A Complete Guide To Data Analytics Using Power BI

Data analysis has created a huge impact in today’s digital age that has come out as a boon for businesses around the world. A data analyst uses data insights to create a profit-oriented business strategy that suggests improvements need to be made in the business model to maximize ROI. The strategy involves various steps that are discussed further in this article which is centered around a business intelligence platform called Power BI. It is a platform that processes data analytics and offers data insights with great visualization that comprehensibly reflects all information in a well-organized manner. Read below the complete guide on Data Analytics Using Power BI. 


What is Data Analytics and What Does It Do?

Data analytics is the process of diligently analyzing the raw data that is provided under the knowledge of certain campaigns initiated to gain insight to make better choices or decisions regarding businesses. It can be thought of as an advisor that provides all significant information to improve the subject’s performance.

This whole terminology does not correspond to anything new as it was already present before its advent because it is just the analysis of the performance. On the other hand, data analytics has automated this into algorithms that analyze the given data and provide the necessary information in a comprehensible form.

A business generally deals with quite a lot of problems that need creative solutions that could not be produced without any deeper insight into the notions that most of the time deal with consumer psychology, market trends, or geo-based trades.

Such issues must be tackled to make profits which is done through multiple applications of data analytics that is descriptive analytics, prescriptive analytics, diagnostic analytics, or predictive analytics. Its wide array of benefits is the result of various software tools utilized in its functions which are data mining programs, data visualization, spreadsheets, open source language, and reporting tools.


Importance of Data Analytics in Business

Before diving into the core subject, it is important to realize the central idea around the specific needs of a business.  A business is successful only when it is profitable and such asset is only there when each choice made is coherent with the requirement needed to be furnished. This basic need can be sufficed easily with the help of data analytics using Power BI as it provides real-time feedback by analyzing the performance of campaigns and could be utilized for predicting the business probability of success.

Most of the business or industries have already adopted this technique for better performance in the market.  The retail industry is constantly utilizing the available data to keep up with the growing and changing demands of consumers. Such trends in the market are prevalent and need to be recognized to form the roots of the business deep in the market.

In data analytics data is gathered and meaningful conclusions are drawn that would help sustain the business for a long. Therefore, it is been emphasized that using data analytics for your business could be worthwhile. And, this article will shed light only on the process of data analytics using Power BI.


Is Data Science and Data Analytics Different?

Data science and data analytics both are different in their approach to processing data and the variety of applications in a field. Also, both professions require somewhat different skill sets as data science being a very broad field encompasses data analytics and includes other portions like machine learning and data engineering.


What is Data Science?

Data science is the analysis and study of large data sets with the help of programming, statistics, machine learning, algorithms, and specific field constitutes multiple techniques, tools, and processes to derive the required result. Data science can identify valuable insights from given raw data sets and can make valuable predictions based on segmentation, collaborative filtering, etc.


Procedures in Data Science

Data science involves a list of procedures that work on data to derive valuable information. There are six methods to achieve the target.

Goal: Data science involves the exploration and study of data with different techniques and tools which is dependent on the type of data. For instance: a data scientist working with business stakeholders needs to define goals and objectives for a certain type of advertisement campaign while a data scientist working with the pharmaceutical industry would be following a similar approach but for different results.

Data integration and management: Before data analysis, the data is converted into a suitable form to integrate the data with the help of various processes involving data replication, ingestion, and transformation to integrate the contrasting types of data into a standardized format.

Analysis and exploration of data: data scientist investigates and explores the large data sets with the data analytics platform using business intelligence tools. This step deals with mainly data analytics-related work which involves the optimization of a business to maximize the revenue.

Model development: This stage involves constructing a model based on business objectives and data investigation which is developed by using algorithms, and capable analytic models with the help of languages such as R, Python, and SQL. And then, various data science techniques are used like statistical modeling, AI, or AutoML.

Model deployment and presentation: Presentation is the last stage involving the data science process which presents the refined data in the comprehensible and visual form. These visual insights are attained through all the previous steps that were conducted on the raw data. All these insights are then shared with the stakeholders and based on their feedback necessary adjustments are made by the data scientist in the model.


Skills Set of Data Scientist

The data scientist has a wide array of skills which makes such a profession a crucial factor in the market given the change in the pace of data moves or changes in today’s business.

These are among the necessary skillsets of a data scientist:

  • Comprehensive understanding of their company or business place in the market
  • Able to use business intelligence tools such as Power BI to investigate data for optimization purposes.
  • Knowing designing and maintaining data integration systems and data repositories
  • Having the ability to make data algorithms and analytical models using languages such as Python, R, and SQL.
  • Knowledge of implementing data science techniques such as statistical modeling, artificial intelligence, and machine learning.
  • Potential to improve data integration and management system


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Types of Data Analytics

Descriptive analytics: it is the simplest of all the data analytics and answers the question “what happened”. For instance, if there occurs a huge surge in sales, this form of analytics tells about this occurrence in a more vivid way which on the other hand would have required more effort to conclude such results if done without any adequate software tools. Descriptive analytics performs best with data visualization which is presented through graphs, charts, and maps. Such representation is easier to comprehend due to its more vivid nature.

Diagnostic analytics: it also plays a quite important role for data analytics in answering crucial in-depth questions related to business ventures. These analytics uncover the “why this happens” question for the presented raw data. Diagnostic analytics provide significant analysis of sales regarding any changes seen in trends

Predictive analytics: when analytics give a glimpse of future trends in the market or answer questions like “what might happen in the future”, then it is called predictive analytics. Such information is given by taking into account the historical trends of the market which is then compared to the present scenario that leads to the data presented with predictions about the possibility of similar trends shortly. These analytics hold significant importance as the business person would arrange suitable conditions for his venture to produce high ROI.

Prescriptive analytics: This is the main purpose of data analytics as the desired changes that need to be made for applying better business strategies are provided in this method. For example:  in amazon, there are hundreds of recommendations shown to a buyer which are actually based on the previous buying history of the customer and also the search history of the browser with various other different factors in action.

Here the example shows how Amazon and other big retail businesses collect a large ocean of data and process it in a prescriptive data analytic system to find out about the product that a customer has a high chance of buying. With such an approach businesses have become more mindful with their investments as the data is already available to them which shows the selling potential of a particular product. Therefore, it is obvious and easy to see that prescriptive analytics is the main aim of data analytics


Power BI

Power BI is an interface for data analytics. Data analytics using Power BI has been among the foremost choice in the business space due to its effective nature. Power BI acts as a business intelligence platform that processes data by aggregating and analyzing it to create a visual representation in a comprehensible form. It is a self-service BI that would require training for kinds of work linked with complicated sets of data but would be self-managing for a small business.

The reports are used to analyze the strategies directed at making sales and marketing reps. The analysis is built on various principles according to the information needed during this data processing. Power BI is an efficient business intelligence platform software that has the capability of providing all sorts of the required information.

Data analytics using Power BI follows certain principles to perform data analytics which cannot be effective in case the following steps are not followed:

  1. Understanding the question

The user must be very clear regarding the objective as the whole analytics will be performed based on a central theme. This specific task is tackled with specific strategies which will be prepared by keeping in mind the rendering theme. Henceforth, knowing the aim of the task will help the business to progress efficiently.

  1. Appropriate data collection

data collection is one of the significant tasks in data analytics. the reliability of data is important as it affects the result of analysis so it becomes incredibly important for data analytics to collect the data from multiple sources which are primary and secondary. Primary source refers to email marketing or internal data structures whereas secondary data sources corresponds to open data sources like the Times of India or UNICEF.

  1. Scrubbing and cleaning of data

Data obtained from secondary sources majorly lacks the quality and consistency in data that is required for analysis. Any missing data value or duplicate values account for the poor quality of data which is a big setback for any enterprise. Therefore, cleaning and scrubbing are required before analysis.

  1. Techniques in data analytics for analyzing the data

Various data analytics techniques are used to meet the main aim of the enterprise. Some data sets can be analyzed using one or two techniques while some require more.

  1. Data visualization

Data visualization is the last step in data analytics which provides insights into data for business. It comprises of 3d models, graphs, or charts that present the information in comprehensible form which is then easily understood by stakeholders and helps them in concluding the discussion regarding the limitations of the enterprise and its objectives.


Features of Power BI

Data analytics using Power BI contains numerous features that help in making your work easy:

  • Cortana integration: Cortana is Microsoft’s digital assistant that allows users to speak the data which is analyzed using natural language and useful information is provided
  • Fast insights: this feature promises quick results as the data can be divided into subsets by using powerful algorithms which then can directly be analyzed. Quick insights provide users with faster access to analytical results.
  • Hybrid deployment support: Microsoft has built-in features for the power BI which just makes it even more demanding as the work is effective and easy because of the numerous features.
  • Modeling view: this allows the conversion of complicated data into a comprehensible form such as by converting data models into diagrams, charts, etc.
  • Questionnaire: This tool provides freedom to users to ask a question and give instant responses in the form of visuals such as charts, tables, or graphs.
  • Real-time data analytics: The data sent or if changed, it is instantly visible on Power BI dashboards which allow users to solve their problems and find any other possibilities instantly.
  • Security: This business intelligence platform promises high security protection which ensures data safety using industry-leading security constituents like end-to-end encryption, sensitive labeling, and real-time monitoring
  • Intuitive reports: The reports in Power BI are comprehensible and well organized which can be shared and they represent information visually relevant to specific business issues by showing valuable insight.


Therefore, it can be concluded that data analytics using Power BI has many significant features that are required to make a business successful.


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Why Choose Power BI instead of Other Available Data Analytics Software? 

Data analytics using Power BI is popular business intelligence software developed by Microsoft

  • scalability: scalability refers to the ability to handle large data sets. Its counterparts like Tableau and QlikView are also scalable but require additional software and hardware support.
  • user-friendly interface: data analytics using Power BI is comparatively easier to use even for non-technical users. Other business intelligence platforms involve somewhat complicated interfaces that would require technical proficiency.
  • broad spectrum of data sources: The data can be contained in various software tools like Excel spreadsheets, cloud-based services, or on-premises databases and still can be accessed through Power BI.
  • mobile friendly: data analytics using Power BI has become a mobile app for IOS, android which makes it handy.
  • cost: It is inexpensive as compared to other business intelligence platforms for similar features
  • ease of excess: As Power BI has a user-friendly interface, it is easy and convenient for the customers performing data analytics using Power BI.
  • data security: it has robust inbuilt security features consisting of secure data access, and role-based security in comparison to other platforms which need plugins or external support to provide security of such level.
  • General connectivity: it is extremely handy as it connects with almost all corporate software like tally, excel, etc. making it one of the most effective and preferred data analytic business intelligence platforms
  • vast visualization options: data analytics using Power BI has a huge array of visualization options to let the professional choose the desired one as per their need. The compression feature that is provided in Power BI can arrange millions of rows of data without facing any glitches in the software. Also, for making interactive data numerous templates are provided but these can be also customized using Python and R
  • Advanced analytics: since analytics has been made possible using Excel, the company’s or enterprise’s work has become easier as there is no need to execute the work separately. In Excel, there is an underlying option for power BI that can be used to organize or assimilate the data.
  • Connectivity with UI and UX: power BI offers design capabilities to create new dashboard designs. Though it is rich in its diversity of uses it has limited features when comes to designing as prototypes made with Adobe or InVision can’t be replicated with high precision.


Components of Power BI

Data analytics using Power BI is comprised of several parts that help conduct the desired activities for data analysis

  1. Power query: The work involving integrating, connecting, discovering, or enhancing the data sources for analyzing the data is done through power query.
  2. Power pivot: power pivot helps in developing data models, calculations, and relationships. It can also model simple and complex data using data analysis expression.
  3. Power view: this component creates visualization effective in terms of comprehensibility as the data presented through charts, graphs, and maps is well organized and easy to understand. A power map connects various data sources and filters out the data for every element of data visualization.
  4. Power BI Q&A: this tool directly answers the question asked by the user instantly. for instance, a user can ask questions and will get the solution. To make this possible, power BI works with Power View to answer the questions in the form of the map like, for example: if a user asks about the no. of products sold in a district then this component will answer the query based on the related data.
  5. Power map: data analytics using Power BI provides many features including a power map which makes the data more interactive as the geospatial data is now visualized in 3D mode. Here geospatial data refers to the location-specific data which can be any state, country, or district. To get the best precision, the power map is in collaboration with Bing Maps to give the best visualization based on the geographical latitude, and longitude of the country, state, or district.


FAQs: Data Analytics Using Power BI


What do data analysts do?

A data analyst has the following responsibilities in handling data sets:

  • Data analysts should know how to handle surveys from customers as their feedback is important to take the business on a smooth financial road.
  • Set business strategies around a central question which is the main aim of business and then analyze the data sets through opted strategy
  • It is important for a data analyst to collect data properly through effective modeling
  • Collecting data from various reliable sources like primary, secondary, or international
  • Develop the back end of a company and be vigilant about its integrity
  • Creating effective ways of visualization for stakeholders
  • Collaboration with other business professional within the same enterprise to create better products


What does the future of data analytics hold?

Data has always been integral to business as the related analysis can decode various insights that will otherwise be hidden if appropriate software tools are not used. These insights reveal various information that helps in molding strategies that were not effective in meeting the desired result. And every enterprise needs data analyst and the market are growing multifold which just increases the demand for such a profession. Therefore, it is well to conclude that the future of data analytics is powerful. There are a variety of software tools that help in data analytics, however, data analytics using Power BI is considered ideal for its array of uses and relatively easy algorithm.


What skills does a data analyst require to work with Power BI?

There are hard and soft skills for a data analyst:

Hard skills-

  • There are mathematical formulas and statistical methodologies that are an essential part of a data analyst skillset.
  • Some basic knowledge of programming language is essential

Soft skills-

  • Candidate must develop critical and analytical thinking to be successful at a job
  • Having great hold at communication skills as a data analyst needs to collaborate with different professionals within the same enterprise to be able to produce the best work



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