Business Analytics

90% of the data in the world today has been created in the last two years….

Business analytics is a buzzing term in today’s business, job, institutes and money market. To understand how and why the business analytics field is soaring, we need to understand, “what does business analytics do?”

To answer this we will look at the goals and tasks handled by a business analyst. Business analytics is a field that drives practical, data-driven changes in a business. It is a practical application of statistical analysis that focuses on providing actionable recommendations..

Above diagram looks very simple but in practical it is very much complex which we shall depict later on.
Analysts in this field focus on how to apply the insights they derive from data. Their goal is to draw concrete conclusions about a business by answering specific questions about why things happened, what will happen and what should be done.
Business analytics juxtaposes management, business and computer science fields. The business aspect requires both a high-level understanding of the business as well as the practical limitations that exist. The analytical part requires an understanding of data, statistics, computer science and also data garbage. This combination of fields allows business analysts to bridge the gap between management and technology. Effective communication and problem-solving are also key elements of business analytics to translate insights from data to information that is easily communicated to executives.
Business intelligence is a related field that also uses data to help understand and inform a business. What is the difference in goals of business analytics compared to business intelligence? Though both fields use data to answer questions business intelligence aims to understand what has happened in an organization to get to where you are. This includes measuring and tracking key performance indicators (KPIs.) Business analytics, on the other hand, aims to inform changes to a business through utilization of predictive models that provide insight into the outcome of proposed changes.
Business analytics utilizes big data, statistical analysis, and data visualization to implement organization changes. Predictive analytics is an important aspect of this work as it involves available data to create statistical models. These models can be used to predict outcomes and inform decision making. By learning from existing data, business analytics can make concrete recommendations to solve problems.
Analytics works on three verticals…
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But it is very much complex due to data volume, garbage data and messy data. We need to find business insights from all the garbage and mess.

Some Examples…
Business analytics has applications in a wide array of different businesses. Some companies are developing innovative ways to use big data in order to improve their customer’s experience and maximize profits.
Food and Bevarage companies like Swiggy and Zomato have begun business analytics to streamline their restaurants and customer call log. No one wants to have a slow experience in a fast-food drive-thru. By monitoring how busy the drive-thru is these businesses can increase efficiency during peak hours. When the line gets long, the digital order boards change.
Other types of business analytics applications do more than just respond to the current situation. These techniques help businesses predict which customers are less likely to return. They can then target advertising and promotions to these customers to improve retention. Here are some examples of predictive analytics in business:
Casinos have embraced business analytics to improve their profits and keep customers coming back. Casinos have a complicated relationship with their customers. Though the house wins most of the time, players need to win enough to enjoy themselves and keep playing. Otherwise, players would quickly lose interest and stop coming back. By tracking players spending, casinos can learn which customers they make the most money from.
Organizations also use data from search engines to find interests and forecast their demand.
Business Analytics Tools
The application of business analytics requires the use of specialized tools to analyse data in a meaningful way. There are data analytics tools that can be used in business analytics to streamline the big data pipeline.
Tools for use in business analytics ranges substantially in complexity. Self-service analytics tools provide a simplified interface, often are paid services that can do basic data analytics tasks in a user-friendly way. Alternatively, advanced statistical analysis tools require programming and software engineering skills to use effectively. Many of these tools are open-source and available for free to users. They require a trained professional to be used but can provide more complex and specialized insights.
The most popular and well-known tools in both data analytics and business analytics are R and Python. Though they require programming and understanding of the underlying statistical techniques, they are flexible and powerful. Apart from these include data visualization tools, advanced statistical algorithms, data scraping tools and much more.
There are also paid statistical programming languages. These include SAS, SPSS and MATLAB. These languages have the advantage of paid support and professional development. However, they are not as popular as open source solutions.
Not all statistical analyses tools require programming. There are many options for statistical analysis with a graphical user interface (GUI). These tools are generally paid and include Tableau, Qlik, Sisense and SAP. These are self-service analytics tools that can take raw data and turn it into user-friendly charts with the click of a button. This user-friendly workflow allows the most useful insights to quickly be visualized.
Selecting the right tool involves balancing financial costs, time costs, complexity of the data and the ease of use.
Benefits of Business Analytics
Business analytics provides a wide array of benefits:
● Enable decision making to increase profits and improve efficiency
● With predictive analytics, allow businesses to plan for the future
● Helps a company make informed business decisions
● By modeling the outcomes and understanding the past, guesswork is minimized
● Present meaningful, clear data to support decision making and convince stakeholder
Business analytics provides a way for businesses to plan for the future. By modeling the trends in businesses’ sales, profits and other key metrics, these indicators can be projected into the future. Understanding the changes that are likely to occur seasonally, annually or on any scale allow businesses to better prepare. This may mean decreasing spending in preparation for a slow season or investing in new marketing campaigns to compensate. Large suppliers can use this data to predict order volume and minimize waste in their warehouses. Planning for future events provides a huge advantage to all businesses.
Business analytics can also enable new types of marketing campaigns. The data collected by businesses give insights into customer behavior which helps businesses understand the effectiveness of advertising campaigns with different audiences. Targeting audiences that are more likely to respond to specific campaigns or products increases efficiency overall. In addition, understanding consumer habits can help businesses improve customer retention. By identifying customers who are less likely to return, businesses can offer targeted promotions. This provides a cost-effective way to gain customer loyalty.
Key Requirements for implementation:
● Top Management support
● Robust business process
Note: Excellent analytics does not solve the bad business process

Energy 2K30

Energy 2K30

Welcome to Next Generation Energy Concept

Power is Money and Power is life. From Einstein to Tesla all indicated that Energy is most valuable but challenging thing to utilize and capture in spite of it’s abundance in nature. Whole universe is running seamlessly with the help of power. Demand for power is increasing exponentially with time, whereas storage of power is a major challenge.
Since its first inception almost hundreds years ago, the concept of Energy Storage has provided communities with increased energy reliability and lower energy costs. Energy sector is relentlessly innovating the ways to store power with minimum energy loss. With the growing integration of and preference for renewable energy sources, as well as the growing demand for power in general, the need for energy storage methods to maintain consistent supply and increased efficiency grows as well. It also allows to supply less whenever requirement is less and store for future without shutting down generation unit and in turn reduce shutdown and startup cost. Although there are many storage methods currently available, and still more being developed, choosing the best method and successfully implementing a project can be daunting without the right experience. Storage unit also promote green energy aim and capture solar and wind energy during day time for distributed requirement in terms of time horizon.
Community energy storage (CES) is expected to contribute positively towards energy transition while accommodating the needs and expectations of citizens and local communities. Yet, the technological and societal challenges of integrating CES in the largely centralized present energy system demand for socio-technical innovation.

The outlook for global energy is not just a matter for energy companies: it’s an issue for all of us. Around the world, there is a lively and important conversation taking place on the choices that face us all – as consumers, producers, investors and policy-makers. By sharing this Energy Outlook, we hope to contribute to that discussion.
In a similar way, the Energy Outlook, which contains our projections of future energy trends, has been used only internally so far. However, we feel it is part of our responsibility as a company to make important information and analysis available for public debate – all the more so if the issue at hand is as vital to all of us as is energy, its relation to economic development on one side, and to climate change on the other.
In this outlook we seek to identify long term energy trends, and then add our views on the evolution of the world economy, of policy, and technology, to develop a projection for world energy markets to 2030. It is a projection, not a proposition, and this is an important distinction.

You will see, for example, that our outlook expects global CO2 emissions to continue rising, along with import dependence in many key consuming regions. This does not mean BP downplays the importance of climate change or the role of energy security in international relations. Rather, it reflects a ‘to the best of our knowledge’ assessment of the world’s likely path from today’s vantage point. To me personally, it is a wake-up call, not something any of us would like to see happening.
We also highlight potential alternative outcomes, assessing in particular a case based on more aggressive policies to address climate change, as well as sensitivities for different economic growth paths. This is done to highlight the economic mechanisms that govern global energy markets, and how these can translate alternative policies into alternative outcomes.
The discipline of building a numerical projection sharpens our thinking, but the precise numbers are less important than the underlying story of the challenges we all face and the choices we make in producing and consuming energy.
In this way the outlook highlights the central role markets and well designed policy can play to meet the dual challenge of solving the energy needs of billions of people who aspire to better lifestyles, and doing so in a way that is sustainable and secure.