August 29

Why Multiple Data Sources Don’t Always Add Up To Insightful Data

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Data merging is complex and tricky. Pulling information from multiple data sources requires expertise, not to mention patience and time. There are many barriers that stand in the way of simply grabbing your data and going, and if a business isn’t careful, it could end up with a mess on its hands.

However, when you understand the risks of extracting data from various sources, you can anticipate the areas you need to examine closely.

Why Multiple Data Sources Can Be Harmful

Many businesses begin to experience serious quality issues with their data when they cast a wide net in their data search. One reason simply has to do with volume; it’s tempting to grab as much data as you can access, but it isn’t needed. In fact, it can get in your way. Too much data at your fingertips can make it hard to sift out the vital information from the unnecessary and irrelevant. It all starts to seem like white noise.

Another problem is that different data sources store data in different forms or use different methods. This can lead to a lot of issues when your company is trying to integrate it since this can cause varied interpretations, causing confusion about the facts.

You are also likely to find that you get a lot of redundant or contradicting data when you seek out multiple data sources. Most organizations find that they get a minimum of 15% duplicate data when they rely on multiple data sources. Read how important good data is to business decisions.

All of this is very time-consuming when it comes to organizing it. As a result, businesses can spend a lot of unnecessary time sorting their information.

There Are Some Pros To Multiple Data Sources

So then, why do organizations use multiple data sources? They aren’t all bad, as long as a company is careful. There can be a couple of positive reasons to get your data from various places.

For one thing, it’s healthy to ensure that your company doesn’t wind up with a biased outlook on an issue because its data was one-sided. A well-rounded look at information is imperative when making any business decision, so companies must take in all the facts, not just the ones they like.

Multiple data sources are, of course, a way of getting more information. However, when an organization assesses what data they have on hand, they may spot gaps in their information. That’s when it is critical to seek out the missing data so you can complete the picture.

What Are The Greatest Challenges With Extracting and Merging Multiple Data Sources

About one-third of respondents to one survey indicated that “integrating multiple data sources” is one of the most significant challenges with data handling and data analytics. Even experienced data analysts agree that taking your data from many sources is a headache and not always beneficial. So, let’s take a closer look at why it can be such a complex issue.

Data Extraction

Data extraction is the act of pulling information from one or more sources with the intention of using it someplace else. However, when your sources use different formats for storing their data, confirming that data can be quite an investment; it’s complex, tedious, and labor-intensive.

However, data extraction is quickly becoming a booming business as it grows in popularity. As a result, the rate at which data is extracted is projected to more than double in this decade. 

Data Integrity

Data integrity means that your information is complete, relevant, reliable, and wholly up-to-date. Businesses always want to ensure the integrity of their data, of course; otherwise, it means nothing to them. Using incorrect or insufficient data has ripple effects. It spoils all the other data based on that wrong information and means that any decision-making based on it was not well-informed.

Considering that the average database has 25% misinformation, using multiple data sources increases the odds of pulling bad data.

Scalability

Scalability refers to a quickly growing volume of data within a database or an increase in traffic.

Typically, when merging data from multiple sources, an organization pre-determines how many sources to use and what kind they’ll be. Unfortunately, this leaves no room for growth; your data needs to grow as your business does. Therefore, it’s essential to leave room to expand. 

When you think about the fact that the amount of data that will be generated in the next three years will amount to more than all the combined data from the previous thirty years, it kind of makes you want to ensure you have room for that increase, doesn’t it?

Data Heterogeneity

Heterogeneous data is data that’s vastly different in structure, format, and type. When different data sources use a variety of storage systems and formats, you can nearly drive yourself crazy trying to untangle it all. This is one of the biggest headaches in all of employing multiple data sources. Not only do you need to pull this data, but then you need to standardize it to a uniform structure. It’s a little like translating from one language to another.

However, if the average Fortune 1000 business made data even just 10% more accessible, that would result in $65 million more annually.  

Duplicate and Conflicting Data

When you have too many chefs in the kitchen, you have too many competing hands trying to do the same job. Extracting data from multiple data sources is a little like that. 

Many sources will have the same data, so why waste time pulling the same information over and over again? This muddies the waters. Plus, some sources may have the same information but are coded in a different way. This can lead you down a path chasing after what you think is new data, only to discover it’s the same. For example, 45% of business leads are bad because of repeat or incomplete data.

However, it’s worse when the sources measure the same thing and get different results. Yikes! How do you determine which is right, or if either is? 

Conclusion

Any experienced data person can tell you that you’ve got your work cut out for you when you’re merging multiple data sources. Don’t allow too many inconsistencies to frustrate your work. The more sources you employ, the longer the process may take since that’s more to extract, convert, and incorporate. It isn’t wrong to use a variety of sources, but it’s helpful to understand the difficulties ahead of time. So grab a cup of coffee; you may need the sustenance.

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