How cement companies use embedded BI dashboards to monitor kiln efficiency and production KPIs in real time
Most cement plants do not lack data. What they lack is timing. The DCS knows the burning zone temperature, the LIMS knows free lime, the weighbridge knows throughput, and the fuel system knows specific heat consumption. The problem is that these systems speak different languages, sit on different screens, and surface insights in reports that arrive a shift, a day, or a month after the loss already happened. The job of a real-time embedded BI dashboard is to close that gap between something going wrong in the kiln and someone with the authority to fix it knowing about it. What follows covers what changes when cement operations run on live Power BI dashboards, which KPIs actually matter, and where digital transformation in the cement industry is heading next. Why does cement plant operational efficiency depend on real-time visibility? A cement plant is one of the most thermally and mechanically punishing operating environments in heavy industry. According to the IEA, the global average thermal energy intensity of clinker production sits around 3.6 GJ per tonne and has barely moved in a decade. The efficiency gains left on the table sit in the speed of operator response, not in kiln design. When burning zone temperature drifts for forty minutes before anyone notices, fuel is wasted, refractory life is shortened, and clinker quality drops. The cost of that delay is measured per shift, not per quarter. The cost of latency in plant reporting A shift supervisor who finds out about a thermal excursion the next morning cannot prevent it. The same supervisor with a Power BI dashboard showing kiln temperature, free lime prediction, and specific heat consumption updating every minute can intervene before quality slips. The enemy is latency, and continuous visibility is the corrective measure. What embedded BI dashboards mean for cement kiln monitoring Embedded BI is analytics built into the workflow, not parked in a separate reporting portal. In a cement plant, kiln temperature trends, fuel ratios, and oxygen levels are displayed inside the operator’s working environment, where decisions actually get made. The difference between a standalone dashboard and an embedded one is adoption. Operators do not switch tabs, maintenance engineers do not log into another system, and information meets the role at the point of action. How embedded analytics changes the operator experience Process engineers see a single pane of kiln health: burning zone temperature, kiln shell temperature profile, specific heat consumption, and free lime trend. Maintenance leads see refractory wear indicators and predicted failure windows. Plant heads see OEE, energy cost per tonne, and production variance live, not summarised on Monday morning. Advaiya’s embedded analytics practice builds this layer using Microsoft Power BI on top of unified data platforms, so the same source of truth serves every role with a tailored view. The production KPIs cement plants track on power BI manufacturing dashboards A useful Power BI manufacturing dashboard in cement operations focuses on a small set of decision-grade KPIs, not a long catalogue of metrics. The discipline is what you remove, not what you add. The KPIs that consistently earn screen space include: Specific heat consumption (kcal per kg clinker) Specific power consumption per subsystem (kWh per tonne) Burning zone temperature and kiln shell temperature profile Free lime, Blaine fineness, and clinker-to-cement ratio Overall Equipment Effectiveness (OEE) for kiln, mill, and packing lines Cooler heat recovery rate Production variance against the shift target Why do fewer KPIs work better than more A dashboard with sixty metrics teaches operators to ignore the dashboard. A dashboard with eight metrics, each tied to a clear corrective action, teaches operators to trust it. The job of a BI reports and dashboards team is to argue for less, not more, until every visible metric earns its place on the screen. How digital transformation in the cement industry connects the operational layers Digital transformation in the cement industry is not a single project. The reality is the slow work of connecting four historically siloed layers: shop floor instrumentation, lab quality systems, enterprise systems, and decision dashboards. The integration pattern that works in cement runs across a Microsoft data stack. Azure IoT brings DCS and SCADA signals into the cloud, Databricks or Microsoft Fabric handles streaming data, Power BI surfaces the result, and Dynamics 365 or the existing ERP closes the loop with production orders, maintenance work orders, and dispatch logistics. Where most cement digitalisation programmes stall Most stalls at the integration step. Plants buy the dashboard, but the data infrastructure that feeds it goes underfunded. The visible failure looks like operators ignoring the screens. The actual failure is upstream, where data quality, latency, and source coverage were never solved. Advaiya’s Peripheral Automation approach is built to address this foundation before any dashboard goes live. What separates a useful kiln dashboard from a decorative one A useful kiln dashboard alters behaviour. A decorative one just sits on a wall. The honest test: do operators change a setpoint, raise a work order, or call a supervisor because of what they see, within the same shift? If the answer is no, the dashboard has failed, regardless of how well it has been designed. The discipline that separates the two comes down to three principles. Role-based views, not universal views The CXO does not need the same information as a shift supervisor, and a kiln operator does not need the same information as a maintenance head. Plants that build one master dashboard and try to serve everyone produce a screen that no one really reads. Closed-loop alerts, not passive displays When specific heat consumption drifts above the benchmark for thirty minutes, the dashboard should do more than colour red. The correct response is to generate a maintenance ticket, notify the shift supervisor on mobile, and log the event. Without the loop back into action, the alert is just decoration. Sustained adoption, not launch-day adoption The conviction at Advaiya is that technology delivers value only when people fully embrace it. A dashboard that operators rely on six months
What Is Enterprise Workflow Automation? 10 Best tools to get started
What is enterprise workflow automation? Are your teams stuck doing the same repetitive tasks over and over? Things like chasing approvals, manually entering data from one system into another, or onboarding new employees with a mountain of paperwork. Small tasks like these add up, slowing down your business and keeping your people from doing their most important work. There’s a better way. You can use enterprise workflow automation. Eric Ries taught us in The Lean Startup that the most successful organizations eliminate waste and optimize for rapid learning. Enterprise workflow automation takes this principle to its logical conclusion—removing friction from every business process while creating feedback loops that enable continuous organizational improvement. A system like this is a core part of any modern digital transformation strategy. This guide will give you a clear look at what an enterprise workflow system can do for you. We’ll cover the real-world benefits, how to choose the right enterprise workflow software, and a list of the best enterprise workflow tools to help you get started. What is Enterprise Workflow Automation? Let’s break it down. A workflow is just a series of steps needed to complete a business process. Approving an invoice, for example, involves several steps: an employee submits it, a manager reviews it, finance checks it against the budget, and finally, payment is issued. Without automation, this process relies on emails and people remembering to do their part. A process like that is slow and it’s easy for things to get lost. An enterprise workflow management system digitizes this entire sequence. An automated system routes the invoice from one person to the next, sends reminders, and keeps a complete record of every action. The software handles the administrative heavy lifting, so your team can focus on the actual decision-making. Key components and technologies Modern enterprise workflow solutions are built on a few key technologies. You’ll often see low-code workflow platforms that let you build processes with visual drag-and-drop tools. Many also incorporate robotic process automation (RPA) for automating tasks in older systems that don’t have modern APIs. Increasingly, artificial intelligence in workflows helps make decisions, predict outcomes, and handle exceptions. Strong API integration for workflows is what ties all your different business applications together into a single, seamless process. Enterprise vs. Standard Workflow Automation What makes it “enterprise”? You’re looking at scale, complexity, and security. Standard workflow tools are great for automating tasks for a single person or a small team. An enterprise workflow system is built to handle complex, cross-departmental processes for an entire organization. An enterprise system includes features like an automation governance framework and robust security to ensure everything runs smoothly and safely at scale. Benefits of Enterprise Workflow Automation Adopting enterprise workflow automation brings tangible benefits that you’ll see across your organization, leading to significant operational efficiency improvement. Improved process efficiency Eliyahu Goldratt’s The Goal teaches us that any system is only as strong as its weakest link. Enterprise workflow automation isn’t just about speeding up individual tasks—it’s about identifying and eliminating the constraints that limit your organization’s entire throughput. When you automate handoffs and approvals, you achieve a dramatic process cycle time reduction. For example, a large landscaping group we worked with made its billing process 7x faster, cutting the time spent from 30 hours down to just 4. Cost reduction and ROI Fewer errors, less wasted time, and more efficient use of resources all lead to lower costs. The cost reduction through automation is one of the most compelling benefits. You’re doing more with the same number of people, which directly impacts your bottom line. A proper automation ROI measurement will show you clear returns from increased productivity and reduced operational overhead. Enhanced collaboration and communication In many companies, departments work in their own silos. Enterprise workflow solutions act as a bridge. A workflow can start in sales, automatically trigger a project in operations, and then notify finance to begin billing, all within a single, connected system. A setup like this breaks down communication barriers and ensures everyone is working with the same information. Error reduction and quality improvement Manual data entry is a major source of errors. A simple typo can cause huge problems. Automation removes the human element from repetitive steps, ensuring tasks are done consistently and accurately every time. One of our clients, a major airport, reduced manual document handling by over 90%, which dramatically improved its data quality and compliance index. How to choose the right Enterprise Workflow Software With so many enterprise automation tools on the market, how do you pick the right one? You’re not just looking at features; you’re looking for a foundation for your future work. Identify your needs: Start by looking at your specific processes. Are you focused on finance workflow automation or HR process automation? The specific use case will influence which tool is the best fit. Assess integration: Your workflow tool must connect to the software you already use. Look for strong integration capabilities, especially with your core CRM and ERP systems. Think about your users: You shouldn’t need to be a programmer to build a workflow. Look for low-code workflow platforms with a simple, drag-and-drop interface that lets your business users create their own solutions. Evaluate scalability: The solution you choose today should grow with your business tomorrow. Make sure the platform can handle more complex workflows and a higher volume of tasks as your company expands. Review security measures: You’re trusting the software with your business-critical data. Ensure the platform has robust security features, access controls, and audit trails to support your compliance automation systems. Getting started with enterprise workflow automation In Leading Digital, the authors identify that digital leaders don’t just digitize existing processes—they reimagine them. Enterprise workflow automation provides the foundation for this reimagining. Define area and scope: Don’t try to boil the ocean. Start with a single department or process that is a known bottleneck. Identify key processes for automation: Look for tasks that are repetitive, rule-based, and involve multiple
AI in Business Intelligence: Uses, benefits and challenges
You’re likely swimming in data. From sales figures and customer feedback to operational metrics and market trends, the information is endless. How do you turn that flood of data into clear, actionable insights that drive your business forward? The answer is in the powerful combination of AI and business intelligence. For years, business intelligence (BI) has helped companies see their performance by organizing data into dashboards and reports. A BI system is great at telling you what happened. Now, infusing BI with artificial intelligence (AI) lets you go much further. As Thomas Davenport predicted in Competing on Analytics, organizations that master data-driven decision making gain sustainable competitive advantages. AI-powered business intelligence is the next evolution of this principle, moving beyond human-limited analysis to machine-speed insights that enable real-time strategic adaptation. You can now understand why something happened, predict what will happen next, and even get recommendations on the best course of action. A powerful synergy is changing decision-making across industries. We’ll walk you through what artificial intelligence in business intelligence means for your business, looking at practical uses, tangible benefits, and the challenges you should know about. AI’s role in business intelligence The introduction of artificial intelligence in business intelligence isn’t a minor upgrade; you’re looking at a fundamental shift in how we interact with and get value from data. AI automates complex processes, uncovers deeper insights, and makes analytics accessible to more people than ever before. Transforming traditional analytics The biggest change is the evolution from hindsight to foresight, a crucial step in business intelligence modernization. A progression like this allows businesses to become proactive rather than reactive, anticipating market shifts and customer needs before they fully materialize. Descriptive analytics (traditional BI): What happened? (“We sold 5,000 units last month.”) Diagnostic analytics (smarter BI): Why did it happen? (“Sales were high because of a successful marketing campaign.”) Predictive analytics (AI-powered BI): What will happen? (“Based on current trends, we predict a 15% drop in sales next quarter.”) Prescriptive analytics (the peak of AI in BI): What should we do about it? (“To avoid the sales drop, launch a loyalty discount for repeat customers.”) A journey from descriptive to prescriptive analytics is the core of what makes AI for business intelligence so valuable. The evolution from manual to automated insights One of the most time-consuming parts of any data analysis project is preparing the data. Analysts often spend up to 80% of their time on automated data cleansing and preparation. AI automates much of this tedious work. Machine learning algorithms can intelligently identify and fix inconsistencies, flag outliers, and merge datasets. Your data experts are then free to focus on what they do best: analysis and strategy. Furthermore, the use of natural language processing in BI has been a game-changer. Instead of writing complex code, a manager can simply ask, “What were our top three products by profit margin in Europe last year?” The AI engine translates the request, analyzes the relevant data, and presents the answer in a clear, understandable format, often using AI-powered data visualization to make the information intuitive. Key benefits and capabilities When you successfully integrate AI and business intelligence, the advantages are significant and can create a strong competitive edge. Putting analytics in everyone’s hands AI democratizes data analysis. When you embed AI into a self-service analytics platform, you give business users—not just data scientists—the ability to ask questions of data and get answers. A setup like this fosters a culture of curiosity and enables faster, more localized decision-making across the organization. Enhanced decision-making through automation With predictive and prescriptive analytics, your teams can shift from being reactive to proactive. Instead of making decisions based on what happened last quarter, they can make strategic choices based on what is likely to happen next. A forward-looking approach, powered by intelligent business process automation, leads to better outcomes, whether you’re launching a new product or allocating your budget. Crafting better data narratives How much time does your team spend building weekly or monthly reports? AI can automate this entire process through automated insights generation. An AI system can pull data from multiple sources, populate a dashboard, and, most impressively, generate a narrative summary of the key findings. These “data stories” explain what the charts and graphs mean in plain language, ensuring stakeholders quickly grasp the important takeaways. Augmented intelligence: less plumbing, faster insights Brynjolfsson and McAfee’s The Second Machine Age reminds us that the most successful AI implementations augment human capabilities rather than replace them. In business intelligence, AI handles the heavy lifting of pattern recognition and data processing while humans focus on strategic interpretation and action. You get a powerful partnership between human insight and machine precision, allowing your team to focus on strategy instead of data plumbing. Improved business agility through real-time insights In today’s fast-paced market, speed is a competitive advantage. Real-time business intelligence, powered by AI, lets you monitor operations, customer behavior, and market trends as they happen. You can react instantly to opportunities and threats, making your organization more agile and resilient. AI applications in business intelligence systems The applications of AI and business intelligence are vast and span every department and industry. Here are some of the most impactful uses that are delivering real value today. Customer-focused applications Predictive analytics for market and consumer insights: AI models analyze historical data and market trends for customer behavior prediction. You can anticipate what customers want next and tailor your offerings accordingly. Sentiment analysis for customer service: Analyzing emails, chat logs, and social media comments with sentiment analysis for business can gauge customer emotion in real-time. You can proactively address issues and improve customer satisfaction, especially with tools like Dynamics 365. Risk and fraud-focused applications Anomaly detection for risk management: AI models excel at learning what “normal” looks like within a system and instantly flagging any deviation. Anomaly detection in operations is critical for identifying potential risks before they escalate. Fraud prevention systems: In finance and e-commerce, fraud detection algorithms analyze transactions in
Business analytics to supercharge sales
Often, Sales is considered a guessing game. Businesses come up with implementation strategies, never knowing whether their efforts are worthwhile. When the revenues grow, there comes an agreement that their methods are successful and continue to move on. If there isn’t much change, we see an adjustment in the approach until the teams close more deals. However, technology has enabled sales teams to see precisely how their strategies are performing. They can monitor customer activities and link them to specific sales efforts by using analytics. With so many companies now using analytics to boost marketing and sales, companies that fail to adopt this technology will eventually find they’re losing to competition. Here are the ways businesses can supercharge their sales by using business analytics. Sales team management: Identifying patterns and drive action-oriented training to the team Your CRM’s potential insights are only useful if your sales team provides the system with accurate information. Once you have confidence in your data, you can analyze the patterns in the behavior of the opportunities in the movement through the sales funnel, and understand the successful techniques and actions that produce desired results. The software for sales analytics, including CRM, contains a range of data that allows for such evaluation with lead scoring and opportunity scoring. The analysis would yield specific actions in the sales process, which can drive your sales coaching and training practices. The insights from the analysis of sales data will answer questions like: Which reps are struggling at identifying all the buying influences? How to assign clear and balanced territories to reps and adjust as needed? Answers to these questions can help you identify and suggest specific actions with successful outcomes or identify the need for guidance to the team. To give you an idea, sales analytics will point out whether an individual rep mainly has contacts in the technical roles, suggesting that he must work on the identification of other buying influences to engage prospects in the sales process earlier. Simply put, it sums up to the reliability of your data, and identification of trends that lead to desired results. Sales effort effectiveness: Exercise data into everyday sales activities Every company operates its sales team differently. A one-off product company does not have the same requirements as a company based on a recurring revenue model. Both companies, though, depend on their sales to reach revenues and profitability targets. In addition to using insights from historical data for the improvement of your sales team performance, you can provide prescriptive guidance to sales professionals on how to improve their chances of making a sale. It can include strategic suggestions for market or account in a specific vertical where demand for a certain product is higher than others, or tactical ideas like product bundling and up-selling or cross-selling of some products that have sold well together in the past. A salesperson often makes more than a hundred calls a day. Using data analytics can be very valuable for deal scoring, determining which opportunities are worth picking up and which ones should be a part of marketing nurture campaigns. The perception that specific calls would be more productive and successful will increase confidence and lead to better interaction with the customer and closing the deal. Sales analytics can be useful for personal interactions, as well. For instance, sales professionals can use data to address pricing questions on the spot. The sales reps can gain tremendous credibility by showing average prices of recent sales for companies located in the same region, vertical or company sizes, to the current prospect. Often this is done on a mobile device during the meeting. Such types of data and analytic benchmarks allow the salesperson to understand how their customers pay attention to a specific price and how flexible and elastic they are in accommodating discounts. Closing thoughts In sales-oriented organizations, the role of data is shifting. It is evolving from management reporting to the development of a strategic tool for sales managers and sales teams. Nonetheless, this change requires overcoming the initial resistance to data quality and reliability barriers and gaining an organizational buy-in. With analytics as a motivator and incentive, the organization can ensure that sales managers and individual representatives are in control of their achievements. The organization can build a highly effective sales team that is capable of prioritizing, taking ownership and making good choices using data as a strategic tool to drive revenue and profits. We, at Advaiya, help you with the right planning, implementation and adoption of business analytics and business applications tailored for your business, enabling you to make better decisions, deliver superior customer experience, increase productivity and induce technology-led innovation.
Gain more value by prioritizing your data initiatives
For our esteemed clients, we are regularly conducting a data discovery workshop to identify information needs related to visibility, control, and information related to business intelligence. These are NOT the typical discovery sessions you may be familiar with, as our clients find this very valuable. At a high level, we conduct these workshops to understand their key objectives, what kind of decisions they are trying to make, how they measure success in different organizational units, what are the data sources and applications from which they want to create reports and insights. The outcome of this exercise is intense and comes in the shape of – A road map and architecture document Initiatives Projects for data integration, aggregation, and warehousing Creating a common taxonomy to use List of various dashboards and reports Since these workshops involve different stakeholders from business and IT and various horizontal functions, everyone has a different priority set and viewpoint on what challenges they are trying to solve (or what makes them awake at night). How to prioritize rationally and thoughtfully is part of our engagement, where we use a framework to prioritize which things to take in the first phase, and what we can plan for later phases. Firstly, we determine what dimensions are affecting prioritization – these could be different based on in what industry or domain the organization is running their business. These can be functional areas, organizational roles which require this information, geographical regions, data sources that need to be integrated, amount of structured and unstructured sources. Let’s look by an example of a healthcare organization – a hospital which is providing patient and clinical services. For those, dimensions would be – Functional Areas Finance Clinical Patients Organizational levels C Level Executives Doctors Clinicians Other dimensions Multicity operations Different hospital management and clinical management applications Datastores residing on-premise and on cloud The second attribute is the purpose of that initiative which can be classified as – Visibility – of what is happening on a regular interval Control – related to compliance or action needed if something goes beyond the baseline Information – Understanding and further analysis So, for each dimension (which could be a combination of dimensions), we put purpose and identify a final set of initiatives for prioritization. To do the prioritization, we generally check for three things and discuss with stakeholder to rate each initiative based on: Strategic Impact Hard impact – Quantifiable Soft impact – Meaningful Complexity What are the variables involved? Which functions and locations involved? Value Cumulative $ value from the decision The opportunity cost of not taking/postponing the decision There are other factors we need to consider related to data like – data availability and relevance, data definitions, data quality, data standards, integrity, uniformity. There are scenarios where if an initiative has a quantifiable hard impact on business and providing cumulative value but complex to implement, this can become a part of phase 1 deliverable. However, it might be a case that costs and time to implement in comparison to other initiatives are more impactful; then, stakeholders can decide which to take as the first phase and what initiative can be part of the backlog. The good thing with this approach is that all stakeholders are aware and informed about the decision, and they also know the roadmap of solution implementation. We know from our own experience that this approach helps a lot. Initially, our customers were skeptical about this whole exercise but appreciated and found this valuable after completion. Let me know if you are planning a data analytics or BI initiative organization-wide or in your SBU. You may find these useful: Related services & solutions Scorecards and dashboards Read more Data discovery and aggregation Read more Intelligence Read more Leveraging data Read more Work management and business productivity Read more Information strategy Read more Data warehousing and infrastructure … Read more Insights Read more Relevant blog Artificial Intelligence: A Silver Bullet for Housing? Don’t just survive, grow through the crises with Power BI The next big thing in business applications Capabilities of dataflows against datasets Business analytics to supercharge sales The Customer Approach: Data for Decisions Not Data for Itself Are you unable to make data-driven decisions? Updates How to Use Analytics to Ramp Up Your Sales Virtual event on DAMA Portland by our President & CTO on saving resources using dataflows
Business intelligence vs analytics: Align your data strategy
Traditionally, enterprises have targeted their information around business intelligence, but the rise of predictive analytics, machine learning, and artificial intelligence, is changing the equation. In the last few decades, organizations have turned to innovative solutions to handle workloads, preserve profitability and ensure competitiveness in their respective businesses. BI solutions accumulate and examine actionable information with the objective of providing insights into enhancing company operations. Are you on the lookout for strategies to understand your business operations? What about find pain points in your workflows? How about examining large data sets to draw insights that are valuable? Analysts and consultants agree that understanding the distinctions between business intelligence and other analytics programs, in addition to the value each brings to the venture. We break down where business intelligence matches in the range of information offerings available today — and the way business analytics is growing, thanks to changes in tools, tactics, and personal needs. Business intelligence vs. business analytics Business Analytics includes approaches and technologies you can use to get and explore your company’s data, with a view to pulling new, data-driven insights to improve business planning and boost decision-making process. Analytics in the broadest sense applies to all technology-enabled difficulty activities. Generally, this involves using predictive analysis and modeling to establish trends, figuring out why things are happening, and making an educated guess about how things will pan out in the future. Business Intelligence is about accessing and exploring your company’s data. And, again, the ultimate aims are to understand how the company is doing, make better-informed decisions which enhance performance, and create new strategic opportunities for expansion. BI is more concerned with the what’s and the how’s than the whys. Business Intelligence lets you employ selected metrics to potentially enormous, unstructured data sets, and covers data mining, querying, online analytical processing (OLAP), and reporting as well as business performance monitoring, predictive and prescriptive analytics. Business intelligence includes services and tools to transform data into actionable intelligence that educates an organization’s strategic and tactical business decisions. It is what enables a company to collect, examine and current analysis of information. If you’re working with masses of raw information, you need extensive control over how you use that data, and you also would like to draw out your requirements and conclusions from the figures, the tools and methods you use will likely fall under BI, instead of BA. Selecting Between Business Intelligence and Business Analytics Organizations have begun to find that content and data shouldn’t be regarded as different features of data management but rather managed in an integrated enterprise strategy. BI utilizes past and present data to maximize the current for present achievement. BA uses the past and assesses the data to prepare businesses for the long run. Size Up Your Requirements Traditionally, business intelligence vendors targeted ventures mostly, but currently, there’s a paradigm change of BI moving to small companies and midsize organizations. Self-service BI is a significant focus of those smaller businesses. Self-service business intelligence (SSBI) is a method of data analysis that permits users to gain access and operate with corporate information although they don’t have a desktop in analytical or information science. It typically offers a user-friendly UI and does not involve communicating, which means that your average joe can run it. Pick with Intention Deciding on the solution for the company is dependent upon your intentions. If you’re happy with your business model as a complete and mostly want to enhance operations, boost efficiency and meet organizational objectives, business intelligence could be an optimum solution. Specifically, businesses that rely on real-time reporting tend to lean toward BI since they’re worried about what they could improve from the here and now. Businesses which need extensive data (e.g., the demand for data warehousing) and instinctive reporting needs to consider company intelligence seriously. BI has the additional benefits of targeting a company’ weak places and providing actionable insights into those issues. Business intelligence tools are excellent options for supervisors who wish to enhance decision making and comprehend their business’s productivity, work procedures, and workers. Then, with this knowledge, enhance their company from the bottom up. If you want help in gaining more insight from your data for better decision, then Advaiya’s experts can help you. Contact us here
Why Microsoft Power BI is the leader in business analytics?
Gartner recently positioned Microsoft’s Power BI as a leader in their annual magic quadrant for business intelligence and analytics Platforms. Also, for the second year in the row, Microsoft is placed farthest in vision within the leader’s quadrant. The reason behind this achievement is mainly because of the new exciting features Power BI team releases every month. Let’s have a look at few of the key features released in february and march 2018. 1. Formatting changes: We can now control the labeling of data in scattering and another kind of cartesian charts. This improves the readability of the report and helps in a situation when data labels overflow the bar in charts. You can also change the size and background color.  Similarly, the size of the axis labels can also be controlled now to increase the percentage of the chart used by the axis labels.  Another formatting feature added is the bar/column padding control. PBI desktop users can now search the analytics/formatting pane. This is helpful because there are many options available under these panes and it has now become easy to browse through them. A live report can be found at Financial Performance Analysis Report to see the formatting changes. 2. Sync slicer: In March, the PBI team delivered the most requested feature on the ideas forum. With the help of sync slicer feature, we can now synchronize all the pages where the sync slicer is applied. This means that if I select any slicer/filter on a page, the same filter selection would be applied to other pages where the sync mode is on. In the following image we can see that four locations are selected on Page 1 When we move to the next page, according to sync slicer information, of these four selected locations should be displayed. Please check the live report at Restaurant Real Time Report  3. Bookmarking: Though bookmarking was released in october 2017 , but now it is generally available. Bookmarking helps users create storytelling like experience. There are many use cases of bookmarks. Collection of bookmarks can help in presenting a series of insights thus enabling storytelling aspect of BI which is important these days. An interesting analysis of Profit Margin is available in this report which illustrates a company’s profit and total sales by changing margin and discount percentage. The Profit-Margin Analysis shows how much of profit would be generated on the specified values of discount, margin and target profit. Click on the image icons on top left corner to see the magic of bookmarks. 4. Custom visuals: One of the main reasons behind the success of PBI is custom visuals. Think of a scenario where you must show your data in the tree visualization format or in box and whisker plot. In other BI tools, you need to create the visualization from scratch thus taking a lot of time. But in Power BI you can make such as visual in few clicks. That is the beauty of custom visuals. There are many custom visuals available in the app source. Let me discuss few of the important custom visuals. TreeViz: TreeViz represents your data in a tree–like structure. It is good when one wants to organize the data hierarchically. Expand the nodes to see the next level in hierarchy. We can add as many levels we want. Funnel by MAQ software: The Funnel with Source custom visual is perfect to track any metric of interest over various stages along with the source of entry of the data point to the funnel. Network visualization and filter: This chart visualizes data as a network which lets you see flow between various categories. Categories are displayed as nodes and are connected via line. The size of the nodes represents the magnitude of a category. The network structure makes it possible to see the connections among many categories. 5. Tool tip: This feature is the latest one which was released in this month. Now we can create visually compelling tooltips which will pop up when you hover over visuals, based on report pages you create in PBI Desktop. These tooltips include all the visuals which are created in the report page of tooltip. This automatically filters the data point of the visual on which mouse is hovered. 5) Tool tip: This feature is the latest one which was released in this month. Now we can create visually compelling tooltips which will pop up when you hover over visuals, based on report pages you create in PBI Desktop. These tooltips include all the visuals which are created in the report page of tooltip. This automatically filters the data point of the visual on which mouse is hovered. These were few of the updates which Power BI releases every month. The Power BI team works continuously on improving and enhancing the capabilities of the entire Power BI ecosystem. This is making Power BI number one choice in the business intelligence and data visualization software industry.Â