Articles

Data democracy is spread across the globe now. These capture many new territories every single day for being full of insights and intelligence, which actually attracts opportunities. Simply put, the prospects are closely connected to information underlying databases. You can figure out what changes you need, where to focus more and which initiatives can actually blow you up with their profit multiplication capacities.
    
    

In short, you have millions of opportunities underlying data collection services to steal the limelight like Netflix, Uber and Amazon that are leading because of their stronghold on a massive set of data, containing customers and performance insights.
    
    

High-Quality Data Wins Opportunities:
    
    

Here, high quality stands for the meaningful and contextual data. The data collection companies have highly qualified programmers and sophisticated web scraping tools like Octoparse or scraper to extract whatever information they require. They drill holes through APIs and take away whatever is meaningful for them, which is later integrated to data mining funnel for reaching artificial intelligence or machine learning algorithms.
    
    

This funnel is set to hit the pre-defined target, such as finding patterns that are closely interlinked with sales or purchase or customer behavior or trend. The methods like association, relational analysis and decision tree are opted out for it. Mapping efficiency and evaluating performance have never been as easier as these are today. These are data collection methods and analysis that have made it possible. And behind the scene, it’s the relevancy of data that qualifies for winning an edge.
    
    

What if we compromise on its quality as happened in 2017, which led to the loss of whopping $15 million to the world economy? Certainly, it would be a big discrepancy that can push us to bankruptcy.
    
    

Here is a roundup of three steps you ought not to skip when it comes to map data quality after collection.
    
    

1. Data Ingestion
    
    

A bad data can lead to bad decision. It’s a big mess if the fundamental of decision has something irrelevant or inconsistent. Therefore, the process of transmitting data from assorted resources to the cloud or warehouse, where these are accessed, deeply analysed and harnessed for decision making, should be infallible. This is also called data ingestion.
    
    

Just imagine a scenario wherein a manual data entry was not made correctly. Consequently, the wrong entry went into mining funnel, where it changed the whole decision. Think what if you enter 10 in place of 100 or just 1 in place of 1K. These missing values and inconsistencies make data redundant.

If you don’t have expertise, you have two options to get flawless data entries. These are:
    
    

· Automation Data Entry Procedure
    

Automation ensures transmitting data into warehouses, servers or the cloud with consistency. This is why many organizations prefer automated tools to copy and paste datasets through predefined rules and functions in an acceptable range and with quality.
    
    

· Outsource Business Data Processing
    

If you are a naïve, outsourcing data processing services can leverage you, as these are some exclusive services dedicated to only data solutions. Therefore, you won’t need to take care of quality, as they already check it twice before delivery. Besides, you save millions of dollars, preventing overhead expenses on such profiles that you don’t have any idea how to deal with.
    
    

2. Optimise Data Storage
    
    

Storage is something that you need to be extra careful about. This is where amassed of high-quality data is collected. But, you have put it there as a complete mess. No proper organization at all! How can you retrieve something valuable on an urgent note from it?

It will be way more difficult to break data silos and access what is needful then. Even, you cannot get where the inconsistent data are. What if you need to measure them up for tapping patterns that can contribute to achieving breakthroughs? You cannot do it. This is why optimizing and well structuring of databases are crucial.
    
    

3. Maintenance Is The Key
    
    

Like health checks, data also needs your attention. These need auditing over and over, be it at the month end or every weekend. This is how you can weed out irrelevant entries and watch on validity. If you skip this vital step, your data collection will suffer quality loss. This would eventually bring some serious implications, which can be related to your marketing campaigns or sales strategy or whatever.

Therefore, data cleansing should be in place frequently to keep right kind of data occupied for contributing to neural systems of automated tools.
    
    

Besides, the governance framework should be strong enough that no oddity can creep in, destroying validity, standardization and integrity of data. Decide authority to access and manipulate apart from executing guidelines to keep up the quality in databases.