Data processing

5 Data Processing Challenges Resolved for Data Scientists

Data processing is an essential step in analyzing large volumes of data and get the right insights to make key business decisions. Not only are data scientists expected to find ways to wade through the vast volume of data that businesses are receiving every day, but they also have to put it together in ways that make sense to the business. The most important processes in business usually come with their own unique set of challenges, and data is no exception.

The sheer volume of data is so high that companies must define what is important to them and how they can use it to their advantage. Finding the right sources of data collection is the initial challenge faced by data scientists, followed closely by dirty data (36%) and explaining data science to others (16%).

Major Data Processing Challenges Faced by Data Scientists and How to Resolve Them!

The primary goal of data science is to use data to provide businesses with useful insights, but that often comes with several challenges. The biggest mistakes in data all stem from the data pre-processing because so many businesses try tapping into all data rather than focusing on key metrics. This eventually leads to data overload, which cascades into other issues like dirty data and storage issues.

Here are the top 5 major data processing challenges and how to address them.

1. Ensuring that the Right Data is Being Collected

A company’s entire data strategy hinges on the way data are collected, which makes the data acquisition phase crucial. Due to the large volume of data that consumers are leaving, the sheer velocity in which data hits companies is overwhelming. In order to drive proper decisions that improve the business, they need to focus on collecting information pertaining to key metrics. Companies that try to take in too much data are going to become crippled by it, leading to a symptom known as data paralysis.

Ensuring that the Right Data is Being Collected

Businesses must equip themselves with the right tools to make the most of the data that’s important to their business. By focusing on these key sources, businesses spend less money and make the collection process much easier. Collecting the right information will become the foundation for a company’s entire data management system.

2. Collecting Data from Multiple Sources

Once the foundation has been established and the load lessened, the focus shifts to the actual data acquisition processes. With data coming in from all kinds of different sources, the challenge of putting it all together can be somewhat overwhelming. In fact, there are so many businesses that are compiling data from different sources manually. This leads to either incomplete or inaccurate insights.

Collecting Data from Multiple Sources

The solution is to develop a comprehensive system that is designed to provide access to all data sources from a single location. These systems will allow data to automatically be compared across sources, verifying its validity. Data science for business can be used to create this kind of centralized system, and often, it’s a good idea to bring in an expert to help at least get the ball rolling.

The key is to ensure that data is being driven into your businesses in a way that is organized and makes it easy for employees to find what they need.

3. Dealing with Unstructured Data

Not all data is going to fit nicely into a database or filter flawlessly into the right categories. Of course, structured data like names and addresses is nice, but the real power of data is locked away behind unstructured data such as social media posts or Twitter conversations. This is where technology starts to play a huge role in the outcome of a business.

Dealing with Unstructured Data

Unstructured data comes with three unique challenges.

It all starts with ensuring that the data being collected is relevant. A real estate business collecting unstructured data from a car mechanic’s social media page is not going to be helpful. Data must be meaningful for the business in question.

Filtering through relevance will naturally funnel down to help with the next challenge, which is managing data volume. The sheer volume of this data requires a rather large infrastructure that some businesses are not equipped to deal with.

Finally, the data must be usable, so businesses will need to be able to organize and store it for easy access. Often, a data scientist will put the data through a vigorous data cleansing process as it’s stored. That way, the data is usable from the moment it hits the database.

4. Ensure Data is Stored Securely and Efficiently

The next data analysis tip reveals what to do with the data once it’s been gathered. However, businesses must navigate the previous three challenges before they reach this point. Storage is dependent on the entire collection process having a stable foundation.

Ensure Data is Stored Securely and Efficiently

With that said, the biggest issue with data storage lies in the infrastructure. Most businesses will underestimate the sheer volume of data they’ll collect, thus end up crippling their process right from the start. However, the advancement of cloud-based technology has made this problem much easier to navigate.

So the problem transforms into cost and security. When it comes to data, real-time processing is going to require an investment. But the right data strategy will provide a significant return on investment.

5. Distributed and Parallel Processing Infrastructure

Cloud technology plays a key role in this final challenge. In most cases, the software is installed on the user’s mobile device that is only capable of simple calculations. Due to its limited computational power, the calculations are carried out on the cloud before the information is relayed back to the mobile device.

Distributed and Parallel Processing Infrastructure

This requires a ubiquitous process known as deep learning, which requires a lot of resources. Deep learning is mostly seen right now in speech recognition and text understanding software. The biggest advantage is that its computational prowess intensifies as the network grows.

However, the biggest setback is the sheer volume of resources required and the long training times.

Data Management Partners Play a Huge Role in Meeting these Challenges

Most businesses are losing data value due to improperly integrated databases that are have been so ingrained into the business processes that even the thought of changing them is overwhelming. Just keep in mind that most companies don’t have a plan for properly managing their data. If the thought of making the transition is intimidating, then consider partnering with a data management company to help develop and implement a solid management process. Here are some of the benefits:

  • Help create a sound data management strategy to build a solid foundation.
  • Implementation that connects business goals with the proper system.
  • Optimize data so that it’s working for the business rather than against it.
  • Develop better training for employees, preparing them for the transition.

Let Data Entry Outsourced (DEO) Help Build a Solid Foundation 

Data Entry Outsourced (DEO) specializes in data structuring and on-demand data conversion and data processing, lessening the impact that businesses feel when making this important transition toward more data-driven strategies. Meet the challenge head-on by partnering with Data Entry Outsourced today!

Source: How to Deal with Data Processing Challenges


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