Data science has come ahead in leaps and bounds in the last decade. With the increase in technologies and computing power, algorithm designs have become more sophisticated, data infrastructure less costly, and the automation of many business functions suddenly viable.
Businesses are becoming more data driven. And it’s connecting the dots for them in profound ways.
Read more: Get Data Smart: Your Data Science 101 Guide
Everyone’s producing data, all the time
With the growth of the Internet of Things (IoT) and falling cost of producing sensors (e.g. your phone’s gyroscope and accelerometer, among many others) and wireless devices, the amount of data we generate has boomed. Since 2012, the volume of sensor data has increased sevenfold, from 4 billion to over 30 billion delivered sensors.
Everyone, whether they know it or not, is creating data. And businesses have woken up to that fact. In the case of B2B and B2C companies, customer relationship management systems, websites, social media—every platform businesses use to connect and transact with a customer—collect customer data in some form. From opening a marketing email to clicking on an online advertisement and, of course, making a purchase—all of this data can be harnessed to help businesses to better understand their customer and/or prospect behaviour.
The problem is computing all that data, knowing how the data is structured and stored, which algorithms to use and why, and extrapolating meaning from the results. Which is where data science comes in.
The power of pattern recognition
Data scientists don’t just make sense of that data. We hunt for patterns in it. We examine the past behaviours of a particular type of consumer to determine what they’ll do next. From there, we isolate similar groups of customers and prospects for targeting. This informs and drives a business strategy.
Over the last 10 to 20 years, our ability to do this has grown exponentially. And with buzzwords like data mining and artificial intelligence fast becoming part of everyday business ‘speak’, companies are catching on to the potential data science offers.
Currently, we’re at a tipping point. Businesses are already using data science to become more efficient—from serving up personalised content on Netflix, to refining automation tools and predicting the success of a marketing campaign. More and more, they are growing confident in data-driven strategies. These businesses are at the forefront, funnelling fuel into the data science engine. We’re a train ready to pull out from the station.
The question is: where are we headed? And, more concerning, what if you miss the train?
The data world is your oyster, if you’re quick
The data science leaders—the early adopters—have got nothing to worry about. You’re already on board, and as each new breakthrough comes out, you’ll be in a position to capitalise on it. You’re leading the way. Keep pushing that fold.
My concern is for the laggards. Businesses that, for any number of reasons—legacy systems, the cost of investing in the infrastructure, siloed data—are not quite at the station yet.
My prediction is that in the next 5 to 10 years all the laggards will eventually take the first steps. BUT they’ll be miles behind the pace of the leaders—with an almost impossible distance to catch up.
In a nutshell, if you miss this train, your competitors that got on board early will get ahead—and probably stay there.
Leader vs laggard: what’s the difference?
Not every leader and laggard will display all of these characteristics, but it is important to recognise what best practice looks like as of 2020 and what key challenges businesses face.
- Are thinking about their data and how they might leverage it—even if they are not 100% sure what that might involve, they are data conscious.
- Have invested in infrastructure to store their data.
- Have made their data accessible.
- Have one centralised data platform.
- Are analysing (or paying someone to analyse) their data for trends, optimisations and automation opportunities.
- Have systems in place to track their progress.
- Use data to inform their business decisions.
- Are not thinking about using their data or are unaware of its potential. They are not data conscious.
- Have not invested (or cannot invest) in the infrastructure required.
- Have customer data split across multiple systems, AKA siloed data. Fragmented data = fragmented view of the customer. In short, if we don’t have the full picture and no work is being done to resolve this issue, it’s especially challenging to uncover accurate and authentic patterns of behaviour.
- Data is inaccessible—it might be incomplete (i.e. fields are missing, such as their name), unclean (e.g. there’s duplicate data for the same person) or they simply have no way to view it.
- Make decisions based on gut feeling, rather than data-driven evidence.
As we head into the 2020s, a business’s ability to capture, store and identify patterns in their data and drive results from the findings is what will set them apart from their competitors. The current market is already teeming with rivals—many of them international—and this is only set to increase. Being “good” will no longer be enough to set your business apart. But harnessing your data might.
And the first step of that journey is easy—in fact, you’re already doing it. Think about data.
Want to do more with data? Discover how to fuel your brand and marketing performance with our FREE guide: Transform with Transactional Data.