Compromising on your Data Stack? Solving Top Analytics Challenges

By neub9
4 Min Read

In today’s digital age, nearly every business leverages data to make informed decisions. The desire to be a data-driven organization is strong for many, as it helps businesses stay competitive, improve decision-making accuracy and speed, enhance operations, and discover untapped opportunities. Despite years of investment and effort, many companies still struggle to unlock the full potential of their data. Analytics teams often face obstacles that impede their ability to access data and pull meaningful insights, leaving business leaders unable to see the possibilities beyond. Research shows that 95% of businesses still struggle with operational challenges around data and analytics.

Amid the current economic climate, organizations are now under pressure to do more with less when it comes to their analytics. The challenge is to keep costs low while harnessing market-leading, real-time performance, regardless of where the data lives. This has led many businesses to make compromises with their analytics databases, ultimately hindering their data-driven initiatives in the long run.

Let’s take a closer look at some of the common compromises businesses make when it comes to their data tools, and the possible impact of these decisions on their analytics capabilities.

Common analytics compromises:

1. Slow processing times and inability to scale. Prioritizing factors such as cost and flexibility over productivity in an analytics tool or database can lead to slow data processing times and insufficient analytic capabilities to support complex workloads and real-time analytics. This forces database administrators and IT staff to spend too much time on data preparation and scrubbing. Worldwide, 62 billion data and analytic work hours are lost annually due to such inefficiencies.

2. Vendor lock-in. Sacrificing flexibility in an analytics tool often hinders an organization’s ability to easily integrate with new technologies and systems, locking them into one deployment option. This can impede expansion and innovation, leading to the time-consuming process of replacing existing systems to meet growing analytics demands.

3. Hidden and exponential costs. Some analytics databases are more expensive to maintain, especially if organizations need to purchase additional third-party tools or performance enhancements. This can lead to unpredictable and complicated pricing models that hinder innovation and experimentation.

Avoiding these analytics pitfalls:

To avoid over-compromising in these areas, organizations can follow best practices, such as:

1. Evaluate legacy database environments. Legacy technologies often lack the necessary scalability and are time-consuming to manage, hindering high-performance computing and real-time analytics workloads.

2. Prioritize financial governance. Ensuring centralized financial governance across teams helps avoid hidden costs and maximizes IT budgets, especially in the cloud.

3. Streamline your data strategy. If complexity and costs are hindering the data processes, organizations may need to pivot to a more streamlined data strategy, utilizing modern data solutions and deploying software as service (SaaS) offerings.

In conclusion, organizations should not settle for compromises in their data analytics tools. Implementing these best practices can help any organization fully optimize and unlock the maximum value of their data.

Share This Article
Leave a comment

Leave a Reply

Your email address will not be published. Required fields are marked *