There’s a joke about the business that loses pennies on the dollar but plans to make it up on volume – what if you could actually run a business that way? What would happen to your competitors? Imagine you’re the CEO of an online retailer that sells fabric direct to consumers and interior decorators. Since founding, you’ve been meticulously gathering data on your product catalog and more abstract information like the geography of your customers, customer traits/personas, seasonality, weather, and even popularity of fabric related terms on social media.
With all of this data, you can sell data to manufacturers that allow them to spot trends, focus their marketing spend, or understand the performance of their competitors’ offerings. No individual-user data has to trade hands, but a huge amount of value does. With this significant revenue stream, you can undercut the prices of everyone of your competitors, seizing market-share from established players. The terrifying truth about this scenario is this retailer doesn’t have to be very large to acquire enough data to execute on this strategy.
What would you do if a disruptive player in your space suddenly didn’t care about margins?
AI-first startups are companies who think of data as a core component of their business model and not as an afterthought. They structure projects, make technology decisions, and even develop products with this lens. Because of this, they are able to derive value from data faster and more efficiently – both to amplify their enterprises but also as a new revenue stream. The disruptors are coming. How can strategic, forward-thinking executives at traditional companies prepare their business for this fierce competition?
The first thing you have to do is find your data. Ask every leader in your organization what data they collect, where they store it, and how they use it. Go a step further and ask what data they want to collect but can’t. Every 21st century leader needs to be able to answer this question.
When you ask many people working with data today if they trust their data and their answer is typically no. You need to push past this answer. Exactly what is untrustworthy? What things are done to compensate for the lack of trust? How could you fix this? Answers to these questions are typically at the practitioner level. This may open your eyes about dashboards you rely on. ### Does the data produce value? In our fabric retailer example, you were able to reduce prices and gain market share. What value does your data produce for your business internally and externally? Are there new applications that could tap incremental value?
Here is where the rubber hits the road. Do you have the talent in place to consolidate the data and package it for use? If you have a world class engineering team working with Data PhD’s, you’re in a great position. If you’re like most companies, you don’t. The three paths forward are: retrain your existing staff, hire new staff, explore how strategic outsourcing can bridge the gap.
If you have engineers and product managers and designers on staff, you could retrain them to become data and AI-oriented. The critical question becomes time. How long will it take to train them on new technologies? Do they want to be trained? What will happen to the projects they are currently working on? If you can push past these questions, then you’re on the right path. But remember, every day the disruptors get closer.
Attracting and retaining the kind of talent required for data-harnessing projects is hard. There aren’t a lot of these people in the industry, they have lots of options of where to work so they’re picky about exactly what they work on and for whom, and they are expensive because everyone else wants them too. If you can find them, pay for them and retain them by consistently feeding them challenging, personally rewarding work, then you’re on the right path.
You may examine the first two paths and realize you won’t be able to accomplish what you need to do to stay competitive on the timetable available. Exploring outsourcing doesn’t mean you can’t build the organization you want in-house, it means you can acquire the talent to get things done fast and buy time to build your organization’s future. CIOs at billion dollar companies have realized Gigster’s data-harnessing projects can help them stay competitive in the new AI-first environment. Connect with us to find out more.