Preventive Measures to Dodge Dirty Data
Bad list, natural data decay or single source or multiple source problems? Yes I know you would think of all these for your “non-functioning marketing strategy”. Well your reasons do validate why your strategies don’t succeed; but what are you doing to address it? Dirty data is one of the biggest problems for companies globally. There you are, drowning in dirty data; while your competitors stand the test and have reached heights by cleansing it.
Unfortunately, organizations still use manual data quality checks as their primary way of ensuring data’s accuracy. Considering that one of the major causes of dirty data is human error, this obviously is a really bad move. In this article we will discuss some preventive measures to fix the dirty data issues.
Data being incorrect has got several reasons like poor communication between humans stapled with it. Human errors are major contributors to towards dirty data. Talking about overall data management, employees would blame it on poor data management strategies. Some organizations surprisingly even claim that they are not equipped with advanced systems or software and special tools to manage data cleansing process for accuracy. With this dirty data, company’s belief of situations getting challenging turns out to be true.
Concerns with Dirty data
The velocity, one of the three “V”s, at which the data is generated and stored across organizations and the drift it creates from making data driven decisions; is completely unimaginable. Businesses depend on data and its processing as they critically want to access data and predict insights based on it. For few businesses like retail and eCommerce it is a mandate to rely completely on real time data but since the data is incomplete, inaccurate and erroneous they face extensive loss because of dirty data.
It’s a proven fact that dirty data can have a serious and negative impact on businesses. Gartner strongly believes that 40% of business initiatives fail to attain success, costing $14.2 million annually. The damages of working with dirty data are estimated to reach $600 billion a year for companies in United States alone, reports TDWI.
If we keep these numbers aside, dirty data leads to lower efficiency, which no company can afford in this hyper-competitive global business ecosystem. It lowers everyone’s confidence in decisions, management, marketing, products and services. It makes missed opportunities and reputational damage, a routine for organizations. It’s time CEOs, CTO, CDOs and all across the enterprise should be concerned about the quality of data used to make strategic decisions and the data hygiene.
The hard truth is that there are chances you may not know how dirty your business data is; and faster you find it the better it is for your organization.
Data hygiene
Now that you know the perils of dirty data, take some preventive measures to save yourselves from failing to understand them.
Data hygiene includes:
- Standardize and automate data entry
- Do not populate the lists unnecessarily
- Monitor trends to identify opportunities for augmenting your data
- Regular monitoring of data sets
- Adhere to data cleansing schedules
- Validate them with the help of expert data professionals
Recent advances in data management has introduced technologies like machine learning algorithms and AI for seriously analyzing data and use it for identifying critical issue. It creates new visions to see data, as they provide autonomous data analytic solutions for identifying data issues. Additionally it helps in pinpointing and rectifying the root cause of dirty data and simplifies it before it becomes a serious issue for your organization.
To wrap up
With increase in data, most of your in-house data professionals are turned into data janitors – who spend hours cleaning data, instead of analyzing it for strategy and business insights. Try and accept the fact that the increase in data is humongous and beyond human capacity.
Companies can go ahead and partner data management firms who hold the expertise in managing data as an assortment of activities right from data collection and processing, to data cleansing and formatting, to conducting analytics and providing visualization dashboards. Gone are the days when companies used manual approach to cleanse and validate data.
About the author:
Hitesh Mistry is one of the key members at HabileData, contributing to lateral growth of the company since its inception. He single handedly manages data processing, customer support, marketing, administrative and people management activities in addition to handling our websites editorial responsibilities.
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