Market intelligence company IDC estimates that the total amount of data created and stored by humanity, every single day, is on track to multiply ten-fold between 2018 and 2025. That’s ten times more emails, ten times more Tweets, and ten times more minable public information. The mind-boggling explosion in the ‘Global Datasphere’, and advances in artificial intelligence and machine learning, make data-driven insights ever more powerful.

In this brave new world, data-driven business strategies are necessary to outsmart the competition. Companies have adopted data processing systems for rapid prototyping and deployment of machine learning models to extract knowledge from data. Business analysts use these models to make strategic decisions and generate actionable insights, in fields including competitive and business intelligence, people analytics, lead generation, revenue management, and more. Ignoring data-driven strategies is a recipe for competitive obsolescence.

Business analysts can overlay external data on existing internal data to discover, measure, and formulate key performance indicators. This allows them to act on business opportunities in real time and in ways that maximize business performance. According to Forrester, 56% of corporate decision-makers say their firms are expanding their ability to source external data; another 21% plan to do so in the next 12 months.

External web data is a natural place to start. As economic and social activity comes online, data trails are scattered on the public web. Social media sentiment, product pricing, job listings, and employee sentiment, to name some examples, all offer actionable business insights. Until recently, savvy businesses considered external data analytics a source of competitive advantage. But as mankind continues on its quest to digitize the world, this is rapidly becoming a competitive necessity.

Rather than pay lip service to external data, here are some real-world customer success stories.

People Analytics: A leading film production and streaming company uses our Job Listings dataset for real-time information on its competitors’ global hiring plans. The dataset can be filtered by job title, job type, location, and date of job posting. This allows the company to stay ahead in the war for talent, keep tabs on who is growing in the film and streaming industry, and glean strategic insights into its competitors’ management.

Lead generation: A food delivery startup uses our Store Locations dataset to map restaurants that partner with its competitors. The company’s sales and marketing teams use these restaurant maps to generate qualified leads, giving them competitive insights in key geographical markets.

Trend Forecasting: one of the world’s top beauty brands uses our Reviews by Vendor dataset as part of their trend forecasting. The dataset allows brand managers and product marketers to track user reviews, including ratings and review title, on a company level. By understanding the number of reviews per product and their rating over time, they can determine which products and ingredients are gaining popularity - and which ones are losing it.

For more examples and use cases, please download our business intelligence deck.

According to expert estimates, data scientists spend 80% of their time cleaning and preparing internal and external data for overlay analysis. Thinknum’s sophisticated ML algorithms were developed in-house to do this work for you, delivering structured datasets that save your company scarce resources.

Through our UI platform, users can glean actionable data insights without the need for expensive data science teams. Our simple visualization tools make it easier to use data in ways you couldn’t have imagined even half a decade ago, giving you the information you need to make fast and intelligent decisions. For example, our Dashboard lets you create customized bookmarks by industry, interest, or any other metric you choose, while our Reports function allows you to get printable PDF data insights delivered straight to your inbox.

Companies with experienced data science teams versed in AI/ML tools, meanwhile, will find our API feed gives them the raw structured data they need.

Shoppers must understand the distinction between data originators and data intermediaries when sourcing external data.

At Thinknum, we have developed sophisticated ML algorithms in-house for scraping, cleaning, indexing, and structuring actionable data from the public web. We are not data resellers and only market our own data. We have a robust customer success team and we proudly stand by our products. Our developers have also worked on custom webscraping projects in the past.