In today’s digital age, almost every business uses data to inform decisions. The desire to be a data-driven organization is strong for many, as it helps businesses remain competitive within their market, improve the accuracy and speed of their decisions, enhance operations and discover untapped opportunity. However, despite years of investment and effort, many companies still struggle to unlock the full potential of their data. Analytics teams often encounter several obstacles that impede their ability to access data and pull meaningful insights, leaving business leaders unable to see what data-inspired possibilities lie beyond. In fact, research shows that 95% of businesses still struggle with operational challenges around data and analytics.
Against the backdrop of the current macroeconomic climate, organizations are now experiencing added pressure to do more with less when it comes to their analytics. The reality is that businesses face a critical data analytics dilemma of keeping costs low, while harnessing market-leading, real-time performance, wherever their data lives. This, in turn, has forced many businesses to make compromises with their analytics databases, ultimately holding back their data-driven initiatives in the long run.
Let’s take a closer look at some of these 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
Analytics teams are frequently pressured to produce efficiency gains without draining their budgets or disrupting existing tech environments. This is often impossible to accomplish without forfeiting one of these key components, like increased productivity, for another, like cost. These sacrifices make it extremely difficult for organizations to realize the full value of their data, and can negatively affect their data-driven initiatives in the following ways:
Slow processing times and inability to scale. By prioritizing factors such as cost and flexibility over productivity in an analytics tool or database, organizations risk burdening themselves with slow data processing times and analytic capabilities that are insufficient to support increasingly complex workloads and real-time analytics. This also forces database administrators and IT staff to spend too much time prepping and scrubbing data, especially as data volumes and varieties continue to grow: 62 billion data and analytic work hours are lost annually worldwide due to analytic inefficiencies.
Vendor lock-in. Modern BI and analytic use cases can be extremely diverse in nature and size, and demand that many organizations invest in additional technology and deployment options to meet growing analytics needs. Therefore, sacrificing flexibility for other elements like productivity and price in an analytics tool often prevents tech stacks from easily integrating with these new technologies and systems. This hinders their opportunity to expand and innovate, and can lock them into one deployment option. The alternative is taking on the time-consuming and burdensome process of ripping and replacing existing systems with new ones in order to meet these growing analytics demands.
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 to fill capability gaps. Without even realizing it, costs can quickly get out of hand with unpredictable and complicated pricing models that hinder innovation and experimentation. Organizations shouldn’t have to pay for better performance as their data, and business, scales. Additionally, businesses can get hit with major cost shocks when they move to run more advanced analytics use cases in the cloud. IDC estimates that planned and unplanned egress charges account for an average of 6% of organizations’ cloud storage costs.
Avoiding these analytics pitfalls
In our present-day analytics landscape, we should be able to deliver productivity gains without overly complicating the tech stack or depleting budgets. Luckily, there are some best practices organizations can follow to avoid over-compromising in these areas. These include:
Evaluate legacy database environments. Many of the challenges listed above stem from legacy database environments. Recent research shows that 88% of organizations continue to be hindered by legacy technologies, as they often lack the necessary scalability and resilience to meet high-performance computing challenges and support the time-sensitive analytics workloads that modern businesses demand. Additionally, they require time-consuming planning, provisioning, deploying, and maintenance – a time-suck that IT staff can no longer afford as it takes away from addressing more strategic business needs and focusing on high-value innovative projects. Avoiding legacy database environments is key to lifting the most value from analytics initiatives.
Prioritize financial governance. To help avoid hidden costs and maximize IT budgets, organizations should focus on fine-tuning financial governance across teams when it comes to applications running in the cloud. Normal approaches to managing infrastructure spend do not apply to the public cloud due to unexpected egress fees and ever-changing cloud capabilities. Therefore, businesses that do not have a handle on their cloud financial management practices are often faced with unexpectedly large bills. When dealing with a limited budget, organizations must make cloud financial management a top priority and a core element of business strategies to ensure they are maximizing the ROI of every project.
Streamline your data strategy. If organizations are seeing growing complexity and costs within their data stack that are hindering data processes, it may be time to pivot to a more streamlined data strategy. Having a top-level, consolidated data approach is critical to conducting effective data-driven decision-making and determining where to invest to help minimize complexity and costs and ultimately enable a business to better leverage their data. Investing in software as service (SaaS) offerings, cloud and other modern data solutions that use infrastructure efficiently can help simplify technical stacks and allow teams to be more agile in their data strategies. Training data analysts to perform more efficiently is key here as well, allowing them to discover new insights faster and more effectively. In many industries, the first one to the insights “wins.”
With the rise of new technologies driving the big business revolution, it’s time organizations stop settling when it comes to their data analytics tools. While the right analytics set up will look different for every business, keeping these best practices in mind will help any organization become fully optimized to help unlock the maximum value of their data.
About the Author
John Knieriemen is General Manager, North America for Exasol. Knieriemen is responsible for strategically scaling Exasol’s North America business presence across industries and expanding the organization’s partner network. Knieriemen has more than two decades of data analytics experience, with deep expertise in new logo acquisition, account management, and solutioning in the data warehousing, big data, and cloud analytics space.
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Great to see this type of forward looking thinking when there is so much legacy development in the market.