Behind BI: Infrastructure basics for data-driven organizations

As businesses strive to become data-driven-organizations, a leading global market intelligence agency called the International Data Corporation (IDC) forecasts that there will be 163 zettabytes of data by 2025. How will all of this data be managed, stored and transformed into useful insights?

Many business are quick to ask “how can we use AI and ML to accelerate our business?” But this is where some leaders miss the mark – you cannot implement and benefit from data science tools without prioritizing data infrastructure. Consider, Monica Rogati’s (former VP of Data at Jawbone) diagram below:

The diagram emphasizes that ML and AI journeys are completely dependent on a simple and efficient data infrastructure to collect, move, store, explore and transform data, and turn it into meaningful insight. If your infrastructure isn’t effective, then your analytics won’t be useful. And in Harvard Business Review’s words – if your company isn’t good at data, then it’s not ready for AI.

So, how can businesses ensure that their infrastructure is capable of efficiently handling petabytes and, eventually, zettabytes, of data? Taking it a step further, how can they then use that data to begin implementing AI and ML to become a fully data-driven organization?

The answer lies in the cloud.

Cloud: the key to addressing big data pain points

The truth is that conventional, on-premise platforms are not able to keep up with today’s data influx. Currently, the ICD reports less than 1% of unstructured data, and less than 50% of structured data, is utilized for business decisions. That figure is especially staggering when you consider that unstructured data accounts for 90% of enterprise data.

Inefficient, complex, and monolithic infrastructures are the main reason for this drastic data under-utilization. Gartner reports that managing data volume and speed with on-premise platforms results in 60-80% higher up-front operations and maintenance costs, and 60% higher risk of failure.

Whether you realize it or not, aspects of your everyday life already are likely supported by cloud data infrastructure. Because a significant amount of consumer data and analytical tools are already in the cloud, companies are turning to cloud-supported data infrastructure. The goal of moving towards cloud data infrastructure is to accommodate increasing amounts of data, and to achieve greater data sharing and collaboration, faster insights and reduced operational costs.

The case for public cloud service providers

Because using data effectively requires the right data architecture, it’s important to understand the elements of an optimized BI environment and underscore how cloud plays an important role – what makes cloud service providers (CSP), like Google Cloud, competent to handle big data pain points?

Big data is often in their DNA

For example, the Google Cloud Platform (GCP) has your big data services run on the same proven and reliable technology principles that power Google’s multi-billion-user services. Cloud providers also integrate analytics, AI and ML to consumer applications.

Cloud’s unique approach to data analytics

CSPs’ main goal with their data tools is to free customers from infrastructure maintenance and allow them to focus on analytics. With GCP, businesses can expect to leave scaling performance, availability, and security to their serverless data platform.

However, In her Google Cloud Summit talk in Seattle, Julie Price (Big Data Specialist, Cloud Customer Engineer) stressed that most enterprise data teams are comprised of data scientists, and not infrastructure specialists (who are notoriously difficult and costly to find and recruit). Price recommends engaging an information technology specialist capable of designing an effective data pipeline. Consumers can engage a Google Premier Partner through their assigned account or sales representative. Other CSPs also offer a certified partner network.

Comprehensive and end-to-end solutions

CSPs provide customers with comprehensive and end-to-end solutions such as modern data warehousing, streaming applications, real-time analytics, advanced data visualization, and ML. Some CSPs also operationalize predictive analytics as a logical next step in a customer’s ML journey.

Serverless data analytics

Serverless data analytics removes conventional IT steps, allowing data teams to spend more time on analysis and insights.

Complete foundation for data lifecycle

One of the pain points of legacy systems is that their infrastructure is siloed. Data does not pass from one application to another efficiently, and applications often do not communicate. CSPs offers tools to ingest data at any scale, reliably stream data through pipelines, implement effective data lakes and warehouses, and lastly, perform advanced analytics.

An example of a cloud data platform (Shira Kimchi, Field Sales Team Manager, Google Cloud)

Summing it up

We can’t stress enough the dangers of the common assumption that businesses can “leapfrog best practices for basic data [infrastructure and] analytics” and directly adopt AI, ML, and deep learning efforts. Businesses that prioritize implementing advanced technologies over infrastructure and “automated processes and structured analytics can end up paralyzed.” The consequence of circumventing infrastructure basics are harrowing: “impenetrable black-box systems, cumbersome cloud computational clusters, and open-source toolkits without programmers to write code for them.”

While this sounds like a monumental effort, cloud-based data solutions are an effective tool for businesses to achieve a scalable, elastic, and automated foundation.