Is Big Data Big Noise? And can we find signal(s) within a never-ending fountain of data noise?
The amount of data and the rate at which it is growing in speed and type is unprecedented with Big Data increasing 50-fold from 2010 to 2020.
Does volume equate to value?
Many believe that the jury on Big Data is still out. Not only are the benefits unclear, but how to achieve those benefits with the right mix of strategy, people, and operational execution remains a mystery.
In the ad tech/digital advertising ecosystem, Big Data is built to fulfill the needs of consumers and CMOs. For consumers, Big Data holds the potential to provide a more relevant digital media experience. For marketers, Big Data attempts to fulfill the need to improve awareness, consideration, and eventually acquisition of customers through digital channels. When a potential customer is in the awareness and consideration phase, ad tech nudges the customer forward to acquisition. But our ecosystem must realize it is only one part of the customer journey. With billions of dollars being spent on improving digital funnel performance from customer awareness to consideration to acquisition, companies across the ad tech industry are wondering if there is more they can do to meet the needs of advertisers worldwide – and not just play outside the firewall.
As marketers, we want to acquire as many customers as possible (GET), retain them as long as possible (KEEP), and upsell/cross-sell (GROW) customers – all to maximize customer lifetime value. In order to meet these goals, marketers utilize the ad tech ecosystem to raise awareness and drive consideration for potential customers; however, once a marketer acquires customers, Business Intelligence plays a larger role in retaining and growing them.
That said, the lack of Big Data quality and its inability to adapt to changing business conditions continues to puzzle marketers across industries. Having Big Data often leads to Big Decisions – they just might not be the right decisions. Is there a smarter way to take advantage of the Big Data Tempest?
It’s time to push past Big Data and Big Noise and set the expectations for Smart Data and Smarter Marketing Decisions.
So how is Smart Data really different from Big Data? What makes Smart Data? Three key attributes do – for data to be smart, it must be accurate, actionable, and agile.
- accurate – data must be what it says it is with enough precision to drive value. Data quality matters.
- actionable – data must drive an immediate scalable action in a way that maximizes a business objective like media reach across platforms. Scalable action matters.
- agile – data must be available in real-time and ready to adapt to the changing business environment. Flexibility matters.
For Smart Data to be accurate it has to be validated through a 3rd party benchmark on a consistent basis while Smart Data customers consistently evaluate their ROI. That’s why marketers and agencies should insist that the data they integrate into their campaigns has been vetted and validated by a leading and respected industry source such as comScore or Nielsen.
The days of analyzing reports for days and then acting upon them are gone – taking action that maximizes reach and activity is critical to success. For a marketer, actionable data must drive revenue or market share growth – core value drivers for marketers across industries. That’s why marketers and agencies need data with global scale broken into thousands of precise target audiences to ensure that their campaigns achieve quality at scale to deliver a more relevant digital experience (ad, offer, service) which results in revenue and market share growth.
The days of waiting weeks to prompt business decisions are gone – agile, real-time data is here to stay and grow. Consumers are making decisions in real-time – and data must follow. That’s why marketers and agencies need data which is available in real-time ready to be fed into any major media platform worldwide – structured to deliver action immediately (no waiting). Smart Data should shift and adapt to changing technical requirements and business conditions by utilizing performance feedback loops and first party seed data.
How do you know if your Big Data may not be Smart Data? Look for these clues:
- Data Quality: Do you have constant data quality issues? Are people always questioning the accuracy of your data?
- Data Scale: Big Data isn’t set up to scale and grow your business. Are people struggling for clear business cases based on your data but failing to provide compelling evidence? Is the data driving Revenue or Market Share? Is there a cost reduction business case to be made?
- Real-time: Does your Big Data run in batch and do you have to wait hours to act on results?
- Lack of Validation: Do you have a 3rd party consistently evaluating your data accuracy?
- Actionability: Can you point to the clear business action the data can drive daily?
- Many Resources Needed to Execute: Does your Big Data require a massive army of consultants, platforms, and vendors to fulfill your Big Data strategy?
- Inflexible Data: Is it painful for your data to adapt to new platforms (like mobile) and changing business conditions?
The time to leave behind Big Data is here. The era of Smart Data has arrived.