From data quality to privacy and consent, from social media to e-commerce, the issues are legion when it comes to leveraging data in today’s business landscape. We spoke to data expert Guillaume Thfoin, Chief Data Officer at Schneider Electric and scientific co-director of ESCP’s Master of Science in Big Data and Analytics, who shared his thoughts on one of the digital world’s most existential problems: when in business, which comes first – the data or the problem?
Hello Guillaume! Let’s get straight into it. How far do you agree with this statement: “No matter how ‘good’ your data is, if you aren’t asking the right question or addressing the right problem then the data becomes irrelevant.”
Even on the internal side this can be the case. During my time at a large consumer packaged goods company, we spent a lot of effort analysing market share data from Nielsen. Our IT team had spent six months building a dashboarding tool that was intended to help analyse this data in even more detail – but the developers had left out a major user need: the data wasn’t exportable to Excel. On paper, it sounded like a great solution, but it wasn’t very user friendly and the tool failed to gain traction. Whether it’s from indiscriminate data harvesting or poor implementation, data is only as good as the means to which you put it. “Build it in the right way, and users will come”, as I like to say.
Data is only as good as the means to which you put it. ‘Build it in the right way, and users will come’, as I like to say.
From a business point of view, how important is data to bottom-line profitability? Does this depend on the sector?
Guillaume Thfoin: Today, we all talk about digital transformation -and digital means data. However, people still tend to think more data means more information – which is true – and thus more profitability – which doesn’t always follow! It’s important to have data that’s easily quantifiable, but it’s also important to take a more holistic view. Data should be used to improve your business consistently, not just with the sole aim of boosting profitability as if it were a magic wand.
Can you share some concrete examples of how data can improve and refine a business idea?
Guillaume Thfoin: Generally speaking, there are three ways in which data can be used to add value. The first is optimising operations and production; the second, to sell more or sell better; and the third is to use it as a currency, which is particularly interesting. By using and monetizing data insights, businesses can further refine and pinpoint key areas of improvement in their strategies – which, in turn, increases profitability, albeit more indirectly. In short, monetizing data goes straight to the bottom line. Once a company has mastered the usage of data for internal benefits, it can extend itself into monetizing its data externally.
This is the route I followed in my previous role in a large Middle East retail conglomerate. As we matured in our internal data usage, we started monetizing insights externally and gained incremental bottom-line impact.
What are some other ways in which big data can be used to refine business strategies?
Guillaume Thfoin: Let’s take a closer look at three different types of data. First-party data is the data that’s owned by a given company. Third-party data comes from partners and can be bought or traded. Finally, zero -party data is data that’s volunteered by the customers themselves.
Data can then be subdivided depending on its level of detail. Aggregate data, which is a form of anonymised data, is interesting for general insights, but its lack of actionability means its value can be limited. Other types of data, such as data for personalisation or data gleaned from loyalty cards, such as seeing where consumers spend with competitors, isn’t anonymised, and thus can provide a much richer customer experience as well as actionable means for improvement.
This data can then be used to refine and personalise the customer journey by, for example, identifying how, where and when consumers spend. Are they regular shoppers who shop small, or do they make the occasional big purchase? The result is a great example of how data can be adapted depending on the company’s key goals of the moment – capturing new markets, satisfying existing clients, or pushing certain products. Of course, this level of detail is where consent comes in.
Leaders should be focusing their time on encouraging consumers to share their data in ways they’re comfortable with, so that both parties receive mutual value from the exchange.
Let’s go further with that. What are some of the most pressing ethics questions accompanying issues surrounding big data today?
Guillaume Thfoin: This is a very interesting question. First, you need to examine how transparent you are with your customers. How comfortable are they with their data being utilized for different purposes? As an example, in light of recent EU GDPR legislation and as a response to negative publicity surrounding certain tech giants on privacy issues, Apple has initiated a huge marketing push focused on user privacy. As a result of one of their latest privacy changes, almost 75% of their users have decided not to opt in to data sharing. As a result, that data has suddenly disappeared from the data marketplace.
Now, let’s look at this in terms of your earlier question about how data can be used. Apple’s approach might appear counterintuitive since apps are now being forced to ask for consent, but we can already see the benefits for Apple. For one, their new policy has hugely improved data quality because of informed consent from users. For other users who’ve chosen not to share their data, Apple is now working with a huge net positive in terms of brand image compared to their peers. In this case, restricting and refining data has been a huge win for the company.
To conclude, ESCP Professor Louis-David Benyayer recently shared some thoughts with us about the future of big data. Where do you think data analysis is likely to take us next?
Guillaume Thfoin: I think that over the next five years retailers are going to begin adopting a two-tier approach to data. In my view, this will lead to AI and automated processes becoming the baseline for any business hoping to scale, particularly in terms of dealing with stock, delivery, targeted ads and other parts of the customer journey where human input isn’t essential. In turn, leaders should be focusing their time on encouraging consumers to share their data in ways they’re comfortable with, so that both parties receive mutual value from the exchange. When it comes to data, quality, not quantity, is the key.
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