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Data processing: methods in the madness

Data is everywhere. With a variety of data collection methods, storage options, and analysis protocols, how do you plan to decipher and use your data? We’ve got the scoop on the structural ins-and-outs of data processing. Trust us, it’s more dynamic than it sounds.

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It’s a mad, mad world of data that we live in, but if you know how to harness its power, data can fuel everything from your SEO to your scalability. Understanding data processing methods is no picnic but we’ve got the guide – and you guessed it, the data – to break it down into byte-sized pieces. Pun intended.

What is data?

Besides a conversation starter…

Data is information (graphs, facts, and statistics) generated by actions and transactions we make online. Try walking into an office, joining a Zoom call, or even hitting your local bar without data coming up in conversation. You might hear “I swear my smartphone is listening to me,” or “did you hear about the new compliance legislation?”

Don’t freak out. This is not a post about sentient phones or data rights (but if you’re interested, check out our posts on the GDPR and CCPA). This blog is about how to manage and process the data that runs your business.

What is data processing?

Data processing turns information collected from a variety of sources into usable information. When we talk about data processing we’re mainly talking about electronic data processing which uses machine learning, algorithms, and statistical models to turn raw data into a format that can be read by machines so that it can be easily processed and manipulated for forecasting and reporting by humans.

Remember elementary school, when you learned about reduce, reuse, recycle? Well, data processing is like recycling. Data is collected and “reduced” or broken down and processed for machine interpretation. Then, it is “reused,” or analyzed and stored for human interpretation and application. Finally, it’s “recycled”, or stored in an active database so it can be used to build more accurate projections and applications.

To fully understand the logistics of data processing, you have to view it almost as a natural resource rather than just collected information.

Image shows the data processing cycle

Types of data processing

Data can be processed in a variety of ways using different algorithms. In fact, you’re really only limited by the capacity of your infrastructure. Let’s take a quick look at the top three ways to process data.

Batch processing

In batch processing, your data is collected and processed in batches. This is the cookie analogy – you can make a giant batter of cookie dough, but you can only cook them in the batch sizes that will fit in your oven.

Have multiple ovens? Great, that means you can process more batches faster. Batch processing is best for really large amounts of data.

Online processing

With online processing, your data feeds directly into your CPU making it the preferred method of processing for data that needs to be available immediately – like if you’re checking out at the grocery store and the cashier is scanning bar codes to recall your items in their system.

Time-sharing and real-time data processing

A simple way to look at the time-sharing method is to think of a vacation timeshare where the resource (the vacation property) is split between multiple families. This method, also known as parallel processing, works by breaking data into small amounts and processing it between multiple CPUs either in parallel or consecutively.

Real-time processing is similar to timesharing in how data is processed. The main difference is how time is interpreted. Real-time processing provides data with very short latency periods (usually milliseconds) to a single application, while time-sharing deals with several applications.

What is data collection?

Data collection is data entry performed by your users. Before you can start processing, you have to get your hands on enough data to make an impact.

There are a lot of ways to collect data and you can (and should) use multiple collection points. Data comes from internet transactions, interactions, observations, and monitoring. Here are a few common methods:

Meth­od

Desc­rip­tion

Meth­od

Tran­sactional trac­king

Data­ capt­ured afte­r an even­t like­ an onli­ne purc­hase, form­ subm­ission, or pass­word rese­t requ­est.

Desc­rip­tion

Onli­ne trac­king

Anal­ysis of the beha­vior of onli­ne user­s i.e.­, brow­ser trac­king, web trac­king, cook­ies.

Surv­eys and inte­rviews

Data­ coll­ected by acti­ve/intentional user­ part­icipation.

Obs­er­va­tional

How peop­le inte­ract with­ your­ prod­uct/site.

Data collection: Compliance and strategy

In general, data compliance is a very political issue in terms of protecting consumers against data mining and other privacy violations. When it comes to data collection, the point in which the data is collected is important. For example, we recommend always growing your contact lists organically over purchasing them. Not only for the quality of the addresses, but for your sender reputation.

The point of collection can also have a lot of compliance legislation surrounding it regarding disclaimers and data policies you need to provide to potential users at the beginning of their journey.

In terms of data collection strategies, you want to approach data collection with the mindset of capturing the most useful and active data that you can. What do we mean? The point of collection is where you can involve users and add Captcha, require confirmation emails, verification codes via SMS, etc. to avoid accumulating a large amount of bot data that won’t benefit you.

Types of data

Not all the data you collect is going to be names and emails. We can break data down into three general types: structured, semi-structured, and unstructured.

Types of data

Structured data

Structured data is formatted to be easily understood by machine language. It is highly formatted and organized repository or dedicated database like My SQL or something similar.

  • Structured data is incredibly specific.

  • Formats for structured data are pre-defined using schema-on-write.

  • Fields can support a variety of information from a name to geolocational data.

Semi-structured data

Semi-structured data is organized but not in a relational database (one or more interrelated tables/rows.)

Semi-structured data has tags and is categorized or organized but it is not classified under a particular database. In other words, semi-structured data doesn’t conform to any one schema, or data format. Semi-structured data can be integrated from many sources, anything from zipped files to TCP packets or XML.

Unstructured data

Unstructured data has no organization or data model and can come in a variety of formats like images, text, or media.

Unstructured data has no predetermined format. Let’s say that structured data is like a roll of pennies, and only pennies can be collected because only pennies fit into the format. Unstructured data is the random loose change in your car. Anything from social media surveys to text files, audio, video, or images applies.

Where structured data uses schema-on-write, unstructured uses schema-on-read which means that data formatting is done on analysis for an individual set of data because the collection of the data is more important than how it’s organized. If we translate this to our coin example, all the coins can be quantified but you must first determine if you’re going to define them by color, value, size, etc.

What is data analysis?

Making a decision with some data is better than decision-making with no data at all. The first step to using your data is data preparation. In order to use the data you have collected, it needs to be machine-readable and then analyzed.

We’ve already talked about the different types of data structure, but what will different data aspects reveal?

Six Vs of data analysis 

Data analytics is the processing of taking big data and breaking it down into a readable format so you can apply the benefits of the data to your business ventures and projections. There are a lot of ways to interpret data, so it helps to break down your analysis into these six segments.

Type

Desc­rip­tion

Type

VO­LUME: Volu­me is abou­t scal­ability.

Volu­me forc­es you to answ­er one big ques­tion, how much­ data­ are you capa­ble of proc­essing? Coll­ecting data­ is one thin­g, but when­ we talk­ abou­t volu­me what­ we’r­e real­ly talk­ing abou­t is the proc­essing powe­r of your­ infr­astructure. How much­ data­ can you stor­e, and how much­ data­ can you mani­pulate at any give­n mome­nt?

Desc­rip­tion

VE­LOCITY: Velo­city is abou­t defi­ning the cond­itions for proc­essing your­ data­ with­in mome­nts to get the resu­lts you need­.

Velo­city enta­ils how fast­ your­ data­ is bein­g rece­ived, such­ as in real­-time or in batc­hed quan­tities. Data­ is in a cons­tant stat­e of flux­, and it beco­mes impo­rtant to be able­ to proc­ess diff­erent type­s of data­ (str­uctured/unstructured) quic­kly in orde­r to seiz­e geol­ocational oppo­rtunities and take­ adva­ntage of real­-time tren­ds.

VA­RIETY: Vari­ety is abou­t how your­ data­ is coll­ected infl­uences how it can be anal­yzed.

Vari­ety spea­ks to the dive­rsity of your­ data­; wher­e it came­ from­, the valu­e of the data­, whet­her it was obta­ined from­ indi­vidual user­s or came­ from­ a larg­er ente­rprise sour­ce, etc.­ In term­s of anal­ysis, vari­ety deal­s with­ how diff­erent data­ is stan­dardized and dist­ributed afte­r you’­ve coll­ected it.

VE­RACITY: Vera­city is abou­t the qual­ity of the orig­in of your­ data­.

How accu­rate is your­ data­? Or, more­ impo­rtantly, what­ is the qual­ity of the orig­in of your­ data­. Vera­city call­s back­ your­ data­ coll­ection proc­ess and the fact­ors you have­ in plac­e to ensu­re the data­ is high­ qual­ity user­ data­ vs. bot data­ or disp­osable emai­ls, etc.­

VA­LUE: Valu­e is abou­t usab­ility. What­ are the appl­ications for your­ data­?

Dete­rmining the valu­e of data­ is subj­ective. One way is to link­ the cont­ribution of the data­ to how it affe­cts your­ bott­om line­. Anot­her is to valu­e it by its usab­ility – does­ your­ data­ have­ prac­tical appl­ications acro­ss init­iatives or serv­e as a valu­able reso­urce?

VA­RIABILITY: Vari­ability is abou­t buil­ding a base­line to comp­are one set of data­ to anot­her for anal­ysis.

What­ is the rang­e of your­ data­? Vari­ability is how spre­ad apar­t your­ coll­ected data­ poin­ts are.­ Vari­ance in a data­ set dete­rmines the rang­e of the data­ coll­ected (fro­m smal­lest to larg­est poin­ts), as well­ as the devi­ation or how tigh­tly your­ data­ poin­ts are clus­tered toge­ther arou­nd the aver­age of the poin­ts.

Storing data

Storing data doesn’t just mean that you collect it and throw it in a box in a digital basement somewhere for later use. Stored data is recorded data, processed so you can retain it on a computer or other device. Storing data also means you are capturing it in order to make it accessible. What’s the point in collecting data if you can’t apply it?

Data centers and the cloud

You have options when it comes to where you store your data. Cloud vs. on premise is its own debate, regardless of whether you’re looking at security or at data storage. In terms of data processing, you can either house your own data storage or you can store on hosted cloud solutions that utilize larger off-site data warehouses.

Data is alive. Ok, not actually alive, but data does grow as it’s collected. And the more data you have, the more powerful your applications can be. So, when you’re thinking about storage it’s a good idea to keep scalability in mind – and that tends to be simpler and more cost-effective with cloud-based solutions.

Data storage and compliance

Possibly the biggest hurdle for data processing as it evolves is secure storage and responsible use. Data compliance legislation is evolving quickly. One way to keep up with the policies is to opt for cloud-based infrastructure solutions that are built to scale as laws change – otherwise you’re on the hook to spend the money to update your own infrastructure.

While the U.S. doesn’t have federal data policies yet, states like California are starting to implement them with legislation like the CCPA. And let’s face it, many businesses operate globally and have to factor in European legislations like the GDPR into their policies.

Processing your data and making it accessible is only half the battle. You can’t use it if it’s not compliant. Curious how Mailgun manages your data? Check out our email and security compliance ebook for all the details.

Applying your data

Data processing is the playbook for making your data usable. Once your data has been evaluated and analyzed by machines, you can apply the data output to your business ventures. Use data to project market trends, user behaviors, and strategies performance improvements.

In our world, the world of email, data helps us offer features like Email Verification that help you validate your emails addresses against our database to catch spam domains, typos, and other inconsistencies with incredible accuracy.

Like we said, data is a natural resource, and it can seem like an unlimited one. Data will likely not run out. As long as people interact online, data will remain a powerful tool, but without proper processing and effective data storage, your data is dead in the water. If you can’t manage your data, you can’t apply it.

Mailgun’s knowledge database

We can’t think of a subject bigger than data, or one that’s more interesting. Data informs everything from your email deliverability to your policies and information systems. It’s a giant topic and our team talks about it a lot.

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