Successful Business Analysis Consulting
Strategies and Tips for Going It Alone
By Pragati Agrawal MBA
# Accounting Analytics :- Accounting analytics, in a nutshell, is the examination of Big Data using data science or data analytics tools to help answer accounting-related questions.
The Emergence of Big Data :- The first piece of the puzzle is `Big Data.’ Big Data is different from previous forms of data in terms of volume, variety, and velocity. Any of the following
might be considered sources of Big Data for an accounting analytics project:
1. Social media data
2. Web search data
3. Journal entries
4. Transactional data (e.g., customer transactions)
5. Call centre transcripts
6. Store videos, web cams, etc.
The Emergence of Data Science :- The second piece of the puzzle is `Data Science.’ Data scientists examine Big Data using a combination of programming skills, statistical skills, and
domain knowledge to answer relevant organizational or societal questions. Data science has emerged in the past decade with the emergence of Big Data and the concurrent development
of readily accessible sophisticated computing tools. At its core, data science is a field, or perhaps an approach, that lies at the intersection of three things:
1) computer programming, or ‘hacking’, skills;
2) mathematical and (especially) statistical skills;
3) domain expertise.
# Marketing Analytics :- Marketing analytics is the study of data to evaluate the performance of a marketing activity. By applying technology and analytical processes to marketing-
related data, businesses can understand what drives consumer actions, refine their marketing campaigns and optimize their return on investment.
The Role of Marketing Analytics :- What sets marketing analytics apart from other business analytics is it’s focus on real market feedback. Marketing analytics keep pulse of the
interests and actions of the subscribers, leads, and customers that your business is focused on serving. Most marketers dipping their toe into analytics, select website and social
media analytics as their starting points. Website analytics typically measure specific actions like clicks, page views and conversions. By connecting all areas of marketing,
including offline efforts, with sales and lead generation results, marketing analytics reveal the direct impact marketing has on pipeline generation and revenue growth.
Common Marketing Analytics
1) Descriptive (i.e. What happened in the past)
2) Predictive (i.e. Predictions on what could happen)\-23
3) Prescriptive (i.e. Suggestions on what to do)
# Financial Analytics :- Finance analytics, also known as financial analytics, provides differing perspectives on the financial data of a given business, giving insights that can facilitate strategic
decisions and actions that improve the overall performance of the business. Related to business intelligence and enterprise performance management, finance analytics impacts virtually all
aspects of a business, playing a critical role in calculating profit, answering questions about a business, and enabling future business forecasting.
Challenges with Financial Analytics :- In many ways, CFOs find themselves pursuing two contradictory goals. As a cost-center for the business, finance must honor stringent cost reduction
imperatives and flat budgets. Yet at the same time, growing regulatory and management requirements demand that CFOs provide unprecedented levels of financial transparency and decision
support. CFOs are being asked to integrate big data at a time when their own financial house is probably not entirely in order—or at least not in an order that provides necessary, actionable
insight into detailed results. Too often, CFOs rely upon a web of unnecessarily complex, disconnected financial systems that require significant manual, error prone reconciliation and validation
labor. This can lead to inconsistently or inaccurately reported results, as well as the internal "data struggles" that erupt when divisions have conflicting definitions of net revenue, gross margin
or selling expense. The resulting arguments delay management’s decisions and negatively impact their quality.
# Business Statistics :- Business statistics takes the data analysis tools from elementary statistics and applies them to business.
For example, estimating the probability of a defect coming off a factory line, or seeing where sales are headed in the future. Many of the tools used in business statistics are built on ones you’ve
probably already come across in basic math: mean, mode and median, bar graphs and the bell curve, and basic probability. Hypothesis testing (where you test out an idea) and regression
analysis (fitting data to an equation) builds on this foundation. Basically, the course is going to be practically identical to an elementary statistics course. There will be slight differences.
The questions will have a business feel, as opposed to questions about medicine, social sciences or other non-business subjects. Data samples will likely be business-oriented. Some subjects
usually found in a basic stats course (like multiple regression) might be downplayed or omitted entirely in favor of more analysis of business data.
1. Introduction to descriptive statistics for displaying and summarizing business data.
2. The use of probabilities and random variables in business decision models.
3. Probability distribution.
4. Statistical inference as a decision-making tool.
5. Sampling of business data.
6. Simple linear regression and correlation.
7. Time series analysis.
8. Use of index numbers in economic data.
# Applied regression Analysis :- Regression analysis is a reliable method of identifying which variables have impact on a topic of interest. The process of performing a regression allows you to
confidently determine which factors matter most, which factors can be ignored, and how these factors influence each other.
In order to understand regression analysis fully, it’s essential to comprehend the following terms:
Dependent Variable: This is the main factor that you’re trying to understand or predict.
Independent Variables: These are the factors that you hypothesize have an impact on your dependent variable.
Why should your organization use regression analysis?
Regression analysis is helpful statistical method that can be leveraged across an organization to determine the degree to which particular independent variables are influencing dependent
variables.The possible scenarios for conducting regression analysis to yield valuable, actionable business insights are endless. The next time someone in your business is proposing a
hypothesis that states that one factor, whether you can control that factor or not, is impacting a portion of the business, suggest performing a regression analysis to determine just how confident
you should be in that hypothesis! This will allow you to make more informed business decisions, allocate resources more efficiently, and ultimately boost your bottom line.
# Data mining :- Data mining is the process of getting the information from large data sets, and data analytics is when companies take this information and dive into it to learn more. Data analysis
involves inspecting, cleaning, transforming, and modeling data. The ultimate goal of analysis is discovering useful information, informing conclusions, and making decisions. Data mining, data
analysis, artificial intelligence, machine learning, and many other terms are all combined in business intelligence processes that help a company or organization make decisions and learn more
about their customers and potential outcomes.
Data mining techniques :-
1. Classification :- This data mining technique is more complex, using attributes of data to move them into discernable categories, helping you draw further conclusions. Supermarket data mining
may use classification to group the types of groceries customers are buying, like produce, meat, bakery items, etc. These classifications help the store learn even more about customers, outputs, etc.
2. Clustering :- This technique is very similar to classification, chunking data together based on their similarities. Cluster groups are less structured than classification groups, making it a more
simple option for data mining. In the supermarket example, a simple cluster group could be food and non-food items instead of the specific classes.
3. Association rules :- Association in data mining is all about tracking patterns, specifically based on linked variables.In the supermarket example, this may mean that many customers who buy
a specific item may also buy a second, related item. This is how stores may know how to group certain food items together, or in online shopping they may show “people also bought this” section.
4. Regression analysis :- Regression is used to plan and model, identifying the likelihood of a specific variable.The supermarket may be able to project price points based on availability,consumer
demand, and their competition.Regression helps data mining by identifying the relationship between variables in a set.
Pragati Agrawal MBA
AirCrews Aviation Pvt. Ltd.