How can we make it easy for computers to process data? group of answer choices

The world of computing is full of buzzwords: AI, supercomputers, machine learning, the cloud, quantum computing and more. One word in particular is used throughout computing – algorithm.

Table of Contents Show

Transformation
Machine learning
What is data processing?
Six stages of data processing
1. Data collection
2. Data preparation
3. Data input
4. Processing
5. Data output/interpretation
6. Data storage
The future of data processing
From data processing to analytics
Become a data processing master.

In the most general sense, an algorithm is a series of instructions telling a computer how to transform a set of facts about the world into useful information. The facts are data, and the useful information is knowledge for people, instructions for machines or input for yet another algorithm. There are many common examples of algorithms, from sorting sets of numbers to finding routes through maps to displaying information on a screen.

To get a feel for the concept of algorithms, think about getting dressed in the morning. Few people give it a second thought. But how would you write down your process or tell a 5-year-old your approach? Answering these questions in a detailed way yields an algorithm.

Input

There are many variables to consider when choosing what to wear. Chris/Flickr

To a computer, input is the information needed to make decisions.

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When you get dressed in the morning, what information do you need? First and foremost, you need to know what clothes are available to you in your closet. Then you might consider what the temperature is, what the weather forecast is for the day, what season it is and maybe some personal preferences.

All of this can be represented in data, which is essentially simple collections of numbers or words. For example, temperature is a number, and a weather forecast might be “rainy” or “sunshine.”

Transformation

Next comes the heart of an algorithm – computation. Computations involve arithmetic, decision-making and repetition.

So, how does this apply to getting dressed? You make decisions by doing some math on those input quantities. Whether you put on a jacket might depend on the temperature, and which jacket you choose might depend on the forecast. To a computer, part of our getting-dressed algorithm would look like “if it is below 50 degrees and it is raining, then pick the rain jacket and a long-sleeved shirt to wear underneath it.”

After picking your clothes, you then need to put them on. This is a key part of our algorithm. To a computer a repetition can be expressed like “for each piece of clothing, put it on.”

Output

The last step of an algorithm is presenting the output. Eternity in an Instant/Stone via Getty Images

Finally, the last step of an algorithm is output – expressing the answer. To a computer, output is usually more data, just like input. It allows computers to string algorithms together in complex fashions to produce more algorithms. However, output can also involve presenting information, for example putting words on a screen, producing auditory cues or some other form of communication.

So after getting dressed you step out into the world, ready for the elements and the gazes of the people around you. Maybe you even take a selfie and put it on Instagram to strut your stuff.

Machine learning

Sometimes it’s too complicated to spell out a decision-making process. A special category of algorithms, machine learning algorithms, try to “learn” based on a set of past decision-making examples. Machine learning is commonplace for things like recommendations, predictions and looking up information.

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For our getting-dressed example, a machine learning algorithm would be the equivalent of your remembering past decisions about what to wear, knowing how comfortable you feel wearing each item, and maybe which selfies got the most likes, and using that information to make better choices.

So, an algorithm is the process a computer uses to transform input data into output data. A simple concept, and yet every piece of technology that you touch involves many algorithms. Maybe the next time you grab your phone, see a Hollywood movie or check your email, you can ponder what sort of complex set of algorithms is behind the scenes.

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Without data processing, companies limit their access to the very data that can hone their competitive edge and deliver critical business insights. That's why it's crucial for all companies to understand the necessity of processing all their data, and how to go about it.

What is data processing?

Data processing occurs when data is collected and translated into usable information. Usually performed by a data scientist or team of data scientists, it is important for data processing to be done correctly as not to negatively affect the end product, or data output.

Data processing starts with data in its raw form and converts it into a more readable format (graphs, documents, etc.), giving it the form and context necessary to be interpreted by computers and utilized by employees throughout an organization.

Six stages of data processing

1. Data collection

Collecting data is the first step in data processing. Data is pulled from available sources, including data lakes and data warehouses. It is important that the data sources available are trustworthy and well-built so the data collected (and later used as information) is of the highest possible quality.

2. Data preparation

Once the data is collected, it then enters the data preparation stage. Data preparation, often referred to as “pre-processing” is the stage at which raw data is cleaned up and organized for the following stage of data processing. During preparation, raw data is diligently checked for any errors. The purpose of this step is to eliminate bad data (redundant, incomplete, or incorrect data) and begin to create high-quality data for the best business intelligence.

3. Data input

The clean data is then entered into its destination (perhaps a CRM like Salesforce or a data warehouse like Redshift), and translated into a language that it can understand. Data input is the first stage in which raw data begins to take the form of usable information.

4. Processing

During this stage, the data inputted to the computer in the previous stage is actually processed for interpretation. Processing is done using machine learning algorithms, though the process itself may vary slightly depending on the source of data being processed (data lakes, social networks, connected devices etc.) and its intended use (examining advertising patterns, medical diagnosis from connected devices, determining customer needs, etc.).

5. Data output/interpretation

The output/interpretation stage is the stage at which data is finally usable to non-data scientists. It is translated, readable, and often in the form of graphs, videos, images, plain text, etc.). Members of the company or institution can now begin to self-serve the data for their own data analytics projects.

6. Data storage

The final stage of data processing is storage. After all of the data is processed, it is then stored for future use. While some information may be put to use immediately, much of it will serve a purpose later on. Plus, properly stored data is a necessity for compliance with data protection legislation like GDPR. When data is properly stored, it can be quickly and easily accessed by members of the organization when needed.

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The future of data processing

The future of data processing lies in the cloud. Cloud technology builds on the convenience of current electronic data processing methods and accelerates its speed and effectiveness. Faster, higher-quality data means more data for each organization to utilize and more valuable insights to extract.

As big data migrates to the cloud, companies are realizing huge benefits. Big data cloud technologies allow for companies to combine all of their platforms into one easily-adaptable system. As software changes and updates (as it does often in the world of big data), cloud technology seamlessly integrates the new with the old.

The benefits of cloud data processing are in no way limited to large corporations. In fact, small companies can reap major benefits of their own. Cloud platforms can be inexpensive and offer the flexibility to grow and expand capabilities as the company grows. It gives companies the ability to scale without a hefty price tag.

From data processing to analytics

Big data is changing how all of us do business. Today, remaining agile and competitive depends on having a clear, effective data processing strategy. While the six steps of data processing won’t change, the cloud has driven huge advances in technology that deliver the most advanced, cost-effective, and fastest data processing methods to date.