business Intelligence: Fast & Accurate Reporting: Companies can create customized reports based on the data pulled from different data sources, including financial, operational & sales data. These reports are generated in real-time in the form of graphs, tables, charts etc. and can be shared easily within the same organization so that the team can make decisions quickly. Most of the visualizations created with BI tools are so interactive that anyone can play with the data by changing the variables. Valuable Business Insights: The reports generated from the BI tools help the organization understand what’s working and what isn’t. Hence, they can take necessary actions regarding the business process. Improved Decision Making: In today’s competitive business world, where customer satisfaction is paramount, it is required to identify the failures or business problems accurately and take necessary steps to stay on top of the industry. Hence, Business Intelligence comes into the picture, which helps to visualize the data rather than manual calculations using thousands of records. So, definitely, BI tools come in handy when it comes to better decision making. Identifying Market Trends: Analyzing new opportunities & building out strategies with supportive data can give organizations a competitive edge, thus impacting the long-term profitability. The companies can leverage market data with internal data & detect new opportunities by analyzing market trends & also by spotting business problems. Increased Revenue: Undoubtedly, this is the ultimate goal for any business. Data visualizations help organizations dig deeper into business problems by asking questions about what went wrong & how to make impactful changes in the business. When organizations take care of customer satisfaction, watch their competitors, & improving their own operations, revenue is more likely to increase. Popular BI Tools in 2021: Here are some popular BI tools which are trending in the market right now : Microsoft Power BI Tableau Board Domo Oracle Analytics Cloud Tibco Qlik SAS Business Intelligence Vs Business Analytics : Business Analytics & Business Intelligence are very similar and somewhat connected. Pat Roche, Vice President of Engineering at Magnitude Software believes, “BI is needed to run the business while Business Analytics are needed to change the business.” Although it’s a debatable topic, most people in the modern business world still believe that Business Analytics & Business Intelligence tend to work well when paired together. The main usage of BI is to present the data in front of the team in the form of various visualizations, thus helping them make the right business decision, whereas the role of business analytics is to ‘analyze the business’ & think of ways to improve a company’s future performance. Generally, both BI & BA requires analytical skills which ultimately helps the business to succeed. However, despite the similarities & differences between Business Intelligence & Business Analytics, we can certainly agree that both

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