How to Skip Data Barriers and Ask Bigger Questions

Introducing “Ask Bigger”

In a world where too many of the critical business questions that you want to ask your data are still off-limits, someone had to make a way for you to ask these bigger questions.

But before we get into that, what do we mean when we say off-limits questions? Well, these are all the critical business questions that should and must be answered by your data. They’re the ones you need to ask to be a data-driven and winning organization.

These questions, however, too often don’t get asked. That’s because it seems like there’s just not enough time to dig into them. So much of what’s involved can take days, weeks, even months, like –loading and preparing large datasets, connecting the data sources, creating the needed reports, and getting started with data insights in the cloud.

And it’s not just about time. Too often no one thinks that it’s even possible to ask these questions, or that it’s too complex and costly, or that the answer is in the 90% of the data that can’t be analyzed.

In fact, today, 90% of data isn’t even being touched, and it’s definitely not being analyzed. On top of that, every year a new 90% (and more!) of raw data is being collected, meaning that the gap is growing rapidly and continually.  

So, how is it that we have all this data but we’re stuck without the answers that we need?

To date, there have been five main data barriers to asking off-limits questions:

• Barrier #1: Scale

When we’re collecting greater and greater volumes of data sets, it gets harder and harder to analyze them. So, organizations choose not to scale with their data analytics and believe that they have no choice but to simply ask smaller questions. The result is that the so-called insights they do get are actually far from being accurate.

• Barrier #2: Skillsets

To analyze masses of data, a company needs a team that comes with the right data-centric skillset. And, as we know, finding talent with the requisite knowhow is a major challenge these days.

• Barrier #3: Location

Location is everything, right? Especially with data. But moving data from one location (source) to another – which is critical to the process of extracting insights from data, requires a tremendous amount of time. Moreover, choosing right when it comes to where to store and analyze the data is crucial, but not always easy to do.

• Barrier #4: Time

Queries that don’t get results within a reasonable timeframe cannot bring the value we need, and unfortunately wind up being meaningless. But accelerating time-to-insights is a major hurdle that’s very difficult to overcome due to the existing architectures that are supposed to be enablers, but which weren’t designed for speed.

• Barrier #5: Cost

Getting to big answers from big questions requires big budgets. You’d think that if you’ve got enough budget you might be able to do away with the above mentioned and any other barrier.

But, as the saying goes, money can’t solve everything. And even if it could, CIOs would still need to allocate their budget with prudence if they are to deliver the best ROI on their data investments, and find the right cost-benefit balance for the organization.

So, when it comes to accelerating time-to-insight, you can’t afford not to scale.  You also need to make sure you can find those skilled engineers who will make the pipeline and queries efficient. You’ve got to analyze data close to where it was created. And you need to be able to handle massive data sets in a short timeframe, regardless of their location, and to do all this in the most cost-efficient way possible.

And , basically, to ask bigger, you’ve got to be able to:

• Dig deeper – uncover more meaningful and impactful insights because you have the ability, augmented by AI/ML, to execute more complex data queries.
• Go faster – get insights much sooner and far more cost effectively because you have a nextgen architecture that can run any query with no data prep and at exceptional speed.
• Reach anywhere – access any data you need because you can query the largest datasets whether on prem, in the cloud, or at the edge, all while reducing your carbon footprint.

 

This is what SQream is all about – helping you dig deeper, go faster, and reach anywhere, breaking down the data barriers of scale, location, time, cost and skillset

For us, Ask Bigger is not just a slogan, it’s in our DNA. This is how we operate as a company and this is what we’re enabling our customers to do – to ask bigger questions from their data – and get the answers.

And this is why organizations ranging from fast-growth startups to Fortune 100s, all rely on SQream.

Stay tuned and start Asking bigger.