Where Did I Put That Detail? How to Navigate Today’s Data Jungle, Without Getting Hopelessly Lost
Data Jungle! Over 2.5 quintillion bytes of data are produced worldwide each day; by 2020, there will be 5,200 GB of data for every person on Earth
If you’re looking for data these days, you’d better bring a magnifying glass – a big one. The jungle of data companies has to search through in order to find the nuggets that can provide actionable insights keeps growing exponentially. For example, when Walmart wants to search for data in order to run a promotion or determine sales patterns, it needs to rummage through almost endless arrays of data – that grows an additional 2.5 petabytes per hour.
There are plenty of statistics that describe how much data there is out there: Over 2.5 quintillion bytes of data are produced worldwide each day; by 2020, there will be 5,200 GB of data for every person on Earth; the average American uses 4.1 GB of data on their cell phone every month, and this is expected to more than double by 2021.
The bottom line is that there is plenty of data to parse through, and when it comes to tracking down a specific piece of data, or even a type of data, the traditional methods of search – usually by BI teams – is to manually write out an API or custom SQL queries to search for the specific information required. But with the sheer amount of data – and its exponential increase – those methods of searching for data are quickly becoming antiquated. What will replace them? What’s needed is a search system that is up to the challenges of the vast array of data, enabling organizations to quickly and easily find what they need.
The only way to do that is to use automated systems that can parse through systems, building an index of the location, relationships, and dependencies of data. The system can then be queried for information organizations need to make solid business decisions. Indeed, the need for an automated data parsing system goes beyond finding specific data to help with sales efforts or to make organizations more effective or efficient.
For example, new regulations, such as GDPR, require organizations to be proactive with their data, agile enough to find information as needed. Under GDPR – which applies to any company that even peripherally works with European residents or entities, meaning just about everyone – companies are required to drop personal information, such as e-mail addresses or purchase histories, on demand. In order to do that, organizations need to be able to track down the information in the many places it is stored – databases, backups, social media posts, etc.
Failure to do so could cost the company in hefty fines. Even if organizations are not asked to find specific pieces of data, they still need to be able to demonstrate to EU authorities that they are capable of doing so. Failure to demonstrate that could also net an organization penalties.
Usually, finding that data is the job of BI, but the manual methods they use to uncover that data would require far more time and effort than they could dedicate to the job.
Exacerbating the problem are issues in the integrity of the data itself, including in the metadata – the classifications used in databases and other storage areas. Often, information in databases is classified or labeled differently. Age, for example, could be listed as birthdate, date of birth, “birthday,” or in different formats (European style, with date preceding month, or American style).
All those differences could make searching for data more challenging, and certainly, make it even more difficult to search for data manually or via simple search routines. Thus the need for an automated metadata management platform that has algorithms already designed to compensate for those issues, and get to the right data, presenting it in a human-readable form for use by the organizations.
In many organizations, there is so much data that they cannot get a handle on what they have. According to a study by NewVantage Partners, 85% of companies are trying to be data-driven, but only 37% of that number say they’ve been successful. Organizations have learned how to collect data, but not how to control it. Don’t let the data control you; in order to get the most out of it, you have to control the data – especially your metadata, the key that helps you unlock the doors to ensuring that you can get what you need out of your data.
Recommended Read: Conversational Commerce: It Takes More Than Great AI