Business analytics (BA) allow us to learn from the past and make better prediction for the future. There are three types of analytics used for the learning from the Past. Descriptive analytics summarizes historical data; exploratory analytics uncovers hidden patterns and explanatory analytics reveals the reason for business results.
BA is used to gain insights that inform business decisions and can be used to automate and optimize business processes. Data-driven companies treat their data as a corporate asset and leverage it for a competitive advantage. Successful business analytics depends on data quality, skilled analysts who understand the technologies and the business, and an organizational commitment to data-driven decision-making.
Big Data: An Overview
WE describes Big Dataas “a term that describes the large volume of data – both structured and unstructured – that inundates a business on a day-to-day basis.” What’s important to keep in mind about Big Data is that the amount of data is not as important to an organization as the analytics that accompany it. When companies analyze Big Data, they are using Business Analytics to get the insights required for making better business decisions and strategic moves.
Business analytics (BA) refers to the skills, technologies, practices that are applied on past data and/or processes to derive insights that can be used for future business planning. It is a field that is now applied across all domains and industries. With more and more data being generated, the requirement for data scientists is estimated to be 4.4 million by the end of 2015. (Source: Gartner)
Analyze, prepare reports and present to Leadership team on a defined frequency
Lead multiple analytical projects and business planning to assist Leadership team deliver business performance.
It can be termed as the study of business data using statistical techniques and programming for creating decision support and insights for achieving business goals. We use statistical techniques and programming by using only business data and not the entire data for Business Analytics. It’s based on analytical tools like R, SAS, SPSS data. Business Analytics is an important activity in an organization in order to create decision support and get an insight. It will help businesses to achieve their goals. It typically enhances the marketing team to channelize their efforts.
Defining Business Analytics
What do you use to help you make decisions? Is it information or data? If you had more of it, would you make better decisions? Probably, yes. The same is true for businesses. Businesses use data to help them make the best decisions possible. They use structured and unstructured data, and lots of it. But it’s not enough to have the data; businesses have to know what to do with it – they have to know how to analyze the data they acquire.
Businesses use business analytics to analyze the data they have accumulated in order to make decisions. Business analytics is the statistical analysis of the data a business has acquired in order to make decisions that are based on evidence rather than a guess.
This is called data driven decision making. Making decisions based on data allows businesses to operate more efficiently, and the more data based decisions they make with positive results, the more they can simplify the process by coming up with formulas for the future. For example, when we do ‘A’, ‘B’ happens 90% of the time and that’s the result we want, so let’s do ‘A’ again.
Some examples of using business analytics include finding patterns and relationships between processes and outcomes, explaining why a result was achieved, determining if a previous decision was appropriate, or predicting the likelihood of a particular result happening.
Analytical Customer Relationship Management (CRM): BA can be applied to analyze a customer’s behavior across the customer lifecycle i.e. (acquisition, relationship growth, retention, and win-back). A lot of business analytics applications such as Direct Marketing, Cross-Sell, Customer Churn and Customer Retention are components of a well-managed Analytical CRM. Predictive analytics forms the backbone of this CRM and is applied to customer data to create a holistic interpretation of the customer after collating information across all departments and locations.
Fraud Detection:Fraud is now a pervasive problem and can come in various forms: intentionally inaccurate credit applications, fraudulent transactions (both offline and online), identity thefts and false insurance claims, to name a few. These problems hence affect credit card issuers, insurance companies, retail merchants, manufacturers, business-to-business suppliers and even services providers. A predictive model can help an analyst distinguish specious data/transactions from other similar data and reduce exposure to fraud. For instance, the Internal Revenue Service (IRS) of the United States uses predictive analytics to mine tax returns and identify tax fraud.
Forecasting and Inventory Management : Retailers are typically interested in predicting store-level or sector-level demand for inventory management purposes. Similarly a manufacturing firm may be interested in predicting GDP figures to analyze demand and hence level of production. Both Forecasting and Machine Learning approaches can be used to find patterns that have predictive power.
Underwriting : Insurance providers need to accurately determine the premium charge for all assets ranging from automobiles and machinery to people. Similarly, Banks need to assess a borrower’s capability to pay before agreeing to a loan. Business Analytics can analyze past data, to predict how expensive an applicant or an asset is likely to be in the future.
Human Resource Department: Business Analytics is used by human resources (HR) departments to create a profile of their most successful employees. Details – such as universities attended or previous work experience of successful employees – can help HR focus recruiting efforts accordingly.
Market Basket Analysis: Market Basket Analysis finds association rules within transaction-based data. It has been used to identify the purchase patterns of the High – Volume Consumer. Analyzing the data collected on this type of customer has allowed companies to predict future buying trends and forecast supply demands.
Numerous Other Applications: Credit – Scoring Analytical Models reduced the amount of time it takes for loan approvals to a few hours rather than days or even weeks. Pricing Models can lead to optimum pricing decisions, which can help mitigate risk of default. Analytics specifically, Pattern Mining and Subject Based Data Mining has even been used to counter Terrorism.
The Business Analytic Process
Business analytics is an iterative process, which means as businesses collect and analyze data, their analysis impacts the future needs of the business. Let’s look at a seven step process:
Business Analytics Process
First the business will determine what it is that they need, or what their goal is for collecting the data.
Next they’ll collect the data. This could be structured data like customers’ demographics (age, gender, political affiliation, etc.), or unstructured data like feelings about customer service.
Once the data is collected, it is analyzed. If the goal was to find out the average age of customers who bought a particular project, the business would do a statistical analysis, perhaps using a spreadsheet to perform the calculations.
Based on the analysis of the data, the business can make certain predictions. For example, if they find the average age for their customer is 25, they can predict that if they target their marketing campaign to that demographic they will increase their sales.
The analysis of the data may have suggested more than one viable route that will meet the business’ need or goal. These need to be evaluated to find the one that best meets their need.
Once the best solution has been found, it must be acted on – someone in the business needs to follow through and take action.
The final step – well, almost the final step – is to evaluate the effectiveness of the decision and update the information the business has access to. This evaluation generally leads back to the first step where the business decision makers can use the information acquired and analyzed for an initial need to determine what other needs the business may have.
The Differences Between Business Intelligence and Business Analytics
Business Intelligence (BI) and Business Analytics are similar, though they are not exactly the same. Business Intelligence involves the process of collecting data from all sources and preparing it for Business Analytics. Business Intelligence is more of a first step for companies to take when they need the ability to make data-driven decisions. Business Analytics, on the other hand, is the analysis of the answers provided by Business Intelligence. While Business Intelligence answers what happened, Business Analytics answers why it happened and whether it will happen again. Business Intelligence includes reporting, automated monitoring and alerting, dashboards, scorecards, and ad hoc query; Business Analytics, in contrast, includes statistical and quantitative analysis, data mining, predictive modeling, and multivariate testing.
Business analytics examples
Business analytics techniques break down into two main areas. The first is basic business intelligence. This involves examining historical data to get a sense of how a business department, team or staff member performed over a particular time. This is a mature practice that most enterprises are fairly accomplished at using.
The second area of business analytics involves deeper statistical analysis. This may mean doing predictive analytics by applying statistical algorithms to historical data to make a prediction about future performance of a product, service or website design change. Or, it could mean using other advanced analytics techniques, like cluster analysis, to group customers based on similarities across several data points. This can be helpful in targeted marketing campaigns, for example.
Specific types of business analytics include:
Descriptive analytics, which tracks key performance indicators to understand the present state of a business;
Predictive analytics, which analyzes trend data to assess the likelihood of future outcomes; and
Prescriptive analytics, which uses past performance to generate recommendations about how to handle similar situations in the future.
Challenges with Business Analytics
Penn State University’s John Jordan described the challenges with Business Analytics: there is “a greater potential for privacy invasion, greater financial exposure in fast-moving markets, greater potential for mistaking noise for true insight, and a greater risk of spending lots of money and time chasing poorly defined problems or opportunities.” Other challenges with developing and implementing Business Analytics include…
Executive Ownership – Business Analytics requires buy-in from senior leadership and a clear corporate strategy for integrating predictive models
IT Involvement – Technology infrastructure and tools must be able to handle the data and Business Analytics processes
Available Production Data vs. Cleansed Modeling Data – Watch for technology infrastructure that restrict available data for historical modeling, and know the difference between historical data for model development and real-time data in production
Project Management Office (PMO) – The correct project management structure must be in place in order to implement predictive models and adopt an agile approach
End user Involvement and Buy-In – End users should be involved in adopting Business Analytics and have a stake in the predictive model
Change Management – Organizations should be prepared for the changes that Business Analytics bring to current business and technology operations
Explainability vs. the “Perfect Lift” – Balance building precise statistical models with being able to explain the model and how it will produce results.
Now, let’s take a look at the 5 most popular paid analytics tools:
SAS – SAS continues to be widely used in the industry. Some flexibility on pricing from the SAS Institute has helped its cause. SAS continues to be a robust, versatile and easy to learn tool. SAS has added tons of new modules. Some of the specialized modules that have been added in the recent past are – SAS analytics for IOT, SAS Anti-money Laundering, and SAS Analytics Pro for Midsize Business.
Tableau – Tableau is an easy to learn tool that does an effective job of slicing and dicing your data and creating great visualizations and dashboards. Tableau can create better visualizations than Excel and can most definitely handle much more data than Excel can. If you want interactivity in your plots, then Tableau is surely the way to go.
Excel – Excel is of course the most widely used analytics tool in the world. I have seldom come across a data scientist who does not use Excel. Whether you are an expert in R or Tableau, you will still use Excel for the grunt work. Non-analytics professionals will usually not have access to tools like SAS or R on their machines. But everyone has Excel. Excel becomes vital when the analytics team interfaces with the business steam.
QlikView – Qlikview and Tableau are essentially vying for the top spot amongst the data visualization giants. Qlikview is supposed to be slightly faster than Tableau and gives experienced users a bit more flexibility. Tableau has a more intuitive GUI and is easier to learn.
Splunk – Splunk is more popular than some of the more known names like Cloudera and Hortonworks. It started as a ‘Google for log files’ which means its primary use was to process machine log files data. It has now become much more than that. Splunk has great visualization options and a web interface makes it easy to use.
Best Business Analytics tools
Business analytics tools are types of application software which retrieve data from one or more business systems and combine it in a repository, such as a data warehouse, to be reviewed and analysed. Most organisations use more than one analytics tool, including spreadsheets with statistical functions, statistical software packages, sophisticated data mining tools and predictive modeling tools. Together, these business analytics tools give the organisation a complete overview of the company to provide key insights and understanding of the business so smarter decisions may be made regarding business operations, customer conversions and more.
While business intelligence tools also collect and display aggregate data, business analytics tools go a step further to not only report the results of the data, but explain why the results occurred to help identify weaknesses, fix potential problem areas, alert decision makers to unforeseen events and even forecast future results based on decisions the company might make. This gives organisations the understanding and confidence to achieve business goals, keep the company competitive and increase overall customer satisfaction.
Business experiments: Business experiments, experimental design and AB testing are all techniques for testing the validity of something – be that a strategic hypothesis, new product packaging or a marketing approach. It is basically about trying something in one part of the organization and then comparing it with another where the changes were not made (used as a control group). It’s useful if you have two or more options to decide between.
Visual analytics: Data can be analyzed in different ways and the simplest way is to create a visual or graph and look at it to spot patterns. This is an integrated approach that combines data analysis with data visualization and human interaction. It is especially useful when you are trying to make sense of a huge volume of data.
Scenario analysis: Scenario analysis, also known as horizon analysis or total return analysis, is an analytic process that allows you to analyze a variety of possible future events or scenarios by considering alternative possible outcomes. Use it when you are unsure which decision to take or which course of action to pursue.
Data mining: This is an analytic process designed to explore data, usually very large business-related data sets – also known as ‘big data’ – looking for commercially relevant insights, patterns or relationships between variables that can improve performance. It is therefore useful when you have large data sets that you need to extract insights from.
Regression analysis: Regression analysis is a statistical tool for investigating the relationship between variables; for example, is there a causal relationship between price and product demand? Use it if you believe that one variable is affecting another and you want to establish whether your hypothesis is true.
Correlation analysis: This is a statistical technique that allows you to determine whether there is a relationship between two separate variables and how strong that relationship may be. It is most useful when you ‘know’ or suspect that there is a relationship between two variables and you would like to test your assumption.
Text analytics: Also known as text mining, text analytics is a process of extracting value from large quantities of unstructured text data. You can use it in a number of ways, including information retrieval, pattern recognition, tagging and annotation, information extraction, sentiment assessment and predictive analytics.
Image analytics: Image analytics is the process of extracting information, meaning and insights from images such as photographs, medical images or graphics. As a process it relies heavily on pattern recognition, digital geometry and signal processing. Image analytics can be used in a number of ways, such as facial recognition for security purposes.
Video analytics: Video analytics is the process of extracting information, meaning and insights from video footage. It includes everything that image analytics can do plus it can also measure and track behavior. You could use it if you wanted to know more about who is visiting your store or premises and what they are doing when they get there.
Voice analytics: Voice analytics, also known as speech analytics, is the process of extracting information from audio recordings of conversations. This form of analytics can analyze the topics or actual words and phrases being used, as well as the emotional content of the conversation. You could use voice analytics in a call center to help identify recurring customer complaints or technical issues.
Importance of Business Analytics (BA)
By having a company, as an investor or an entrepreneur you need to know what you are getting yourself into. You need to have all the data to back up your goals and visions for the company. We should always keep asking ourselves, if it even makes sense to make this particular move. For this reason, you need to do a Business Analysis.
You check for profits. If the market shows signs of return on investment you will be encouraged to invest. Similarly if the market isn’t profitable then you will not make the move.
By analyzing, you try to understand the customers. You try to find out what it is that they want or the problems they have. Then, we try to provide them with that exact product or services that caters to them.
Business analytics is a methodology or tool to make a sound commercial decision. Hence it impacts functioning of the whole organization. Therefore, business analytics can help improve profitability of the business, increase market share and revenue and provide better return to a shareholder.
Facilitates better understanding of available primary and secondary data, which again affect operational efficiency of several departments.
Provides a competitive advantage to companies. In this digital age flow of information is almost equal to all the players. It is how this information is utilized makes the company competitive. Business analytics combines available data with various well thought models to improve business decisions.
Converts available data into valuable information. This information can be presented in any required format, comfortable to the decision maker.
Allows company to control their cost & identify efficient ways
Businesses often fail because of poor financial management strategy or a lack of planning. For your business success, developing and implementing financial and management systems is vital. It is important to keep updating the original business plan. When reviewing your finances, It benefits to consider the following:
Evolution of Business Analytics
Business analytics has been existence since very long time and has evolved with availability of newer and better technologies. It has its roots in operations research, which was extensively used during World War II. Operations research was an analytical way to look at data to conduct military operations. Over a period of time, this technique started getting utilized for business. Here operation’s research evolved into management science. Again, basis for management science remained same as operation research in data, decision making models, etc.
As the economies started developing and companies became more and more competitive, management science evolved into business intelligence, decision support systems and into PC software.
Scope of Business Analytics
Business analytics has a wide range of application and usages. It can be used for descriptive analysis in which data is utilized to understand past and present situation. This kind of descriptive analysis is used to asses’ current market position of the company and effectiveness of previous business decision.
It is used for predictive analysis, which is typical used to asses’ previous business performance.
Business analytics is also used for prescriptive analysis, which is utilized to formulate optimization techniques for stronger business performance.
For example, business analytics is used to determine pricing of various products in a departmental store based past and present set of information.
Data for Analytics
Business analytics uses data from three sources for construction of the business model. It uses business data such as annual reports, financial ratios, marketing research, etc. It uses the database which contains various computer files and information coming from data analysis.
Business analytics can be possible only on large volume of data. It is sometime difficult obtain large volume of data and not question its integrity.
The availability of Big Data, low-cost commodity hardware, and new information management and analytic software have produced a unique moment in the history of data analysis. The convergence of these trends means that we have the capabilities required to analyze astonishing data sets quickly and cost-effectively for the first time in history. These capabilities are neither theoretical nor trivial. They represent a genuine leap forward and a clear opportunity to realize enormous gains in terms of efficiency, productivity, revenue, and profitability.
The Age of Big Data is here, and these are truly revolutionary times if both business and technology professionals continue to work together and deliver on the promise.
Thank you for taking the time to read our book and we hope you enjoyed reading it as much as we did writing it.
I actually don’t really care about the promise of data unless they can deliver on that promise that comes with the data.
To illustrate the applications of analytics to answer many critical business questions, a list of top 10 questions in every area of business was discussed. Within each question, ways of applying analytics were also suggested. The vital lessons learned in more than a decade of applying analytics in businesses were also shared. To avoid the common trend of investing in analytics without using it, pitfalls were also highlighted based on the author’s own experience.
The main lesson is to tightly link analytics to business. To do this, you have to create safe and productive intersections staffed by analytics deciders who are well-versed in both business and analytics, supported by an analytics sandbox as part of the holistic BAP.
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