dirty data business

Impacts of ‘dirty’ Data on Business

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The world is producing data at exponential rates. If it continues then by the year 2025, the global data sphere will include 10 times the amount of data generated last year alone.

Many business firms suffer from analytic paralysis without an input of quality data. They never use the analytic to clean the data errors. Those data errors are called ‘dirty’ Data.

‘Dirty’ data really offers no good to an organization. It is basically the data that contains erroneous information. It has been costing companies millions of pounds each year. As a matter of fact, dirty data costs organizations almost £500 billion each year, based on the reports from The Data Warehousing Institute (TDWI).

Maintaining the quality is very important for any business firm. It is important for organizations to practice data management. Dirty data can really be inevitable.

Types of ‘dirty’ data that can bring harm to business.

  1. Incomplete data

Incomplete data is the most common event of dirty data as well as important fields of master data records, useful for the business, are often left blank. Business operations cannot move on to the next step if the data are incomplete. For example, if firm hasn’t classified their customer data by industry, they cannot segment their sales and marketing initiatives by industry.

  1. Duplicate data

Most of the companies face issues like duplicate customer records, but duplicate materials are also very common. This can be costly to companies due to excess in inventory and sub-optimal procurement decisions. In contrary to lack of data, there is a duplicate data. Most duplicate data involves customer profiles, redundant values in the database, and repetitive elements. This can be costly for organizations especially when they are performing an inventory.

  1. Incorrect data

Incorrect data occur when field values are created outside of the valid range of values. There were situations when the given data are incorrect. Incorrect data can really affect the data quality. For Example, the date and address which are given on the website are incorrect than the data recorder will record the incorrect data.

  1. Inaccurate data

Apart from incorrect data, some data are factual; however, there are certain elements that are inaccurate. It is possible for data to be technically correct but inaccurate given the business context. Inaccurate data mostly occur in the address. It can cost businesses a lot. For example, minor errors in customer addresses can result in deliveries at the wrong locations or vice versa.

  1. Inconsistent data

Redundancy of data results in inconsistency. Data redundancy the same field values stored in different places-often leads to inconsistencies. This error happens mostly with profile names. These redundant elements may decrease efficiency in business operations. Moreover, it also leads to wrong actions.

  1. Business rule violations

Business rule violations are dirty data that occurs when certain enterprises follow varied data procedures and formats. Most common examples for this are units of measurement, currency, address and date format, etc.

‘Dirty’ Data bring harm to Business

Having a bulk of data is commendable, but only if the business firm has the means and the mindset to keep a high bar on data quality. That goes only for timely, relevant and accurate information. Dirty data can freeze the firm’s revenue growth, cut down business reputations, and cause operational inertia. Sales teams may be forced to waste 50% of their time looking at, verifying and correcting data.

Dirty data may lead a business value to a dead-end or even worse, it can have a negative impact on business value. Dirty data can cause minor problems or be catastrophic. A catastrophic problem would be losing a customer or having to take a major financial write-off due to inventory problems. Less of an impact is carrying too much inventory. Even less of an impact is an invoice going to the wrong department at a customer site but the customer routing it correctly to fix your mistake, again and again. The impression you leave with the customer is that you are out of control or that you do not care.

Dirty data can bring more harm than just operational hiccups. For example, wrong diagnostics can kill patients, wrong test data forced airbag manufacturer Takata to eventually file for bankruptcy.

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