So, you have a wealth of data. What next?

In the minute or so it takes you to read this, 4,416,600 YouTube videos would have just been posted. 456,000 tweets would just have flown. Google would have processed 6.3 million searches and 120 new LinkedIn users would have filled in their online resumes. Just short of 1500 lonely hearts would have made their first right swipe on Tinder. And 103,447,520 spam emails would have flooded into our collective inboxes. 

In 2020, every person generated an average of 1.7 megabytes of data a second. While much of this information may seem trivial and personal, it becomes actionable business data for companies interested in harvesting it. And, harvest it they do, to the tune of a staggering 2.5 quintillion bytes a day. To put things into perspective, in the last two years, nine times more data has been collected as compared to the rest of recorded history.   

Data generation of this magnitude is the result of a variety of factors – cheap and infinite cloud storage, powerful microprocessors, an explosion in personal devices, embedded technology and widespread adoption of social media being just some.  

The question is, do we really need all that data? 


When faced with such a huge stream of data pouring into from various sources, holding onto it all would seem to make business sense. But there are inherent problems with prioritizing the quantity of data with its quality. 

The primary issue is that much of the data collected goes straight into silos - internal and external databases and cloud repositories - without being tagged or integrated into an organized database.  

Decentralized data storage results in much of this data simply not being used. A Forrester Research study reveals 75 percent of all data collected by enterprises goes unused for analytics. Another global survey by Sigma Computing shows that, on an average, 63% of respondents feel that the products and tools at their disposal do not give them timely insights to help in decision-making. For businesses who spend big bucks to gather data, that’s a waste of a huge investment.  

Added to this is data duplication, which results in a muddled, overloaded system that doesn’t deliver anything close to what it should. Finally, using inaccurate or irrelevant data can erode customer sentiment towards a brand. Sending offers to people who aren’t the target audience, or sending introductory offers to an existing customer, for example, can give a company the appearance that it is callous, or worse, that it just doesn’t know what it is doing. 


A knock on issue is that available data is simply not available for timely decision making – the reason why it was collected in the first place. Not having all the relevant information when it is needed can lead to decisions going awry. For example, a product’s low sales in a market could be interpreted as a product defect by a business leader. But, if clickstream data is also available, it could point out to a website navigation flaw. Or, a distribution database could reveal that the product is understocked, or losing shelf space to a competitor offering better margins.  

All this information needs to be a click away and up to date. But the urge to achieve this often creates in another problem – multiple dashboards, data feeds and databases that work on different platforms, creating a confusing, cobbled-together system.


The FORMCEPT approach to efficiently using data is to not see a data platform as a database with a single view. Instead, it is an architecture that allows multiple functional areas instant access and powerful tools to get a single viewpoint of the data. 

Every bit of data is tagged as it is received by the system. It then resides in a unified pool, making data duplication and junk data impossible. After this, the entire data ecosystem is seamlessly connected. This ensures that every stakeholder in the business process gets up to date data, exactly when they need it. So, the same set of data that a sales team uses to predict demand for a product will also be the basis for production teams to fulfil this demand, and for the distribution team to plan on getting the right SKUs to the right regions.

[caption id="attachment_6076" align="aligncenter" width="729"]Modern Data Platforms: Rationalizing data, Unified Data, Multiple Views It isn’t the quantity of business data that counts. Categorizing it at source and giving it relevance maximizes its value.[/caption]

Storage, analytics, visualization and middleware all work on the same data pool, eliminating the chance of any relevant data being missed. A unified container-based tech stack allows systems to talk to each other, flexibly and scalability. Finally, an easy-to-use interface allows users to exactly pinpoint information relevant to their needs.  

For example, let’s say an airline customer tweets about lost baggage. A modern data platform will simultaneously relay this information to multiple nodes. The legal and finance teams will know about a potential liability, the customer support team will be alerted to call the customer, the marketing team will be asked to assess what compensation or special offer can be extended. And, of course, the operations team can instantly begin to track the lost baggage, at which point the customer support gets the same information to send to the customer. The result is that a disgruntled passenger could turn into a frequent flyer, just because her issue has been solved quickly, proactively and professionally. 

Modern Data Platforms ensure that data becomes an asset that can be utilized to its maximum. It can be toyed with, examined from every angle and use case and arrives just in time to maximize accurate decision making. And, best of all, it ensures that you never have too much data, but the right data, served up exactly when and how you want it.