Author: carolhargreaves

Business Analytics – What is Business Analytics?

Business Analytics – What is Business Analytics Carol Hargreaves

Interest in Business Analytics is growing each day. Many still do not understand the difference between Business Analytics & Business Intelligence. There is quite a big difference between the two.

Business Intelligence is your dashboards, reporting tools, etc that allow you to see how the company is doing in meeting its KPIs or to see how the company is doing now compared with last week or last year this time. Business Intelligence is a summary of how your company is doing usually presented visually using pie charts, bar charts or trend charts.

Say for example your reporting system presents your sales this week as an upward trend. Business Intelligence tells you your sales is increasing but, does not tell you why it is increasing. Secondly, when seeing your Business Intelligent information, your action is likely to be reactive.

On the other hand, Business Analytics allows you to model your sales and better understand why your sales is trending up. Using models such as the logistic regression or decision tree, you are better able to understand what is driving your sales. And, in turn, you may act proactively and do more of “what is driving your sales”. Business Analytics allows you to be proactive and hence gain competitive advantage over your competitors.

The 7-Step Business Analytics Process

The 7-Step Business Analytics ProcessThe 7-step Business Analytics Process

Real-time analysis is an emerging business tool that is changing the traditional ways enterprises do business. More and more organisations are today exploiting business analytics to enable proactive decision making; in other words, they are switching from reacting to situations to anticipating them.

One of the reasons for the flourishing of business analytics as a tool is that it can be applied in any industry where data is captured and accessible. This data can be used for a variety of reasons, ranging from improving customer service as well improving the organisation’s capability to predict fraud to offering valuable insights on online and digital information.

However business analytics is applied, the key outcome is the same: The solving of business problems using the relevant data and turning it into insights, providing the enterprise with the knowledge it needs to proactively make decisions. In this way the enterprise will gain a competitive advantage in the marketplace.
So what is business analytics? Essentially, business analytics is a 7-step process, outlined below.

Step 1. Defining the business needs
The first stage in the business analytics process involves understanding what the business would like to improve on or the problem it wants solved. Sometimes, the goal is broken down into smaller goals. Relevant data needed to solve these business goals are decided upon by the business stakeholders, business users with the domain knowledge and the business analyst. At this stage, key questions such as, “what data is available”, “how can we use it”, “do we have sufficient data” must be answered.

Step 2. Explore the data
This stage involves cleaning the data, making computations for missing data, removing outliers, and transforming combinations of variables to form new variables. Time series graphs are plotted as they are able to indicate any patterns or outliers. The removal of outliers from the dataset is a very important task as outliers often affect the accuracy of the model if they are allowed to remain in the data set. As the saying goes: Garbage in, garbage out (GIGO)!

Once the data has been cleaned, the analyst will try to make better sense of the data. The analyst will plot the data using scatter plots (to identify possible correlation or non-linearity). He will visually check all possible slices of data and summarise the data using appropriate visualisation and descriptive statistics (such as mean, standard deviation, range, mode, median) that will help provide a basic understanding of the data. At this stage, the analyst is already looking for general patterns and actionable insights that can be derived to achieve the business goal.

Step 3. Analyse the data
At this stage, using statistical analysis methods such as correlation analysis and hypothesis testing, the analyst will find all factors that are related to the target variable. The analyst will also perform simple regression analysis to see whether simple predictions can be made. In addition, different groups are compared using different assumptions and these are tested using hypothesis testing. Often, it is at this stage that the data is cut, sliced and diced and different comparisons are made while trying to derive actionable insights from the data.

Step 4. Predict what is likely to happen
Business analytics is about being proactive in decision making. At this stage, the analyst will model the data using predictive techniques that include decision trees, neural networks and logistic regression. These techniques will uncover insights and patterns that highlight relationships and ‘hidden evidences’ of the most influential variables. The analyst will then compare the predictive values with the actual values and compute the predictive errors. Usually, several predictive models are ran and the best performing model selected based on model accuracy and outcomes.

Step 5. Optimise (find the best solution)
At this stage the analyst will apply the predictive model coefficients and outcomes to run ‘what-if’ scenarios, using targets set by managers to determine the best solution, with the given constraints and limitations. The analyst will select the optimal solution and model based on the lowest error, management targets and his intuitive recognition of the model coefficients that are most aligned to the organisation’s strategic goal.

Step 6. Make a decision and measure the outcome
The analyst will then make decisions and take action based on the derived insights from the model and the organisational goals. An appropriate period of time after this action has been taken, the outcome of the action is then measured.

Step 7. Update the system with the results of the decision
Finally the results of the decision and action and the new insights derived from the model are recorded and updated into the database. Information such as, ‘was the decision and action effective?’, ‘how did the treatment group compare with the control group?’ and ‘what was the return on investment?’ are uploaded into the database. The result is an evolving database that is continuously updated as soon as new insights and knowledge are derived.

Challenges in Business Analytics

Today, data is everywhere (internet, emails, sms, linkedin, twitter, facebook, online magazines, online newspapers, online journals, etc) and easily accessible and the result is ‘Big Data’ (data in a variety of forms, of different sizes and complexities). With many trillions of click stream records being generated every second, and aggregated to records with thousands of attributes, massive data and databases are being built. But how do we make sense of the massive data? There is a clear need and increasing demand for business analytics techniques to find patterns in the data and to present these in a simple way and at the same time display insights.

One of the challenges today is, in the past, business users relied on statisticians to analyze the data and to report the results. And, the results typically raised more questions and generally it took a long while, sometimes months, before business users could actually act on the results. Business users, while being domain knowledge experts in their particular areas, are not likely to be experts in statistics and business analytics.

Business users are also placing more demands on IT for more information in near real time. For organizations to better understand and improve the results of their business processes along one or more dimensions, organizations need data science teams which consist of experts in business analytics, domain knowledge and IT. Another key challenge for organizations is the transition of analytics onto mobile devices. Leading organizations have started to store data for mobile distribution.

With the growing variety in data, organization’s need to determine the types of data they want to analyze. It is also critical for organization’s to be able to leverage on both structured & unstructured data.  The major challenge that organization’s face in today’s competitive environment is to identify which data is relevant and useful and then to transform the data into useful knowledge for business decisions.

Organizations need to have tools or roadmaps to put their analysis results and insights into use for actionable decision making. Organizations also need to share information throughout their enterprise, so that analytics can be used in a proactive way, to grow profits and streamline operations in the hope of gaining a competitive advantage over their competitors.

Is there a difference between Business Analytics, Business Intelligence & Statistical Analysis?

Today, businesses are being challenged by the amount of data they have, as they don’t quite know what to do with the data. Further, there is a short supply of skilled business analytic talent.
Recently, there has been an increased demand for Business Analytics, and this will continue to rise. Many today are also confused about the difference between Business Aanlytics, Business Intelligence and Statistical Analysis.
Business analytics is all about turning data into information and information into knowledge thus helping a business to take the right decisions. Business Analytics encompasses 5 key areas:
1. Business analytics is all about what is likely to happen in the future.(Prediction)
2. Business analytics is all about using historical data to forecast future values (Forecasting)
3. What is the worst that can happen? What is the best that can happen? (Simulation)
4. How should we take action? How will we measure the impact of our actions? (Optimisation)


 5. Which action will result in the most ROI? (Optimisation)

While Business Intelligence (BI), is about providing a platform that displays historical and current views of  the business operations in the form of  cubes, reports, dashboards, etc.

Statistical Analysis are the methods applied to business problems to make sense of the data. Statistical analysis is all about better understanding your data using graphical displays and summary statistics. It is useful for making comparisons between two or more groups and determining whether one variable is related to another. Statistical analysis also helps to identify drivers of performance.

What is Business Analytics?

What is Business Analytics?

What is Business Analytics?

•Business analytics is all about what is likely to happen in the future.(Prediction)
•Business analytics is all about using historical data to forecast future values (Forecasting)
•What is the worst that can happen? What is the best that can happen? (Simulation)
•How should we take action? How will we measure the impact of our actions? (Optimisation) 
•Which action will result in the most ROI? (Optimisation)

What’s new about Business Analytics?

What is Business Analytics?The year 2012 certainly increased the importance of business analytics…. what’s new about business analytics? Business Analytics is not new. To me, it’s simply statistics applied to relevant business data, providing solutions to business needs in a timely manner.

The actual techniques (decision trees (1950s), logistic regression (1970s), neural networks (1950s), etc) used in business analytics has been used for many years. So, why all the hype?

With the speed at which new information technologies are being introduced, information is becoming more readily available to users. So many business users are demanding information near real time if not real time.

Research has shown that organisations who apply business analytics in their organisations, gain competitive advantage over their competitors and are more likely to be the leaders in their industry.

One of the key reasons why there is so much hype, is because, business analytics has a strong mathematical/statistics component and not many people have these skills. There is a huge shortage of business analytics talent globally. With this, many organisations are looking at automating their business processes & analytics so that information & insights are derived faster and businesses are able to proactively make scientific business decisions and not make decisions on gut feel.

I think we are living in exciting times. Statistics was not so long ago the most dreaded subject on earth and now,…., many are hungry to learn more about what is statistics, shat is business intelligence, what is business analytics, what’s the difference?…Stay tuned

Why all the hype about Business Analytics?

Business analytics is the new kid on the block. But what is business analytics????

In my opinion, business analytics is simply ‘statistics applied to business data, deriving insights in a timely manner so that business users can be proactive and make scientific decisions instead of making business decisions on pure gut feel.

With the speed at which information technology is changing, many people have access to data very easily, and in fact, many business users are demanding information and business insights in real time or almost real time. With the result that many hand held technologies that provide business insights in real time are becoming the order of the day.

Businesses are now being challenged to get up to speed in using Business Analytics. Some of the key challenges are:

1. Shortage of business analytics talent

2. Data Quality Issues

3. Centralised Database for all business users

4. Need for mobile devices to keep business users up to date with business insights

5. Identifying and implementing effective information systems that can automate  business processes and business analytics insights in real time to users

There are of course many more challenges, so what are organisations doing to get up to speed with the ‘nuts & bolts’ of business analytics? Do organisations know how to approach the whole business of getting on board with Business Analytics?