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Tech Guides - Business Intelligence

7 Articles
article-image-what-does-a-data-science-team-look-like
Fatema Patrawala
21 Nov 2019
11 min read
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What does a data science team look like?

Fatema Patrawala
21 Nov 2019
11 min read
Until a couple of years ago, people barely knew the term 'data science' which has now evolved into an extremely popular career field. The Harvard Business Review dubbed data scientist within the data science team as the sexiest job of the 21st century and expert professionals jumped on the data is the new oil bandwagon. As per the Figure Eight Report 2018, which takes the pulse of the data science community in the US, a lot has changed rapidly in the data science field over the years. For the 2018 report, they surveyed approximately 240 data scientists and found out that machine learning projects have multiplied and more and more data is required to power them. Data science and machine learning jobs are LinkedIn's fastest growing jobs. And the internet is creating 2.5 quintillion bytes of data to process and analyze each day. With all these changes, it is evident for data science teams to evolve and change among various organizations. The data science team is responsible for delivering complex projects where system analysis, software engineering, data engineering, and data science is used to deliver the final solution. To achieve all of this, the team does not only have a data scientist or a data analyst but also includes other roles like business analyst, data engineer or architect, and chief data officer. In this post, we will differentiate and discuss various job roles within a data science team, skill sets required and the compensation benefit for each one of them. For an in-depth understanding of data science teams, read the book, Managing Data Science by Kirill Dubovikov, which has interesting case studies on building successful data science teams. He also explores how the team can efficiently manage data science projects through the use of DevOps and ModelOps.  Now let's get into understanding individual data science roles and functions, but before that we take a look at the structure of the team.There are three basic team structures to match different stages of AI/ML adoption: IT centric team structure At times for companies hiring a data science team is not an option, and they have to leverage in-house talent. During such situations, they take advantage of the fully functional in-house IT department. The IT team manages functions like data preparation, training models, creating user interfaces, and model deployment within the corporate IT infrastructure. This approach is fairly limited, but it is made practical by MLaaS solutions. Environments like Microsoft Azure or Amazon Web Services (AWS) are equipped with approachable user interfaces to clean datasets, train models, evaluate them, and deploy. Microsoft Azure, for instance, supports its users with detailed documentation for a low entry threshold. The documentation helps in fast training and early deployment of models even without an expert data scientists on board. Integrated team structure Within the integrated structure, companies have a data science team which focuses on dataset preparation and model training, while IT specialists take charge of the interfaces and infrastructure for model deployment. Combining machine learning expertise with IT resource is the most viable option for constant and scalable machine learning operations. Unlike the IT centric approach, the integrated method requires having an experienced data scientist within the team. This approach ensures better operational flexibility in terms of available techniques. Additionally, the team leverages deeper understanding of machine learning tools and libraries – like TensorFlow or Theano which are specifically for researchers and data science experts. Specialized data science team Companies can also have an independent data science department to build an all-encompassing machine learning applications and frameworks. This approach entails the highest cost. All operations, from data cleaning and model training to building front-end interfaces, are handled by a dedicated data science team. It doesn't necessarily mean that all team members should have a data science background, but they should have technology background with certain service management skills. A specialized structure model aids in addressing complex data science tasks that include research, use of multiple ML models tailored to various aspects of decision-making, or multiple ML backed services. Today's most successful Silicon Valley tech operates with specialized data science teams. Additionally they are custom-built and wired for specific tasks to achieve different business goals. For example, the team structure at Airbnb is one of the most interesting use cases. Martin Daniel, a data scientist at Airbnb in this talk explains how the team emphasizes on having an experimentation-centric culture and apply machine learning rigorously to address unique product challenges. Job roles and responsibilities within data science team As discussed earlier, there are many roles within a data science team. As per Michael Hochster, Director of Data Science at Stitch Fix, there are two types of data scientists: Type A and Type B. Type A stands for analysis. Individuals involved in Type A are statisticians that make sense of data without necessarily having strong programming knowledge. Type A data scientists perform data cleaning, forecasting, modeling, visualization, etc. Type B stands for building. These individuals use data in production. They're good software engineers with strong programming knowledge and statistics background. They build recommendation systems, personalization use cases, etc. Though it is rare that one expert will fit into a single category. But understanding these data science functions can help make sense of the roles described further. Chief data officer/Chief analytics officer The chief data officer (CDO) role has been taking organizations by storm. A recent NewVantage Partners' Big Data Executive Survey 2018 found that 62.5% of Fortune 1000 business and technology decision-makers said their organization appointed a chief data officer. The role of chief data officer involves overseeing a range of data-related functions that may include data management, ensuring data quality and creating data strategy. He or she may also be responsible for data analytics and business intelligence, the process of drawing valuable insights from data. Even though chief data officer and chief analytics officer (CAO) are two distinct roles, it is often handled by the same person. Expert professionals and leaders in analytics also own the data strategy and how a company should treat its data. It does make sense as analytics provide insights and value to the data. Hence, with a CDO+CAO combination companies can take advantage of a good data strategy and proper data management without losing on quality. According to compensation analysis from PayScale, the median chief data officer salary is $177,405 per year, including bonuses and profit share, ranging from $118,427 to $313,791 annually. Skill sets required: Data science and analytics, programming skills, domain expertise, leadership and visionary abilities are required. Data analyst The data analyst role implies proper data collection and interpretation activities. The person in this job role will ensure that collected data is relevant and exhaustive while also interpreting the results of the data analysis. Some companies also require data analysts to have visualization skills to convert alienating numbers into tangible insights through graphics. As per Indeed, the average salary for a data analyst is $68,195 per year in the United States. Skill sets required: Programming languages like R, Python, JavaScript, C/C++, SQL. With this critical thinking, data visualization and presentation skills will be good to have. Data scientist Data scientists are data experts who have the technical skills to solve complex problems and the curiosity to explore what problems are needed to be solved. A data scientist is an individual who develops machine learning models to make predictions and is well versed in algorithm development and computer science. This person will also know the complete lifecycle of the model development. A data scientist requires large amounts of data to develop hypotheses, make inferences, and analyze customer and market trends. Basic responsibilities include gathering and analyzing data, using various types of analytics and reporting tools to detect patterns, trends and relationships in data sets. According to Glassdoor, the current U.S. average salary for a data scientist is $118,709. Skills set required: A data scientist will require knowledge of big data platforms and tools like  Seahorse powered by Apache Spark, JupyterLab, TensorFlow and MapReduce; and programming languages that include SQL, Python, Scala and Perl; and statistical computing languages, such as R. They should also have cloud computing capabilities and knowledge of various cloud platforms like AWS, Microsoft Azure etc.You can also read this post on how to ace a data science interview to know more. Machine learning engineer At times a data scientist is confused with machine learning engineers, but a machine learning engineer is a distinct role that involves different responsibilities. A machine learning engineer is someone who is responsible for combining software engineering and machine modeling skills. This person determines which model to use and what data should be used for each model. Probability and statistics are also their forte. Everything that goes into training, monitoring, and maintaining a model is the ML engineer's job. The average machine learning engineer's salary is $146,085 in the US, and is ranked No.1 on the Indeed's Best Jobs in 2019 list. Skill sets required: Machine learning engineers will be required to have expertise in computer science and programming languages like R, Python, Scala, Java etc. They would also be required to have probability techniques, data modelling and evaluation techniques. Data architects and data engineers The data architects and data engineers work in tandem to conceptualize, visualize, and build an enterprise data management framework. The data architect visualizes the complete framework to create a blueprint, which the data engineer can use to build a digital framework. The data engineering role has recently evolved from the traditional software-engineering field.  Recent enterprise data management experiments indicate that the data-focused software engineers are needed to work along with the data architects to build a strong data architecture. Average salary for a data architect in the US ranges from $1,22,000 to $1,29, 000 annually as per a recent LinkedIn survey. Skill sets required: A data architect or an engineer should have a keen interest and experience in programming languages frameworks like HTML5, RESTful services, Spark, Python, Hive, Kafka, and CSS etc. They should have the required knowledge and experience to handle database technologies such as PostgreSQL, MapReduce and MongoDB and visualization platforms such as; Tableau, Spotfire etc. Business analyst A business analyst (BA) basically handles Chief analytics officer's role but on the operational level. This implies converting business expectations into data analysis. If your core data scientist lacks domain expertise, a business analyst can bridge the gap. They are responsible for using data analytics to assess processes, determine requirements and deliver data-driven recommendations and reports to executives and stakeholders. BAs engage with business leaders and users to understand how data-driven changes will be implemented to processes, products, services, software and hardware. They further articulate these ideas and balance them against technologically feasible and financially reasonable. The average salary for a business analyst is $75,078 per year in the United States, as per Indeed. Skill sets required: Excellent domain and industry expertise will be required. With this good communication as well as data visualization skills and knowledge of business intelligence tools will be good to have. Data visualization engineer This specific role is not present in each of the data science teams as some of the responsibilities are realized by either a data analyst or a data architect. Hence, this role is only necessary for a specialized data science model. The role of a data visualization engineer involves having a solid understanding of UI development to create custom data visualization elements for your stakeholders. Regardless of the technology, successful data visualization engineers have to understand principles of design, both graphical and more generally user-centered design. As per Payscale, the average salary for a data visualization engineer is $98,264. Skill sets required: A data visualization engineer need to have rigorous knowledge of data visualization methods and be able to produce various charts and graphs to represent data. Additionally they must understand the fundamentals of design principles and visual display of information. To sum it up, a data science team has evolved to create a number of job roles and opportunities, but companies still face challenges in building up the team from scratch and find it hard to figure where to start from. If you are facing a similar dilemma, check out this book, Managing Data Science, written by Kirill Dubovikov. It covers concepts and methodologies to manage and deliver top-notch data science solutions, while also providing guidance on hiring, growing and sustaining a successful data science team. How to learn data science: from data mining to machine learning How to ace a data science interview Data science vs. machine learning: understanding the difference and what it means today 30 common data science terms explained 9 Data Science Myths Debunked
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Packt Editorial Staff
04 Aug 2018
16 min read
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Effective Product Development needs developers and product managers collaborating on success metrics

Packt Editorial Staff
04 Aug 2018
16 min read
Modern product development is witnessing a drastic shift. Disruptive ideas and ambiguous business conditions have changed the way products are developed. Product development is no longer guided by existing processes or predefined frameworks. Delivering on time is a baseline metric, as is software quality. Today, businesses are competing to innovate. They are willing to invest in groundbreaking products with cutting-edge technology. Cost is no longer the constraint—execution is. Can product managers then continue to rely upon processes and practices aimed at traditional ways of product building? How do we ensure that software product builders look at the bigger picture and do not tie themselves to engineering practices and technology viability alone? Understanding the business and customer context is essential for creating valuable products. In this article, we are going to identify what success means to us in terms of product development. This article is an excerpt from the book Lean Product Management written by Mangalam Nandakumar. For the kind of impact that we predict our feature idea to have on the Key Business Outcomes, how do we ensure that every aspect of our business is aligned to enable that success? We may also need to make technical trade-offs to ensure that all effort on building the product is geared toward creating a satisfying end-to-end product experience. When individual business functions take trade-off decisions in silo, we could end up creating a broken product experience or improvising the product experience where no improvement is required. For a business to be able to align on trade-offs that may need to be made on technology, it is important to communicate what is possible within business constraints and also what is not achievable. It is not necessary for the business to know or understand the specific best practices, coding practices, design patterns, and so on, that product engineering may apply. However, the business needs to know the value or the lack of value realization, of any investment that is made in terms of costs, effort, resources, and so on. The section addresses the following topics: The need to have a shared view of what success means for a feature idea Defining the right kind of success criteria Creating a shared understanding of technical success criteria "If you want to go quickly, go alone. If you want to go far, go together. We have to go far — quickly." Al Gore Planning for success doesn't come naturally to many of us. Come to think of it, our heroes are always the people who averted failure or pulled us out of a crisis. We perceive success as 'not failing,' but when we set clear goals, failures don't seem that important. We can learn a thing or two about planning for success by observing how babies learn to walk. The trigger for walking starts with babies getting attracted to, say, some object or person that catches their fancy. They decide to act on the trigger, focusing their full attention on the goal of reaching what caught their fancy. They stumble, fall, and hurt themselves, but they will keep going after the goal. Their goal is not about walking. Walking is a means to reaching the shiny object or the person calling to them. So, they don't really see walking without falling      as a measure of success. Of course, the really smart babies know to wail their way to getting the said shiny thing without lifting a toe. Somewhere along the way, software development seems to have forgotten about shiny objects, and instead focused on how to walk without falling. In a way, this has led to an obsession with following processes without applying them to the context and writing perfect code, while disdaining and undervaluing supporting business practices. Although technology is a great enabler, it is not the end in itself. When applied in the context of running a business or creating social impact, technology cannot afford to operate as an isolated function. This is not to say that technologists don't care about impact. Of course, we do. Technologists show a real passion for solving customer problems. They want their code to change lives, create impact, and add value. However, many technologists underestimate the importance of supporting business functions in delivering value. I have come across many developers who don't appreciate the value of marketing, sales, or support. In many cases, like the developer who spent a year perfecting his code without acquiring a single customer, they believe that beautiful code that solves the right problem is enough to make a business succeed. Nothing can be further from the truth Most of this type of thinking is the result of treating technology as an isolated function. There is a significant gap that exists between nontechnical folks and software engineers. On the one hand, nontechnical folks don't understand the possibilities, costs, and limitations of software technology. On the other hand, technologists don't value the need for supporting functions and communicate very little about the possibilities and limitations of technology. This expectation mismatch often leads to unrealistic goals and a widening gap between technology teams and the supporting functions. The result of this widening gap is often cracks opening in the end-to-end product experience for the customer, thereby resulting in a loss of business. Bridging this gap of expectation mismatch requires that technical teams and business functions communicate in the same language, but first they must communicate. Setting SMART goals for team In order to set the right expectations for outcomes, we need the collective wisdom of the entire team. We need to define and agree upon what success means for each feature and to each business function. This will enable teams to set up the entire product experience for success. Setting specific, measurable, achievable, realistic, and time-bound (SMART) metrics can resolve this. We cannot decouple our success criteria from the impact scores we arrived at earlier. So, let's refer to the following table for the ArtGalore digital art gallery: The estimated impact rating was an indication of how much impact  the business expected a feature idea to have on the Key Business Outcomes. If you recall, we rated this on a scale of 0 to 10. When the estimated impact of a Key Business Outcomes is less than five, then the success criteria for that feature is likely to be less ambitious. For example, the estimated impact of "existing buyers can enter a lucky draw to meet an artist of the month" toward generating revenue is zero. What this means is that we don't expect this feature idea to bring in any revenue for us or put in another way, revenue is not the measure of success for this feature idea. If any success criteria for generating revenue does come up for this feature idea, then there is a clear mismatch in terms of how we have prioritized the feature itself. For any feature idea with an estimated impact of five or above, we need to get very specific about how to define and measure success. For instance, the feature idea "existing buyers can enter a lucky draw to meet an artist of the month" has an estimated impact rating of six towards engagement. This means that we expect an increase in engagement as a measure of success for this feature idea. Then, we need to define what "increase in engagement" means. My idea of "increase in engagement" can be very different from your idea of "increase in engagement." This is where being S.M.A.R.T. about our definition of success can be useful. Success metrics are akin to user story acceptance criteria. Acceptance criteria define what conditions must be fulfilled by the software in order for us to sign off on the success of the user story. Acceptance criteria usually revolve around use cases and acceptable functional flows. Similarly, success criteria for feature ideas must define what indicators can tell us that the feature is delivering the expected impact on the KBO. Acceptance criteria also sometimes deal with NFRs (nonfunctional requirements). NFRs include performance, security, and reliability. In many instances, nonfunctional requirements are treated as independent user stories. I also have seen many teams struggle with expressing the need for nonfunctional requirements from a customer's perspective. In the early days of writing user stories, the tendency for myself and most of my colleagues was to write NFRs from a system/application point of view. We would say, "this report must load in 20 seconds," or "in the event of a network failure, partial data must not be saved."  These functional specifications didn't tell us how/why they were important for an end user. Writing user stories forces us to think about the user's perspective. For example, in my team we used to have interesting conversations about why a report needed to load within 20 seconds. This compelled us to think about how the user interacted with our software. It is not uncommon for visionary founders to throw out very ambitious goals for success. Having ambitious goals can have a positive impact in motivating teams to outperform. However, throwing lofty targets around, without having a plan for success, can be counter-productive. For instance, it's rather ambitious to say, "Our newsletter must be the first to publish artworks by all the popular artists in the country," or that "Our newsletter must become the benchmark for art curation." These are really inspiring words, but can mean nothing if we don't have a plan to get there. The general rule of thumb for this part of product experience planning is that when we aim for an ambitious goal, we also sign up to making it happen. Defining success must be a collaborative exercise carried out by all stakeholders. This is the playing field for deciding where we can stretch our goals, and for everyone to agree on what we're signing up to, in order to set the product experience up for success. Defining key success metrics For every feature idea we came up with, we can create feature cards that look like the following sample. This card indicates three aspects about what success means for this feature. We are asking these questions: what are we validating? When do we validate this? What Key Business Outcomes does it help us to validate? The criteria for success demonstrates what the business anticipates as being a tangible outcome from a feature. It also demonstrates which business functions will support, own, and drive the execution of the feature. That's it! We've nailed it, right? Wrong. Success metrics must be SMART, but how specific is the specific? The preceding success metric indicates that 80% of those who sign up for the monthly art catalog will enquire about at least one artwork. Now, 80% could mean 80 people, 800 people, or 8000 people, depending on whether we get 100 sign-ups, 1000, or 10,000, respectively! We have defined what external (customer/market) metrics to look for, but we have not defined whether we can realistically achieve this goal, given our resources and capabilities. The question we need to ask is: are we (as a business) equipped to handle 8000 enquiries? Do we have the expertise, resources, and people to manage this? If we don't plan in advance and assign ownership, our goals can lead to a gap in the product experience. When we clarify this explicitly, each business function could make assumptions. When we say 80% of folks will enquire about one artwork, the sales team is thinking that around 50 people will enquire. This is what the sales team  at ArtGalore is probably equipped to handle. However, marketing is aiming for 750 people and the developers are planning for 1000 people. So, even if we can attract 1000 enquiries, sales can handle only 50 enquiries a month! If this is what we're equipped for today, then building anything more could be wasteful. We need to think about how we can ramp up the sales team to handle more requests. The idea of drilling into success metrics is to gauge whether we're equipped to handle our success. So, maybe our success metric should be that we expect to get about 100 sign-ups in the first three months and between 40-70 folks enquiring about artworks after they sign up. Alternatively, we can find a smart way to enable sales to handle higher sales volumes. Before we write up success metrics, we should be asking a whole truckload of questions that determine the before-and-after of the feature. We need to ask the following questions: What will the monthly catalog showcase? How many curated art items will be showcased each month? What is the nature of the content that we should showcase? Just good high-quality images and text, or is there something more? Who will put together the catalog? How long must this person/team(s) spend to create this catalog? Where will we source the art for curation? Is there a specific date each month when the newsletter needs     to go out? Why do we think 80% of those who sign up will enquire? Is it because of the exclusive nature of art? Is it because of the quality of presentation? Is it because of the timing? What's so special about our catalog? Who handles the incoming enquiries? Is there a number to call    or is it via email? How long would we take to respond to enquiries? If we get 10,000 sign-ups and receive 8000 enquiries, are we equipped to handle these? Are these numbers too high? Can we still meet our response time if we hit those numbers? Would we still be happy if we got only 50% of folks who sign up enquiring? What if it's 30%? When would we throw away the idea of the catalog? This is where the meat of feature success starts taking shape. We  need a plan to uncover underlying assumptions and set ourselves up for success. It's very easy for folks to put out ambitious metrics without understanding the before-and-after of the work involved in meeting that metric. The intent of a strategy should be to set teams up for success, not for failure. Often, ambitious goals are set without considering whether they are realistic and achievable or not. This is so detrimental that teams eventually resort to manipulating the metrics or misrepresenting them, playing the blame game, or hiding information. Sometimes teams try to meet these metrics by deprioritizing other stuff. Eventually, team morale, productivity, and delivery take a hit. Ambitious goals, without the required capacity, capability, and resources to deliver, are useless. Technology to be in line with business outcomes Every business function needs to align toward the Key Business Outcomes and conform to the constraints under which the business operates. In our example here, the deadline is for the business to launch this feature idea before the Big Art show. So, meeting timelines is already a necessary measure of success. The other indicators of product technology measures could be quality, usability, response times, latency, reliability, data privacy, security, and so on. These are traditionally clubbed under NFRs (nonfunctional requirements). They are indicators of how the system has been designed or how the system operates, and are not really about user behavior. There is no aspect of a product that is nonfunctional or without a bearing on business outcomes. In that sense, nonfunctional requirements are a misnomer. NFRs are really technical success criteria. They are also a business stakeholder's decision, based on what outcomes the business wants to pursue. In many time and budget-bound software projects, technical success criteria trade-offs happen without understanding the business context or thinking about the end-to-end product experience. Let's take an example: our app's performance may be okay when handling 100 users, but it could take a hit when we get to 10,000 users. By then, the business has moved on to other priorities and the product isn't ready to make the leap. This depends on how each team can communicate the impact of doing or not doing something today in terms of a cost tomorrow. What that means is that engineering may be able to create software that can scale to 5000 users with minimal effort, but in order to scale to 500,000 users, there's a different level of magnitude required. There is a different approach needed when building solutions for meeting short-term benefits, compared to how we might build systems for long-term benefits. It is not possible to generalize and make a case that just because we build an application quickly, that it is likely to be full of defects or that it won't be secure. By contrast, just because we build a lot of robustness into an application, this does not mean that it will make the product sell better. There is a cost to building something, and there is also a cost to not building something and a cost to a rework. The cost will be justified based on the benefits we can reap, but it is important for product technology and business stakeholders to align on the loss or gain in terms of the end-to-end product experience because of the technical approach we are taking today. In order to arrive at these decisions, the business does not really need to understand design patterns, coding practices, or the nuanced technology details. They need to know the viability to meet business outcomes. This viability is based on technology possibilities, constraints, effort, skills needed, resources (hardware and software), time, and other prerequisites. What we can expect and what we cannot expect must both be agreed upon. In every scope-related discussion, I have seen that there are better insights and conversations when we highlight what the business/customer does not get from this product release. When we only highlight what value they will get, the discussions tend to go toward improvising on that value. When the business realizes what it doesn't get, the discussions lean toward improvising the end-to-end product experience. Should a business care that we wrote unit tests? Does the business care what design patterns we used or what language or software we used? We can have general guidelines for healthy and effective ways to follow best practices within our lines of work, but best practices don't define us, outcomes do. To summarize we learned before commencing on the development of any feature idea, there must be a consensus on what outcomes we are seeking to achieve. The success metrics should be our guideline for finding the smartest way to implement a feature. Developer’s guide to Software architecture patterns Hey hey, I wanna be a Rockstar (Developer) The developer-tester face-off needs to end. It’s putting our projects at risk
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Sunith Shetty
25 Jul 2018
4 min read
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Why should enterprises use Splunk?

Sunith Shetty
25 Jul 2018
4 min read
Splunk is a multinational software company that offers its core platform, Splunk Enterprise, as well as many related offerings built on the Splunk platform. The platform helps a wide variety of organizational personas, such as analysts, operators, developers, testers, managers, and executives. They get analytical insights from machine-created data. It collects, stores, and provides powerful analytical capabilities, enabling organizations to act on often powerful insights derived from this data. The Splunk Enterprise platform was built with IT operations in mind. When companies had IT infrastructure problems, troubleshooting and solving problems was immensely difficult, complicated, and manual. It was built to collect and make log files from IT systems searchable and accessible. It is commonly used for information security and development operations, as well as more advanced use cases for custom machines, Internet of Things, and mobile devices. Most organizations will start using Splunk in one of three areas: IT operations management, information security, or development operations (DevOps). In today's post, we will understand the thoughts, concepts, and ideas to apply Splunk to an organization level. This article is an excerpt from a book written by J-P Contreras, Erickson Delgado and Betsy Page Sigman titled Splunk 7 Essentials, Third Edition. IT operations IT operations have moved from predominantly being a cost center to also being a revenue center. Today, many of the world's oldest companies also make money based on IT services and/or systems. As a result, the delivery of these IT services must be monitored and, ideally, proactively remedied before failures occur. Ensuring that hardware such as servers, storage, and network devices are functioning properly via their log data is important. Organizations can also log and monitor mobile and browser-based software applications for any issues from software. Ultimately, organizations will want to correlate these sets of data together to get a complete picture of IT Health. In this regard, Splunk takes the expertise accumulated over the years and offers a paid-for application known as IT Server Intelligence (ITSI) to help give companies a framework for tackling large IT environments. Complicating matters for many traditional organizations is the use of Cloud computing technologies, which now drive log captured from both internally and externally hosted systems. Cybersecurity With the relentless focus in today's world on cybersecurity, there is a good chance your organization will need a tool such as Splunk to address a wide variety of Information Security needs as well. It acts as a log data consolidation and reporting engine, capturing essential security-related log data from devices and software, such as vulnerability scanners, phishing prevention, firewalls, and user management and behavior, just to name a few. Companies need to ensure they are protected from external as well as internal threats, and as a result offer the paid-for applications enterprise security and User behavior analytics (UBA). Similar to ITSI, these applications deliver frameworks to help companies meet their specific requirements in these areas. In addition to cyber-security to protect the business, often companies will have to comply with, and audit against, specific security standards, which can be industry-related, such as PCI compliance of financial transactions; customer-related, such as National Institute of Standards and Technologies (NIST) requirements in working with the the US government; or data privacy-related, such as the Health Insurance Portability and Accountability Act (HIPAA) or the European Union's General Data Protection Regulation (GPDR). Software development and support operations Commonly referred to as DevOps, Splunk's ability to ingest and correlate data from many sources solves many challenges faced in software development, testing, and release cycles. Using Splunk will help teams provide higher quality software more efficiently. Then, with the controls into the software in place, it will provide visibility into released software, its use and user behavior changes, intended or not. This set of use cases is particularly applicable to organizations that develop their own software. Internet of Things Many organizations today are looking to build upon the converging trends in computing, mobility and wireless communications and data to capture data from more and more devices. Examples can include data captured from sensors placed on machinery such as wind turbines, trains, sensors, heating, and cooling systems. These sensors provide access to the data they capture in standard formats such as JavaScript Object Notation (JSON) through application programming interfaces (APIs). To summarize, we saw how Splunk can be used at an organizational level for IT operations, cybersecurity, software development and support and the IoTs. To know more about how Splunk can be used to make informed decisions in areas such as IT operations, information security, and the Internet of Things., do checkout this book Splunk 7 Essentials, Third Edition. Create a data model in Splunk to enable interactive reports and dashboards Splunk leverages AI in its monitoring tools Splunk Industrial Asset Intelligence (Splunk IAI) targets Industrial IoT marketplace
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Natasha Mathur
07 Jun 2018
11 min read
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A tale of two tools: Tableau and Power BI

Natasha Mathur
07 Jun 2018
11 min read
Business professionals are on a constant look-out for a powerful yet cost-effective BI tool to ramp up the operational efficiency within organizations. Two tools that are front-runners in the Self-Service Business Intelligence field currently are Tableau and Power BI. Both tools, although quite similar in nature, offer different features. Most experts say that the right tool depends on the size, needs and the budget of an organization, but when compared closely, one of them clearly beats the other in terms of its features. Now, instead of comparing the two based on their pros and cons, we’ll let Tableau and Power BI take over from here to argue their case covering topics like features, usability, to pricing and job opportunities. For those of you who aren’t interested in a good story, there is a summary of the key points at the end of the article comparing the two tools. [box type="shadow" align="" class="" width=""] The clock strikes 2’o'clock for a meeting on a regular Monday afternoon. Tableau, a market leader in Business Intelligence & data analytics and Power BI; another standout performer and Tableau’s opponent in the field of Business Intelligence head off for a meeting with the Vendor. The meeting where the vendor is finally expected to decide to which tool their organization should pick for their BI needs. With Power BI and Tableau joining the Vendor, the conversation starts on a light note with both tools introducing themselves to the Vendor. Tableau: Hi, I am Tableau, I make it easy for companies all around the world to see and understand their data. I provide different visualization tools, drag & drop features, metadata management, data notifications, etc, among other exciting features. Power BI: Hello, I am Power BI, I am a cloud-based analytics and Business Intelligence platform. I provide a full overview of critical data to organizations across the globe. I allow companies to easily share data by connecting the data sources and helping them create reports. I also help create scalable dashboards for visualization. The vendor nods convincingly in agreement while making notes about the two tools. Vendor: May I know what each one of you offers in terms of visualization? Tableau: Sure, I let users create 24 different types of baseline visualizations including heat maps, line charts and scatter plots. Not trying to brag, but you don’t need intense coding knowledge to develop high quality and complex visualizations with me. You can also ask me ‘what if’ questions regarding the data. I also provide unlimited data points for analysis. The vendor seems noticeably pleased with Tableau’s reply. Power BI: I allow users to create visualizations by asking questions in natural language using Cortana. Uploading data sets is quite easy with me. You can select a wide range of visualizations as blueprints. You can then insert data from the sidebar into the visualization. Tableau passes a glittery infectious smirk and throws a question towards Power BI excitedly. Tableau: Wait, what about data points? How many data points can you offer? The Vendor looks at Power BI with a straight face, waiting for a reply. Power BI: For now, I offer 3500 data points for data analysis. Vendor: Umm, okay, but, won’t the 3500 data point limit the effectiveness for the users? Tableau cuts off Power BI as it tries to answer and replies back to the vendor with a distinct sense of rush in its voice. Tableau: It will! Due to the 3500 data point limit, many visuals can't display a large amount of data, so filters are added. As the data gets filtered automatically, it leads to outliers getting missed. Power BI looks visibly irritated after Tableau’s response and looks at the vendor for slight hope, while vendor seems more inclined towards Tableau. Vendor: Okay. Noted. What can you tell me about your compatibility with data sources? Tableau: I support hundreds of data connectors. This includes online analytical processing (OLAP), big data options (such as NoSQL, Hadoop) as well as cloud options. I am capable of automatically determining the relationship between data when added from multiple sources. I also let you modify data links or create them manually based on your company’s preferences. Power BI: I help connect to users’ external sources including SAP HANA, JSON, MySQL, and more. When data is added from multiple sources, I can automatically determine the relationships between them. In fact, I let users connect to Microsoft Azure databases, third-party databases, files and online services like Salesforce and Google Analytics. Vendor: Okay, that’s great! Can you tell me what your customer support is like? Tableau jumps in to answer the question first yet again. Tableau: I offer direct support by phone and email. Customers can also login to the customer portal to submit a support ticket. Subscriptions are provided based on three different categories namely desktop, server and online. Also, there are support resources for different subscription version of the software namely Desktop, Server, and Online. Users are free to access the support resources depending upon the version of the software. I provide getting started guides, best practices as well as how to use the platform’s top features. A user can also access Tableau community forum along with attending training events. The vendor seems highly pleased with Tableau’s answer and continues scribbling in his notebook. Power BI: I offer faster customer support to users with a paid account. However, all users can submit a support ticket. I also provide robust support resources and documentation including learning guides, a user community forum and samples of how my partners use the platform.  Though customer support functionality is limited for users with a free Power BI account. Vendor: Okay, got it! Can you tell me about your learning curves? Do you get along well with novice users too or just professionals? Tableau: I am a very powerful tool and data analysts around the world are my largest customer base. I must confess, I am not quite intuitive in nature but given the powerful visualization features that I offer, I see no harm in people getting themselves acquainted with data science a bit before they decide to choose me. In a nutshell, it can be a bit tricky to transform and clean visualizations with me for novices. Tableau looks at the vendor for approval but he is just busy making notes. Power BI: I am the citizen data scientists’ ally. From common stakeholders to data analysts, there are features for almost everyone on board as far as I am concerned. My interface is quite intuitive and depends more on drag and drop features to build visualizations. This makes it easy for the users to play around with the interface a bit. It doesn’t matter whether you’re a novice or pro, there’s space for everyone here. A green monster of jealousy takes over Tableau as it scoffs at Power BI. Tableau: You are only compatible with Windows. I, on the other hand, am compatible with both Windows and Mac OS. And let’s be real it’s tough to do even simple calculations with you, such as creating a percent-of-total variable, without learning the DAX language. As the flood of anger rises in Power BI, Vendor interrupts them. Vendor: May I just ask one last question before I get ready with the results? How heavy are you on my pockets? Power BI: I offer three subscription plans namely desktop, pro, and premium. Desktop is the free version. Pro is for professionals and starts at $9.99 per user per month. You get additional features such as data governance, content packaging, and distribution. I also offer a 60 day trial with Pro. Now, coming to Premium, it is built on a capacity pricing. What that means is that I charge you per node per month. You get even more powerful features such as premium version cost calculator for custom quote ranges. This is based on the number of pro, frequent and occasional users that are active on an account’s premium version. The vendor seems a little dazed as he continues making notes. Tableau: I offer three subscriptions as well, namely Desktop, Server, and Online. Prices are charged per user per month but billed annually. Desktop category comes with two options: Personal edition (starting at $35) and professional edition (starting at $70). The server option offers on-premises or public cloud capabilities, starting at $35 while the Online version is fully hosted and starts at $42. I also offer a free version namely Tableau Public with which users can create visualizations, save them and share them on social media or their blog. There is a 10GB storage limit though. I also offer 14 days free trial for users so that they can get a demo before the purchase. Tableau and Power BI both await anxiously for the Vendor’s reply as he continued scribbling in his notebook while making weird quizzical expressions. Vendor: Thank you so much for attending this meeting. I’ll be right back with the results. I just need to check on a few things. Tableau and power BI watch the vendor leave the room and heavy anticipation fills the room. Tableau: Let’s be real, I will always be the preferred choice for data visualization. Power BI: We shall see that. Don’t forget that I also offer data visualization tools along with predictive modeling and reporting. Tableau: I have a better job market! Power BI: What makes you say that? I think you need to re-check the Gartner’s Magic Quadrant as I am right beside you on that. Power BI looks at Tableau with a hot rush of astonishment as the Vendor enters the room. The vendor smiles at Tableau as he continues the discussion which makes Power BI slightly uneasy. Vendor: Tableau and Power BI, you both offer great features but as you know I can only pick one of you as my choice for the organizations. An air of suspense surrounds the atmosphere. Vendor: Tableau, you are a great data visualization tool with versatile built-in features such as user interface layout, visualization sharing, and intuitive data exploration. Power BI, you offer real-time data access along with some pretty handy drag and drop features. You help create visualizations quickly and provide even the novice users an access to powerful data analytics without any prior knowledge. The tension notched up even more as the Vendor kept talking. Vendor: Tableau! You’re a great data visualization tool but the price point is quite high. This is one of the reasons why I choose Microsoft Power BI. Microsoft Power BI offers data visualization, connects to external data sources, lets you create reports, etc, all at low cost. Hence, Power BI, welcome aboard! A sense of infinite peace and pride emanates from Power BI. The meeting ends with Power BI and Vendor shaking hands as Tableau silently leaves the room. [/box] We took a peek into the Vendor’s notebook and saw this comparison table. Power BI Tableau Visualization capabilities Good Very Good Compatibility with multiple Data sources Good Good Customer Support Quality Good Good Learning Curve Very Good Good System Compatibility Windows Windows & Mac OS Cost Low Very high Job Market Good Good Analytics Very Good Good Both the Business Intelligence tools are in demand by organizations all over the world. Tableau is fast and agile. It provides a comprehensible interface along with visual analytics where users have the ability to ask and answer questions. Its versatility and success stories make it a good choice for organizations willing to invest in a higher budget Business Intelligence software. Power BI, on the other hand, offers almost similar features as Tableau including data visualization, predictive modeling, reporting, data prep, etc, at one of the lowest subscription prices today in the market. Nevertheless, there are upgrades being made to both of the Business Intelligence tools, and we can only wait to see what’s more to come in these technologies. Building a Microsoft Power BI Data Model “Tableau is the most powerful and secure end-to-end analytics platform”: An interview with Joshua Milligan“ Unlocking the secrets of Microsoft Power BI      
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Amey Varangaonkar
31 May 2018
7 min read
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Best practices for deploying self-service BI with Qlik Sense

Amey Varangaonkar
31 May 2018
7 min read
As part of a successful deployment of Qlik Sense, it is important IT recognizes self-service Business Intelligence to have its own dynamics and adoption rules. The various use cases and subsequent user groups thus need to be assessed and captured. Governance should always be present but power users should never get the feeling that they are restricted. Once they are won over, the rest of the traction and the adoption of other user types is very easy. In this article, we will look at the most important points to keep in mind while deploying self-service with Qlik Sense. The following excerpt is taken from the book Mastering Qlik Sense, authored by Martin Mahler and Juan Ignacio Vitantonio. This book demonstrates useful techniques to design useful and highly profitable Business Intelligence solutions using Qlik Sense. Here's the list of points to be kept in mind: Qlik Sense is not QlikView Not even nearly. The biggest challenge and fallacy is that the organization was sold, by Qlik or someone else, just the next version of the tool. It did not help at all that Qlik itself was working for years on Qlik Sense under the initial product name Qlik.Next. Whatever you are being told, however, it is being sold to you, Qlik Sense is at best the cousin of QlikView. Same family, but no blood relation. Thinking otherwise sets the wrong expectation so the business gives the wrong message to stakeholders and does not raise awareness to IT that self-service BI cannot be deployed in the same fashion as guided analytics, QlikView in this case. Disappointment is imminent when stakeholders realize Qlik Sense cannot replicate their QlikView dashboards. Simply installing Qlik Sense does not create a self-service BI environment Installing Qlik Sense and giving users access to the tool is a start but there is more to it than simply installing it. The infrastructure requires design and planning, data quality processing, data collection, and determining who intends to use the platform to consume what type of data. If data is not available and accessible to the user, data analytics serve no purpose. Make sure a data warehouse or similar is in place and the business has a use case for self-service data analytics. A good indicator for this is when the business or project works with a lot of data, and there are business users who have lots of Excel spreadsheets lying around analyzing it in different ways. That’s your best case candidate for Qlik Sense. IT to monitor Qlik Sense environment rather control IT needs to unlearn to learn new things and the same applies when it comes to deploying self-service. Create a framework with guidelines and principles and monitor that users are following it, rather than limiting them in their capabilities. This framework needs to have the input of the users as well and to be elastic. Also, not many IT professionals agree with giving away too much power to the user in the development process, believing this leads to chaos and anarchy. While the risk is there, this fear needs to be overcome. Users love data analytics, and they are keen to get the help of IT to create the most valuable dashboard possible and ensure it will be well received by a wide audience. Identifying key users and user groups is crucial For a strong adoption of the tool, IT needs to prepare the environment and identify the key power users in the organization and to win them over to using the technology. It is important they are intensively supported, especially in the beginning, and they are allowed to drive how the technology should be used rather than having principles imposed on them. Governance should always be present but power users should never get the feeling they are restricted by it. Because once they are won over, the rest of the traction and the adoption of other user types is very easy. Qlik Sense sells well–do a lot of demos Data analytics, compelling visualizations, and the interactivity of Qlik Sense is something almost everyone is interested in. The business wants to see its own data aggregated and distilled in a cool and glossy dashboard. Utilize the momentum and do as many demos as you can to win advocates of the technology and promote a consciousness of becoming a data-driven culture in the organization. Even the simplest Qlik Sense dashboards amaze people and boost their creativity for use cases where data analytics in their area could apply and create value. Promote collaboration Sharing is caring. This not only applies to insights, which naturally are shared with the excitement of having found out something new and valuable, but also to how the new insight has been derived. People keep their secrets on the approach and methodology to themselves, but this is counterproductive. It is important that applications, visualizations, and dashboards created with Qlik Sense are shared and demonstrated to other Qlik Sense users as frequently as possible. This not only promotes a data-driven culture but also encourages the collaboration of users and teams across various business functions, which would not have happened otherwise. They could either be sharing knowledge, tips, and tricks or even realizing they look at the same slices of data and could create additional value by connecting them together. Market the success of Qlik Sense within the organization If Qlik Sense has had a successful achievement in a project, tell others about it. Create a success story and propose doing demos of the dashboard and its analytics. IT has been historically very bad in promoting their work, which is counterproductive. Data analytics creates value and there is nothing embarrassing about boasting about its success; as Muhammad Ali suggested, it’s not bragging if it’s true. Introduce guidelines on design and terminology Avoiding the pitfalls of having multiple different-looking dashboards by promoting a consistent branding look across all Qlik Sense dashboards and applications, including terminology and best practices. Ensure the document is easily accessible to all users. Also, create predesigned templates with some sample sheets so the users duplicate them and modify them to their liking and extend them, applying the same design. Protect less experienced users from complexities Don’t overwhelm users if they have never developed in their life. Approach less technically savvy users in a different way by providing them with sample data and sample templates, including a library of predefined visualizations, dimensions, or measures (so-called Master Key Items). Be aware that what is intuitive to Qlik professionals or power users is not necessarily intuitive to other users – be patient and appreciative of their feedback, and try to understand how a typical business user might think. For a strong adoption of the tool, IT needs to prepare the environment and identify the key power users in the organization and win them over to using the technology. It is important they are intensively supported, especially in the beginning, and they are allowed to drive how the technology should be used rather than having principles imposed on them. If you found the excerpt useful, make sure you check out the book Mastering Qlik Sense to learn more of these techniques on efficient Business Intelligence using Qlik Sense. Read more How Qlik Sense is driving self-service Business Intelligence Overview of a Qlik Sense® Application’s Life Cycle What we learned from Qlik Qonnections 2018
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Amey Varangaonkar
29 May 2018
7 min read
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Four self-service business intelligence user types in Qlik Sense

Amey Varangaonkar
29 May 2018
7 min read
With the introduction of self-service to BI, there is segmentation at various levels and breaths on how self-service is conducted and to what extent. There are, quite frankly, different user types that differ from each other in level of interest, technical expertise, and the way in which they consume data. While each user will almost be unique in the way they use self-service, the user base can be divided into four different groups. In this article, we take a look at the four types of users in self-service business intelligence model. The following excerpt is taken from the book Mastering Qlik Sense, authored by Martin Mahler and Juan Ignacio Vitantonio. This book presents expert techniques to design and deploy enterprise-grade Business Intelligence solutions for your business, by leveraging the power of Qlik Sense. Power Users or Data Champions Power users are the most tech-savvy business users, who show a great interest in self-service BI. They produce and build dashboards themselves and know how to load data and process it to create a logical data model. They tend to be self-learning and carry a hybrid set of skills, usually a mixture of business knowledge and some advanced technical skills. This user group is often frustrated with existing reporting or BI solutions and finds IT inadequate in delivering the same. As a result, especially in the past, they take away data dumps from IT solutions and create their own dashboards in Excel, using advanced skills such as VBA, Visual Basic for Applications. They generally like to participate in the development process but have been unable to do so due to governance rules and a strict old-school separation of IT from the business. Self-service BI is addressing this group in particular, and identifying those users is key in reaching adoption within an organization. Within an established self-service environment, power users generally participate in committees revolving around the technical environments and represent the business interest. They also develop the bulk of the first versions of the apps, which, as part of a naturally evolving process, are then handed over to more experienced IT for them to be polished and optimized. Power users advocate the self-service BI technology and often not only demo the insights and information they achieved to extract from their data, but also the efficiency and timeliness of doing so. At the same time, they also serve as the first point of contact for other users and consumers when it comes to questions about their apps and dashboards. Sometimes they also participate in a technical advisory capacity on whether other projects are feasible to be implemented using the same technology. Within a self-service BI environment, it is safe to say that those power users are the pillars of a successful adoption. Business Users or Data Visualizers Users are frequent users of data analytics, with the main goal to extract value from the data they are presented with. They represent the group of the user base which is interested in conducting data analysis and data discovery to better understand their business in order to make better-informed decisions. Presentation and ease of use of the application are key to this type of user group and they are less interested in building new analytics themselves. That being said, some form of creating new charts and loading data is sometimes still of interest to them, albeit on a very basic level. Timeliness, the relevance of data, and the user experience are most relevant to them. They are the ones who are slicing and dicing the data and drilling down into dimensions, and who are keen to click around in the app to obtain valuable information. Usually, a group of users belong to the same department and have a power user overseeing them with regard to questions but also in receiving feedback on how the dashboard can be improved even more. Their interaction with IT is mostly limited to requesting access and resolving unexpected technical errors. Consumers or Data Readers Consumers usually form the largest user group of a self-service BI analytics solution. They are the end recipients of the insights and data analytics that have been produced and, normally, are only interested in distilled information which is presented to them in a digested form. They are usually the kind of users who are happy with a report, either digital or in printed form, which summarizes highlights and lowlights in a few pages, requiring no interaction at all. Also, they are most sensitive to the timeliness and availability of their reports. While usually the largest audience, at the same time this user group leverages the self-service capabilities of a BI tool the least. This poses a licensing challenge, as those users don’t take full advantage of the functionality on offer, but are costing the full amount in order to access the reports. It is therefore not uncommon to assign this type of user group a bucket of login access passes or not give them access to the self-service BI platform at all and give them the information they need in (digitally) printed format or within presentations, prepared by users. IT or Data Overseers IT represents the technical user group within this context, who sit in the background and develop and manage the framework within which the self-service BI solution operates. They are the backbone of the deployment and ensure the environment is set up correctly to cater for the various use cases required by the above-described user groups. At the same time, they ensure a security policy is in place and maintained and they introduce a governance framework for deployment, data quality, and best practices. They are in effect responsible for overseeing the power users and helping them with technical questions, but at the same time ensuring terms and definition as well as the look and feel is consistent and maintained across all apps. With self-service BI, IT plays a lesser role in actually developing the dashboards but assumes a more mentoring position, where training, consultation, and advisory in best practices are conducted. While working closely with power users, IT also provides technical support to users and liaises with the IT infrastructure to ensure the server infrastructure is fit for purpose and up and running to serve the users. This also includes upgrading the platform where required and enriching it with additional functionality if and when available. Bringing them together The previous four groups can be distinguished within a typical enterprise environment; however, this is not to say hybrid or fewer user groups are not viable models for self-service BI. It is an evolutionary process in how an organization adapts self-service data analytics with a lot of dependencies on available skills, competing established solutions, culture, and appetite on new technologies. It usually begins with IT being the first users in a newly deployed self-service environment, not only setting up the infrastructure but also developing the first apps for a couple of consumers. Power users then follow up; generally, they are the business sponsors themselves who are often big fans of data analytics, modifying the app to their liking and promoting it to their users. The user base emerges with the success of the solution, where analytics are integrated into their business as the usual process. The last group, the consumers, is mostly the last type of user group that is established, which more often than not doesn’t have actual access to the platform itself, but rather receives printouts, email summaries with screenshots, or PowerPoint presentations. Due to licensing cost and the size of the consumer audience, it is not always easy to give them access to the self-service platform; hence, most of the time, an automated and streamlined PDF printing process is the most elegant solution to cater to this type of user group. At the same time, the size of the deployment also determines the number of various user groups. In small enterprise environments, it will be mostly power users and IT who will be using self-service. This greatly simplifies the approach as well as the setup considerations. If you found the above excerpt useful, make sure you check out the book Mastering Qlik Sense to learn helpful tips and tricks to perform effective Business Intelligence using Qlik Sense. Read more: How Qlik Sense is driving self-service Business Intelligence What we learned from Qlik Qonnections 2018 How self-service analytics is changing modern-day businesses
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Ed Bowkett
04 Dec 2014
4 min read
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Top 4 Business Intelligence Tools

Ed Bowkett
04 Dec 2014
4 min read
With the boom of data analytics, Business Intelligence has taken something of a front stage in recent years, and as a result, a number of Business Intelligence (BI) tools have appeared. This allows a business to obtain a reliable set of data, faster and easier, and to set business objectives. This will be a list of the more prominent tools and will list advantages and disadvantages of each. Pentaho Pentaho was founded in 2004 and offers a suite, among others, of open source BI applications under the name, Pentaho Business Analytics. It has two suites, enterprise and community. It allows easy access to data and even easier ways of visualizing this data, from a variety of different sources including Excel and Hadoop and it covers almost every platform ranging from mobile, Android and iPhone, through to Windows and even Web-based. However with the pros, there are cons, which include the Pentaho Metadata Editor in Pentaho, which is difficult to understand, and the documentation provided offers few solutions for this tool (which is a key component). Also, compared to other tools, which we will mention below, the advanced analytics in Pentaho need improving. However, given that it is open source, there is continual improvement. Tableau Founded in 2003, Tableau also offers a range of suites, focusing on three products: Desktop, Server, and Public. Some benefits of using Tableau over other products include ease of use and a pretty simple UI involving drag and drop tools, which allows pretty much everyone to use it. Creating a highly interactive dashboard with various sources to obtain your data from is simple and quick. To sum up, Tableau is fast. Incredibly fast! There are relatively few cons when it comes to Tableau, but some automated features you would usually expect in other suites aren’t offered for most of the processes and uses here. Jaspersoft As well as being another suite that is open source, Jaspersoft ships with a number of data visualization, data integration, and reporting tools. Added to the small licensing cost, Jaspersoft is justifiably one of the leaders in this area. It can be used with a variety of databases including Cassandra, CouchDB, MongoDB, Neo4j, and Riak. Other benefits include ease of installation and the functionality of the tools in Jaspersoft is better than most competitors on the market. However, the documentation has been claimed to have been lacking in helping customers dive deeper into Jaspersoft, and if you do customize it the customer service can no longer assist you if it breaks. However, given the functionality/ability to extend it, these cons seem minor. Qlikview Qlikview is one of the oldest Business Intelligence software tools in the market, having been around since 1993, it has multiple features, and as a result, many pros and cons that include ones that I have mentioned for previous suites. Some advantages of Qlikview are that it takes a very small amount of time to implement and it’s incredibly quick; quicker than Tableau in this regard! It also has 64-bit in-memory, which is among the best in the market. Qlikview also has good data mining tools, good features (having been in the market for a long time), and a visualization function. These aspects make it so much easier to deal with than others on the market. The learning curve is relatively small. Some cons in relation to Qlikview include that while Qlikview is easy to use, Tableau is seen as the better suite to use to analyze data in depth. Qlikview also has difficulties integrating map data, which other BI tools are better at doing. This list is not definitive! It lays out some open source tools that companies and individuals can use to help them analyze data to prepare business performance KPIs. There are other tools that are used by businesses including Microsoft BI tools, Cognos, MicroStrategy, and Oracle Hyperion. I’ve chosen to explore some BI tools that are quick to use out of the box and are incredibly popular and expanding in usage.
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