- Navid Nassiri
When used properly, data significantly informs your decisions. So how do you extract value from data
We are living in an increasingly data-driven world, with daily volumes only set to grow. By some estimates, the amount of data generated each day will reach 463 exabytes by 2025. There will be 175 zettabytes of data in the global datasphere by then.
Ingesting and leveraging large quantities of data for your business is critical to your success, whether honing your marketing, allocating future resources, or understanding customer behavior. One of our own enterprise clients warns that incorrect forecasting for two consecutive quarters can literally mean the difference between an IPO and a failed business.
The sheer volume of data coming each day from affiliate ecommerce, newsletter subscribers, subscription paywalls, acquisition funnels and third-party cookies means that extracting meaningful insights is more complex than ever before. The task of bringing all this data together and enabling your teams to make sense of it seems daunting... because it is. How do you access this data? Is the data accurate? What do you do with it? How will it benefit your rev ops and business teams? And crucially, is there a business case for data automation?
This, in turn, has put a premium on new tools and techniques to measure performance. Yet data complexity, volume, and engineering challenges continue to block even the most intrepid business teams. The emerging science of Data Operations (DataOps) has been evolving to meet this challenge, and enterprises now stand at the crossroads of data and efficiency.
Either you master your data so your business can evolve based on quantified insight, or your data will keep your teams in an ever-lengthening cycle of manual reporting. Not to mention uncertainty over the accuracy of the data being reported.
To help navigate the data road ahead, we are publishing a series of blog posts to make the business case for data automation, no matter where you are in your data journey. Our goal is to help you glean the business-driven insights that will put an end to the guesswork and enable smarter decision-making.
The content for this series comes from the team at Switchboard Software, the data engineering automation platform used by some of the largest and most data-reliant companies in the world such as Spotify, Dotdash Meredith, the Financial Times, Orangetheory Fitness, and others.
In the forthcoming posts, we will share with you:
How DataOps can unify and automate the measurement of monetization, inventory, audience reach and content performance in real time
Which data sources and formats data-centric companies must tie together to create timely and accurate business KPIs
The best approaches to help you consolidate disparate data in a scalable manner, and the strengths and weaknesses of these approaches
In the meantime, check out some of our other blog posts