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3 Common Data Challenges Enterprises Face, and How to Tackle Them

common data challenges: this is a corn maze.


We’ve talked about the rate at which data is growing in enterprise organizations. It was a major finding in the recent MarketPulse Research survey from Matillion and IDG research. It’s definitely a topic on everyone’s mind, and a topic that’s been well traveled over the years. But there are other common data challenges that enterprise data professionals cite that can be barriers in leveraging data for analytics.


These common data challenges can be caused by the increase in data. They can also be a byproduct of growing pains as enterprises outgrow traditional data systems and processes. All of them can be addressed by modernizing your data architecture and employing the right tools to prepare data for analytics. Here are three of the biggest challenges enterprises face, and how to help solve them.

Common Data Challenge 1: Inability to use data volumes for actionable insights


One in 10 respondents reported that their data volumes are growing by 100% or more per month. And more than 20% of enterprises report drawing from 1000 or more data sources. On the surface, it seems impossible to even attempt to keep up with that kind of growth and the challenge of importing and integrating data from 1000 different places. But it can be done.

Employ modern ELT to gain faster time to insight


A modern, cloud-native ELT tool like Matillion can make that data analytics-ready on two fronts. First, with connectors for dozens of different data sources, extracting data and moving it to the cloud is much easier than it would be with traditional tools.


However, that’s half the battle. You also need to be able to simply and quickly transform that data to get it into a format that’s useful for business intelligence tools.


In the IDG MarketPulse survey, only 28 percent of respondents were loading data into the cloud and transforming it there.  Cloud data transformation is rapidly becoming essential. It gives you access to the kind of power, and elasticity you need to quickly prepare huge volumes of data for analytics. Transforming data more quickly enables you to get insights faster. Scaling up and down as needed helps you control both workloads and cost.


Common Data Challenge 2: Lack of data granularity


Useful data is data that can give organizations deep insights into specific business questions as well as overall trends. Businesses need to retain granularity of data to help examine brand performance, personalize customer experiences, improve forecasting, mitigate security and financial risk, and more. Access to raw, granular data allows businesses to make specific, targeted adjustments in the right areas to improve sales, product and profitability.

However, due to the inherent overhead that comes with traditional ETL tools that do transformation outside of the cloud, you may only have access to aggregated data that lacks the detailed records of the original data. You can retain that raw data in an on-premises data warehouse. But with data volumes today being what they are, you may not want to tie up or add resources to handle all of the raw data.

Granular data, where and when you need it


Instead, you can use Matillion ETL to load that raw, granular data into a cloud data warehouse and store it. Then it’s so it’s available when needed. Matillion can then also transform that data to make it analytics-ready for both broad and targeted analytics.

Common Data Challenge 3: A growing skills gap for data professionals


Enterprises face a data skills gap on several fronts. First of all, data scientists are few and far between. Organizations need to be able to empower others on the team to do the work of parsing and analyzing data based on evolving business challenges, to help a company achieve faster time to insight.


Second, hand-coding is a common bottleneck in transforming data. IDG found that 37 percent of businesses still use hand-coding to transform data for analytics. With massive volumes of data, it isn’t feasible from a human resources or cost standpoint to hire enough highly skilled data engineers to keep up with that hand-coding.

Simplify ETL to democratize data


Matillion helps solve this challenge. It allows data developers and engineers to run complex workflows, orchestrate jobs, and automate ETL steps. This helps relieve the burden of hand-coding and democratize data access and analysis.


Enterprises can bridge the technical skill gap by effectively creating ‘citizen’ data scientists various lines of business. This will ensure access to data for analytics as needed.


Embrace the cloud and modern ELT


As cloud data warehouse adoption increases, organizations are finding new solutions to data challenges that have plagued them for years. But having data in the cloud is only part of the solution. Modern ELT tools that are purpose-built for the cloud are an integral part of ensuring that all of your data is useful for gaining insight, making faster decisions, and innovating quickly.


Just need the facts? Here’s an infographic of the survey findings:

IDG MarketPulse/Matillion infographic


To see how Matillion ETL software can help you solve your common data challenges. Get a quick demo from one of our data transformation experts.

The post 3 Common Data Challenges Enterprises Face, and How to Tackle Them appeared first on Matillion.