Power BI is a business analytics solution that let’s you visualize your data and share insights across your organization or embed them in your app or website. Connect to hundreds of data sources and bring your data to life with live dashboards and reports.
Business intelligence is the key component in the drive to be agile, competitive and effective. This is why business intelligence systems are being embraced globally. Putting an analytical system in place to effectively analyze data helps an enterprise discover useful information, suggest conclusions and support enhanced decision making.
Microsoft with its proven track record in the field of technology has come to the forefront with the introduction of its compelling analytics tool, Power BI. This report authoring tool allows enterprises to easily analyze and visualize their enterprise data to transform their operations. When it comes to the field of business intelligence, Microsoft’s Power BI will give you everything you need to take you from data to actionable insights in minutes with the accelerated decision making your organization depends on.
HOW DOES IT WORK?
Power BI will connect to a multitude of data sources, simplify your data preparation and drive ad-hoc analysis in a way that suits you in just one view – whenever and wherever you need to see it. Power BI gives access to a range of stunning reports that can be generated within just a few minutes and published for everyone to see on the web and across their mobile devices.
BENEFITS OF USING POWER BI
Power BI is a cloud-based business analytics service that enables the following;
- Fast and easy access to data
- A live 360 view of your business
- Data discovery and exploration
- Insight from any device
- Collaboration across your organization
- Anyone can visualize and analyze data
ADVANTAGES IT HAS FOR CONSUMERS
- Pre-built dashboards and reports for popular SaaS solutions
- Real-time dashboard updates
- Secure, live connection to your data sources, on-premises and in the cloud
- Intuitive data exploration using natural language query
- Integrated with familiar Microsoft products and utilizes commitment for scale & availability in Azure
- Fast deployment, hybrid configuration, secure, and integrated with existing IT systems.
WHO CAN USE POWER BI?
- Retail institutions use Power BI immediate insights into customers, pricing, inventory, POS activity, and so much more—across markets and channels, online and in-store
- Finance and Insurance Institutions is used for the performance of claim handling processes, Client preferences in handling type, Changes in notification type preferred by clients, Trends in average compensation paid, visualize your finance data and uncover new, insights into cash flow, profitability, and customers. Share your QuickBooks Online data with colleagues in a Power BI report and dashboard, refresh data on a schedule or on demand. Monitor the latest income and expenses on the go with the native Power BI app for mobile devices.
- Manufacturing Organizations use power BI to track metrics including: Revenue by division year on year variance by expense category, headcount, and division. Top customers and Balance sheet trends.
- Other Institutions such as Telecommunication Network and Service Providers, Media Houses and institutions, Educational and Research institutions, Government Agencies such as the Electoral commission, Statistical Service, Transport and Travel industry organizations like the Airport where customer data is critical to business decision and investment are all likely candidates for Power BI usage.
WHAT ARE THE KEY FEATURES?
Data is at the center of all business decisions today. The challenge before organizations is to manipulate data with other datasets to derive meaningful information. An effective way to do this is to collect various datasets and organize it in different visual ways for better understanding. This form provides a much deeper understanding of data. The information obtained from these data sets with the help of Power BI visual representation organization can have an edge over the competition.
Import Data from different sources
Power BI supports cloud data sources: Azure, Office365, Dynamics, non-cloud: Salesforce, Twilio, Marketo, Quickbooks, etc. and on-premise data
Every organization function in a different manner and they have their own way of doing things, sometimes the standard way is always the way to go. The same applies to visualization. Power BI has a default standard and might not be sufficient in some cases due to complex data. However, you can access the custom library of visualization to design a visualization that meets your needs.
Power Q&A Question Box
This integrated function within the Microsoft Power BI allows you to explore data by asking questions and the software gives answers in the form of visualizations. You can use the Q&A to modify dashboards and reports. Q&A also helps you find the right question and provides the most correct answer.
Data Science is a blend of various tools, algorithms, and machine learning principles with the goal to discover hidden patterns from the raw data. It is a “concept to unify statistics, data analysis, machine learning and their related methods” in order to “understand and analyze actual phenomena” with data. It employs techniques and theories drawn from many fields within the context of mathematics, statistics, computer science, and information science.
BENEFITS / ADVANTAGES OF USING DATA SCIENCE
- Empowering Management and Officers to Make Better Decisions
An experienced data scientist is likely to be a trusted advisor and strategic partner to the organization’s upper management by ensuring that the staff maximizes their analytics capabilities. A data scientist communicates and demonstrates the value of the institution’s data to facilitate improved decision-making processes across the entire organization, through measuring, tracking, and recording performance metrics and other information.
- Directing Actions Based on Trends—which in Turn Help to Define Goals
A data scientist examines and explores the organization’s data, after which they recommend and prescribe certain actions that will help improve the institution’s performance, better engage customers, and ultimately increase profitability.
- Challenging the Staff to Adopt Best Practices and Focus on Issues That Matter
One of the responsibilities of a data scientist is to ensure that the staff is familiar and well-versed with the organization’s analytics product. They prepare the staff for success with the demonstration of the effective use of the system to extract insights and drive action. Once the staff understands the product capabilities, their focus can shift to addressing key business challenges.
- Identifying Opportunities
During their interaction with the organization’s current analytics system, data scientists question the existing processes and assumptions for the purpose of developing additional methods and analytical algorithms. Their job requires them to continuously and constantly improve the value that is derived from the organization’s data.
- Decision Making with Quantifiable, Data-driven Evidence
With the arrival of data science, data gathering and analyzing from various channels has ruled out the need to take high stake risks. Data scientists create models using existing data that simulate a variety of potential actions—in this way, an organization can learn which path will bring the best business outcomes.
- Testing These Decisions
Half of the battle involves making certain decisions and implementing those changes. What about the other half? It is crucial to know how those decisions have affected the organization. This is where a data scientist comes in. It pays to have someone who can measure the key metrics that are related to important changes and quantify their success.
WHO CAN USE DATA SCIENCE?
The financial industry is one of the most numbers-driven in the world, and one of the first industries that adopted data science into the field. As it is fairly known, financial companies are information-driven, and data science is the perfect helper to get actionable insights and obtain a sustainable development for financial institutions such as banks. Data science helps in risk assessment and monitoring, potential fraudulent behavior, payments, customer analysis, and experience, among many other utilizations. The ability to make data-driven decisions creates a more stable financial environment and data scientists make the backbone of the industry.
By connecting pattern recognition, analytics, statistics, and deep learning algorithms, data science makes healthcare more efficient. The demand for data scientists in the healthcare area grows rapidly, according to research published by the Journal of the American Medical Informatics Association. The ability to quickly process large volumes of data for clinical and laboratory reports, data scientists enable a more precise diagnosis process by utilizing deep learning techniques. There are also many companies that market smart wearables, used to track and detect health conditions, and data science is in the heart of the process. This allows data scientists to reduce the risk of health issues, and directly impact the state of human wellbeing, not just in the US, but in the entire world.
Travel personalization has become an increasingly deeper process than it used to be. The possibility to create customer profiles based on segmentation, offering personalized experiences according to their needs and preferences, has its foundations in data science.
The energy industry experiences major fluctuations in prices and higher costs of projects – obtaining high-quality information has never been so important. Data scientists help in cutting costs, reducing risks, optimizing investments and improving equipment maintenance. They use predicting models to monitor compressors, which, in turn, can reduce the number of downtime days. Regarding the (data science) tools used in extracting and evaluating data, it can range from Oracle, Hadoop, NoSQL, Python, and various other software and solutions that can manipulate and analyze large datasets.
Often referred as industry 4.0 (with the introduction of robotization and automation as the 4th industrial revolution), the manufacturing industry keeps growing in need of data scientists where they can apply their knowledge of broad data management solutions through quality assurance, tracking defects, and increasing the quality of supplier relations. Similar to the energy industry, utilizing preventive maintenance to troubleshoot potential future equipment issues is another focus where data scientists can find good usage of their skills. Avoiding delays in the production process, implementing artificial intelligence and predictive analytics offers the possibility to manage frequent manufacturing issues: overproduction of products, logistics or inventory. In short, data scientists help in identifying inefficiencies and tuning the production process.
There are 2.5 billion gamers across the world, and the industry is becoming the heart of entertainment. Data science is used in the industry to build models, analyze optimization points, make predictions or identify patterns to ultimately improve gaming models. Not just limited to the production process, data scientists also work in the monetization, where they need to identify the most valuable players and analyze general consumer behavior to increase the profitability of the company (the more the players spend, the higher the profitability). Another area where data scientists can put their skills to use is in fraud detection; security levels in the gaming industry must be of highest standards, thus, machine learning algorithms allow faster identification of suspicious account activities.
Connected to human health, the pharma industry has also emerged as an industry where data science is increasing its application. For example, a pharmaceutical company can utilize data science to ensure a more stable approach for planning clinical trials. The patent exclusivity “starts roughly at the same time of its first clinical trial,” therefore, companies need to resort to data science in order to build precision into their calculations of the potential success or failure of the clinical trials. Another application can be seen before the trial even starts, by identifying suitable candidates based on their body structure such as chemical structure, medical history or other important characteristics. Data scientists read, evaluate, monitor and perform these analyses.