What is big data analytics

Big data analytics uses data collected at scale in order to ease the corporate decision-making process while yielding actionable, and monetizable insights. This post will walk you through the workflow
7 min read
What is big data analytics

In this article, we will discuss:

Define big data analytics 

Simply put, big data analytics is the process of taking large quantities of data and analyzing them for customer or competitor activities. When examining this data at scale, one is able to eliminate short-term/fading consumer trends and short-lived competitor tactics. Big data analytics helps surface more meaningful insights that can be substantiated in digital interactions and then acted upon in order to gain a competitive advantage. 

Why big data analytics can be pivotal for business 

The importance of big data analytics is especially apparent when looking to collect/ingest open-source web data. It can shed light on and lead to: 

  • The incorporation of a new product into a company’s catalog is based on consumer-evident demand.
  • A shift in the marketing campaign’s messaging/imagery in accordance with the target audience’s social media sentiment.
  • Improving/broadening a company’s omnichannel retail experience based on competitor Point of Sale (PoS) architecture

All of these examples demonstrate how data collection and analysis at scale can help ease the corporate decision-making process while yielding actionable and monetizable insights. 

What is the workflow of big data analytics 

The process of big data analytics is as follows:

Step one: Data collection from multiple sources

Open source web data can be collected in multiple formats (JSON, CSV, HTML) and from a variety of sources, including:

  • Social media
  • eCommerce marketplaces (eBay, Amazon)
  • Databases (government, investment) 

Step two: Data transformation, cleaning, and delivery 

The transformation step includes taking data in different formats and transitioning them into a uniform one so that it can be processed by target systems (e.g. data points collected in JSON, CSV, and HTML can be converted into Microsoft Excel so that decision-makers can more easily quantify trends).

This step also includes ‘cleaning’, which entails removing duplicate data points and values, as well as identifying and removing corrupted data files as well as file fields. 

And finally, the formatted/cleansed data is delivered to the target system/warehouse/data pool/ algorithm. 

Step three: Data analytics, and insight-generation 

Delivered data is then moved into the analytics stage, which can utilize one of the following methodologies:

  • Manual analytics: This can be carried out by a team leader or fund manager who requests data in a Microsoft Excel sheet, for example. He or she can simply glance at the volume of trades on a security, which can then help determine if it is a buy or sell, for example. 
  • Natural Language Processing (NLP): This is when natural text and speech can be understood and mined for insights by computers/machines. For example, a company looking to analyze a competitor’s product reviews in order to identify ways to undercut them (e.g., frustrated customers can be won over with quicker shipping times). 
  • Algorithm training (AI/ML): Algorithms are fed data in the ‘training stage’ as well as on a day-to-day operational level. This initial/ongoing ‘nourishment’ empowers algorithms to understand the existing relationships or make educated guesses based on large amounts of data (essentially data-driven AI/ML in a nutshell).

As you can see, data analytics very quickly morphs into insight generation. This often also sets the stage for predictive analytics, which is the process of building behavioral models in order to help forecast consumer/competitor demand, trends, or actions.

Big data collection tools and tech 

Large scale web scraping can be accomplished using a fully autonomous solution that leverages third-party resources. This essentially takes care of stages one and two previously described while outputting large quantities of real-time industry data to target ‘business consumers’ (team leaders, algorithms). One of the leading industry tools in this context is Bright Data’s Web Scraper IDE. It requires zero hardware/software infrastructure, zero coding, as well as zero technical staff (DevOps, IT, data collection specialists). 

All companies need to do is:

Step one: Choose a target website 

Step two: Define the delivery timing, and data format 

Step three: Have data delivered directly to where it will then be used for analytics (webhook, email, Amazon S3, Google Cloud, Microsoft Azure, SFTP, or API)

Applying big data analytics for a competitive advantage (incl. Use cases) 

Here are the top-five big data analytics which can help illustrate its uses, and applications:

Big data analytics in eCommerce 

Big data analytics can provide value in the field of digital commerce in a number of ways, including supply chain management. In this instance, vendors can collect large amounts of data on competitors who are ‘out of stock’ on certain items and then be sure to drastically increase shipments of those items in order to appeal to eager shoppers with little patience. 

Other eCom applications include employing a dynamic pricing model, as well as optimizing shipping, warehousing, and supply routes. The former can be accomplished by collecting information on industry-wide pricing, enabling increases or drops aimed at maximizing conversions/profits. 

The latter can be accomplished by mapping out where clusters of products are being ‘ordered from’/’shipped to’, so that ballpark quantities can be kept in warehouses in targeted geolocations. 

Big data analytics in insurance 

The insurance industry can see major benefits from big data patterns as far as risk mitigation is concerned. For example, analyzing crime rate data in an area where a company is applying for insurance coverage can shed light on the potential risk involved as well as the pricing of a policy premium. Weather data from previous years can also help calculate the probability of climate-based damage on a structure’s future insurance plan. 

Big data analytics in marketing 

Marketing teams/agencies can benefit from big data analytics by understanding current search trends on engines, by analyzing competitor marketing campaigns, as well as analyzing target audience engagement on social media. A shoe retailer may cross-reference a top-trending search query (‘where to find vintage sneakers?’), with competitor ads pushing ‘worn shoes’ together with high engagement on social media posts featuring shoes of this variety. Each insight on its own is ‘interesting’ but as a large-scale, cross-reference  is no longer a correlation but a concrete trend to be capitalized on. 

Big data analytics in healthcare

Exercise input and output data from wearables and fitness apps are shedding light on what types of workout routine people like doing, where they like doing them, as well as the biggest fitness challenges amongst different target audiences. Good sources of relevant data points include posts on social media of people sharing their fitness accomplishments, such as ‘Huray, I just finished running 10 miles in Central Park’. Enough of these posts, and companies can deduce that running in parks is a major trend in New York and use that to promote healthcare products such as knee braces for runners’ damaged cartilage. 

Big data analytics in real estate 

Big data in real estate can have major input for construction companies as well as Real Estate Investment Trusts (REITs). Construction companies can use data analytics to predict the increase of building materials such as timber and concrete and preemptively stock up on those before price surges. 

REITs looking into getting involved in new property development plans can look at current/future supply and demand, sales volume, as well as government records and zoning committee plans (to build infrastructure, public transportation, and parks), all of which can contribute to a property being a worthwhile long-term investment. 

The bottom line 

Big data analytics has a lot to offer businesses in terms of really understanding their current target audience and competitor trends. When done properly, it can yield better marketing, product development, sales processes, and risk mitigation. But properly carrying out big data analytics is contingent upon being able to collect quality, real-time data at scale in order to feed analytics systems that can spit out insights on an ongoing basis. It is in this context that many companies choose to outsource their data collection so that they can focus on data analytics and practically implement the derived insights in the context of their day-to-day operational business workflow. 

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