Doing business today often means dealing with an abundance of raw data. And typically, raw data is unstructured and can be messy. It may come in big chunks of unstructured information sequences that can barely be recognized as something meaningful. But that is not the case. To use raw data properly, one has to find meaning in it. Here are the pros and cons of building your own data parser.
This quest for meaning starts with data parsing. Broadly it can be understood as a process of structuring the unreadable mess of data into something that can be made sense of.
Articulating the data
To better understand what parsing is, let’s look at where the term comes from. The word “Parsing” comes from “pars”, which is Latin for a part or a piece of something. Therefore, parsing means dividing something into parts with recognizable meaning.
Usually, it refers to a part of a speech or a sentence. Sentences can be divided into words and grammatical structures by what function they perform to carry the meaning of the sentence.
But parser can be used not only for natural language analysis, but for computer language as well. Lexical analysis is when the parser categorizes all units of data by the type of function they perform in the dataset. Then syntactic analysis, by building a parse tree, puts a data in its proper place, so to speak, showing the relationship between different types of data.
Thus, data parsing is a sort of articulation of data, analogical to the way natural language is articulated by breaking down the structure of the sentence into functional units. This structuring of data makes it easier for computer programs to read it and for programmers and analysts to find sense in.
Making use of the data
The strategies that comprise business intelligence use data to arrive at valuable insights regarding how the business should move forward. But for data to count as business intelligence it has to be, well, intelligible.
This is why data parsing is important. No matter how much data you have, you cannot utilize it if it is stored in a big pile without any apparent order. Through parsing, data becomes easily accessible to analyze and extract meaning from. Parsed data can be used as intelligence that informs decisions regarding future strategies of the business.
Benefits and burdens of a self-built data parser
From what has been said, it is clear, that data parser is a much-needed tool for businesses dealing with a lot of unstructured data. Building such a tool is something that these businesses could attempt to do themselves. The question is whether they should.
Let’s look at some benefits of building your own data parser.
Pros of building a parser
It’s yours. That is, you control it completely. You look after it, you decide how to maintain it, what and when to do with it.
You are making it therefore, by definition, it can be custom-made. This means, that you can build your data parser to suit your data parsing needs precisely.
Usually, it is cheaper to build your own parser than to buy it.
However, as usual, where there are pros, there are also cons. Here are some cons of building your own data parser.
Cons of building a parser
Having your own data parser may become a burden. You will have to maintain it, to make sure it is running properly. Whenever there is a problem, it will be on you to solve it. Thus, it will add extra workload and demand attention that could otherwise be directed elsewhere.
- Extra training
You are going to need to hire or train a team to build and maintain the parser.
- Server size
You will need a server that is capable of parsing your data fast enough to meet your needs.
DIY or buy?
The alternative to doing it yourself is buying a data parser. That would mean allowing other companies to do your data parsing for you. So, which route is better? As it is usually the case, that depends on where you stand as a business and how you wish to carry on with your daily proceedings.
The difference here is pretty much the same as the difference between going to a restaurant and making your own meal. The meal at the restaurant will be more expensive than making it at home. But then you wouldn’t need to shop for ingredients, you wouldn’t need to cook the meal and you wouldn’t need to set the table. You would just pay and eat.
Ultimately, every business needs to decide whether it is prepared and willing to build and maintain its own data parser. If the resources are there and the additional workload and responsibility is not an issue, then you could take-on building your own data parser. If you would rather concentrate on other aspects of running the business, you might be better off leaving it to the professionals.