Data has never been more crucial for businesses. It’s the most valuable form of modern currency. A world backed by smart data insight drives connected cities, influences real-time business decisions and makes every customer interaction, personal and bespoke.
Yet amongst this deluge of data, most organizations are either unaware of the vast amount of data that is available to them, unsure of how to tap in to it all, or don’t have a strategy for looking at it end-to-end to make complete decisions.
It’s why a modern data analytics strategy is critical to deriving the most value from data, but many organizations struggle to get it right. For example, 60 percent of organizations are trying to integrate between four and nine disconnected data silos. Without the right foundation in place, that can be really challenging.
Why invest in data analytics?
In a recent research project we undertook with Enterprise Strategy Group (ESG) we examined the benefits for companies that invest in analytics, compared with those who don’t. The study revealed some fascinating insights, and showed that organizations with the most mature data analytics capabilities powered ahead of the competition.
Compared to organizations not using data analytic capabilities, these companies were 3.2x more likely to outperform on customer satisfaction, 2.4x more likely to have increased revenue per employee over the past two years and 2.7x more likely to see a shorter time to market.
Roadblocks to analytics - three key challenges
But while the benefits of investing in a mature analytics platform are clear, there are several challenges that prevent companies from being able to achieve their analytics goals and dreams.
Firstly, and the most common challenge we see, is around performance. As log management architectures scale, their performance begins to become harder to predict, causing slowdowns in search queries and subsequent processes. As a distributed system that manages a magnitude of ingested data, a large part of search performance relies on the ability of the administrator to predict which data will be queried. But as companies evolve their pipelines and utilize more and more data to glean insights – it is becoming harder for administrators to accurately forecast what data should live where and for how long.
As the analytic platform matures and more data is ingested, IT infrastructure can easily be overwhelmed and search capabilities across the board impacted. This can lead to overprovisioning of infrastructure and reduced efficiency.
Second, in addition to unpredictable performance, are issues around the tightly coupled nature of compute and storage that traditional log analytic deployments use, leading to disruptions and complexity as these environments scale. As capacity needs grow, customers are forced to deploy unnecessary compute resources as well and experience lengthy and impactful rebalancing processes. Likewise, if a customer needs to grow their compute resources, they are forced to grow the capacity as well whether they need to or not.
Thirdly, and by no means last, is that often the teams that run and manage log analytic applications are not the same teams that manage infrastructure. Because of this, there are often dramatic impacts to data pipelines in the form of performance issues, strained resources or outages. The application owners struggle to meet demand of their systems due to the struggling infrastructure, and the infrastructure teams don’t understand the application requirements and dynamics in order to quickly adapt to the ever changing demands.
Data has to work hard
As businesses face even greater challenges and competitiveness than before, there’s not much that analytics can’t help with to move businesses forward. Around the world, enterprises are making investments in data analytics a top priority. Their goals: to boost efficiency, product delivery, and time to market; to grow business revenue, and to improve customer experience and retention.
This is testament to the benefits of data analytics, and how having a concrete data strategy is helping organizations to constantly learn and adapt to customer preferences.
Whilst many organizations have expanded their analytics capabilities by capturing “big data” to explore new business capabilities, forward thinking businesses are those that are accelerating those capabilities by moving beyond experimentation with analytics toward more mature investments and capabilities.
The need for speed
When it comes to analytics, fast matters. It’s why many organizations are turning to the power of all flash, which, coupled with the ability to scale in multiple dimensions, enables forward thinking businesses to experience the speed of distributed systems with the simplicity of a consolidated platform.
It’s also about scale - having the ability to scale capacity, performance and concurrency on a unified fast-file and object (UFFO) platform, allowing data architects to use the same system for a multitude of analytic applications. This means data scientists can focus on their data pipelines instead of battling the infrastructure needed to run them.
In addition, a modern data architecture fit for data analytics needs to protect a customer's investment, ensuring that they can innovate now and well into the future, without unnecessary, often repeat, spend. Like any other business critical app, an analytics pipeline cannot afford downtime. Any outage, planned or unplanned will have a detrimental impact to analytic pipelines and business insights. That’s why businesses are looking for solutions that offer upwards of six nines of availability.
The lifeboat of analytics
When it comes to data analytics, the rewards are plentiful, and while challenges persist, the technology barriers are steadily being broken down. So in the age of the modern data deluge, if you feel like you’re sinking - rest assured that there are solutions to suit your strategy, and if your analytics capabilities are more mature and you already feel like you’re swimming, the only question is how far do you want to go?
- Patrick Smith, Field CTO, EMEA, Pure Storage.
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