Today I saw an interesting headline informing the public that "leveraging big data is the new price of entry for the manufacturing industry." Indeed, in an evolving world where the digital is touching virtually every industry that statement holds true. Big data implementation used to be more of a leg-up and gave companies a competitive edge; now in this leveled playing field companies must leverage big data simply to keep pace with their competitors. Although the article in question specifies the manufacturing industry, is it possible that different industries must implement big data analytics differently to achieve the same value? How?
E-commerce- You don't have surveillance cameras to track your customers' journey through your store. You don't have the ability to see or hear your consumers travel through your physical space, making comments or stating complaints. You need some way to track their path online while connecting this to behaviors and reactions, like purchases, conversions, and bounces.
A stupid company doesn't continuously evolve, improve, and address mishaps. Data analytics is necessary for any company who wants to figure out what they're doing right, what they're doing wrong, and what they have potential to do differently. A webpage has a high bounce rate? Boom. Change it. Your conversion rates are low? Boom. Make your products look more attractive with better images or more specific content.
Brick and mortar retail- These companies profit from the benefits of existing in the sensory physical world but also are burdened with several challenges that accompany a more physical experience. For one, they have a clear, eye-to-eye view in and around their store and a clear ability to connect physical actions and behaviors to purchase. However, they also have more moving parts at play that influence their success. They have to ensure that their location is appropriate and determine if people in the surrounding area will want to buy their products (a.k.a. if their target demographic is accessible), their store layout, setup, physical branding, etc.
E-commerce sites exist in competition on more of an equal playing field in the sense that they all exist in cyberspace and are accessible to all. Their location is where they show up on google search, and their store "interior" is their digital space that they can craft to reflect their branding. In fact, this actually heightens the level of competition because the benefits of physical location (like being a neighborhood store) will not influence buyers to purchase from them over others. Basically a consumer's only deciding factor when choosing to purchase or pursue a relationship with an e-commerce retailer is what's in their site and their online reviews. Data analytics can help them gain a more focused sense of their digital surroundings - who is searching for them, what consumers say about them, and how people respond to and interact with their online presence.
What do these two have in common?
1. Brick-and-mortar retailers increasingly need an online presence to survive- They need analytics to manage their online presence for the same reason that e-commerce sites do...in addition to aiding them in forming a cohesive unit between it and their physical presence.
2. With e-commerce, the "company" may be online, but the product is still physical. E-commerce and brick-and-mortar retail still have physical products and supply chains, physical people behind the computer screens, and need data to make sense of their physical operations. The physical world is a large jumbled space - relationships and correlations are often difficult, if not impossible, to identify and tease out in that world. We need data analytics to record information and put pieces on paper, then side-by-side to see what we find. Valuable activities and indicators to watch are departmental spending, revenue per employee, trends in purchases, etc.
3. Manufacturing has no website to worry about or social media presence to manage. They can use data analytics on a strictly internal basis. Manufacturing is a complicated process, dealing with many automated systems that all have unique relationships with each other, from each step of the supply chain to the production processes. Big data analytics can be used to connect operations by continent, country, city, etc. and figure out lag times, isolate problems, and identify patterns, in addition to measuring productivity in terms of value added per employee or equipment.
These industries may be different and implement big data for varying purposes, but the message is still the same: big data deals with numbers, and every company across every industry needs to sit down and look at their numbers if they want a hope of increasing a special little number called revenue.