Data is becoming increasingly relevant in our everyday lives, and larger companies invest heavily to acquire, store, and manage that data. Right now, over 12% of Fortune 1000 businesses have invested more than $500 million into big data and analytics, according to a NewVantage Partners survey. But are they using it effectively? In the same survey, less than a quarter of businesses interviewed responded that they were data-driven organizations. In a world where data is king and highly relevant to everyday occurrences, these organizations must begin taking a different approach to their data and cultivating their data culture to ensure data management and data teams are effectively dissecting and interacting with their data functions. 

Data Culture’s Impact on Effective Data Use 

Data culture, when embedded correctly into an organization’s processes and approaches, allows enterprises to effectively organize their data in ways that can accelerate necessary functions. Organizing data systems that provide the right people with access to the right data allows for faster and more efficient decision-making, and it makes decision-related processes about data less risky. Data is collected en masse in today’s world. It’s critical that companies utilize the insights these data pools can provide and begin taking a proactive approach to their data analytics and management. 

While large enterprises may have bigger budgets or more resources available, this doesn’t mean they are using these assets to their full potential. Oftentimes, these larger organizations are wasting money on numerous software applications that are ineffective because of a lack of a strong data culture. Ad hoc solutions and reports become the standard in these environments. 

Enterprises of all sizes and across all industries have an opportunity to study and form assessments on data behavior, data anomalies, and potential opportunities for improvement. With a strong data culture, even smaller enterprises can build an efficient team of data engineers who can proactively recognize and fix data anomalies as they arise, helping to build data trust across the organization. They aren’t just reacting to a perceived fire, but instead initiating an effective preventative strategy that removes all guesswork from the process. 

Dark Data: Is It Leverageable? 

A byproduct of any company is the collection of data, but what happens to data left unused? Dark data is the information assets that organizations collect, process, and store during regular business activities, but fail to use towards other purposes. This means that any data not immediately of use is collected, stored, and forgotten – often at great expense.

Some enterprises may not have the budget to put toward expensive data storage, meaning they either can’t collect as much dark data or they delete it after a period of time. But what if they could do more without spending the money larger competitor enterprises do for storage? With effective data culture and management, enterprises of all sizes could go a step further than their larger counterparts. By taking the time to analyze dark data, these companies can actually:

• Provide context for and tag floating dark data
• Create further insights into collected data
• Form an indexable library of data that can be accessed 

When it comes to data collection and analysis, dark data isn’t necessarily a disadvantage – businesses just need the right tools and strategy. 

How Enterprises of All Sizes Can Leverage Cloud Strategy

Companies of all sizes are experiencing today’s digital transformation, but how well are they embracing it? Today, businesses maintain legacy on-premises technologies and may be enduring a slow transition into the cloud. Creating and employing a cloud strategy with data observability could be a solution to a faster, more efficient datascape. 

According to the IDC, most companies are currently split between using dedicated cloud infrastructure versus non-cloud data infrastructure. However, the same report predicts that in 2022, cloud infrastructure investments will see a 21.7% increase, compared to a marginal decrease in traditional infrastructure. This shows that while on-premises and legacy technologies will still be relevant, both hybrid and cloud infrastructure technologies will see a rise in relevancy. Companies are already seeing end of support for many on-premises infrastructures, and this is likely to continue over time. Making the transition into the cloud will almost be necessary in the future.

Cloud strategy is still new, but it’s growing. In order to stay competitive, companies are realizing they need to shift towards cloud data management. Enterprises who begin using cloud infrastructure alongside an effective data strategy could increase efficiency, lower costs, and simplify data scalability in comparison to slower moving organizations. 

Data Observability as an Accelerator of Digital Transformation

A strong data culture and an efficient data infrastructure are critical ingredients to compete in today’s digital data race. How are these accomplished? Simply put, data observability. 

Multidimensional data observability provides companies the opportunity to observe data processing, data and data pipelines through a single tech-agnostic source. This source then delivers insights and analytics to identify, predict and fix critical issues as data travels from consumption, through transformation and consumption. Any roadblock, anomaly or failure in the system can be noted and analyzed with data observability. As stated before, companies enabling their data teams with the right tools can begin to transition away from only reacting to data fires. Reactive responses to anomalies within the data pipeline will only serve to increase repair costs and even damage overall productivity by creating bottlenecks. With data observability, these data teams can instead begin taking a proactive approach to managing and observing data as it journeys through the pipeline. 

Unfortunately, most companies utilizing data observability tools are only monitoring data, not data pipelines or data compute. Not only do they lack the full picture of their data environment, but they cannot effectively scale their data operations and continually lag behind in the data race. Companies are missing out on critical infrastructure observability, and it’s actually costing them more than just money in the long run. What this means for all businesses looking to outcompete larger competitors is that there are gaps opening for them to win if they have the tools, strategy and foresight to do so.