When someone puts the word ‘Big Data’ and ‘censoring’ together, it might raise a few brows. However, censoring in this context is actually not the word we are familiar with (and occasionally dread).
What it really means is that data needs to be filtered out, cleaned up (and hence, censored) in order for us to see relevant statistics.
It is directly related to the very same problem that big data was supposed to handle: the problem of obtaining relevant information in even bigger quantities. And in a large-scale operations like a supply chain network, both accuracy and quantity are paramount. There are vast amounts of information but they’re now also in a sea of statistical noise.
That said, however, it goes to show that your company’s culture shouldn’t still be stuck in a mentality that treats big data like it’s still an experimental technology. We are way past that! We are already in the age where big data censoring is so efficient, that you now have entire businesses selling this accurate information as a byproduct to other companies. The Harvard Business Review offers one such example in the form of United Health:
…UnitedHealth built a business with $5 billion in annual revenue by reusing the aggregate information contained in the vast number of claim forms it processes. Drug companies pay for the data to see how their products are used, how effective they are, and how well they are competing with rival drugs. UnitedHealth’s resale of its data led to the creation of a new business, OptumInsight, that, in recent history, experienced multiples of both profit margin and growth rate vs. UnitedHealth overall…
For many consumers, this comes off as alarming but that is where the censoring comes in. Before, there wasn’t even enough processing power to trim large streams of data in answering simple business questions like, “What product are customers buying the most?”
These days though, you have major banks like Westpac now aggregating the buying histories of millions of consumers into an anonymous block of information that will just purely answer such questions:
Inquiries by The New Daily found that Westpac is selling anonymous, aggregated information about customers’ spending habits to other businesses through a data sharing platform called ‘Data Republic’, which it jointly owns with NAB, ANZ and Qantas.
It’s actually good news for consumers as well. With their permission, banks can now privately share a customer’s information more easily should they decide to switch banks. Other benefits can trickle down in the form of more accurate advertising.
This can also allow them to use other forms of big data directly by themselves! NoiseNet is one good example, with its way of acquiring anonymous data about residential trends to better inform potential homebuyers and property renters.
Data Republic is just the beginning, however. Banks like Westpac are also pioneering initiatives like FuelD in the hopes that censored big data will become a vital commodity that will help accelerate future businesses and grow the economy.
In other words, if you were to integrate big data censoring into your supply chain network now, you would soon be sitting on a gold mine that you can also sell yourself! Censoring is but the art of really bringing out that data’s core value and preserving it.
One question remains though: Exactly how does one company go about properly filtering/censoring information down to relevant facts? The key here is to set the criteria for relevant data. Here are some basic factors to consider while you are implementing these large-scale analytics into your operations.
#1. Information’s connection to main business goals.
Whether it is long-term, short-term or quarterly goals, the information you need from big data should have the most direct link to them. Are you trying to boost sales, and hence, need more information about what often gets in your customers way? Are you trying to ramp up production but don’t know where your supply chain’s main inefficiency lies? Maybe you only recently systemized your supply chain towards a more demand-driven approach and want to know exactly how the right numbers are generated.
We are now in the age where curated and censored data is as diverse as the items on grocery store shelves. And much like when shopping, the most efficient way is to know what your supply chain’s primary needs are and what information directly empowers you to meet them.
#2. The cost of methods behind analytics.
Supplementing technology with the best practices in analytics is a must. However, there are many ways to go about it. You can hire a strong team of experienced data scientists or you can set up your systems to incorporate AI to help censor the vast amounts of streaming data being generated by your entire operation. Other methods even require the expertise of senior IT and data specialists, who can help develop more fine-tuned solutions for acquiring and censoring data.
All of these come at a cost. How you manage these among your cost and value drivers is entirely up to you. However, it does help to remember it is not necessarily a choice between powerful AI or human ingenuity. Analytics today has more often been a combination of both.Think of AI as doing the heavy lifting for us humans, so we simply need to have the right empowering questions to ask.
#3. The ever-present privacy question among target customers.
Perhaps the most important question you need to ask first is whether or not your target market is the type to favour their information being used at all. Sure, there are definitely some fringe groups who would rather drop off the grid completely. These may not necessarily reflect the entire bulk of your target market.
That doesn’t eliminate the possibility that some of them will only allow you to know certain things and nothing else (e.g. their favorite food flavours, preferred time of delivery, favorite product colours etc). In a way, data censorship can certainly tie in to these preferences by using methods that narrow down to specific queries that prompt these specific answers. Still, that is only all the more reason why the privacy question has to be tackled.
Big data became a big thing long ago, when major businesses realized that there is a wealth of information being generated by technology. Censoring this data is the process in which the core value of this information is derived and then used to accelerate business via sound decision-making.
Therefore, don’t be alarmed when experts say that it’s necessary to integrate this technology into your supply chain. Start now and continuously evolve your data processes. The real cause for alarm is when you know that you could have begun something amazing for your business, but haven’t bothered to start.
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