Data-Rich But Insight-Poor: Is Your Business Just Another Victim?
The Data Struggle
The amount of information being processed daily all around the world is starting to be less of a blessing and more of a curse. The sheer volume of data ingested means that there are higher costs for taking in the data, accessing it, processing it and finally storing it. Depending on what kind of storage you need - active, passive or backup storage, the costs can vary widely.
Most businesses process and store more than enough data to use in an effective way. The reality however is that these companies are data-rich but insight-poor - and this is where the key difference between Big Data and Deep Data emerges.
If you think your business fits in this category, ask yourself a simple question, how well do you know your customers?
What is Big Data?
Big Data is used when companies choose to ingest and store all data related to their business activies. From internal client IDs to weather corresponding to the time of when purchases were made, the possibilities are limitless.
Is this the way to go though or is there a better way to do this?
A few years ago, Big Data was one of the most common buzzwords floating around the internet (before Cryptocurrencies came along), with industires ranging from finance to medicine and back using Big Data to supposedly optimize systems, find trends and make predictions for future use-cases through advanced analytics used to draw conclusions from these large repositories of data - we're talking exabytes, or millions of gigabytes of data in each industry.
Since then, things have changed with increasing amounts of data being processed and available to companies which has resulted in massive costs for data ingestion, enrichment, storage, and finally, the biggest costs of all - accessing and processing this data.
Whether it's the time, processing power or analyst burnout, Big Data often created more problems than it solved through information overload - especially through redundant data.
Let's take an example: local weather data might significantly affect industries such as the board game industry (who doesn't love a good board game in the winter?) or utility companies with the amount of load on the electric grid, it is much less likely to be a significant player in financial market performance which are operating on an international scale.
This is where Deep Data comes in, through the application of what's basically the Pareto Principle - meaning that 80% of the effects can be traced back to 20% of the causes. This means that board game sales fluctuate for example can be tied to only 10 of 50 analyzed metrics.
What is Deep Data?
Deep Data is like Big Data's parent - a wiser, more actionable version of the excited young mind that wants to take literally everything in.
Think of Big Data as a curious child who wants to know the smallest detail about anything and everything while Deep Data is the parent that has learned to filter the important things out and focus on the essentials.
This is essentially the core of Deep Data - a filtered, slowed down version of Big Data where the most important pieces of information are chosen, ingested and processed in order to come up with actionable insights within the least amount of time to produce tangible results.
A good example of Deep Data is the use of RFV metrics for publishers to drown out the noise and make sure they take the right actions at the right time to grow susbscriptions, increase ad revenue and ensure subscriber retention.
Should I use Big Data or Deep Data in my business?
This question is a tricky one. The answer is, as it is for most questions - it depends.
If you believe Deep Data Analytics can help you move from simply ingesting, processing and then storing data then you're on the right track. While some firms may need and benefit from Big Data capabilities, all businesses can reap the benefits from Deep Data Analytics.
Some things to consider are the following:
1) Your data scale: is your data scale large enough to justify investing in Big Data solutions?
2) Your analytics capabilities: do you have a team of analysts or data scientists who can be dedicated to sift through the huge amounts of data coming in with Big Data solutions and effectively pick them apart to gain valuable insights?
3) Your budget: are you able to invest a sizeable amount of financial resources and time into implementing the solutions? Should you do this in-house or outsource it?