Click to learn more about author Mark Marinelli.

Think about your day-to-day life. How much of it revolves around technology? We live in a world of constant connectivity. Data is at the very core of how we go about business:

• As a consumer, you expect companies to use data to give you a personalized, seamless shopping experience.
• As a manager you rely on data to help you make smarter business decisions that increase revenue while reducing cost.

The issue with the above scenarios is that they make very lofty assumptions that data is easily accessible and actionable. But more often than not, this is not the case.

The Three Common Data Problems We Hear Most from Organizations

There is a data crisis happening across organizations. While flawed, traditional methods of data mastering (ETL and MDM) worked well enough so that some sense of data could be made. But the growth rate of data collection has far outpaced the limited capabilities of these methods.

Additionally, there is a lack of education around effective and affordable ways to tackle data mastering at scale, which can be very costly (when relying on old methods). Many organizations simply don’t have the resources to invest in this seemingly insurmountable problem and don’t realize there is an alternative solution, so they tread down a narrow path with whatever data they can manage.

As a result of the above, stakeholders across organizations continually face challenges when trying to access their data. Below are the four most common struggles we hear:

Problem 1: It Takes too Long to Prepare the Data

Businesses need to make decisions in real-time. That’s why it’s crucial that the data they rely on to make these key decisions is up to date. But the ETL and MDM methods of data mastering take time. These processes center around rule creation that is extremely laborious, and it can take months or even years to make sense of the data.

Problem 2: I’m Not Able to Analyze the Data

Even worse than delayed data is incomplete data; where users only gets a portion of their data (the easiest portion to access). This forces stakeholders to make assumptions without knowing the whole customer story leaving their decisions flawed at best and wildly off base at worst.

Incomplete data is far too common of a reality for many organizations due to limitations when it comes to data mastering. There are a number of reasons this may happen ranging from data being siloed across various systems to not having the right tools or business experts in place to master it effectively.

Problem 3: I Don’t Trust Our Data

Having access to the data is one problem, being able to glean reliable insights is another. Many organizations know that their data is simply unreliable due to limited business expert involvement and precarious data mining. This is an even worse situation to be in because the time and money invested into data mastering is wasted.

The Quintillion Pound Elephant in The Room

The three problems above are prevalent across organizations that collect large amounts of data. As a result, stakeholders must rely on hunches or leave business processes largely unchanged and inefficient. This problem is only getting worse as data collection grows.

According to Forbes, there’s an astounding 2.5 quintillion bytes of data created every day and in the past two years alone over 90% of the data in the world was generated. Yes, you read that correctly.

The data created and collected by organizations across the world is incomprehensible. As is the rationale that we should use the same approach to data mastering that has been the industry standard for the past twenty plus years.

This is why it’s essential to look at the data problem through a new lense using modern day processes and technologies. An agile approach to data mastering with support from machine learning completely transforms the process making it easy, efficient, and effective to accomplish.