When the Fitbit debuted in 2009, skeptics dismissed it as a glorified and not particularly accurate step counter. Thirteen years later, the Fitbit is recognized as one of the creators of the modern data-infused wellness culture, with the now-Google subsidiary credited with leveraging the power of statistics and positive feedback to promote healthier lifestyles for countless individuals.
The original Fitbit was super simple, only able to track steps and distance traveled. With each subsequent release, Fitbit added more metrics: heart rate, breathing rate, “active” minutes. When combined with a user’s age, height, and weight, Fitbit could even generate personalized insights. Now, it’s even delivering sleep insights by tracking nighttime movements, heart rates, and skin temperatures.
The rise of the FitBit and the growth of performance-based wellness parallels the rise of the data-driven business. Uber, Doordash, TikTok and others showed the power of their disruptive data-driven business models. Now, every other company is trying to catch up by deploying real-time data to solve real-time business problems. This now requires the real-time ingestion and analysis of data and a way to combine all that data into a convenient, comprehensive, and usable source.
Databricks has become a leader in this effort with its ability to unify data warehouses and data lakes in a single platform. And while no one would argue that Databricks has made the management of data easier, data teams still need to continuously monitor, evolve and optimize their Databricks environments to get maximum trust, scale, and control. In other words, they need a FitBit for data, and it comes in the form of data observability.
The Rise of Unhealthy Corporate Data Cultures
As companies embrace data-driven processes, they are also creating a culture around using and managing their data. A healthy data culture is one in which data is trusted, reused, and supports the business in scalable, risk-minimized and cost-efficient ways. A healthy data culture is not optional. It can determine the success or failure of your data-driven business initiatives, and Databricks is increasingly seen as a critical component of this effort.
Many companies are rushing to migrate their data into Databricks environments, build real-time data pipelines, and/or infuse data into key operations. Without creating a healthy underlying data culture first, they are creating bloated, unfit data architectures, full of duplicative pipelines and unused pools of dark data that are expensive to maintain.
What are some symptoms of an unhealthy data culture, you ask? Here are a few:
But this is precisely what Databricks is trying to solve, and it’s improving the way that enterprises manage and operate with their massive data stores. Thankfully, Databricks allows users to be flexible, which makes it particularly attractive. Data workloads are fairly free-form, and use newer constructs, but they also have fewer native guardrails than some other data platforms. Every Databricks user will be helped with the use of compute capabilities that ensure that, first, data is reliable, but then secondly, that data is operating in an optimal way. Thirdly, users need spend controls for their Databricks usage so they are getting full advantage of their environments without overpaying or under-spending when more resources are needed.
Getting Fit with Data Observability
As we saw, the Fitbit transcended from being a mere gadget to blazing a new trail — easy-to-access personal health observability — that enables and drives today’s wellness culture.
Companies that want to combine a healthy data culture with optimization of their Databricks environments are turning to data observability because it can maximize the return on their Databricks investment with insight into data reliability, performance, cost, and more. Specifically, data observability can provide:
Such a SaaS platform provides an extremely easy-to-use and overarching view over your entire data architecture. Besides real-time monitoring and alerts, it would provide correlated insights to free your engineers from false alarms and alert fatigue, and tailored recommendations to protect your data and data pipelines. This helps create a positive data culture, in which data is inherently trusted, bottlenecks and errors are addressed immediately, and data pipelines are reused, data costs are optimized, and data ops teams are efficient, productive and happy.
Building a positive corporate data culture is impossible without ongoing, multi-layered and data observability. Without a Fitbit to provide visibility into their data operations, they won’t realize that for every step forward they take as a data-driven business, they are also taking two steps backward.
Acceldata Platform Uses AI and Actionable Analytics to Improve Data Culture
Here’s an example, a multinational provider of data and AI-driven insights to more than 90 percent of the Fortune 500.
The heart of this company’s business is its mission-critical data cloud: a multi-petabyte data warehouse storing 460 million business records in AWS EMR. That data literally drove all of the company’s $2 billion in annual revenue.
However, the company had an unhealthy data culture due to its reliance on a legacy data integration tool. The company’s data was not well-trusted due in part to the lack of a global view into or control over its data quality. Its data cataloging features were inadequate, making it difficult and time-consuming for internal employees to find relevant, reusable datasets and data pipelines. Scaling and automation were also lacking, creating too much manual labor for its demoralized data ops team.
To improve the health of its data culture, the company chose to deploy Acceldata. Our data observability platform is helping the company eliminate bottlenecks and outages, enable single-click scalability, and optimize the price-performance of data workloads.
This will also help the company automate monitoring, validation and remediation of more than 4 PB of existing data, as well as validate all new data entering the company’s data supply chain. Acceldata will also generate comprehensive metadata that the company can use to create and automate its data management policies.
Learn more about how the Acceldata data observability platform is helping Databricks users contextualize delta lake and data science projects, improve data trust, eliminate bottlenecks and prevent incidents, and improve resource efficiency and align cost to value.
Photo by John Schnobrich on Unsplash