Types of visualisation ideal for presenting pitches

Types of visualisation ideal for presenting pitches

Information and Advice from Minerra

In today’s digital world where everything is measured according to the visual appeal, every manager should read at least one data visualisation book to understand the power that it has over people’s judgment. In this article, we are going to cover the types of visualisation that are ideal for presenting pitches to your colleagues or clients.

Choosing the right chart to convey your message can be a game changer during a meeting, and knowing your subject and how to present it well with the help of a visualisation tool can win you the account.

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Minerra dashboard software is an excellent tool for merging data from multiple platforms and creating a great and understandable visualisation for the presentation of data. When you are creating a strategy for results promotion, you need to think it through and decide on the story you want to tell and on the punch lines that would catch everyone’s attention.

You will also want to make your charts clean, simple and pretty to look at, so the point you want to convey to people can be understood by just looking at the board.

This is not always easy; on the contrary, however, there are multiple options you can choose from and find the one that helps you present your idea in the best possible way. Everyone loves a useful chart, so put your thinking cap on and let’s give you some ideas and tips about chart selection in the next section of the article.

Types of charts and graphs

The following suggestions contain the most commonly used types of charts and graphs by industry professionals. We included descriptions, tips and tricks you might find beneficial when you’re choosing the one that best tells your idea.

Number charts

A real-time number chart is the simples form of keeping people up to date with sales/trends/customers/expansion. It’s relatively easy to build because you just have to input the tracking numbers, but don’t forget to mark the period you’re tracking for the objective because it will help you emphasise your point.

You can also add a trend indicator so you can compare numbers. However, you should be careful not to use too many number charts in one presentation since they can distract people from your story.

Line charts

Line charts are perfect for showing trends of volatility and tendencies of the market (like Bitcoin line charts). They can help you show how data changes over specified periods of time, and they are very easy for reading and understanding. You need to be careful not to add to many line variables in the creation of the line chart, because what was supposed to be simple can easily become complicated and confuse your audience.

Pie Charts

The great side to pies is that everyone is familiar with the concept and can read them well without any previous knowledge, and they are convenient if you want to show sections of the whole and how that translates into percentages. On the other hand, this can be useful to you only if the pitch you want to make doesn’t require precision and it’s limited on past interpretation of data.


Maps are one of the most beautiful types of visualisation you can use during a presentation, and they are beneficial in showing geographical data. Maps have been instrumental at showing world trends for governments, NGOs, big companies and other successful businesses around the globe. However, no matter how remarkable they are as a visualisation tool, you shouldn’t use them if your pitch has nothing to do with geographical data.

Bar graphs

Bar graphs come in two main categories, horizontal and column graphs, and both of these are used for specific purposes. You should use horizontal graphs when you’re doing a comparative analysis and columns for showing serial data at one particular period. Another option is to use the stacked column chart, which is perfect for showing percentages.

Scatter plots

Scatter plots are perfect if you have one independent variable and one depended variable combined with a lot of data sets you want to present cleanly and elegantly. The correlation between the variables is clearly marked on the chart, and you can show how positive, negative or nonexistent it is. As you understood by now, this chart is only usable if you have to show a correlation between variables.

Gauge charts

The speedometer charts use one assessment/measure within a numerical context to show progress against key business indicators. You should use them if you want to point out one specific line of development over a specified period of time; any more variables, and you might puzzle the people at the meeting. They also take up a lot of space on the dashboard, so if you have to point out more than one, then it would be better to choose a different type of chart.

Spider charts

Spider charts (they look like webs) are good for working with more than three quantitative variables for evaluation on one or more subjects. The trick with these charts is that they are not easy to pull off, even though they are showing in-depth analysis.

Moreover, people are not that familiar with them so you might have to explain them in more details. However, if you make them comprehensible and integrate them into your story, they can become an excellent asset during the presentation.

Area charts

Area charts are perfect for following trends and tendencies, and they can be instrumental if you want to present consecutive data points in a timeline. Area charts are flexible as a visualisation tool, but it’s not recommended for you to use more than three variables, and even for that, you would have to do a stacked area chart.

Chart types to compare multiple categories

You can use most of the above chart types to compare multiple categories. However, for a prolific type of visualisation, you should use the spider chart. If you want to play it safe, the bar graphs will do the job.

Contact us for additional information on our business intelligence software and visualisation tools!

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Top Data Warehouse Tools And Business Intelligence Software

The importance of data warehouse tools

Information and Advice from Minerra

Data warehouse tools come in many shapes and sizes as to fit every organisation’s specific needs. When it comes to customisation, options are close to endless.

One could say it’s a global trend. Many BI and analysis vendors offer decision-focused BI technology, coupled with business consulting. We are talking about fully automated solutions that shorten the painfully long ELT processes to days and weeks.

Contact Minerra for product demonstration and consulting services!

The benefits of data warehouse tools

The benefits of data warehouse tools are numerous. First of all, there is a huge difference between ready-to-use and decision-focused solutions. The first address only the most common issues, whereas the second can be tailored to support the business decisions that you need to make.

Minerra offers only customised BI analytics and data warehouse (DWH) tools. We know that one size doesn’t fit all; it never has, and it never will. Each organisation has its specific business goals, plans and objectives. With the market changing at a rapid pace, your information requirements will change and you need a solution that will adapt.

We take all those variables into account when presenting our data warehouse automation tools. But, first of all, we provide business consulting, that we believe to be the crucial step to a long-term success. After all, Minerra was founded by business people with a unique combination of business experience and a passion for technology.

Data warehouse tutorial

Every data warehouse tutorial run by an organisational at the cutting edge of their field should cover data warehouse automation in detail. Data warehouse automation is the key to allowing your data warehouse to keep up with your changing business environment.

Types of data warehouse tools

Analytics applications, can be very broad in definition, and include Excel spreadsheets, data mining tools, OLAP and dashboards (also referred to as performance dashboards), etc.

  • Excel spreadsheets are often used to extract data from operational systems. Albeit heavily relied upon in business, they have a huge flaw. Namely, they call for manual data export, manipulation, integration, and preparation, which is a very resource-intensive and time-consuming process.
  • Data science, advanced analytics and data mining tools use the data stored in various locations (such as a data warehouse) to correlate information on multiple levels. They define emerging trends and discover all kinds of issues and opportunities. Organisations use data mining tools to help identify the patterns of the ever-changing market. E.g., these tools can show that customers who buy wine often purchase dark chocolate. By putting dark chocolate next to wine, the sale is certain to skyrocket.
  • Performance dashboards are advanced data warehouse tools. They display data in a summarised form, often in graphical format.

Dashboards measure KPIs (key performance indicators) such as marketing, production, growth, customers, financial, etc. They are commonly used by senior decision makers who need to understand business performance metrics at a glance.

Business analytics tools

There are numerous business analytics tools, roughly divided into two categories: business intelligence and data science / statistical analysis.

Business intelligence tools inspect historical data and provide an insight into business operations over a selected period of time. As such, they deal with past performance. Statistical analysis / data science applies statistical algorithms to historical data in an attempt to predict the future performance of any given aspect of the business.

Business analytics incorporates a palette of approaches as to inspect, compare and predict future trends and performance. Descriptive analytics deals with present performance. Prescriptive analytics inspects past performance in an attempt to provide recommendations for similar situations in the future. And these are only some of the examples.

Nowadays, business analytics tools are increasingly being automated. To keep track of the hectic demand, manual methods are insufficient, way too slow in coming and tremendously expensive. Manual coding used in the ELT process is lengthy and incapable of handling rapid updates.

But, that isn’t to say that the ELT is a bad method. On the contrary! By automating the entire process, changes to data warehouses can be deployed rapidly (in hours and days as compared to weeks and months, in case of manual ELT).

Minerra data warehouse tools

Minerra data warehouse tools are fully automated and made to order. No matter your requirements, we are confident of being able to offer just the solution you need. We also provide business consulting and training. We make certain that our clients know how to get the best out of their DWH tools at all times.

Contact Minerra for product demonstration and consulting!

Want to learn more? We can help you start your analytics journey on the right foot and set you up for success.

The Misuse of Statistics

The Misuse of Statistics

Information and Advice from Minerra

The misuse of statistics is a delicate subject. However, it’s a discussion worth having because statistical analyses can be beneficial for your business only if the data is adequately gathered, processed and analysed. In the words of Karl Pearson, an English mathematician and biostatistician, “Statistics is the grammar of science”, but only when it’s used accurately, and the conclusions drawn from the numbers are not affected by biases.

In today’s world, the misuse of statistics is common, and it can be spotted everywhere, from news to marketing, and in this article, we are going to focus on the ways you can detect that practice and avoid it in your business.

Data Science is about collecting and processing meaningful information from massive amounts of raw data. The goal is to detect patterns and trends from the gathered information and to prepare them in a way which would afterwards help you in the decision-making process.

The most significant advantage of data science and statistical analysis is that numbers don’t lie, but you are free to interpret them and present them in a way that does.

For a business manager, the hardest part about data is to interpret the numbers, which is where Minerra’s business intelligence experts can help. Most of the time, when it comes to “reading the numbers right”, the potential for them to be misleading is high, because they can sometimes be used as half-truths that paint a different picture than the one that is accurate. You can find examples of this practice everywhere, from social media to news outlets, from advertising companies to simple sale postings.

A study was done by Dr Daniele Fanelli from The University of Edinburgh in 2009, which showed that 33.7% of data scientists use questionable research practices. This can result in modifying results, subjective testing, biased interpreting of data, withholding of important analytical details and more.

The damage this can cause to your business is more significant than merely operating on gut feeling when it comes to decision making, because what you thought was backed up by numbers is, in reality, a modified “truth”.

Misleading statistics examples

In this section of the article, we are going to give you misleading statistics examples so that you can be aware of the misuse of statistics and be more cautious in the future.

Faulty polling

Polling is the most common way to track people’s opinions and tastes in society; you can see this practice in every political campaign executed in the last 50 years. However, the one thing about polling that might make you reconsider the results is the way the question is asked and formulated. For example:

  • Do you support a tax reform that would imply bigger taxes?
  • Do you support a tax reform that would be beneficial for social redistribution?

Both of the questions are essentially asking the same thing, but they will not have the same effect and results in the polls. This is called the “loaded questions” run-through.

Polling should be impartial, and if you want the “real picture” and opinions of the subjects, you would have to ask a question that doesn’t imply the answer and is not affecting emotional responses. If you’re not sure about the statistics from a poll, always take a look at the question that was presented to the target group.

Faulty correlations

Weak correlations happen when you process so much data that eventually you’re going to recognise patterns that are emerging just because you processed a lot of data. This is a type of common statistical manipulation in cases where there is not enough evidence to prove causation, but the amount of analysed data makes it possible. You can recognise this in the news when an absurd factor is taken into consideration as the cause for an unrelated issue, like millennials eating avocado is affecting the diamond industry.

Data fishing

Data fishing or data dredging is when you analyse vast amounts of data (as in the case of weak correlations) to discover affiliations between data points, without having a working hypothesis. You can see examples of this every day and in every industry branch, where a scandalous fact was proven with data mining, and it was contradicted one week later by another fact more outrageous than the one before, again with data mining.

Confusing data visualization

Dashboards and visual, statistical graphs and charts are only useful if they provide insights on a specific subject in an orderly manner. If you don’t have the context or if you’re not familiar with the basics of the issue, then data visualisation can be confusing and, at the same time, damaging, because the conclusions drawn from it would be faulty.

The most prudent way to go around this is to implement intelligent solution automation in order to apply a variable data point comparison, which is, in fact, helpful for the growth of your business.


Purposeful bias is the most hazardous statistical misuse because it implies manipulation with results and deliberate efforts to impact data findings. As it was found in the study conducted by Dr. Daniele Fanelli that we mentioned above, 33.7% of the scientists have done faulty analysis, and that is only in the science community.

How statistics can be misleading

The easiest way to answer the question of how statistics can be misleading is to give you the Colgate example, which the U.K.’s Advertising Standards Authority (ASA) deemed was in breach of U.K. advertising rules. The slogan said “More than 80% of Dentists recommend Colgate”, but in reality, the slogan was found to be misrepresentative and misleading. An independent research company did not even do the polling.

You can find these examples of misuse of statistics in news, politics, advertising and even in science. What you can do about these types of faulty statistics is to take them with a reserve, and use experts and business software which are not affected by bias.

Contact us to consult with our business intelligence experts, and to avoid statistical mistakes that affect the governance of your company!

Want to learn more? We can help you start your analytics journey on the right foot and set you up for success.

Efficient data collection methods

Efficient data collection methods

Information and Advice from Minerra

The speed and efficiency of data collection methods are truly crucial in today’s rapidly changing environment. What serious business person does not need an innovative approach to help their employees work faster and more cost-effectively?

Long story short, market research used to be expensive and take more time than businesses had to spend. Done the traditional way, manual coding would take months and years to conclude. With big data entering the stage, manual ELT is being replaced by real-time insights that are delivered through fully automated BI and analytics platforms.

How to analyse data

That all sounds nice, but how to analyse data in a timely, cost-efficient manner? Data automation is the key. On top of allowing researchers to get faster results, it also provides invaluable insights which are shareable across the board.

For example, imagine that you need one answer only. Instead of digging through all the data, you can simply ask the BI and analytics platform to select best answers for you. These tools perform such operations by allowing customised values. But, what does that mean?

We need to keep in mind that business analytics techniques are many. Roughly, they are divided into basic BI and statistical analysis. Basic BI tools deal with historical data, namely, with past performance. Statistical analysis deals with predicting the future performance by applying statistical algorithms to historical data. Waiting for conclusions from either of the techniques does take time. So, what is the solution?

Business analytics tools are becoming automated rapidly, as the entrance of big data has made it impossible to keep track of all the changes by using manual methods alone. Data science is expanding to add new skill sets and improved algorithms, as to better predict operational performance.

Data automation is beneficial in two ways. Firstly, it is cost-effective and secondly, and it takes considerably less time to generate results. Minerra offers cutting-edge BI analysis tools, coupled with personalised training with one objective in mind: to enable our customers to always be capable of making data-driven decisions.

Ajilius (data warehouse automation tool) and Yellowfin (BI and analytics platform) are fully integrated and customisable, generating interactive dashboards that are shareable across the board. They incorporate both the benefits mentioned above, allowing for a range of customisation options that will help you get detailed answers to your specific questions. Let’s see how that works in practice.

Defining data mining tools

Data mining tools generate patterns inside large sets of structured data. They employ the CRISP-DM methodology (cross-industry standard process for data mining). The process comprises six major steps, as follows:

  • Business understanding – This step focuses on understanding the objectives and requirements for attaining them. It uses the intel to define the data mining problem and draft a preliminary plan.
  • Data understanding – This step implies data collection, followed by a set of operations aimed at understanding the data, identifying problems and generating preliminary insights.
  • Data preparation – This step constitutes a range of operations which are aimed at constructing a structured dataset.
  • Modelling – This step deals with the application of modelling techniques.
  • Evaluation – This step sees models being built and tested. In the end, the best model(s) is being selected.
  • Deployment – The final step that deploys the model into an OS. Once finished, it enables the model to treat new raw data, added afterwards in the same manner as the previous information.

As regards data collection methods, they encompass a set of steps aimed at generating structured data. At a glance, data collection starts with accumulating information from a number of sources. The information is then being stored and shaped into suitable database frameworks, which serve as a model for all additional data to be added later. The final step is information organisation, which sorts raw data and transforms them into structured data.

Shortly put, data mining tools attempt to create patterns capable of predicting future outcomes. The patterns are normally run through data analysis and BI tools, and then used in a number of ways. For example, the insights gained in this way may help a business increase revenues, reduce expenses, and select top performers and best sellers.

Marketing analytics benefits greatly from data automation tools, as the entire lengthy process is shortened — considerably. When dealing with big data, traditional manual methods are ineffective, too slow and way too expensive.

Automated BI and analytics tools come in many shapes and sizes and tackle all aspects of BI. For example, there are statistical analysis tools, self-service analytics platforms, data visualisation tools and so on. Self-service analytics platforms have been gaining popularity of late and are being developed and perfected at a rapid pace. Namely, with businesses being metadata-driven, the need to speedily extract information from all kinds of sources is a must.

Moreover, with such volumes of information, it is important that it be generated in such a way that it will be easy to understand at a glance. Interactive dashboards allowing for additional parameters are the best example of the practice. Yellowfin is one of those. It takes the practice one step further by enabling descriptive attributes to be added.

Finally, research has shown that automation has increased engagement. According to market research expert and managing director of The Future Place consultancy Ray Poynter, “in real-time tracking, automation has enabled the data-gathering to become seamless, and painless, for the participants.” He adds that market research automation can increase productivity, as it allows analysts to focus on thinking, instead of on processing.

Automated data collection methods are here to stay

Automated data collection methods are the future of analysis. On top of data mining tools, data warehousing tools are also being perfected by the minute. Data warehousing extracts data from all sources, loads them into a centralized location and then shapes them into structured data.

Structured data are displayed in an integrated view and are, as such, report-ready. Data warehouses store fact tables and summarized business events. The warehouses analyse historical data, focusing on data changes over time.

We offer the finest of BI analysis tools and train the customers to use them to their full potential.

Contact Minerra for product demonstration and consulting!

Want to learn more? We can help you start your analytics journey on the right foot and set you up for success.

Data mining techniques

Data mining techniques

Information and Advice from Minerra

The most important task of data mining is the selection of appropriate data mining techniques. These are chosen based on the business type and the issue they are to address. The techniques range from general to more specific ones. If unfamiliar with the term, data mining is the process of extracting usable data and patterns and using them to predict future trends.

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Data wrangling and data mining

Data wrangling (also: data munging) is the process of clearing raw data and transforming them into a format suitable for analysis. Data wrangling solutions are designed with the purpose of allowing the user to explore data for downstream uses.

Data mining: How it works

Data mining involves three steps: exploration, pattern identification and deployment.

Exploration is the process that sees data cleared and converted into another format. Pattern identification creates the most suitable pattern which is capable of the best predictions. Deployment sees the patterns being used to achieve the desired outcome.

Some of the benefits of data mining include automated prediction of future trends, speed of analysis, variety of models, automated hidden pattern discovery, overall implementation (e.g., data mining tools fully implement with existing platforms and new ones alike), etc.

Minerra offers some of the finest automated BI and analysis tools that will help you to always make data-driven decisions. We also offer personalised training and BI consultancy. Give us a call to see for yourself how automated data mining can benefit your business in the long run.

Data mapping

Before we dive deeper into the mining techniques, it is necessary to explain data mapping. It is the process that sources data fields to their related target data fields. These comprise metadata storing information on the individual data parts, fields, attributes, objects, etc. As such, data mapping is critical in helping decision makers make optimum business choices. Coupled with data mining, it is one of the most important aspects of BI.

Types of data mining techniques

Generally speaking, there are seven main data mining techniques. Note the term “main.” Certainly there are additional ones, but these seven are the most commonly used ones. They include: statistics, clustering, visualisation, decision tree, neural networks, association rules and classification.

  • Statistics

Statistics deals with data collection and description. It helps with pattern discovery and predictive models. Statistics answers general questions, such as “what is the probability of certain event occurring?” or “which pattern is the most useful one to the business?” Statistical reports collect data through varied approaches, most common of which are median, variance, min, max, histogram and linear regression.

  • Clustering

Clustering is among the oldest data mining techniques. Clustering analysis identifies similar data and attempts to comprehend the similarities and differences between them. This process is also known as “segmentation.” Similarly to statistics, clustering uses a number of approaches, best known of which are model-based methods, partitioning methods, grid-based methods, density-based methods and hierarchical agglomerative methods.

The most famous clustering algorithm is called the “Nearest Neighbour”. It gives the answer to the so-called “travelling salesman problem” by answering the question: “given a list of cities and the distances between each pair of cities, what is the shortest possible route that visits each city and returns to the origin city?” Applying the answer to data, the algorithm predicts what the estimated value of one record looks like for similar estimated values.

The main thesis is that objects close to one another have similar prediction values. The Nearest Neighbour technique is most commonly used for text retrieval, where it finds the documents that share the same attributes as the main one. Clustering is an integral part of all analytics tools.

  • Visualisation

Visualisation is used for discovering data patterns and is commonly the starting point of data mining. This technique converts raw data into structured data and allows for the usage of various data mining methods that discover hidden patterns.

  • Decision tree

Decision tree is a predictive technique whose pattern resembles a tree. Each branch is structured as a question and each leaf — as a part of data related to the answer. Decision tree is commonly used for prediction and exploration analysis. It is good at relating databases and stops “growing” if the segment contains a single record, or if all the records share the same features, or if the growth cannot branch further.

  • Neural networks

Neural network is another technique used as the starting point of data mining. As the name suggests, neural networks are related to AI and, hence, require the user to know some basic answers. E.g., what are the nodes and how are they connected? A neural network comprises the node and the link, where the node is connected to the neuron in the human brain, with the link to the neuron connections.

Since neural networks comprise many interconnected neurons that form network architecture, they are not easily understood by the average user. Still, they remain one of the most precise predictive modelling techniques. Because of that, many businesses are applying it either as a solution integrated into a single app, or they accompany it with expert BI consulting services.

  • Association rule

As the name portends, the association rule finds associations between two or more variables. The technique is useful for discovering hidden patterns and identification of frequently occurring variables. The association rule answers two basic questions:
• How often is the rule applied?
• How often is the rule correct?

There are three basic types of the rule: quantitative, multi-level and multi-dimensional. All of them are most commonly used for finding sales patterns.

  • Classification

In data science, classification is one of the most frequently used data mining techniques. It contains a number of pre-classified samples used to create a model that will later be used to help classify larger datasets. It works similarly to clustering and uses either a neural network or a decision tree.

Classification comprises two phases: learning and classification. There are numerous classification sub-types, best known of which include Support Vector Machines (SVM), classification based on associations, classification by decision tree induction and Bayesian classification.

The importance of automated data mining techniques

Automated data mining techniques are what every business needs in order to always be able to make data-driven decisions. Data automation is rapidly gaining momentum, as speed, efficiency and affordability are not to be taken lightly. Minerra will help you achieve best business results with the help of expert BI consulting and cutting-edge automated analysis tools.

Contact Minerra for analytics training and mentoring!

Want to learn more? We can help you start your analytics journey on the right foot and set you up for success.

Business analytics training Australia

Business analytics training Australia

Information and Advice from Minerra

If you are looking for expert business analytics training Australia, Minerra is the right choice for you. Our substantial industry experience makes us stand out from the competition. We don’t offer just simple software tutoring. Rather, we develop customised training services that convey the soft skills required to back up your business decisions.

Contact Minerra for product demonstration and consulting!

Customised business intelligence training

At Minerra, we believe in a personalised approach to business intelligence training services. We offer decision-focused courses suitable for beginners and professionals alike. Our programs encompass a balanced approach to services, training and products certain to boost your organisational performance in no time.

The services and products offered at Minerra include the Ajilius data warehouse automation tool, the Yellowfin BI and analytics platform, data integration and preparation, warehouse design and development, and customised dashboard and report design.

BI analytics tools offered by Minerra are cutting-edge, fully customisable, and deliver optimum results. I.e., merely switching to automated data warehousing (Ajilius) will save you precious time and provide you with richly visualised, report-ready insights.

Our supporting services include, among others, data modelling and data warehouse design review. Minerra is the leader in market innovation, driven by the goal of simplifying the decision-making process. Making data-driven decisions is the key to superb operational performance. The tools, consultancy services and training offered by Minerra will help you reach your business goals with convenience and ease.

Analytics training at Minerra

Minerra offers analytics training on fundamentals of analysis and data-driven decision making, best practice dashboards, report and data visualisation principles, and developing a data-driven culture in your organisation. As stated above, our training services are personalised.

Obtain the best analytics training Australia and learn how to make the best out of any situation. Minerra also offers Ajilius and Yellowfin training, aimed at enabling you to build data warehouses using the Ajilius data warehouse automation platform and utilise Yellowfin’s interactive dashboards to their full potential.

Yellowfin training services at Minerra include Yellowfin content creation, Yellowfin view creation and system administration, Yellowfin data exploration and analysis and Yellowfin for busy managers.

About Minerra

Our team comprises experienced business people passionate about helping the clients develop core skills necessary for increasing their effectiveness and performance. We apply a creative approach to all challenges, and provide BI analytics training services, as well as consulting.

The courses offered at Minerra come in various shapes and sizes, notably group training, one-on-one mentoring, seminar-style courses, structured learning and customised training. We also host presentations and conference seminars.


Minerra offers industry-leading BI analytics tools that make data discovery, analysis and sharing a seamless task. Ajilius and Yellowfin are one of their cutting-edge BI solutions that increase business productivity by optimising data and offering richly visualised interactive dashboards. Both are fully automated and customisable, as to suit every client’s specific needs.


Ajilius is a web-based data warehouse automation tool that extracts data from most data sources (Excel spreadsheets, CSV files, ODBC/JDBC sources). Ajilius builds star-schema data warehouses, following the Kimball methodology. The model steps up data warehouse development and eliminates the need for manual coding.

Ajilius also features a number of built-in of functions, while also allowing for custom coding. Data warehouses are stored in a centralised location for easy accessibility. The documentation auto-generated by Ajilius is backed up by a full dependency diagram.

Ajilius easily updates multiple data warehouse instances, as it supports multi-user data warehouse environments. It is especially useful for OEM applications, which require multiple customer data warehouses.

The tool generates SQL code for a number of platforms, including Microsoft SQL Server, Azure SQL, Redshift, Snowflake, ExaSol, PostgreSQL/EnterpriseDB and Pivotal Greenplum.

Finally, Ajilius features the three-click migration option, which allows data migration between different DB platforms, cloud-to-cloud warehousing included. Ajilius generates documents, DDL and DML for ELT processing, batch scripts and job schedules. Ajilius is fully integrated with the Yellowfin BI and analytics platform.


Yellowfin is a BI tool that analyses data from all sources and delivers them through an integrated platform. It displays interactive dashboards, which are mobile and shareable across the board. Yellowfin answers your target questions, rather than providing general answers. The tool runs a series of algorithms before displaying the most relevant results. You can choose which ones you want analysed further and thus save precious time on data mining.

Yellowfin’s reporting system is state of the art. It sends proactive alerts (pop-ups included, where applicable) via your chosen medium.

The tool can be accessed from any device and the dashboards shared even with people who don’t have access to Yellowfin. Easy sharing is one of the biggest benefits Yellowfin offers. Whether you need to share metadata, dashboards, storyboards, schedules or agendas, Yellowfin will perform it instantaneously.

Most importantly, Yellowfin is fast in its ways. Forget about lengthy manual coding and late updates. The data generated by Yellowfin are richly visualised and highly browsable. I.e., performance dashboards are summarised and measure KPIs. Accessing data on products, customers, performance and events takes mere minutes.

Additionally, the data are report-ready and easy to compare, too. With the process being fully automated, the comparison will take considerably less than manual ELT.

Ajilius and Yellowfin analytics training Australia

The benefits of Ajilius and Yellowfin are easy to grasp. In order for such superb tools to truly shine, the users need to know how to best utilise them. Minerra offers specialised analytics training Australia that will help you learn all there is to know about world’s best BI and analytics tools.

Contact Minerra for product demonstration and consulting!

Want to learn more? We can help you start your analytics journey on the right foot and set you up for success.

Big data solutions

Big data solutions

Information and Advice from Minerra

Big data have changed the rules of the game. Keeping connected with the data becomes increasingly difficult in the metadata-driven world. In an attempt to keep pace with constant changes, BI and analytics tools are getting increasingly automated.

Generating useful information from piles of raw data and making it report-ready is what businesses of today crave. And that is exactly what we at Minerra offer: self-serving BI and analysis solutions, along with expert training and consultancy.

Contact Minerra for analytics training and mentoring!

Why is data integration so important?

There are multiple definitions of “data integration,” which can often cause confusion if left unexplained. According to IBM, data integration is “the combination of technical and business processes used to combine data from disparate sources into meaningful and valuable information.”

No matter the definition, the key factor of the process is getting “meaningful information.” Namely, unless refined and sorted, raw data are unusable. In order for data to be analysis- and report-ready, they needs to be structured, comparable and understandable at a glance.

Minerra offers two automated solutions to that end: Ajilius (data warehouse automation tool) and Yellowfin (BI and analytics platform). Ajilius integrates data, and Yellowfin generates shareable interactive dashboards that allow for adding custom values.

Data warehouse automation does not only step up data processing, but it also generates alerts when the thresholds are met or have failed. And those thresholds are defined by you.

Data integration and business analysis

Only structured data are usable in business analysis. Because they comprise meaningful information, they are easy to compare, analyse and share.

However, the result varies depending on the data collection methods used. All of the methods compile data into an integrated view, but that is where their similarities end. Data solutions are as diverse as the information they treat, and with the rapidly changing markets, new requirements keep them evolving.

Common data integration techniques

Data consolidation

Data consolidation compiles data from various sources and multiple systems, and then stores them in a single data storage. The technique is based on the traditional ELT technology, which extracts data from multiple sources, transforms them into a usable format and finally transfers them to a single data warehouse.

Data virtualisation provides a real-time data view through an interface. The technique pulls data from multiple sources, unifies them and displays them in an integrated format. Data are accessible from one location, but they are not physically stored there (they remain scattered).

Data federation is similar to virtualisation, as it uses a virtual database to create a common pattern for all pieces of data located in multiple sources. Also, similarly, data are brought together and displayed from a single location. The data generated in such way can be analysed through various specialised apps.

Data propagation copies data from one location to another. Data propagation can be performed in two ways: synchronously or asynchronously. Synchronous data propagation supports a two-way data exchange link (a source-target link).

Both enterprise application integration (EAI) and enterprise data replication (EDR) technologies are based on this method. EAI uses application systems for data exchange and is often applied in real-time processing. EDR transfers large volumes of data and uses logs to track the changes.

Data warehousing (DWH) is a process of compiling, cleaning, transforming and storing data in a single warehouse. Data generated in such way are presented in an integrated view and are report- and analysis-ready. In the case of automated DWH, warehouses are usually shareable as-is.

What is a data warehouse? A data warehouse is a compilation of data derived from multiple sources (both internal and external) designed to allow for data consolidation, analysis and reporting.

Generally speaking, DWH tools comprise everything from Excel spreadsheets to OLAP (online analytical processing) to data mining tools to performance dashboards. Data mining tools use data to consolidate information on multiple levels, as to define emerging trends. OLAP tools analyse metadata with the same goal. Performance dashboards summarise data and measure KPIs.

Data warehouse architecture may vary, depending on the type of the technique used. Most common architecture schemas include the star, snowflake and fact constellation. The star schema is the simplest, where the centre displays a fact table and the points display dimension tables. The snowflake schema is an upgraded version of the star schema. Namely, whereas the star schema displays de-normalised dimension tables, the snowflake schema shows normalised ones. Finally, the fact constellation schema displays multiple fact tables and multiple dimension tables.

Minerra offers some of the finest BI and analytics platforms that generate interactive dashboards, instantaneously shareable and understandable at a glance.

Not only are Minerra’s DWH tools fully automated, but they are also customisable. We cater to each customer’s specific needs and provide just the solution to boost their business operations. All of our BI and analytics platforms have one thing in common: they allow for adding descriptive attributes that make browsing as easy as it gets. I.e., you will be able to search for top customers by revenue, overall revenue trends, revenue by periods, and so on.

Choosing optimum data solutions

There is no universal advice when it comes to optimum data solutions, as every business has its respective goals. However, what is certain to benefit every company is data warehouse automation. One does not have to be an expert to realise that big data spell innovative approaches.

With everything being metadata-driven, the challenge of data consolidation becomes increasingly difficult. If we add to that client information, social networks and all kinds of smartphone apps, it becomes painfully clear that manual ELT methods simply cannot keep up anymore.

What we at Minerra offer is cutting-edge BI and analysis technology that will help you make the best data-driven decisions. Coupled with expert BI consultancy and training, you will learn how to make the best out of the messy data that keeps piling up.

Contact Minerra for analytics training and mentoring!

Want to learn more? We can help you start your analytics journey on the right foot and set you up for success.

BI Consulting Services

BI Consulting Services

Information and Advice from Minerra

Get your business on the right foot: find a BI consulting partner you can trust.

Obtaining expert BI consulting services from a consulting partner you can trust is vital to the success of your analytics initiative. It will not only ensure that the reports and dashboard delivered fully support the decisions you need to make, but also provide specialised advice on how to use data to make informed, data-driven decisions.

Minerra’s BI consulting services will help you monitor and manage business performance, respond to market changes and have confidence in the critical business decisions you need to make.

Contact Minerra for product demonstration and consulting!

Top business intelligence training Singapore

Headquartered in the Lion City, Minerra also organizes business intelligence training Singapore. We are passionate about training and pride ourselves on providing much more than just “how-to” software training.

We draw from our extensive industry experience and background in academic research to create customised training programs that empower you with the soft skills required to support business decisions within your organisation.

At Minerra, our unique approach involves us developing a tailored solution to meet your specific needs. Whether you are just starting on your analytics journey, or are looking to take your existing environment to the next level, we can help. We will provide the right balance of products, services and training to empower you and your staff to maximise the return on your investment.

Some of the diverse products and services that we offer to help you reach your goals are as follows:

  • Data integration and preparation
  • Ajilius data warehouse automation
  • Data warehouse design and development
  • Custom-built dashboard design
  • Custom-built report design

In addition, we also provide supporting services, including data visualization, data modelling, data warehouse design review and data warehouse testing.

Minerra is your one-stop shop for all things BI. Get in touch to request specialized analytics consulting, training services or a product demonstration.

Generating ROI: best business practices

  • Marketing analytics

In the increasingly digitized world of today, marketing analytics is critical to the success of an organisation. However, many organizations do not have ready access to the valuable information stored in the various platforms that are being used.

One aspect that is important to measure is the return on advertising spend (ROAS). Advertising on digital platforms is vital, but can very quickly burn through your marketing budget. You need access to analytics to enable you to identify the advertising strategies that are giving you the best ROI, so you can channel your marketing dollars towards strategies that work.

Marketing analytics can also provide insights into sales and lead generation, customer preferences, trends and segmentation. These aspects are closely related, and an integrated approach works best to identify what information is required to support decision-making in marketing.

To make the best use of your data, therefore, you need to identify the right way to measure your marketing return on investment. Several options are available, including the following:

The MMM (marketing-mix modelling) measures the effectiveness of investments by channel. Although it can be used for both near-term planning and long-term strategic purposes, it has certain restrictions.

Firstly, it is slow in delivering applicable information (usually it takes years to collect high-quality sales and marketing investment data). Secondly, it does not measure minor changes in activities nor the extended effects of single reference point (touch point) investments.

The RCQ (reach, cost and quality) disperses reference points into their respective components, providing data on the number of consumers reached, cost per reference point and the quality of the engagement. Its drawback is that it doesn’t take network interactions into account.

Attribution modelling uses algorithms to determine how the budget used to convert traffic to sales is assigned to online reference points (e.g., websites, social-networks and email campaigns). Attribution modelling is an emerging approach to analytics.

As such, it has not yet been developed to its full potential, but is gaining momentum fast. Its drawback is that it is heavily dependent on cookies, which makes attributing the importance of the reference points a challenging task.

  • Automated ELT/ETL

Marketing analytics is the deciding factor for future sales. However, in order for the data to be useful, they need to be structured and easily comparable. The usual methods for collecting data from various sources and displaying them in an integrated view are the ELT (extract-load-transform) and the ETL (extract- transform- load).

These are time-consuming processes with many unpredictable variables. Often, it takes months and even years to get a reliable picture, which translates into months and years of pending ROI. What is to be done to step up the process?

At Minerra, we believe the future of structured data lies in automation. Our solutions incorporate exactly that, providing our customers with state-of-art interactive dashboard software that enables instant analysis, reporting, and data sharing. Customizable fact tables and summarized business events are only some of the options offered.

Our BI analytics tools transform data from various organisational sources into a data structure optimised for reporting and analytics. Your data warehouses will always be updated with all relevant information pulled from all sources — spreadsheets, CRMs, HRs, ERPs and cloud sources. What’s more, the time required for data warehouse testing is dramatically reduced as all ETL / ELT code is automatically generated by our data warehouse automation software, thus producing error-free and consistent code.

Professional BI consulting services guarantee fast ROI

Professional BI consulting services are certain to set your business on the right track ASAP. Minerra offers cost-effective solutions, personalized training and business consultancy for companies of all sizes and structures. Be ahead of the competition at all times. Get that ROI rolling in days and weeks, not months and years.

Contact Minerra for product demonstration and consulting!

Request your expert BI consultant.

Contact us for an expert BI consultant to help you get the information you need to execute your strategic plan. Minerra’s experienced consultants will help you access and understand your data, and then use this data to make decisions. Our tailored approach can cover your entire analytics environment from data preparation and integration, to data visualisation, dashboard design and training.

Minerra’s solutions will give you the tools and knowledge to analyse and visualise your data with ease. We can empower you to create dashboards and reports covering all areas of the business, including marketing, operations, finance and sales.

When we undertake BI consulting, we choose to work with best-of-breed tools that have been carefully selected by our team. Using the right technology mix, we will design and implement a high-quality solution that will deliver significant business value in a short time-frame. Furthermore, we will give you the knowledge and skills to maximise the value from your investment in analytics.

Want to learn more? We can help you start your analytics journey on the right foot and set you up for success.

BI analytics tools

BI analytics tools

BI and Analytics Tools You Can Trust

BI analytics tools

BI analytics tools are the future of data management. With the market changing at a rapid pace, the task of keeping databases updated gets rather hectic. Businesses are struggling to keep the pace – manual ELT simply doesn’t seem to keep up.

It is a well-known fact that manual coding takes time, is expensive and is often late in delivering relevant results. Automated analytics tools are just the solution: they are fast, cost-effective and allow for regular updates in a matter of hours.

Choosing among BI solutions

BI solutions are not a new concept; they have been around for decades. However, with the market changing rapidly, they have been struggling to evolve as to meet the ever-expanding requests of businesses, both large and small. Various analytics approaches have emerged over the years, in an attempt to match the diverse ways businesses analyse data.

Broadly speaking, BI analytics tools fall into three categories: guided analysis and reporting tools, self-service BI and analysis tools, and advanced analytics tools.

Guided analysis and reporting tools

Guided analysis and reporting tools encompass traditional BI approaches that have been used by businesses for years. They deal with recurring analyses of target data, such as, i.e., comparison of marketing campaigns’ performance/KPIs.

Originally they generated static reports, but have now expanded to include various features, such as filtering, selection and visualisation. The key premise remains unchanged, however, guided analysis and reporting tools use predefined data sets and metrics. The usual styles of these tools include dashboards, scorecards, reports, integrated spreadsheet, BI search and performance management.

Self-service BI tools

Self-service BI tools are in high demand. They perform on-demand data analysis and are better at dealing with large data volumes than guided analysis and reporting tools. Innovative self-service BI tools are metadata driven, which steps us the analysis process and allows for timely updates.

Self-service BI tools either perform a one-time-only analysis or formulate recurring analyses to be shared across the board. As such, these tools are more often used by professional analysts (business, financial, etc.), who communicate the results with decision makers and analytics producers.

Unlike guided analysis and reporting tools that use predefined data sets and metrics, self-service BI tools allow for adding data and new metrics. The usual styles of these tools include ad-hoc analysis and reporting, data discovery, OLAP cubes and data visualisation.

Good examples of self-service BI tools are the Ajilius data warehouse automation tool and the Yellowfin BI and analytics platform. Whereas the first automates the ELT process, allowing for quick data warehouse changes, the latter analyses the data and displays the result in the form of report-ready interactive dashboards.

Both tools can be customised to suit customers’ specific needs. Minerra offers decision-focused versions of both and also delivers training on how to best utilise them.

Advanced analytics tools

Advanced analytics tools make use of predictive analytics, data mining, big data analytics and statistical modelling. They are used by data scientists to predict market trends, and require professional training.

Business intelligence and data warehousing tools

Business intelligence and data warehousing tools (when employed to their full potential) are the key to organisational success.

In order to choose optimum BI analytics tools, it is necessary to keep both front-end and back-end in mind. Some tools are web-based; others are deployed on on-premises servers and private clouds. E.g., IBM and Amazon host such services.

The decision on the analytics tool most often depends on accessibility, speed and shareability. Large organisations need to cope with massive data volumes where update speeds are of immense importance.


In those terms also, Ajilius and Yellowfin are good choices. These tools are cost-effective and fast. Yellowfin’s interactive dashboards are state of the art, and the platform integrates with all popular web connectors, such as Google Analytics.

Yellowfin generates dashboards and reports, with the data being accessible in real time. Most importantly, unlike the majority of other BI tools, Yellowfin explains business results on top of monitoring them.

The platform also features Assisted Insights, which provide you with only the answers you need. Simply ask Yellowfin the question, and it will show only the most relevant results. Yellowfin’s Insight Wizard is set in place to enable you to provide the context to the question. You can then choose the results you find most relevant and keep fine-tuning them.

Yellowfin provides seamless data point comparison, with the results being sorted by relevance. They are explained in a natural language and include summary- and detail-level explanations.

Yellowfin creates both static reports and storyboards. The latter can be edited with text, images and video, and are updated in real time.


Ajilius is an automated web-based data-automation tool. It is fast and cost-effective, with the automation taking no more than several hours (depending on data volumes).

Ajilius creates detailed data warehouses that feature table and column names, documentation and lineage.

Ajilius delivers on a number of DW platforms and cloud-based databases: SQL Server (Windows and Linux), PostgreSQL/EnterpriseDB, Exasol, AWS Redshift, Azure SQL, SQL Server Parallel Data Warehouse, MariaDB Column Store and Snowflake Elastic Data Warehouse.

Ajilius integrates with Yellowfin and other popular visualisation tools. The integration process is what this Ajilius is famous for. Namely, it features a three-click migration between supported platforms (cloud-to-cloud warehousing included).

BI analytics training

Minerra offers BI analytics training on Ajilius and Yellowfin, as well as professional business consultancy. Learn how to always make data-driven decision and employ the world’s leading BI and analytics tools to generate best results. Learn all there is to know about best report and data visualisation principles and best practice dashboards.

Our training services are developed as to suit each client’s needs. We organise one-on-one mentoring, group training, structured learning and customised training. We are also available for conference seminars and presentations.

Contact Minerra for product demonstration and consulting!

Want to learn more? We can help you start your analytics journey on the right foot and set you up for success.

Agile business intelligence to kick-start your operations

Agile business intelligence to kick-start your operations

Information and Advice from Minerra

Agile business intelligence solutions are the key to making data-driven decisions at all times. Automated data warehousing tools that display visualised structured data are just the thing a business needs to enhance its operations. Getting connected with your data has never been easier, too. Minerra’s BI and analytics solutions will bring your business to the next level.

Contact Minerra for product demonstration and consulting!

Agile business development to tackle all challenges

Agile business development is a fresh approach to building a flexible business capable of adjusting to unpredictable changes. The process combines principles encompassing flexibility, strong communication skills and adaptability.

Minerra offers BI and analytics tools supporting the concept, notably Ajijus and Yellowfin. The two are fully integrated, with Ajijus performing data warehouse automation and Yellowfin displaying the data in a single, integrated platform.

Yellowfin delivers all the business intelligence crucial to making core decisions at any given moment. Rich visualisation and interactive dashboards make the task seamless.

Yellowfin at a glance

Yellowfin is a cutting-edge browser-based automated ETL platform that connects to all your data sources and transforms the data into visually rich insights. In addition to tables, SQL and flat files, Yellowfin also integrates with Google Analytics and other popular web connectors. The platform uses a drag-and-drop GUI interface to visualize the insights, making it user-friendly.

Not only does Yellowfin provide the established transformation steps (aggregating, calculation, merging, and so on), but it also taps into geo-coding and popular data science models such as PFA, PMML and H20.ai. Most importantly, the transformed data are accessible in real time.

When it comes to security, Yellowfin allows for coding custom authentication and authorisation solutions that don’t disrupt its performance.

Yellowfin seamlessly integrates with third-party applications using programming languages supporting the generation of WSDL or functional stubs. The Javascript API allows for embedding interactive dashboards and reports into external websites.

Yellowfin generates flexible dashboards and reports, displaying the most relevant content presented in an easy-to-understand narrative. The platform makes app integration easy and can be customized to resemble your existing application.

Contact Minerra with your requests to get the best of Yellowfin – your way. Get to easily share insights across the board as needed. Users can see all relevant data through their personalized timeline, add commentary and annotations, and share the insights with ease.

Yellowfin Assisted Insights

What makes Yellowfin truly stand out is its Assisted Insights . While most BI tools simply monitor business results, Yellowfin also explains what has brought them forward. Done the traditional way, the decision maker has to consult a data analyst for answers. Yellowfin generates full self-service analysis by providing automated help.

To bring you the exact answers you need, Yellowfin combines machine-learning with human insight. That allows for immediate answers without you having to dig through all the data. Instead, you can ask Yellowfin the question, and it will, in turn find the exact information to analyse and run it through a series of steps to define the most relevant results.

Next, the results are presented in an easily understandable manner and ready to share. What’s more, the process is fully automated. Simply drag the data you wish to analyse to the report builder, and click the Smart Analysis button.

To further simplify things, Yellowfin also features the Insight Wizard that will help you provide the context to the question you need answered quickly. The depth of analysis is also up to you; Yellowfin is fully customizable. In this way, Yellowfin displays only the most accurate charts; you only need pick the ones you find most relevant and define them further.

Yellowfin Data Storytelling

The key value of business intelligence technologies is seamless data sharing and collaboration. While Yellowfin does provide instant insights, its main benefit is even more sophisticated. Namely, it allows for comparing multiple data points. The insights are displayed by relevance and explained in natural language. Both summary and detail-level explanations are provided, with the user in full control of the results.

By no means does Yellowfin only provide static reports. Forget about PowerPoint presentations and start weaving vivid stories with your data. You can embed live charts in your presentations by using Yellowfin’s Storyboard presentation module. It’s also possible to combine reports with text, images and video to animate the audience.

Moreover, Storyboard allows for building live presentations that are updated in real time. The feature is all about mobile delivery. Get to share your presentations on the go.

Agile business intelligence made easy by Minerra

In addition to decision-focused, fully automated business analysis tools, Minerra also offers agile business intelligence solutions. We know how important expert BI consulting is; our team of seasoned business people will offer specialised advice to suit your exact needs.

At Minerra, we believe in a unique approach. Every business is different; every decision maker has their own ideas. We will listen to you first and only then devise the best approach to help you achieve your goals in as short time as possible.

Our experts will advise you on how to best monitor and improve your business performance and on how to make the best of market changes. Learn how to optimise your organisational data and make data-driven decisions with ease.

Minerra offers various trainings on data integration, data warehouse automation, and dashboard and report design development. Get professional advice and the best BI analysis tools in one go.

Contact Minerra for product demonstration and consulting!

Analytics as a Service

Want to learn more? We can help you start your analytics journey on the right foot and set you up for success.