The advancement of technology has brought forth new problems and solutions to deal with them. Now more than ever, data generation keeps growing at an exponential rate. Today, more data means better analytics methods for the benefit of enterprise efficiency and competitive advantage.

As a result of that, several improvements have been made to the way big data is analyzed to shed off the many struggles that data analysts face in the bid to effectively squeeze out insights from data.

This piece takes the backseat to correctly approach data infrastructure from the lens of a data analyst to understand their struggles and how they hope they will be solved.

How it all started…

Tons of messy data lying fallow in various places and in distinct formats is quite a scare when data analysts think about the efforts required to assemble all of this data in one place for proper cleaning to be done.

Data analysts are expected to unravel a solution to this without pushing the organization to break the bank. Now, this is very tricky because the challenge appears unending with the continuous generation of vast amounts of data.

Most of the time, the problems to be solved with this data are time-sensitive. If data analysts decide to resort to manually cleaning this data by writing several lines of code, time will not be optimized for other tasks, thereby lessening productivity.

The data has to be properly cleaned to speak the right insights. This is not possible with several errors like duplicate values and missing values to be resolved manually.

For the errors to be resolved, this data has to be examined. Tons of data can't be examined manually to fault errors and this is where automation comes in.

Choosing the right analytics tool(s) is a different ball game. Having to work with many tools could complicate an already complex situation. With bearing in mind that cost must be managed, how possible is it to get a tool that carries out ingestion, cleaning, and other transformations without data analysts depending on multiple vendors to solve just one problem?

By depending on multiple vendors, there is already a scare of data breaches that can primarily affect a business's reputation.

What Data Analysts Want

Honestly, data analysts just want to do their job, which is to identify insights from data. The labyrinth of this path is unnecessary.

Here is a list of major things that would make the path visible and less demanding for data analysts. All they need is a conducive environment for:

• Reproducible data

The craving is for insights and these insights would be needed regularly. Is it possible that the environment available allows for regular analysis of data as it comes on a daily, weekly, monthly, or yearly basis?

• Doing their job without worrying about DevOps or Cloudops

The presence of so many technicalities doesn't ultimately get the job done, rather it makes the routine more complex. Data analysts want an environment that lauds simplicity and doesn't force technicalities on them to get their work done easily. Yes, simple presentation matters!

• Scalability and Ingestion without limits

The limitlessness of an environment to cater to big data as it is in all its formats and the residing domain is a breather for data analysts- a large percentage of their problems are solved. They can simply put together data from wherever and have it transformed in one place without fear of any weight or performance limit. Now that's a huge flex!

• Less effort, better data

What's better than minimizing energy and recording more output? This is what data analysts yearn for -an environment that demands less and fine-tunes data at its best.

Data Infrastructure To The Rescue…

Data infrastructure should house a robust architecture for all that data analysts want without splitting tasks with multiple vendors and saving costs.

• Seamless ingestion

  Magnetize all data from different sources together without writing code or eliminating some parts of the data.

• Scalability

  Enjoy the huge flex of analyzing big data with an infrastructure that is built for heavy data demands.

• Reusability

  Save time by cloning previous workflows to get similar work done.

• Enjoy simple

  Clean and carry out other complex transformations on data without learning technical stuff.

Data analysts deserve a working stack that is unbothered by technicalities and other glitches.