Data Analytics Using Power BI – Why Should You Combine Both For Growth
Data Analysis in the digital age has turned out as a boon for businesses around the globe. They can use data-driven insights to turn up the profits for their products and services with relative ease. The steps to do so are plenty but the pivotal process of data dissemination stands far above all others in terms of priority. To derive the crux of data is essential and then conveying it is just as important. Data analytics using Power BI is one way of visualizing data that cannot be neglected in business circles for its incredible data insight expression.
But before we entangle ourselves in the labyrinth of data analytics using Power BI and the components of Power Bi that separate it from all other data visualization software, we must do our due diligence. Learning about data analytics and its various courses takes precedence. Power BI can’t be used effectively if there is a murky understanding of data analysis, integration, and the processes therein. Secondly, Data analytics using Power BI can’t be possible without understanding the features and the use of Power BI in the data analytics process.
Building the foundations is key to good retention and conceptualization of any tenet found in any discipline across the spectrum. Learning the standard practices of the entire process follows shortly behind. Learning a new discipline is like driving a train and the standardized procedures are the carrying compartments. Without them, a train may as well be of little use. Furthermore, we’ll also intimate you of some other software that is used by data analytics professionals to supplement Power BI or used in its stead.
Such imparting of knowledge on our part is rendered to gain a comprehensive outlook on the entire field and not limit yourself solely to BI. In this volatile field of work, employer demands and software frameworks can limit you to using certain software that might be far away from your repertoire if you only learn Power BI. Thus, there is inexpressible value in learning data visualization as a whole process and all the software that permits you to paint a glorious and vivid picture for stakeholders. Lastly, you’ll be informed of a few courses that you can find online for learning more about data analytics using Power BI.
Some among them will even provide certifications, boot camp opportunities, and internships that can add a completely different charm to your writing. Becoming a data analyst is not a walk in the park though it might be possible for anyone who carries sufficient determination within themselves. A beginner-level understanding of some concepts might be required but your main concern should be to relay business-related insights that you elicit from data sets. Converting jargon into simple and digestible chunks is an important criterion for differentiating between a good and a great data analyst. Data analytics using Power BI can be transformative if used correctly so ingrain it within your psyche let us begin by understanding the value of analytics and what is construed as…
Data Analytics in the Business World:
In layman’s terms, data analysis refers to ascertaining valuable trends in business data sets that can lead to effective production, brand marketing, product management, and efficient data management. They work based on data available from established records to formulate an understanding of the current situation. This situation is supplemented by a lot of interaction and input with important business stakeholders and concerned potential officials to lay the foundation for meaningful modeling of data.
These statistical models can crunch numbers to bring out insights from big data. Valuable insights can be shaved away from others and the importance of their application has to be presented to the stakeholders to provide the required information for decision-making. Data Analytics using Power BI contributes immensely to this process by converting data analytics results into digestible chunks.
It is a process that requires specialized software and systematic procedures to organize business data and utilize it effectively. Data analytics falls close to data science, business analytics, and business intelligence.
The Difference is as Below:
Data Science vs Data Analytics:
Data analytics using Power BI might sound better than Data Science using Power BI but they are uncanny in their similarities. The major difference lies in their approach to data; functions and the aspirational outcomes to be achieved. Data analysts are tasked with answering the questions that plague business practices and lie well within the scope of knowledge of businesses. Understanding the patterns that undergird business problems and crucial ways to rectify them quickly is the job of a data analyst. Probing Big data and previous databases of the company of work may be necessary.
However, visualization of the results of data analysis is the most important aspect of data analysis. Businesses hire data analysts to solve business problems and to convey the direness of the issue at hand in jargon that would be missing the entire point of data analysis. Therefore, data visualization into graphs, charts, and unique interactable visual media is essential for data analysts. Data science on the other hand establishes the significance of the questions and their focus. Data scientists run models on this evaluation and basis and resulting in the emergence of rising trends, adjustments, and accurate optimizations in business procedures.
Data Analytics vs Business Analytics:
Data Analytics using Power BI works more along the basis of prescriptive and predictive analyses. Data sets are configured and used on the fly in data analytics because the locus of the entire exercise lies in finding trends and patterns in data sets to give a meaningful competitive edge to a business.
Business analytics handles itself more on descriptive and retrospective approaches opting to redefine the value of datasets and use them to the end of deriving a truth that can be applied to business processes to multiply their efficiencies and efficacy. Business analytics also possesses a host of requirements in its procedures and programs to fulfill and institute before any analyses can be developed whereas data analytics is more about figuring out datasets and constructing hypotheses to work them out later.
Moving on to our journey to understand data analytics using Power BI, we try to understand the basic categories of analysis and the procedure of data analysis. They are as follows:
- Descriptive Analysis of Data: Descriptive analysis is the simplest and most rudimentary of all analyses. It is the process of describing the fundamental data that will be used further by other analyses. It doesn’t erect any relationships of causality between factors, nor does it explain how the previous data arose. It simply describes the data as valid and important to the analysis. Two techniques are used in this type of analysis. Data aggregation and data mining. Both these techniques are incredibly important to the process of data analysis.
- Diagnostic Analysis of Data: Where descriptive analysis sticks by simply defining the state of data and data sets recorded, diagnostic analyses check on all data anomalies that cannot be explained via the blanket analysis of the whole data. Diagnostics is tasked to find and locate causation and explain the phenomena seen in the data at hand, so it has to be rigorous and immaculate in both its extent and sources. The discovery phase is embarked upon to find new sources of information that could explain away the given anomalies in data sets. Furthermore, in this stage causal relationships are defined and set up through various methods of data analysis like probability theory or time series analytics.
- Predictive Analysis: Data analytics using Power BI is based on the results of predictive analysis. It actively predicts the future by using multiple data sets derived from historical data and aims to deliver the perfect prediction for businesses to prepare for in the upcoming period. Despite the accuracy of predictions prioritized on priority being somewhat shaky, the plethora of predictions does display a broad-stroke direction of the business and the abundant pitfalls that might be encountered along the way. Ultimately, the bull’s eye is brought closer to the business so that the business can take appropriate actions and contingencies.
- Prescriptive Analysis: It is the ultimate aim of data analytics; to prescribe the right process or method to lend an edge to their business and improve the overall profit of the organization. At this stage, different outcomes and the differential weight that they carry are measured by all stakeholders. This part requires efficient data analytics using Power BI to help the proper understanding of complex data structures and machine learning outputs. This analysis contains research that may be of use outside the boundaries of the data analysis process. This analysis is by far the most essential since it has direct effects on the bottom line of the organization and directs real word decision-making.
The aforementioned are the varied types of analysis you’ll encounter along the journey. Moving on to understand the other aspect of data analytics using Power BI, we shall now try to understand the value of data visualization and what it means to data visualize using Power BI.
Power BI: Data Visualization
Understanding visualization is not a simple task without any prior knowledge of the standardized data analytics process. Data analytics using Power BI cannot be fruitful without this process. It follows five different steps in a sequence. One draws another and leads to the fulfillment of insight development in the long run. The steps are described in some detail for your understanding below:
Defining the Question:
Any action you proceed to do should revolve around the central question. Good data analysts always fixate on a clear central question around which the entire exercise of data analysis will be conducted. The details of the concept under investigation, the formation of a well-defined problem, and a sharp-ended hypothesis that deals with effective prodding can be adjusted as per need. Furthermore, providing clean references to the necessary data sources and the means of a collection of data goes a long way to enhance the transparency and ease of working with the business.
Collection of the Appropriate Data:
Data collection might appear like a tedious task, but the data is crucial to the quality of your results. The credibility of your data can proportionally affect the results of your analysis as well. Data analysts know the ways to accrue data from multiple sources. Primary data sources like internal data structures and email marketing information, secondary tools such as a variety of open data sources like the New York Times, and governmental or international data sources like UNICEF or the government of your nation.
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Data Cleaning & Scrubbing:
Data found from primary sources may be ready for analysis as it exists, but data garnered from other sources lacks the quality and consistency of data that is necessary for proper analysis. Duplicates of data values, anomalies, and missing data values can be dead giveaways of the poor quality of data. These require cleaning and processing before analysis. Even though this is a rather monotonous process, it is crucial for obtaining dependable results from modeling and analysis.
Analyzing the Data through Techniques of Analysis:
This step includes the use of the various data analysis techniques mentioned previously. Some data sets might require the use of only one or two techniques whereas others may benefit from sequential use. Cohort analysis and factor analysis are some previously unmentioned techniques of analysis.
This is the final step in the data analysis process where data turns into insights for the business. Differing from other steps, this is a type that benefits greatly from developed soft skills such as communication and presentation skills.
They are presented in the form of charts and graphs and help the conveyance of important data concepts and facilitate discussion among stakeholders to arrive at the correct decision and what that may be considering the limitations of business and its objectives.
Data Visualization is key for businesses because it shows the most likely path to success or improvement for the business in the long term. Data analytics using Power BI is conducted through effective data visualization. Power BI can be described as a joint and buildable platform for securing self-service and effective business intelligence through visualizations of data that automatically adjust to the given data. Power BI is a reliable visualization tool that can enhance the look of any presentation or document with seamless integration of charts, graphs, etc.
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10 Ways Power BI is Different:
Some features that supplement the popularity of Power BI are as follows:
1. Connection with Other Microsoft Products
For most professionals, Excel has become a part of life. For modeling, simple analysis and data organization Excel cannot be trumped. The convergence of components, software materials, and convenience provided by using products from the same company cannot be denied. Microsoft Excel and Power BI are both effective tools and their partnership fulfill all the requirements of a data analytics professional.
2. General Connectivity:
Power BI is extremely adjustable and easily connects with most corporate software, thus, it is a jack of all trades and can be used easily with most of the prevalent software of the corporate world like Tally, etc.
3. Extensive Visualization Options:
Power BI offers extensive options for visualization in its user interface for the benefit of the professional at work. Few of the very unique features of this software are below:
- Monthly Updates: Microsoft has continued to add significant features ever since the launch of the software in 2015. The community that works with the application can vote for new features and the selected ones are prioritized by the developers accordingly.
- Data Insights Drawn from Datasheets: The compression feature in this program helps in forming millions of rows of data without a hiccup in the smooth functioning of the software. It also utilizes aggregations to speed up the process of answering queries and processing important reports.
- Customized Visualizations in Python & R: Despite the innumerable templates and basic designs present in the arsenal of the software, there is an option for customizable visualizations like graphs and charts, etc. through the help of Python and R Programming.
- Beautiful Location-Based Mapping that can be easily color-coded.
4. Advanced Analytics:
Microsoft made Analyze in Excel available. As a result, Excel users now have the option to connect directly to the underlying Power BI data and do ad-hoc reporting and tabular reporting. If you’re confident in your ability to create PivotTables in Excel, you may refer to similar data points in the datasheets utilized in Excel and leverage this comfortable experience to slice and dice your data as needed. Additionally, users can modify their Excel connections to acquire the most recent data from Microsoft Power BI databases. For enterprises torn between using Excel and Power BI, this accessibility changes the game.
5. Governance of Data:
Power BI contains features for data control, authority, and management that can be effectively sourced to demarcate different data sets depending on user rank and role in the organization or render only limited access to freelancing professionals hired on contract.
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6. Data Exploration with Power BI:
This software has multi-level and multi-faceted data exploration options, and it simplifies deriving insights from data and is an essential feature for top-down methodological processes in data analysis.
7. Connectivity with UI & UX:
As a business tool for experts and employees in the corporate environment, Power BI was developed in the beginning. Its main users are data analysis specialists and Power BI software consulting partners. Though it is among the BI tools with the best usability and UI due to the platform’s business design. By building outlines that can be applied to all of your reports, Power BI also enables you to automate the process of adjusting your findings to reflect the image of your business.
8. Available Power BI App for the convenience of Users
9. Easy to Use
10. Easy Handling of Data through Automatic Refreshing of Data
The aforementioned features of Microsoft Power BI are important factors in its rise in popularity as a data analysis software. It is quick, intuitive, and possesses easy integration with Office and other third-party software. Though it has some limitations with the handling of big data and general integration with R language when compared to its more widely used counterpart Tableau.
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Some Courses to Learn This Incredible Software Are as Follows:
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FAQs: (Data Analytics using Power BI)
Q1. What are the responsibilities at work for a data analyst?
Data analysts may be responsible for a couple of important tasks in the business. These may be:
- Knowing the importance of satisfaction surveys from customers and knowing how to handle them properly as devices for data collection
- Coordinate with business owners to standardize success criteria, and collective definitions and set up important interactions for data-driven decision-making
- Configure businesses toward the central questions that need to be answered. Collect data from research and use it effectively for thorough analysis
- Garner data from multiple credible sources from the different categories: primary, secondary, or international
- Develop the backend code of a company and keep a constant check on its integrity
- Ensure the proper collection of data through effective modeling
- Learning the effective ways of visualization for stakeholders
- Collaborating with other data professionals for the designing of the best products as per customer demand
Q2. What skills does a data analyst require for using Power BI?
Data analysts’ job description stipulates that they possess hard and soft skills for the job at hand. Some of these are:
- Understanding Statistical Methodologies and Mathematical Formulations
- Programming Knowledge
- Analytical & Critical Thinking
- Competent Problem-Solving Skills
- Precise Control and Finesse in Communication Skills
These data analytics skills will unlock the latent potential of Power BI for users.
Q3. What does the future of data analytics look like?
Big data is the big buzzword in the market today and every company is head over heels for gaining leverage in it. The collection of data has never been higher and without analysis, it may as well collect figurative dust in cloud storage. In 3 years the market has doubled in size and has a sizable growth rate prediction for the upcoming years as this prolific multinational corporation phenomenon makes its way down the business hierarchy. So the future of data analytics is bright and gleaming.
We hope you enjoyed the article and learned something that can be of use to you in the future.