Money never sleeps, dataflows never slow, and a data leader’s job is never done. You aren’t just thinking about how to make sense of the data you already have, but also the data you need now, the data you may someday need — even the datasets that don’t yet exist, but that you’ll inevitably have to incorporate into your models.

That’s because, in today’s fast-changing commercial landscape, new challenges and questions crop up endlessly, in all areas of business. If they are to stay competitive, your colleagues need answers to these questions fast. As a data leader, they’ll look to you when it comes to understanding and taking actions to effectively implement data science for business. 

Data science for business: future-proofing your models

No matter how brilliant your algorithms, they’ll only deliver great results with the best possible data. Your internal data is a great place to start, but it can only take you so far. Your job is to really interrogate the limits of the data you have and think carefully about where the gaps are that you still need to fill. 

That means you need to continually think about whether the data in your datasets that really do unveil the most valuable insights and trends on their own — and, if not, where you might look externally to source the data that does.

Let’s take a look at some examples of the most promising ways you can use data science for business, but specifically data, to future-proof each use case.

Better advertising

Machine learning models help your marketing team translate real-time data into valuable predictions by split testing your ads, emails, landing pages, website text, pop-ups, product descriptions, and landing pages. It’s also incredibly useful for predicting people’s preferences, allowing marketers to micro-target the right people at exactly the right time, maximizing conversions and sales.

That said, marketing and advertising are volatile fields. Social media users and online buyers can be fickle, and strategies that play well for their novelty one week may fall flat the next. Your marketing colleagues need to be versatile when it comes to the data and lead scoring models they use. 

If you can step in to offer high-quality, up-to-the-minute datasets that provide demographic insights and information on social media activity and search terms, you will prove indispensable to this team.

For example, if you provide demographic information to your marketing colleagues, they can make better-informed decisions about ad placement and overall targeting strategies. Combining social media activity data with current event and engagement data can unlock invaluable insights and reveal trends that guide retargeting strategies and churn analysis. This, in turn, can help your colleagues to improve customer retention metrics and conversion rates. 

Or consider the need for your marketing team to stay continually one step ahead of evolving trends and other external factors. How might a better understanding of seasonal weather patterns inform how and when they launch a campaign? Could tracking responses to key global events help them to refine their brand voice, marketing strategy, and demographic targeting? How might sales figures for popular books, movies, and other types of entertainment help them get into the headspace of their target audience, or predict when a particular ad set is most likely to strike a chord?

This type of insight can enhance existing models and broaden your team’s understanding of what’s happening out there in the market, positioning them for greater success. 

Improving efficiency and resource management

From process bottlenecks to unnecessary waste, to duplicated tasks, all kinds of problems may be slowing down productivity and progress in your organization. But you need data to tell you that. 

Collecting and analyzing IoT and other data you have on business-critical processes and activities helps your colleagues gain a better understanding of where issues form. From here, they can tweak the way they do things to try and streamline operations, boost productivity and cut out costs along the way. 

That’s a great start, but there’s something missing: it doesn’t help your colleagues anticipate what the impact will be if circumstances change at short notice. What happens if orders surge? If there’s a major disruption in the market? If other external factors change normal patterns of supply and demand?

More importantly, what clues can you extrapolate from available datasets that will tell you that these kinds of changes are on the horizon? To manage and mitigate risks in your supply chain and operations, in order to avoid work disruptions, supplier issues and outages?

The right data can provide those answers. Instead of your colleagues looking at past sales figures for the same quarter and saying “well, here’s what we forecast for this time around”, you can bring valuable new datasets into your predictive models that take into account market pressures and trends that are happening right now. 

For example, let’s say you’re a fast fashion brand and a hot new starlet has just been photographed at an awards ceremony wearing a dress that fans are going crazy for on social media. By accessing datasets by external sources that track and aggregate mentions and shares in real time, you can spot opportunities to market or even commission similar items at lightning speed, seizing on the opportunity to drive up sales. 

In an industry like manufacturing, where you need to order in just the right supplies at the right time to create your products and meet demand, the knock-on effects of anticipating surges and slumps in demand can be huge. On one end of the spectrum, you’re less likely to find yourself stuck with heaps of inventory and warehousing costs during an unexpectedly slow period. On the other end, you’re sensitive to market events and trends that mean your product will fly off the shelves and can put in the orders you need and ramp up production accordingly. But this is only possible with the right data at the right time.

Why data science needs great data

Knowledge is power and that knowledge can only come from great data, no matter how sophisticated the algorithms you design. From machine learning products and models through to full-funnel AI, businesses of all sizes use a data science platform in business to capitalize on quality, varied data to improve and grow, and it’s a trend that looks set to continue. Just remember that the success of these predictive models are entirely contingent on the quality, relevance, and scope of the data you feed into them. 

Final thoughts: looking beyond your own data

The right machine learning models are a gift for departments trying to hit their KPIs, figure out how to tweak their strategies, and set achievable targets for the future. At the same time, predictive modes are only as strong as the data you feed into them. If this isn’t accurate and high-quality enough, your colleagues won’t get the results and insights they need to stay ahead of the game.

That means you can’t afford to rely on your internal data stores anymore. Not if you really want to position yourself to power ahead tomorrow. You also need to look outside your organization, figuring out what kind of data you lack, where you can find it, and how it can integrate with your existing machine learning products, predictive models, and data science strategy as a whole. Choosing a platform that connects seamlessly to external data sources and automates cleaning and harmonization tasks is a great first step, as you’ll be able to adapt and evolve quickly to the changing needs of your colleagues. 

In short, to position your company for tomorrow, it’s not enough to have a brilliant strategy for interrogating the data you have today. You need to be poised and ready to incorporate valuable new data sources at speed at the drop of a hat. That’s the secret to safeguarding your success.