Skip to main content

What Are the Stages of Data Processing?


Data is considered as the new oil, which is true. But without processing, it is good for nothing. In short, its processing is important, as it structures knowledge in a comprehensive format. You get through what is interesting or what can really transform the way your business typically goes. It helps to evolve such intelligence that can easily resonate with your domain.

A business analyst picks something meaningful out of a massive set and then, the journey to its makeover begins via cleansing. Upon proper structuring, such models are put aside that have the potential or that can actually add on opportunities to make you run ahead of time and competitors. This is why data processing is important. Certainly, being digital helps any organization to do it quickly. You just set your codes to scrape information via APIs. That’s it. You get all what you need to observe for getting insight.

1.       Collection
Typically, it is about collating information from various sources upon thoroughly getting through the goal or aim. One of the most suitable methods from interviews, questionnaires, observations, documents & records, focus groups and case studies or datalakes/warehouses are mined to get the valuables. If the real-time details are required, IoT devices come first to extract.  
What you need to think about is the authenticity. Facts from trustworthy sources get transformed into high-yielding decisions upon analysis.

2.       Preparation
Also called pre-processing, this stage ensures filtering and cleansing of raw information. The corrupt records are set apart through de-duplication and validation methods from the useful ones. If required, the research team keeps with normalization or standardization to eliminate redundancies. Eventually, the high-quality compilation is made processing-ready.

3.       Input
The facts and figures are now ready to put in the proper place or destination to translate them through analysis into learning for the lifting as per goal. This procedure may need to reformat so that whatever you are going to place looks compatible to your repository, like the Cloud or server.

4.       Processing
This stage is intertwined with machine learning algorithms and your goal. The goal, certainly, is one of the prior things that you should go through thoroughly. Then, the algorithms are fed inside so that these can process those particulars in the lakes or warehouses or CRMs without encountering with frictions. This is how several ‘if or not’ conditions are passed through many-a-times in Python, R or any language that is hired for functionality testing specifically. Finally, data scientists get the breakthroughs in the form of patterns or models that they are looking for.

5.       Output
This is the stage where tried-and-tested models or patterns are aligned separately for steering them to the next level of data processing.  The makeover of these patterns is done so that even a layman can read and understand them through graphs, charts, videos and images or plain text. In short, these patterns are prepared for analytics.

6.       Storage
A proper storage will provide an opportunity to access the output over and over again for the transition in the future. However, nothing decays as fast as the real-time details. So, some information is used immediately to meet the end goal, whereas the rest of its pieces are put safely. In the meantime, data regulation policies and guidelines are considered on a serious note.

This is how the process goes on to ground up a platform for predictive or descriptive analysis. These stages can be incorporated with the particulars of any industry or any domain. The outcome will certainly prove groundbreaking because the decisions would be drawn from the observation of performance.

Labels

Labels:

Comments

Post a Comment

Popular posts from this blog

Advantages and Disadvantages of Outsourcing Data Entry Services

Outsourcing data entry project can be advantageous as well as disadvantageous. Before nodding for it, look at its cost, risk, repo and track record to avoid the bundles of inaccuracies in data entry .   Outsourcing has become familiar term for acquiring data entry services. Advantages and disadvantages walk hand in hand in terms of cost, risk, repo and track records of the data services. An entrepreneur should be aware of the facts regarding data entries that can make them enriched overnight or can land you behind bars in allegation of hacking or misinterpreting or loosing data. Reduced overhead expenses as well as risk, cost-effective, time to focus on key areas, improved production are the merits or advantages that certify data entry services should be outsourced. On the contrary, data hacking, low quality of data entries, exceeding the deadlines for the project, over expenses and streamlining process of these appear as the demerits or disadvantages of the outsourcing...
Post a Comment
Read more

Retail Market Analysis

Let us first understand the meaning of Retail Marketing. Retail marketing is the range of activities performed by a retailer to develop knowledge, awareness and sales of the company’s products. This is quite different from other types of marketing because of the factors and elements of the retail trade like selling finished goods in small number or amount to the consumer or end user, generally from a fixed location. Retail marketing uses the common principles of the marketing blend i.e. product, price, place and promotion. A study of retail marketing at university level includes effective vending and selling strategies, shopping and consumer behavior, branding i.e. categorizing and advertising. Retail marketing is especially essential to small retailers trying to compete against large chain stores.
Post a Comment
Read more

5 Email Marketing Tips to Ensure Subscribers in Thousands

Email Marketing Running an ad campaign through bulk of emails is email marketing. A commercial message is sent to the customers directly. Thus, it is specified as direct marketing tool. Existing as well as potential customers are targeted through them. And its generator harvests email addresses of the subscribers in return. So, the emails pitch the customers. And its subscribers become goldmine for the company. Conversion is an ultimate motto of dropping emails. Otherwise, all exemplary emails will be futile. So first, one should have email addresses to which subscriptions follow. And then, conversions occur. Most of the people tend to skim newsletters because they lack time. But how one can tap subscribers if they don’t want to? Well, its answer lies in mind-blowing & exceptional email strategy.  Following these 5 email marketing tips will ensure thousands of subscribers as a cakewalk: 1. Landing pages should have short and crisp bottom lines: The hy...
Post a Comment
Read more