Now that we know that the global data size is nearly 2.5 quintillion bytes a day, it is obvious that challenges in understanding and processing them will be more.
For businesses, there may be a dozen opportunities will be inside. Simply put, the overwhelming data brings prospects and opportunities together with a ton of challenges and hiring developers for startup can help you solve them. So, you have to find the best way to deal with that ton of data for filtering values out from them.
This post will help you find some awesome ways to find useful data for processing from the mountain of datasets.
Let’s get started to learn how to do so.
Determine What Type of Data You Need & How
This journey starts with the discovery of which data can fulfill your requirement and where to get it. However, there are multiple websites, IoTs, and other sources that provide records. But, the challenge is to get valuable data from the right sources via web scraping or capturing.
Mainly, there are two sources that can help you to have sufficient and valuable data, which are given below:
- Internal Sources: Web data analytics results or web/app journey can prove a great help in this matter. You can have records suggesting customer behavior, demographics, intent, etc.
- External Sources: These can be data vendors that sell data for making money. For instance, US-based data entry companies like DISQO, IBM, and Salesforce provide processed data.
So, these sources enrich you with two types of datasets, which are processed (such as product details, number of suspicious transactions, etc.) and unprocessed or unstructured data (including event flags, additional variables, etc.).
Now that you have both sources, find the most preferable and valuable source. Certainly, the value of internal sources would be way more than any other sources in terms of relevancy. They carry the following useful details:
- Data on payments, expenses, & revenues
- Telecom data
- Social activity data
- Biometrics data
- Behavioral data
- Alternative data
Having expertise in collecting datasets won’t guarantee that you would have the right set of information. You should be capable of effectively analyzing the right datasets & their sources after processing. This is deep analysis, which helps in seeing beyond what is seen or performed.
How to Find the Right Source of Data
Here is how you can come across this challenge.
- Think of the Right Questions
Premeditate what you want to discover from that database. These can be the terms and conditions of data sources, and associated costs. Precisely, these questions can help you to consider the right question:
- Is the data fresh?
- What part of the customers’ database do you require?
- How is the fresh data associated with the existing database?
- Does the fresh database prove valuable in view of credibility and affordability?
- What is the standard value you want to achieve from that data?
- What sources are available and how quickly you can get records?
- How often is that source data updated?
- Is that information legally extracted & processed?
- How much does it cost?
These all queries can prove a milestone in effective data processing later. Certainly, you would have the right and fresh data to understand and take a step ahead.
But, what about their accuracy?
Well, its answer lies in the next section.
- Test What You Get
An overwhelming amount of data requires high maintenance costs, which may not be ideal to bear for any business. So, it’s better to measure the value instead of just quantity. However, data vendors or data processing companies often save time and money by cutting corners. They follow the “fit-for-all-types” approach (which is not an ideal approach). Wise businessmen often avoid this practice. Instead, they follow these steps:
- Review History of Data Provider
It may be difficult, but the end result would be fruitful. So, you must review the worth of previous data processing projects of the selected data vendor. He may have hundreds of customers. But, only a few might be happy with the way they processed their datasets. So, do review their historical records, their quality, and how they maintain those records. It would help you to find if he is worthy to consider for the next step.
- Find out Relevancy Early than Being Late
It’s an extensive process for both, the data provider and the lender. So, you may ask for a pilot project to complete at first. It’s like a demo to show how the delivery is likely to be. It provides you with an opportunity to analyze the quality of work. This sampling proves a key to saving on costs that you may incur because of erroneous data. You may ask to provide a sample or do a pilot project before signing an agreement. This is going to be a benchmark in determining if the process works, or if the data will be accurate. In short, you win an opportunity to opt in or out beforehand.
- Cost Calculations
Now that you would have fair value in your data, it’s time to talk about the costs. This is never going to be so easy for you because negotiating the cost is an art. It involves assumptions, calculations, and variables that may not be right every time. A bit of hesitation or wrong calculation may end up in a costly deal. So, do follow this suggestion to avoid costly and inaccurate data processing results.
- Make Decisions
However, it’s the upper management that makes the final call about the data processing services provider. It also is essential to determine what information is actually required and how the data should look like. It may also follow the aforesaid steps to determine how to get useful data for business intelligence processing. Possibly, it would be capable of predicting the intended result. So, let it decide and make a decision in the end.
Get More Data, Avoid Refusing More
The aforementioned steps are not for rejecting a majority of datasets from the vendors, but to finalise the data provider. Often, entrepreneurs focus on the negative data during that selection. They may prove a key enabler for those providers to understand who the suspicious customers would be (what should be rejected). In short, you can manage the risk by predicting those customers.
While doing so, we should not ignore the positive data also, which can bring opportunities for upselling, and loyalty building.
Let’s say, a data provider comes up with exceptional services and value-additions. But, his packages may be higher, which makes it challenging to onboard him. In this case, the value additions and exceptional services can be a major breakthrough. This deal can leverage you with overwhelming revenues, which may be far above what you expected. So, never ignore the brighter side of any deal. Instead, taking time to think about all upsides and downsides can be beneficial.
Although there is no specific answer that can fit all organizations’ requirements. But the best ways have already been discussed in this post. Do follow the steps if you are really serious about data-driven results. You may access as many data sources as possible. Then, find out their samples through a pilot project to score its data processing models. Measure the quality of that processed database. This doing can help you to have a useful and goal-specific database. With it, you may manage risks, draw opportunities for growth and efficiently process intelligence.
To find useful data for processing like risk management, business intelligence, or upselling, there are multiple steps. Find the sources and check their relevancy. Then, go with the accuracy check. It can be easier if you ask for a pilot project. Once done, negotiate for the cost to avoid costly deals.