What is data analytics

Data analytics is the process of analyzing raw data in order to make conclusions about that information. Alternatively, it is the process of inspecting, cleansing, transforming and modeling data with the aim of discovering useful information, suggesting conclusions and supporting decision making. These qualitative and quantitative techniques and processes enhance productivity and business gain and also allow us to make informed decisions disregarding guessing. The tool or software used in realizing these processes is called the analytic tool.

Why Data Analytics Matters

Data analytics is important because it helps businesses optimize their performances. Implementing it into the business model means companies can help reduce costs by identifying more efficient ways of doing business and by storing large amounts of data.

A company can also use data analytics to make better business decisions and help analyze customer trends and satisfaction which can lead to new and better products and services.

Data Analytics is no more a nice option in the running of business but has become an integral part of businesses. In this era of technological advancement and the rapid growth of AI, it has become imperative for businesses to analyze transactional data to enable them make critical decisions, identify income or revenue leakages that may go unnoticed.

Types of Data analytics

The main goal of data analytics is to help organizations make smarter decisions for better business outcomes. Data analytics is broken down into four four basics types – Descriptive, Diagnostic, Predictive and Prescriptive Analytics

Descriptive analytics

As the name implies, it summarizes raw data and convert it into a form that can be easily understood. Descriptive analytics answers the question of what happened over a given period of time. E.g. Has the number of views gone up? Are sales stronger this year than last year? This is perhaps the most used type of analytics.

Diagnostic analytics

This focuses more on why something happened. It allows the analyst to dig deeper into an issue so that they can arrive at the source of a problem. E.g. Did the weather affect beer sales? Did the latest marketing campaign impact sales?

Predictive analytics

Predictive analytics helps businesses to forecast trends based on the current events and suggests a course of action. It uses the findings of descriptive and diagnostic analytics to detect clusters and exceptions, and to predict future trends, which makes it a valuable tool for forecasting. E.g. If the likelihood of a hot summer is measured as an average of these five weather models is above 58%, we should add an evening shift to the brewery and rent an additional tank to increase output

Prescriptive Analytics

This type of analytics explains the step-by-step process in a situation. It literally prescribe what action to take to eliminate a future problem or take full advantage of a promising trend. For instance, a prescriptive analysis is what comes into play when a company is able to identify opportunities for repeat purchases based on customer analytics and sales history.

Steps to Data analytics

The process involved in data analysis involves several different steps: (Where and how did we come to the steps below)

1. Undertstand the Business / Define the Business Need This stage involves defining the need of the organization by interacting with the people in the organization whose processes or business you aim to improve with data. Clearly define the problem you aim to solve with the analysis, identify the sources of data, set metrics to track along the way.

2. Data Collection With the business need clearly defined, it’s time to collect the data that will be used in the analysis. The first step is to determine the data requirements or how the data is grouped. Data may be separated by age, demographic, income or gender. Data values may be numerical or be divided by category. Data can be taken various sources depending on the organization or the needs of the project.

Some sources of data are:

Primary source: Data can be collected from the organizations internal sources o. Usually, this is gathered from the CRM software, ERP system, Core Banking Application, marketing automation tools, and others. The IT team of the organization will make it available or setup a private database for you.

Secondary source: While it may not be required to gather from secondary sources, it is sometimes useful. Some sources include; review sites, social media APIs, economic trends APIs, google analytics, google trends, depending on the business requirements of the analysis.

3. Data Cleaning / Preparation Once you’ve gotten your data from all the necessary sources, you will have to clean and sort through it. Data preparation is crucial to the quality and accuracy of results generated. This step usually involves, purging duplicate data, removing unwanted data, masking sensitive data, restricting, and removing any other inconsistencies that could skew the analysis. This step helps correct any errors before it goes on to a data analyst to be analyzed.

4. Analyze Data After you’ve collect the data and cleaned it time to perform analysis on it. A software like Arbutus Analyzer can be used to perform analysis on the data collected. Data can be analyzed as graphs, pivot table, summary table, etc.

5. Interpret the Results The final step is interpreting the results from the data analysis. After going through all the stages of data analytics, you can make better decisions for the business or agency because your choices are backed by data that has been robustly collected and analyzed.

Importance of Data analytics:

• Helps companies to spend their advertising budget with maximum effect.
• Objective answers that can put an end to an argument
• Identify the important trends
• Identify business performance problems that require some sort of action and provide corrective measures.
• Providing data analysis in a visual manner, which leads to faster and better decisions
• Better awareness regarding the habits of potential customers
• Providing companies with an edge over their competitors

Data analytics is no longer a nice option, it’s the core of the business.

Arbutus Analyzer

Analyzer is the flagship analytics product offered by Arbutus that support desktop based data access and analytics of challenging and disparate data sources. Arbutus Analyzer is a powerful data access and analysis solution that auditors, business analysts, and fraud investigators use to access and analyze data and simply.

In some cases, you can start analyzing your data as soon as you have imported it into Arbutus Analyzer. However, in most cases, the data need to organized or worked on before analysis. The process of performing one or more preparatory tasks to the data is known as Data Prepration.

What is Data Preparation

Data preparation is the process of gathering, combining, structuring and organizing data so it can be analyzed. Simply put, it means cleaning and transforming raw data to a form suitable for further analysis and processing.

Data preparation is a lengthy process for data professionals or business users. It has been estimated that data preparation accounts for 60%-80% of time spent in data analysis project.

The quality and accuracy of results generated as a result of analysis is dependent on the quality of data preparation performed. This makes the step of data preparation very essential. It allows for efficient analysis, limits errors and inaccuracies that can occur to data during processing Hence the saying, “garbage-in garbage-out”.

Stages of Data Preparation.

Completely streamline unique resources after user friendly schemas. Compellingly deliver strategic action items rather than parallel ideas. Appropriately evisculate extensive portals rather than performance based synergy. Holisticly streamline parallel products without premier e-tailers. Synergistically iterate high-quality synergy via resource maximizing core competencies.

“Revenue assurance is about ensuring that ALL transactions for ALL events have been correctly billed without losing revenue to fraud. (, system malfunction or error)” -Pitta Satya Sai Kumar

It involves a process or software solution that enables an organisation to monitor all income heads to be sure that all revenues or charges are accurately captured by the organisation’s system for all services rendered. Instead of correcting errors after they occur, or not detecting and correcting them at all, revenue assurance enables the organisations to examine and plug dozens of actual or potential leakage points throughout the application system.

Why Revenue Assurance?

The need for a Revenue Assurance arises out of the many opportunities for revenue to be ‘lost’ either through system failure or through human error. Revenue Leakage can occur due many reasons, organisations generally have reasonably complex process and technical infrastructure with many points of data generation, input, transformation and output.

The advent of digital services and the ever-changing Technology is adding even more complexity to system process. With the complexity and often real-time nature of the environment, things can sometimes go awry in a big way. Common examples of issues contributing to revenue leakage includes:

• Errors or mis-representation in receivables and payable
• System failure to process income or charges accurately.
• Human errors and mistakes.
• Incorrect service parameter and charging definitions…etc.

In fact, the majority of risks and issues of revenue leakages are rooted in human error and system process failure. Although technical challenges do present issues, these often stem from incorrect specification or configuration of charges or income in the system.

Resources that would otherwise be engaged in generating revenues maybe lost due to one of the above stated reasons.

Impact levels of revenue leakage will vary depending on the nature, scope and sophistication of the controls deployed. Even with Risk Assurance functions and processes in place, poor design and operation can lead to more significant exposure. Organizations have recovered millions of potential revenue leakages after performing Revenue Assurance exercise within their organisation.

Now, due to competitive pressure companies are beginning to focus on internal tightening of their process to stop revenue losses. Revenue Assurance process is one of the simplest and easiest ways to stop revenue leakages.

Send me a message if you are interested in conducting a revenue assurance exercise for your organisation or you need more information. Connect with me and lets discuss further. You never know what your company is loosing in terms of revenue until you perform this all important exercise...

Get a free Arbutus Evaluation License for Revenue Assurance