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How to Mitigate the Issues That Big Data Presents

While many organizations strive to utilize information for their benefit in things like testing metrics, there are numerous problems that they can encounter in the process. Let's take a look at some of the biggest issues big data presents and how to mitigate them.

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Mobility and the Internet of Things have made our world more connected, and with sensors built into everyday objects, more data is being generated than ever. In fact, 2.5 quintillion bytes of data are generated every day, according to VCloudNews. While many organizations strive to utilize this information for their benefit in things like testing metrics, there are numerous problems that they can encounter in the process. Let's take a look at some of the biggest issues big data presents and how to mitigate them:

1. Overwhelmed systems

One of the most obvious problems is that there's just not enough space or resources for organizations to house and manage the increasing load of information. To make matters worse, most of the data is kept in silos and is highly unorganized. IBM's Big Data & Analytics Hub contributor Tom Groenfeldt noted that it can be easy to miss important insights when there's so much data in spreadsheets, and individuals spend a lot of time cleaning up the information and integrating it with other sources. Doing all of this by hand has a lot of drawbacks including the substantial loss of time and the high likelihood of human error.

The problem is further compounded by the fact that most analytics tools are outdated and cannot handle the onslaught of information inherent in big data. Organizations need to ensure that they constantly have the most updated information, otherwise, it may not be useful anymore and can waste resources trying to be incorporated into gathered insight reports. Teams must ensure that they have the software development tools and infrastructure available to capably adjust to the scale and type of data inflow.

2. Privacy concerns

Sensors built into objects like fridges, smartwatches and more are tracking every move that is made. For many users, this brings up a slew of privacy concerns, especially since it may not be clear what companies are actually using this information for. TechTarget contributor Lynn Goodendorf noted that some organizations may sell consumer behavioral data to help other businesses improve their marketing and sales initiatives. This type of effort is why more retailers are using geolocation capabilities to send push notifications to smartphones when a customer is walking around the store. These alerts often signal when someone is near a sale for a certain product and can even customize ads based on that user's previous behavior and transactions.

Obviously, this is a major problem that businesses must address in the years to come, but there is one solution that will help alleviate concerns. Goodendorf suggested that rather than forcing consumers to participate unknowingly, simply encourage and invite them to take part in the big data process and IoT testing. They can do this through reviews, enabling customers to verify that the data is correct. It's also important to issue out privacy disclosures on applications and websites to ensure that users understand what data is being collected and how it's being leveraged.

3. Industry and legal regulations

In sectors like health care and finance, there is a great deal of personally-identifiable information including patient records, credit card data, and other documents. If someone is using a Fitbit for their health, they may share this information with their doctor, but it's also important that it's protected under industry rules. Inside Counsel contributors Jennifer Rathburn and Simone Colgan Dunlap noted that violating privacy and compliance laws related to this data could result in significant fines and criminal penalties. Organizations must address these concerns and constantly evaluate whether their practices are in line with compliance efforts.

4. Analysis

As mentioned in point one, many organizations have outdated analytics systems, but there are also issues concerning the most important metrics to keep track of. With so much data flowing through the pipeline, teams must give careful consideration to what pieces of information are going to be critical to monitor. This effort should be constantly reevaluated to add or subtract metrics and get the results that will help improve quality. Test automation integration could be beneficial here as analytics tools will generate reports and track testing metrics without manual interaction.

Big data is a force that has already started to change businesses. There are a number of considerations to make, but by understanding the potential risks, it will be easier to prepare for and manage big data to your advantage.

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Published at DZone with permission of Francis Adanza. See the original article here.

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