As villains continuously strive hard to demoralize the hero in your favorite movies and books but never succeeds, similarly, the wrong and bad data should not be able to weaken your success. Poor verdicts made on obsolete and inaccurate data can spoil your success at the most crucial time of the year. Nevertheless, don’t let bad data panic you as data cleansing tools can help you take control of your data.
It is vital for businesses to have an updated database, both for efficient contact with their customers and to maintain compliance standards. Data cleansing tools help you recognize and correct inaccurate data from a data set. Data has a huge prospective to transform your business and reveal new opportunities. Only good data can shape your business for the better. When you make business decisions, you need correct and updated information. Having confidence in the quality of your data, you can boost efficiency and improve your bottom line. That’s why using data cleansing tools is important for capturing organized data.
Data Quality Improvement – What Does It Entail?
Improving data quality entails small steps such as to make sure that emails are sent to the right addresses. Most businesses still struggle with these types of problems as well as negative retention rates. Minor details such as inaccurate email, address, contact numbers or names might seem less than important, but they can actually damage your bottom line. So, how can you improve data quality, is a question that managers constantly deal with.
Accurate data lies at the heart of the strategic, tactical and operational steering of every business. Having proper data cleansing solutions in place directly link with a company’s ability to make the right decisions, thereby assuring its success. Making the right decisions at the right time, in front of the right audience can help drive the desired customer behavior. Doing so is not only about capturing more data, it’s about capturing data that can drive better decisions. Nowadays, marketers are well aware that data is the cornerstone of any effective marketing campaign. It’s essential for customer acquisition, retention, and increasing overall relationships. As we move into 2018 and beyond, data-driven marketing is getting bigger and better and more integrated. Meeting the data challenges means employing the right resources and data cleansing tools to help you uncover valuable data.
How Do We Get Bad Data?
In large business firms, information is often dispersed across different sectors, processes, and applications. This result in partial, incorrect and with varying information. When data isn’t settled because of one system and detached departments, errors in entries proliferate. What’s more worse is that modernizing the data in one system doesn’t constantly mean it gets rationalized in every other system in the organization. All of this makes a lot of possibly bad and unpredictable data.
In case you are not certain if the data you hold is correct, it’s hard to make the best choices. Unreliable data could outcome in undesirable decisions every day leading to financial harm. Moreover, without cleaned data, it’s difficult to go deeper into data analytics to discern unexploited opportunities. Those lost prospects could be the costliest blunders of bad data.
Consequences of Bad data
Bad data can result in bad analytics which might lead to bad business decisions. It’s important to fix your data quality now so it won’t be able to sway your business in the future. The potential consequences of bad data include:
The major consequence of bad data is the possibility you take while making important decisions. In case your data contain errors or missing information it means that the analysis will be utterly wrong. Let say, if you are running details and it tells that all of your sales are coming from cold calls, you are going to make hiring decisions according to that information. But you don’t understand that you are using incorrect data – in reality, most of your leads are coming from advertising emails. Since the bad data showed you the wrong information, you ultimately ended up making the wrong decision.
As the bad analysis is absolutely dangerous for your business, poor quality means that you don’t have accurate data to analyze. If you don’t possess the data you need in the first place, you can’t try to evaluate it for sales trends or analytics. Data cleansing tools can help you handle the missing values. The sources of missing data can be:
- Data was not entered
- Data was lost while transferring
- Programming error
Things To Know Before Data Cleansing Process
There are certain things you should know before applying data cleansing tools or solutions. You need to be aware of data quality factors and the standards of data cleansing. The major data quality factors are:
- Accuracy – this factor deals with conformity, which decides whether a specific data set beats the standards of true value.
- Validity – it outlines whether a specific dataset comes within the valid region of data characteristics. This factor contains different data checks such as type, mandatory, unique, etc.
- Completeness – it describes the completeness of the requested data set. This is the most threatening factor against data cleansing. It is not possible to clean an incomplete data set.
- Reliability – it outlines how reliable the data set is. It will be able to prove its character with other databases.
Now that you are aware of the importance of data quality for better decision making, make sure to use data cleansing tools and solutions. Data cleansing should be performed at least once a month so that your business decisions are made on high-quality and accurate date. Else, messy data causes problems that are hard to fix. Data cleansing solutions can help you recognize and convert different opportunities into success factors while overcoming the obstacles. These solutions and tools can help you make more profits, and can influence the insights into improved advertising and sales. Make the correct investment for your business and spend on data cleansing solutions.