Data Anaysis is a body of methods that help to describe facts, detect patterns, develop explanations, and test hypotheses. It is used in all of the sciences. It is used in business, in administration, and in policy. Most of this information can be gained from the income reports and balance sheets of the companies involved. Pertinent pieces of financial analysis data measure earnings, debt, assets, liabilities, cost of goods sold, and much more.

Companies today have complex organizations and stretched supply chains, face intense competition, and have demanding customers. Every day, executives and their support organizations sift through large amounts of non-standardized data trying to get to "One Version of the Truth" that will enable them to keep their organizations functioning and help them make strategic decisions. Our experience tells us that many companies are not adequately prepared to address operational and strategic challenges due to their inability to manage massive amounts of data across their diverse organizations. Examples of poorly managed data include:

Outdated and inaccurate customer data hampers an organizations ability to analyze customer trends, view performance across geographical boundaries, forecast sales company-wide and rationalize its SKUs

Fragmented supplier data leads to an inability to view spend data across organizational components, sub-optimal procurement decisions, and less productive vendor relationships. Companies spend more money than required on purchases because they can’t adequately analyze what they are buying and who they are buying from.

Scattered customer information leads to poor sales effectiveness.  e.g.:  an inability to drive further sales, lower than desired customer satisfaction, less than optimal customer retention and customers scrambling to address customer service and informational requests.

Non-standardized data input hinders the performance of IT systems and data integrity

Misaligned financial information impedes strategic decisions on innovation and growth.

Dake believes, based on extensive implementation experience and research that organizations require additional focus on data improvement in order to arrive at “One Version of the Truth” and improve decision making processes. Dake further believes that companies can implement a series of business and technical improvements in order to standardize and organize data to make effective decisions.

Benefits of Data Analysis:

It helps to identify the discrepancies and minimize the risk
Increases the ability to forecast and make better decisions
 Bottom line is if you know your customer better you can serve better

Customer Data Analysis:

A typical customer database in a service organization consists of customer address, contact numbers and services used. Most often there is an inconsistency in the data available either the address is incorrect or the contact numbers. This is because when the customers change their address for some reason it is not updated in the database leading to inconsistency in the database.

Here’s how the data can go wrong: In a service based organization, address information is relied on for customer billing and communication. A single bad address can set off a ripple effect of operational consequences. For example, when customers receive bills too close to their due date or worse, not at all, the call center is flooded with service calls. When mail is returned to the office, staff members spend time researching address details and then re-working the documents originally sent to customers. Additionally, when customer bills aren’t paid on time the cash flow is impacted.

Financial Data Analysis:

In any financial data the common problems areas are – inconsistent terminology and format, data volume, transaction complexity, information variability, and financial statement limitations. These discrepancies can be reconciled with the help of data analysis.

Data analysis is also used for expenses classification, vendor classification, revenue classification etc. further it also helps to identify various data like for example – What type of revenue is more outstanding and the related service, vendors that are more expensive, manual errors like if an employee typed an extra zero on an account inadvertently, ability to forecast income and expenditure, risk factors, etc.

Consumption / Service Data Analysis:

It allows the organizations to have more accurate data in terms volumes of consumption for the services offered. More importantly it also gives inputs in terms of the consumption trends for example – usage of electricity high and low based on season, water usage is strongly dependent upon factors such as the time of year and the weather, since most water is used on gardens. Also, being able to analyze the effectiveness of the service based on the revenue and customer satisfaction.

Debtors Data Analysis:

One more analysis that can be done is the debtor’s data and this is most critical data for the organizations. Knowing who owes you is not important as knowing when it was owed and this data can be classified into 4 categories using data analysis – 1.Pending payments for the past month (30 days), 2. All those invoices that are now 31 – 60 days from the invoice date, 3. Those who did not paid you after 60 days from the invoice date, 4. Those who did not paid you for 3 months since the invoice date.  

Also, data can be classified into – type of services that are not yielding minimum required amount of revenue, payment trends based on age groups for example:

Age groups from 20 – 30 years are more defaulters; 30 – 50 years pay the bills regularly etc.

These types of data analysis / categorization will help organizations to pin point the areas of issues and take necessary actions. Having said this, data analysis will help organizations to take corrective measures / decisions which will significantly increase their revenues.

Thus, data analysis provides businesses with greater insight and relevant data that can be used to offer service in a more consistent manner.