As predictive analytics and big data become central to the success of modern marketing strategies, you may feel overwhelmed with the variety of new terms being tossed around. But with a big data revolution already in motion, you can begin implementing and understanding some of these tools to propel your business efforts even further.
Here, we’ll separate some commonly confused methods and tools to lay out the purposes and possibilities with each. For the three data essentials your company should be focusing on, check out our other post: What’s the Best Way to Visualize Your Data?
When you think of business intelligence (BI) you might think of simple data collection. But true business intelligence involves transforming data into useful guidance. Thankfully, BI is no longer a set of unknown tools existing only for mega-corporations who can afford it. Essentially, BI tools offer answers to big questions that matter through reports, KPIs, and trend-reviewing.
BI software allows companies to gather their data into one program, rather than juggling several lower-capability tools like Access and Excel. BI software typically stores data in warehouses, allowing for easier collaboration and collective decision-making within a company.
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On the surface, data science may sound identical to business intelligence, but there are several differences – the most important being that BI provides data visually, while data science extracts data to gain more insight.
Using data science, you can uncover the significance of questions that you didn’t even think to ask. You might also discover rich information, like what kinds of content are most likely to go viral, ways to optimize emails or your rate of customer churn. Instead of being warehoused, data can be shared in real-time.
Data science opens up unlimited opportunities, as businesses can conduct more experimental research and delve into new markets of which they were previously unaware.
Predictive analytics is a branch of data science. It can strengthen your ability to understand customers and tweak your decision-making to be more accurate. Companies can finally have a data-informed strategy for out-performing competitors.
The specific power of predictive analysis is that it gives companies insight into relationships and correlation – does A impact B, and how? While you may not prove causation, the simple finding of a correlation can guide strategic moves and ultimately boost your sales, audience size, and more.
Many mistake machine learning (ML) for artificial intelligence. However, the purpose of ML is not to create technology with advanced cognition. The purpose is to streamline the process of solving certain business problems. Using mathematical and statistical algorithms, ML allows businesses to use data to fine-tune marketing campaigns for maximum effectiveness.
While it may seem that ML is a brand new function in digital marketing, examples of it can be found in many common places – Google’s “did you mean” capability that corrects spelling errors is just one example.
One factor you must keep in mind is that the quality of your data greatly impacts the usefulness of ML tools. Many predict that ML will enable a much higher capacity for “data storytelling,” answering many of the “why’s” that businesses couldn’t determine before.
While you may not grasp each and every concept in big data just yet, or know precisely how and when to utilize every tool, you can begin a slow immersion — one that will eventually lead you to harness more information, make better decisions and build a stronger business.
For more information on these methods and tools, simply reach out to us or visit our website.