Getting Started with Power BI: Introduction and Key Features

Getting Started with Power BI: Introduction and Key Features

As You are here, congratulations on the first step toward a data-driven business journey. In today’s data-driven world, we all rely on data to make informed decisions in one way or another. Power BI was developed by Microsoft, a powerful business analytics service that empowers users to harness the potential of their data and transform it into actionable insights. In this comprehensive introduction to Power BI, we’ll explore what it is, its key features, why it matters, and real-world use cases.

What is Power BI?

At its core, Power BI is a versatile data analytics and data visualization tool. Users can connect to various data sources, transform raw data into meaningful insights, and create interactive business dashboards and paginated reports. 

Key Features and core concepts to start with Power BI 

To understand Power BI, it is necessary to understand the core elements of Power BI that encompass data analytics and reporting suites. 

1. Creative Dashboard Design: 

Power BI should be considered a self-service BI tool as it provides a wealth of data visualization options, using charts, cross-tab tables, maps, and more with great formatting functionalities for informative reports and dashboards. These dashboards can be printed or shared using Power BI services. 

2. Smart Dataset Filtering 

Using Power BI massive datasets can be filtered to zoom in on the stuff we want to refer to. It’s like focusing on specific data without drowning in the entire sea of information.

3. Data Connectivity

To analyze data, we first need the connectivity of different data sources. Power BI supports diverse data sources; including databases (SQL Server, Oracle), cloud services (Azure, AWS), online services (Google Analytics, Facebook), and local files (Excel, CSV).

4. Data Modeling

Once various data sources are connected to Power BI, the importance of data modeling can’t be overstated as it forms the foundation; establishing relationships between different data tables, and improving data quality and well-structured data contributes to better query performance. 

5. Explore Data

Power BI’s drill-down functionality allows users to seamlessly explore data from high-level summaries down to granular transaction details. This hierarchical navigation provides transparency into the intricacies of the data, fostering a dynamic and interactive reporting experience. With customizable detail views, users can tailor their analyses, making Power BI a powerful tool for in-depth data exploration and insight generation.

6. Real-time Data

Power BI allows for real-time data analysis, ensuring that you’re always working with the most current information.

7. Power Query

Power Query is a built-in data transformation tool that enables you to shape and transform your data before analyzing it. It helps clean, filter, and format your data for optimal use.

8. The Statistical Language R

Power BI supports widely used Statistical Language R., Data Analytics is not just representing data in charts and table format. Statistical models can recognize patterns within datasets and can predict or forecast based on historical data. Statistical models also help in Hypothesis testing, risk management, decision support, segmentation & targeting, along machine learning.


You can refer link to Microsoft has comprehensive details about data sources supported by Power BI

Above are core concepts to start with Power BI, though Power BI is distributed among the following platforms, 

  • Power BI Desktop – A free desktop application for Dashboard and Reports creation. 
  • Power BI Service – Microsoft-owned cloud platform to collaborate & distribute visualization/dashboards and reports. 
  • Power BI Mobile – We need data at our fingertips, thus Power BI mobile is a mobile application that allows us to interact with reports and dashboards over mobile devices and tablets. 
  • Power BI Report Builder – Power BI Report Builder is the modern successor to SSRS and it is part of the Power BI suite with enhanced capabilities to build paginated reports, effectively integrating traditional reporting functionality with the broader Power BI platform. Power BI Reports can be easily embedded into your custom web applications since Power BI provides a simple-to-implement framework. 
  • Power BI Report Server – On-premises report server to distribute reports, it is available through a Power BI Premium license.

For a better basic understanding of Power BI, refer to the following basic FAQs related to Power BI. 

1) How do I download and install Power BI Desktop?

To download and install Power BI Desktop, visit the official Microsoft Power BI website, click on the “Download free” button, run the downloaded installer, and follow the on-screen instructions to complete the installation on your Windows machine.

2) What are the system requirements for Power BI Desktop?

Power BI Desktop requires a 64-bit version of Windows 7 or later, at least 1 GB RAM (2 GB recommended), and a display with a resolution of 1024×768 or higher. It’s compatible with x86 or x64 processors.

3) Is Power BI available for Mac?

Presently, Power BI Desktop is exclusively supported on Windows and isn’t directly installable on Mac systems. To use Power BI Desktop on your Mac, you may explore options such as running it on a virtual machine or utilizing a cloud service.

4) How can I create a Power BI account?

To create a Power BI account, go to the official Power BI website, click on “Sign up free,” provide your email address, follow the registration steps, and create a password. You’ll then have access to Power BI service and can start building and sharing reports.

5) What is the difference between Power BI Desktop and Power BI Service?

Power BI Desktop is a standalone application for creating reports and visualizations on a local machine, while Power BI Service is a cloud-based platform for sharing, collaborating, and publishing reports online. The desktop is used for report development, and the Service is used for hosting and sharing those reports in a collaborative online environment.

6) Can I use Power BI without an internet connection?

Yes, you can use Power BI Desktop without an internet connection for creating and modifying reports locally on your machine. However, to share, publish, and collaborate on reports, you’ll need an internet connection to utilize Power BI Service, which is cloud-based.

7) How do I get data into Power BI?

In Power BI, you can get data by clicking on “Get Data” in Power BI Desktop, selecting a data source (such as Excel, SQL Server, or online services), connecting to it, and importing or transforming the data as needed. Power BI supports a wide range of data sources for analysis and visualization.

8) What data sources does Power BI support?

Power BI supports various data sources, including Excel, SQL Server, Azure services, SharePoint, online services (such as Google Analytics and Salesforce), and many others. It also allows connections to on-premises data sources and supports a wide range of file formats.

9) Can I connect to on-premises data sources with Power BI?

Yes, Power BI enables connections to on-premises data sources using methods like DirectQuery or importing data. You can connect to on-premises databases, servers, and other sources to analyze and visualize the data within Power BI.

10) Is there a mobile app for Power BI?

Yes, there is a mobile app for Power BI available for both iOS and Android devices.  You can download it from the respective app stores to access and interact with your Power BI reports and dashboards on the go.

11) What is Power BI Pro and Power BI Premium?

Power BI Pro is a subscription plan that allows individual users to collaborate, share, and consume content created in Power BI. Power BI Premium, on the other hand, is a capacity-based licensing model designed for larger organizations with heavier workloads. It offers dedicated resources, enhanced performance, and the ability to share reports with free Power BI users.

12) How do I share reports and dashboards with others?

To share reports and dashboards in Power BI, you can publish them to the Power BI service, create a dashboard, and then share it with specific users or groups. For advanced collaboration, you may need Power BI Pro or Power BI Premium subscriptions, depending on your organization’s needs.

13) Can I embed Power BI reports in other applications? 

Yes, you can embed Power BI reports in other applications using the Power BI Embedded service or leveraging the Power BI REST API. This allows you to integrate Power BI reports seamlessly into custom applications or websites.

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Data Visualization Best Practices for Complex Datasets

Data Visualization Best Practices for Complex Datasets

Data visualization is essential for visually representing and conveying complex data sets to a larger audience. Typically, data is presented in charts and diagrams; however, converting it into a narrative-driven story can significantly improve its accessibility and contextual comprehension for executives. 

What is Data Visualization?

Data visualization is the process of converting unstructured data into more visually appealing forms like graphs, charts, and maps. It has rapidly gained notoriety for publishing information on the Internet.

It’s used everywhere, from Business intelligence to journalism, to understand better and communicate the meaning of data. Displaying data visually facilitates comprehension and analysis and is regarded as an industry best practice.

It also helps people make decisions faster and find patterns, both new and hidden, that help them understand challenging concepts in a better way.

When information is represented graphically, anyone can understand the most complex data visualization concepts easily and quickly. Power BI and Tableau are two examples of technical knowledge-free tools that make learning easier without technical skills.

What are Data Visualization’s Primary Objectives?

When creating datasets for visualizations, it is essential to set goals and objectives. Here are some factors to consider when creating your data visualization datasets:

  • Data visualizations are beneficial for emphasizing data trends, patterns, and correlations.
  • Data visualization is an efficient decision-making measure that enables management to make well-informed, data-driven decisions.
  • Data visualizations facilitate the comprehension of large datasets and the combination of datasets from diverse sources.
  • It is an effective method for communicating ideas supported by data through narratives.
  • Data visualizations must capture the focus of the intended audience while being simple to comprehend and interpret.
  • Data visualizations facilitate monitoring crucial metrics and key performance indicators (KPIs).

Data visualization best practices

1. Identify the target audience and their specific needs

There are better ways to give customers information than creating visualizations and putting them all on a dashboard. One may not fit all. We must do more than compare notes, keep tabs on conduct, and evaluate output. It would help if you asked this question before preparing data visualization datasets.

Who is our target audience?

Understanding our most important user persona is crucial when creating a dashboard. Where do we stand in terms of overcoming challenges? What can we do to overcome those challenges? Create a dashboard that anyone can use and has all the features they need.

What do I want the user to decide?

Decision-making guidance is required. Is this a risky move? Is this profitable? If I decide to purchase this house or not. Multiple meetings per day, week, or month may be necessary to conclude a particular matter. Therefore, your visualization should be made to provide you with a yes/no answer.

2. Choose the right data visualization graphs

Choosing the right type of data visualization graph can increase your visualizations’ clarity and readability and make them more interesting to the viewer. Make smart comparisons and employ charts appropriate for the data type when using these visualizations. Choosing the right visualization approach requires considering your data’s context, audience’s needs, and objectives.

Examples of effective data visualization graphs are provided below. Bar charts, or charts with rectangular bars, are frequently used to graphically represent categorical data. They are widely employed due to their efficiency in conveying information and facilitating value comparisons and their utility in illustrating distributions.

Line graphs: Graphs that use lines to connect data points are called “line graphs.” They come in handy when drawing a graph to show recurring patterns in data, like a time series. Variables such as the weather, stock markets, sales, etc., are good examples.

Scatter plots: Relationships between variables are displayed graphically in scatter plots, which are useful for depicting how variables impact each other and spotting data patterns.

Pie charts: Pie charts are a type of bar chart in which each section of the circle represents a different size of the displayed data. Multiple charts, each representing a different period, illustrate the distribution of, or change in, a given quantity or percentage.

Box and Whisker plots: Graphical representations of the minimum, maximum, median, first quartile, and third quartile values in a dataset are known as box plots (or box and whisker plots). In particular, they help you identify outliers and understand the overall structure of your data.

Violin plots: Like box plots, violin plots show how a dataset is distributed. Kernel density plots can be used to display the minimum, maximum, median, and quartiles of a dataset and its distribution. You can use them to find outliers in your data or examine how data is distributed across different groups.

To view more details about how to choose the best data visualization tools

3. Carefully and deliberately apply text

The most important information should be located in the top left corner, where the eye naturally travels first. You should include at least three or four views in a single dashboard. Because adding too many graphs will make the presentation clearer. Group filters into one and frame it with a transparent border to make it look nicer.

4. Design following the standard format

The human eye is very good at picking up cues that aid in making sense of data. Visualization can be difficult to understand if the patterns you can discern are random or make no sense. Whether sequential or numerical, data must be presented in a way that is easy to understand for the audience if we are to gain insight into how the human mind works.

Any charts or graphs you use should be legible and make it easy to see how your data fits together. Avoid causing your audience any unnecessary confusion; this technique is not recommended, and users can easily navigate from one point to another.

5. Hierarchy

The idea of hierarchy is a fundamental principle in design, and it is especially useful when creating data visualizations and dashboards. As mentioned, there is a standard widget layout that users will recognize immediately and understand without any training. A proper hierarchy is crucial for making data visualizations and dashboards scannable.

A few easy ideas define a hierarchy:

  • The top left corner of the dashboard is a prime location because that’s where most people’s eyes naturally go first. 
  • The widgets after the top level should provide context for the KPIs there. 
  • You can use text widgets to make titles to help explain the information better.
  • Blank space can be just as effective. It’s better to leave a void than to fill it with something unnecessary.

6. Keep Titles and Labels Brief

Labels and titles should be used for your visualizations and be easy to understand. The purpose of the visual representation and its intended meaning should be communicated via these labels.

A number of guidelines should be followed when designing titles and labels, such as making sure to use legible fonts, using a large enough font size, positioning labels strategically, and using a color that is easy on the eyes.

Avoid using unnecessary abbreviations and keep labels at a reasonable distance from one another to improve readability. If you must use abbreviations, provide a key explaining what they stand for.

7. Select Appropriate Colors 

Colors are a powerful tool in data visualizations, keeping the viewer interested in the dashboard’s (or report’s) content and drawing their attention to key points.

The colors you use for your titles, labels, charts, etc., should be chosen with brand consistency (such as using your company’s brand colors) and readability.

Visuals should be appealing and should not have too many colors. Also, consider how the psychology of color might affect how your audience responds. Finally, people with visual impairments should be considered when choosing color schemes for their visualization.

Keep your visuals simple and organized by using a limited color palette.

8. Analyze Business Intelligence

Finding data interaction is challenging in today’s competitive environment. One of its most crucial recommendations is to focus on discovering the connections between data and new business insights.

Every company should review these thoughts to make sure they are heading in the right direction. It’s useful for keeping up with the times to serve your customers better.

It’s also useful for spotting issues before they become problematic. You can boost sales and revenue by keeping up with the latest developments.

9. Use clean and current data

For visualizations, data quality is very important. You’ll need to clean and precondition your data to remove any unusual patterns or mistakes.

This procedure may include data normalization, the removal of duplicates, etc., may be part of this process. Aside from the accuracy and clarity problems that dirty data introduces to analysis and visualization, it also makes it harder to use.

When making decisions in your company, you don’t want to use inaccurate or outdated information. You should use the most recent and applicable data available to guarantee that your visualization is up-to-date and accurate.

When Creating data visualization best practices, What should we look for?

Now that we’ve covered some data visualization best practices let’s talk about what makes good datasets for visualization. 

  • A well-executed data visualization makes complicated connections clear, concise, and clutter-free.
  • The main ideas you want to convey are easily understood. Your audience should be able to quickly and easily grasp your insights.
  • The visualizations should show the concepts and details in the data by using the right visual elements.
  • Good data visualizations can cater to a wide range of viewers by using easily readable fonts and various text sizes. Make good use of contrast and color schemes. Due to the prevalence of red-green color blindness, avoiding combinations involving those hues is best.
  • Good data visualization is uncomplicated and easy to understand, with no unnecessary components.
  • Accurate, up-to-date, and trustworthy data is the foundation for good data visualization.

Which Data Visualization Method Is Most Effective?

The most effective data visualizations are those that immediately convey the intended meaning. The data is presented in a way that makes sense for the data being dealt with. Design, readability, and understandability are also taken into account.

How to Recognize a Good Data Visualization?

The quality of data visualization can be evaluated according to the following criteria:
Look at how the visualization uses color, labels, and other design elements.

  • Do the texts have the right amount of information, and are they easy to read?
  • Does it make the data easier to understand and use for your intended audience?
  • Is the dashboard/visualization displaying data in the correct proportions?
  • Is it possible to read the visualization for those who have low vision?
  • Is the visual representation clear and interesting?
  • Does the visualization’s color scheme draw attention to significant patterns?

Final Words On Data Visualization Best Practices

Data visualizations are the best way to keep track of your company’s metrics and key performance indicators (KPIs). If you follow the best practices for making a data visualization, you can make reports that are easy to understand and help you find patterns or outliers. 

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The Small Business Owner’s Guide To Data Analytics

The Small Business Owner’s Guide To Data Analytics

Dear small business owners,

I hope that the oncoming depressing sentence doesn’t end up upsetting you but if it does, please bear with me because I empathize with you and want to suggest something which will not only be useful but it may open up a new horizon for your profitability.

In comparison with the large businesses, our position is quite vulnerable in multiple aspects such as financial standing and cash flow, strategy, client reliance, being updated with the market, leadership dependence, balancing quality and growth, access to cutting-edge technology, customer retention, supply chain management, inventory control, handling price fluctuations, employees evaluation, sensing a customer going away, control on operations, etc to name a few. As small businesses, we do want to help our customers and offer them great deals but there are limitations on how generous we can be to them.

In such a scenario there is a tool if applied correctly, effectively, and smartly can give us a great help to smartly counter the challenges posed by the mighty large-size competitors. That tool is Data Analytics.

What exactly is Data Analytics?

Data Analytics is a buzzword nowadays, and it’s also branded as ‘the next big thing.’

Data analytics is a set of processes to collect data from various sources, transforming that data using well-defined algorithms, and organizing it in a way that it reflects meaningful conclusions to support informed decision-making and also to predict future trends and patterns.

Companies collect large amounts of data every moment from many types of data-feeding sources such as mobile phone sniffers, loyalty cards, financial transactions, point of sales, web page visits, online purchases, social media interactions, text input on any net-connected devices, and literally from every device we can and can not think of.

But that data as it is collected in large amounts (which is called raw data) doesn’t make any sense unless we have those exceptional superhuman abilities (thank God we don’t have that.)

Therefore we need ‘something’ which can synthesize and analyze the raw data and derives some meaningful and useful information out of that intimidating jumble. ‘Data Analytics’ is ‘something’ that can do that for us. 

It’s an art or science which can create a picture of meaningful information for us from scattered jigsaw pieces called raw data. 

Data Analytics is a process of analyzing raw data to help us extract useful ‘insights’ which are not only important but inevitable to make business decisions.

Why should small business owners use data Analytics?

If I want to describe it in the shortest way possible, It’s “if you want to figure out how to provide exactly the right product at the right time, exactly to the right customer, data analytics is the tool you must use.”

Data analysis allows you to conduct an objective assessment of your business.

You have to use Data Analytics to give your customers better service, reward them for their loyalty, to offer them a supportive product/service for the product they are going to buy. And also to predict if they’re going away from you.

You can also predict what’s happening in your customer’s life by looking at the data of their buying behavior (for example if a customer starts purchasing nappies and infant milk powder, there has been the arrival of a baby in their life. So you can help them get those things that are necessary for their parenthood.)

As owners of small businesses, it is crucial to understand what all that data means and what messages that data is conveying to us. Only then we can make informed decisions that can lead the business to healthy growth. 

Here are some of the things small business data analytics can tell you:

  • Where your business stands now 
  • Where your company goes if the trends remain the same 
  • The growth potential of your business 
  • How long it should take to expand your brand 
  • The steps to take to make the expansion happen


  1. The data must be analyzed daily, weekly, monthly, quarterly, and annually to get answers to all these questions.


  2. But one problem is the struggle to understand data. But we will see in the coming pages that it’s also not an issue provided you have a trusted pair of hands on which you can rely to take care of all the data-related operations. 

What are the benefits of Data analytics?

Data can point us in the right direction, and prevent us from getting in the wrong direction by showing us objective and unbiased facts.

  • For instance, by looking at the crowd in a giant supermall one can be tempted to get a place in the same supermall. But the reality can only be reflected when we look at the numbers of people visiting and people actually spending money there, and what is selling in what amount.

We can study the trends through various patterns in the data that help us in describing what happened, diagnose the exact issues, predict how the market will behave, and prescribe appropriate actions to deal with or take optimum advantage of the oncoming trends.

We can adapt data-directed thinking processes and decision-making.

If we want to understand our customers and figure out better and more profitable ways to help them, and also understand the behavior of our own organization to make it more operationally efficient, we certainly need to give data strategic importance.

We can logically devise strategies for expansion.

Other than these, there are a few more ways we can also apply Data Analytics to gain many other benefits. Some of them are as follows.

  1. It helps us reduce costs by shortening tasks, and in many cases eliminating them altogether.
  2. Organizational efficiency can be increased significantly by Increased operational efficiency.
  3. Data doesn’t lie. It helps us identify the exact weaknesses and failures.
  4. We can design new products and services based on Predictive Analysis and Prescriptive Analysis.
  5. Data can give us 360-degree customer reviews so we don’t have to rely on subjective spot surveys.
  6. Through thoroughly conducted Data Analytics it becomes easy to spot leakages which makes it easy to identify and prevent fraud.
  7. Data Analytics can also help us optimize pricing strategies.

In short, Data Analytics helps us make smarter logical business decisions.

Which BI Tools / Which technology tool should they select?

There are a number of BI tools to choose from, with different specialties. So it depends on your business needs and which functions of a BI tool you want to employ. Are your needs basic, or do they demand complex analysis? However, these are the basic criteria one should consider to select the best fit for one’s business.


  • Capabilities to collect data collection
  • Analytical abilities
  • Visualization facilities
  • Customizable reporting tools
  • Customizable dashboards
  • Predictive analytics
  • Integration ability with other tools
  • Security 

Here we are briefly explaining a few good BI tools. If you wonder which would be a better option for your business needs, please contact us for the best data analytics services and solutions. They will be happy to support you in choosing the right Data Analytics tool for you.

  1. Microsoft Power BI enables you to transform, explore, and analyze data on-premise and in the cloud. Also, it creates real-time visualizations and can connect relatively easily to your own data sources.


  2. Zoho Analytics has perhaps the most beautiful interactive dashboard. It supports multiple source data collection, and the data can be easily integrated through a simple interface and exports the results to various platforms and ecosystems.


  3. Scoro is good for its customizable KPI dashboard and real-time overview of every aspect of your work.


  4. Dundas BI is an end-to-end business intelligence platform with an open API across the entire platform. With drag-and-drop tools, it can quickly transform raw data into the form of dashboards, reports, and visual data analytics. Its ability to connect and integrate with other data sources is remarkable.


  5. Sisense can incorporate AI-enabled applications that can be embedded and integrated with a wide variety of sources and doesn’t require specialized training. It can get real-time data feeds to create intuitive dashboards and reports.


  6. MicroStrategy supports both data mining and visualization. It offers a multi-functional dashboard, big data solutions, and advanced analytics.


  7. Halo combines automated data processes with manual data manoeuvres for custom results. Its data integration, supply chain analytics, and visualization are automated and available in a single solution. For supply-chain management, this is most suited. Its intuitive interface allows multiple users to collaborate in real-time.


  8. Oracle has a large array of BI capabilities. It uses the Common Enterprise Model for calculations and business analytics and offers inbuilt tools for mining data, sending alerts, and data discovery which is rather agile. Its workspace is also easy to use and allows multi-user collaboration.


  9. SeekTable can perform ad-hoc analysis of all multiple sources of business data at once. It comes with facilities such as data restriction, live interactive reports, sorting, filtering, etc. It offers data analytics while allowing users access to reports.


  10. Tableau is a long-time tried and tested BI tool for live visual analytics. Its highly intuitive interface and drag-and-drop facility allow users to observe live trends. It features a mobile BI strategy and in-memory architecture for data visualization and exploration. It’s easy to integrate with Microsoft SharePoint and offers one-click reporting.


  11. GROW allows the extraction of data from over 115 sources, including Dropbox, Salesforce, Twitter, Google Analytics, etc. It features a highly intuitive UI with several data visualization elements. It also facilitates importing data from social media platforms such as Facebook, Twitter, LinkedIn, and more, helping optimize the marketing budget.


  12. Datapine facilitates and allows the visualization of many key metrics simultaneously. It’s an interactive BI tool featuring enabling versatile filters, mobile optimization, ad-hoc data source queries, fast and efficient connections to multiple data sources, predictive analytics, and data alarms based on customizable triggers. 

Other than these there are many more intelligent BI tools such as Syn Enterprise, BigID, Qualtrics Research Core, Active Batch, Salesforce Analytics Cloud, Board, CXAIR Platform, Looker, Reveal, Yellowfin, Periscope Data, AnswerDock, etc.

If you are looking for the best fit BI tool for your business, the best course of action is to talk to an expert at DataNectar who will understand your business, its processes, and your objectives and then figure out which one will be the best option.

Which types of skill sets do small business owners need for their organization?

To be very frank, you don’t need any skills to employ data analytics. This may come as a shock but think about it.

I’m sure you have heard this famous proverb “Do your best and delegate the rest.”

That’s the way forward to progress and growth. If you end up doing everything yourself, when will you think about expanding your business? To paraphrase Michael Gerber, if you end up working ‘in’ your business, when will you work ‘on’ your business?

Therefore the best answer I can give to this question is “Have a BI & Data Analytics partner like DataNectar on your side to take care of your BI needs.”

Having said that, let’s as well discuss what types of skills can be helpful to take optimum advantage of Data Analytics.

  • SQL (Structured Query Language) – It’s a programming language widely used for databases.
  • Oracle – It is a database commonly used for running online transaction processing, data warehousing, and mixed (OLTP & DW) database workloads.
  • R and Python – These are the most popular statistical programming languages used to create advanced data analysis programs
  • Machine Learning – an aspect of artificial intelligence that uses algorithms for pattern recognition in data
  • Statistical skills such as calculating probability to be able to analyze and interpret data trends
  • Data management – proficiency in collecting, organizing, and storing data
  • Data visualization – competence to visualize and illustrate data through graphic aids such as charts, graphs, and various figures
  • Econometrics – the skill to create mathematical models from the data trends that can predict future trends
  • Mathematical & statistical ability
  • Soft-skills:
    • Analytical mindset – An analyst must be able to analyze the data from multiple points of view to understand what’s happening and to dig deeper if necessary.
    • Problem-solving skills: Data analytics is all about answering questions and solving business challenges, and that requires some keen problem-solving skills. Data analysts have a wide variety of tools and techniques at their disposal, and a key part of the job is knowing when to use what. 
    • Communication skills: Once you’ve harvested your data for valuable insights, it’s important to share your findings in a way that benefits the business. Data analysts work in close collaboration with key business stakeholders and may be responsible for sharing and presenting their insights to the entire company. So, if you’re thinking about becoming a data analyst, it’s important to make sure that you’re comfortable with this aspect of the job.

What will be the role of a Data Analyst in your organization?

A data analyst collects all the scattered pieces of a large complex jigsaw data puzzle and creates a meaningful picture so that others can use that information. So if you choose to employ a full-time Data Analyst, his/her responsibilities will be like these.


  • To manage the delivery of user behavior surveys and create reports based on the results.
  • Work with clients to develop requirements, define success metrics, manage and execute analytical projects, and evaluate results.
  • Monitor practices, processes, and systems to identify opportunities for improvement.
  • Coming up with good questions and translating them into well-defined analytical tasks.
  • Gather new data to answer client questions, collating and organizing data from multiple sources.
  • Devise, build, test, and maintain back-end code.
  • Establish data processes, define data quality criteria, and implement data quality processes.
  • Work as part of a team to evaluate and analyze key data that will be used to shape future business strategies.

As a business leader, it must be an obvious matter for you to be aware of how crucial thing Data Analytics is. Also how vast a subject it is, and what level of complexities it involves. Therefore you must have employed a proper Data Analytics system and experts to run that system. 

However, the subject being a relatively recent phenomenon, it’s far from being practical that every organization would have its own team of Data Analytics experts.

Therefore it’s wise to have an external partner like us to direct and manage this matter.

We, at DataNectar, have a team of veterans who understand not only Data Science & Engineering but also the business processes in multiple industries thoroughly. They will be able to objectively study your business and come up with the parameters for analysis and also devise appropriate algorithms to extract information from the data.

We employ a system to extract the data from multiple data sources.

Clean the data up and store them in a defined order in a warehouse.

Take the data through a transformation procedure. And Create various visually understandable dashboards and analyses. 

By the way, we will choose the best tools for different steps in the entire BI exercise, and also set up automation wherever required.

After having done this exercise,

We sit with the team of the leaders of your business to support the brainstorming for interpreting the analysis.

We also support brainstorming for predicting the oncoming trends and Strategizing to take optimum advantage of those trends. 

At this stage, we’d like to offer you a free consultation for 15 minutes over the phone to understand your business issues. At the end of that conversation, either party can decide whether we are fit to work together or not. If we feel there is a synergy, we can set up a time for the next meeting, and if we don’t, we can still be friends. 

Feel free to contact us at [email protected] or visit our website at

How to Choose the Best Data Visualization Tools

How to Choose the Best Data Visualization Tools

Data is getting immense with every passing year and in nearly all industries. As metrics pile up considerably, you, as an organizational decision-maker, may find yourself confused about which data points collected are essential and in what approaches they can assist your business operations. 

All of this data is tough for the human brain to grasp. It is tricky to comprehend numbers more significant than five for a human brain without sketching some abstraction. Data visualization professionals can play a vibrant role in generating those abstractions.

Big data is impractical if it can’t be understood and digested conveniently. That is why data visualization plays a significant role in the whole thing, from economics to technology, enabling decision-makers in IT companies and end users of BI technologies like hospitals and industries like manufacturing.

By converting multifaceted numbers and other pieces of data into visual elements, content becomes simpler to comprehend and use in diverse applications.

So, here, you require data visualization techniques and need to select the best tools that can maximize your utilities.

What is Data Visualisation?

Data visualization in simple terms is an arrangement of visual elements of a set of data that is highly interactive, intuitive, personalized, and easy to share. 
For instance, text-based data is visualized graphically in the outline of charts, graphs, tables, Infographics, and maps to analyze business or operational scenarios. 

So, by manipulating big data sets in the form of visual formats, you can clearly understand the story your data depicts at a swift glance, instead of working on piles of tables and numbers for long hours.

How does it Enable Business Intelligence Dynamics?

Now coming to the context of Business Intelligence (BI) dynamics, data visualization is used and applied in two ways. 

Data is visualized in form of Dashboards that represent business data from every angle by allowing one to measure its performance in any dimension. Data can be drilled down and dissected any information. We can slice & dice the information in any unit size.

Do you want to know what valuation Business Intelligence (BI) can bring to your organization?

Data Visualisation can Assist your Organisation with Diverse Approaches

How does data visualization help decipher digital information?

Large and ever-altering quantities of data related to your business’s health, such as customer interactions, user experiences, staff performance levels, and expenditures can robustly impact and influence the overall decision-making at crucial moments. However, this is only probable when such data is clearly understandable even by non-data professionals.

With data visualization, you can translate scores of text and numbers to instinctively understandable insights. A step further, visualization tools can transform raw metrics into insightful stories that can be easily shared and worked upon.

How can data visualization help discover trends swiftly?

Data visualization facilitates your organization to spot alterations in customer behavior and market conditions swiftly. For instance, by utilizing heat maps, one can rapidly spot expansion opportunities, which is not evident in spreadsheets. 

On the other hand, Radius maps enable you to focus on spatial relationships for realizing enhanced business efficiencies or oversupply.
Further, with territory mapping, your sales teams can easily view their territories and ensure they are aligned or not.

How does data visualization help with decision analysis?

When you feed precise and neutral data visualizations into the decision-making tools, you can make enhanced decisions for your organization. Accurate data visualizations don’t deform the original information with unreliable displays. 

Additionally, charts and dashboards should be updated with dynamism using the newest information keeping the decision-making analysis highly applicable and relevant.

How data visualization reveals flaws, fraud, and anomalies?

Erroneous data can lead to a severe threat to businesses that depend on their correctness and accuracy levels. Data visualizations like charts and graphs can swiftly highlight large discrepancies in data readings, specifically signaling, where more careful reviewing of the numbers may be crucial.

Identifying and visualizing data patterns

Data visualization software enables you to identify and visualize data patterns with relationships that occur amid daily operations and overall business performance. 

However, you should be cautious of inappropriate comparative visualizations as if your organizational data analysis is puzzling or tough to compare; your visualizations might be doing more damage than enhancements.

Following are two charts that illustrate: 
a) Poor Data Visualisation,
b) Enhanced Data Visualisation through Dashboard.

a) Poor Data Visualisation: 

b) Enhanced Data Visualisation through Dashboard.

Let us further explore the bad data visualization and good data visualization examples in detail.

Example of Bad Data Visualization 

#1: Pie chart with multiple categories

bad data pie chart

Pie charts are leveraged when 2 to 3 product items make up the complete data set. Any more than that, and it is tough for the human eye to differentiate between the parts of a circle.

Notice how difficult it is to differentiate the size of these diverse parts. 

What is the exact difference between India and Russia?

It is rough to calculate the exact size difference. Rather, substitute this with a bar chart.

Example of Good Data Visualization: Precise Bar Chart

good data bar chart

Here you can explicitly calculate the difference between India (6.80%) and Russia (4.90%).

Bar charts will be your go-to option for exact data visualization.

7 Best Data Visualization Tools Which Are Popular In 2022-23

1. Power BI

Power BI is effortless to set up with dashboards and data connectors to on-premise and cloud-based sources such as Salesforce, Azure SQL DB, or Dynamics 365. The open framework enables the creation of custom visuals. 

It possesses default data visualization elements with bar charts, pie charts, maps, and even complex models like waterfalls, funnels, gauges, and other components. 

Power BI is developed and enabled with machine learning abilities, so it can automatedly spot patterns in data using them to make informed predictions through “what if” scenarios. These estimates facilitate users to make forecasts and meet future demands or significant metrics. 

A user can easily save his work to a file, and publish data and reports through Power BI to share with other stakeholders. Power BI is utilized to develop custom dashboards as well as reports as per the relevancy and access of data. 

Through custom visuals SDK, one can generate stunning visualizations, based on rich JavaScript libraries like D3, jQuery, and R-language scripts.

You also might like to read more about our best case study which is Remodelling advertising pricing strategy with Data Analytics 


2. Tableau

Tableau has an extensive customer base of more than 57,000 accounts because of its capability to generate interactive visualizations far beyond those offered by standard BI solutions. 

It is best for managing massive and quickly altering datasets utilized in Big Data operations, machine learning, and artificial intelligence applications. Further, it can be integrated with modern database solutions including Amazon AWS, Hadoop, My SQL, Teradata, and SAP.

Developing content in Tableau doesn’t need conventional source control or dev-test-prod-related techniques. You can integrate Tableau content development and deployment into your present development systems.

Publishing data to Tableau is integral to sustaining a single source for accessible data. Publishing facilitates sharing data with colleagues; even those not using Tableau Desktop, however, have required editing permissions. 

The top features of Tableau include Tableau Dashboard, Collaboration and Sharing, Live and In-memory Data, Data Sources, Advanced Visualizations (Chart Types), Maps, Mobile view, and robust security. D3.js is an exclusive JavaScript library that is utilized for Tableau data visualization.

3. MicroStrategy

MicroStrategy provides intuitive tools with data discovery and big data analytics features with an extensive library to visualize data. 

The MicroStrategy platform backs engaging dashboards, scorecards, advanced reports, thresholds, alerts, and automated report distribution. The tool can connect to over 200 data sources which include RDBMS, Cloud data, OLAP, and Big data.

Dossiers are MicroStrategy’s advanced and modern dashboards. To make the dossier to be presentation-ready, one requires to certify it to validate that the content is trustworthy. Once certified, you can share it with the enterprise environment for collaboration and publishing.

MicroStrategy Library is a unique and personalized virtual bookshelf that enables you to access dossiers from one common location. Through the MicroStrategy library, you can reach out to subject matter specialists and have a conversation regarding your data visualizations.

4. Qliksense

The vendor has 40,000+ customer accounts across 100+ countries, offering a highly adaptable setup and extensive features. 

Along with its data visualization abilities, the Qliksense tool even provides business intelligence, and enables the storytelling capacity of dashboards, data analytics, and reporting with a sleek user interface. 

There is also a sturdy community and 3rd party resources obtainable online to assist fresh users in understanding how to incorporate it into their current projects.

The Qliksense dashboard is an influential feature to showcase values from multiple fields simultaneously, and its functionality of data association in memory can showcase the dynamic values in all the available sheet objects. 

Qlik DataMarket® is an integrated data-as-a-service (DaaS) of Qlikview offering an all-inclusive library of data sets from reliable sources. Qliksense developers can use the same and effortlessly enable their analyses with external data sets to have an “outside-in” perspective for deeper insights.

5. Google Data Studio

Google Data Studio is a tool that enables communication and acts on tailored data sets. Programmers, executives, and worldwide team members from diverse departments can match, filter, and well-organize the precise data sets they require swiftly in one single report. No more waiting for numerous and static data reports to fill their inbox.

Data Studio is now an integral part of Google Cloud’s BI solutions. By blending Data Studio with Looker, Google Cloud has the finest of both ends – a structured semantic model and a self-served, simple-to-use front-end app with Data Studio that enables the analysis of unstructured/ungoverned data sets.

6. Apache Superset

Apache Superset is an advanced exploration and data visualization platform. It can substitute or enhance proprietary BI tools for many teams. It blends well with a diversity of data sources.

It offers a no-code interface for swiftly crafting charts. It provides a powerful web SQL Editor for progressive querying and a lightweight semantic layer for rapidly defining custom dimensions and precise metrics.

It provides an extensive array of attractive visualizations to display your data sets, ranging from straightforward bar charts to geospatial visualizations.

7. Looker

Looker Studio is a self-service BI with unmatched suppleness for intelligent business decisions. It helps tell powerful stories by building and sharing interactive reports and data visualizations. 

It assists in transforming your data sets to business metrics and dimensions with intuitive, intelligent reports. The tool enables professionals with significant business metrics by sharing automated dashboards. It helps you generate shareable, tailored charts and graphs with merely a few clicks.

Moving Forward

Extract, transform & load (ETL) are 3 data processes, enabled after data collection. 

Extraction takes data, collected in varied data sources with diverse structures and formats, to the staging database. 

Transformation takes fetched data and applies predefined rules to it, and load takes the transformed data and stores it in Data Warehouse (DW). 

However, this data is multifaceted until it is parsed and showcased in a simplified way. 

Specialists at Data Nectar enable the seamless consumption of significant insights by transforming the data analysis into visual representations with the assistance of Reports and Dashboards to decipher trends, anomalies, and data usage patterns.

At Data Nectar, a data analytics and visualization technology company, we know the real significance of Data Visualization for multiple stakeholders, and we can assist you in choosing precise tools in line with your requirements. 

Further, we enable SMEs and Enterprises with analytics-driven technology solutions to realize enhanced performance and maximize ROI in the process – through data. 

If you all too, as your organization’s decision-makers are willing to discover the vast possibilities Data can bring to your business or industry operations, Call Us Today!