Data infrastructure optimization, availability & security software
Data integration & quality software
The Next Wave of technology & innovation

4 Signs of Big Data Information Overload

If your organization doesn’t have a plan for big data before you start acquiring or generating it, you can end up with a big mess instead. In the old days, you used business intelligence to find answers to questions you already had, but today, there’s so much data you will almost certainly use data itself to lead to questions you have never considered before.

Big data is being used by businesses and organizations of every type, but though adoption of big data is prevalent, analytics hasn’t kept pace, and the result can be information overload. Here are 4 signs that your organization is suffering from big data information overload.

1. Your BI tools are reaching their limits

Business intelligence platforms offer a top-down approach to data, excelling at querying existing repositories. They’re terrific for monitoring, reporting, and answering traditional marketing research questions. But big data turns BI on its head, because big data promises ground-breaking insights, once you know what to look for. With big data, you have more of a bottom-up approach, an approach that is called data discovery, for which tools are newer. Data discovery tools from companies like Tableau are facilitating this process, making it easier to explore data and formulate new questions to which big data holds answers.

2. You’re having a harder time identifying business goals

With a mountain of big data containing insights you may never have considered, it’s hard to identify what your business goals should be with respect to it. Deploying big data without an overarching goal for what you want to obtain from it can be an exercise in futility. Your data science and analytics teams as well as subject matter experts are your allies here. By working with these key players, you can develop and prioritize business goals, and use your data discovery tools to start learning what questions to ask and what objectives to define in pursuit of your overall goals.

3. Data Management involves too many manual steps

Manual processing steps quickly become untenable with large data volumes.

When organizations start experimenting with big data they quickly realize that manual steps that may have worked with previous data analysis initiatives are no longer practical. Management and analytics with big data must eliminate as many manual steps as possible to be useful on a large scale. Specifically, with big data you need to be able to:

• Access data and be able to collect and store it in real-time, near-real-time, or batch modes

• Prepare data for analysis and collect metadata to make huge data sets easier to use and reuse

• Browse data, discover patterns and trends, and curate and manage data sets

• Manage and distribute data to end-users to fully realize its value

4. You’re afraid to “swim” in your data lake

A so-called data lake is a big, object-based repository that holds your data in its native format until it is needed. The more data you have, the better your chances of developing comprehensive insights, but you have to manage your data lake properly, or you’ll end up with a metaphorical overgrown swamp that you might be afraid to enter.

Hadoop is the main way organizations manage the data lake, because it is a scalable raw file system that only seeks out metadata to understand information when it is processing data. Lately, numerous tools have become available to put the power of Hadoop into the hands of businesses. One tool is Syncsort’s ETL (Extract Transform Load) solution – Connect ETL – which lets companies develop analytics tasks without coding and scale up affordably. Such tools make the data lake a much better setting for exploration of data.


Big data involves mining and analyzing enormous sets of often unstructured information. That information may come from social media, sensors, and countless other sources. It’s a different game from BI, which creates analyses from structured data stores. Big data doesn’t mean BI is going away, but that analytics now has an exciting new branch of data discovery opening up.

In the new age of big data analytics, discovery is part of the process, and this will naturally cause goals to shift as discoveries emerge. Syncsort’s big data products give organizations the infrastructure, automation, and processing power that allows them to transition easily from data exploration to insight development, to actionable information.

Related Posts