What is pervasive analytics?
During eras of global economic shifts, there was always a key resource discovered that became the spark of transformation for groups of individuals that could effectively harness it. Today, that resource is data. In no uncertain terms, we are witnessing a global data rush and leading companies realize that data will grow enterprise over the next several decades as much as any capital asset. These forward-looking companies realize that to be successful, enterprises must leverage analytics in order to create a more predictable and valuable organization. In some cases they must package data in a way that adds value and informs employees, or their customers, by deploying analytics into decisions making processes everywhere. This idea is referred to as pervasive analytics. But to drive a pervasive analytics strategy and win the data rush, successful companies also recognize the need to transform the way they think about data management and processes in order to unlock the true value of data.
Leading enterprises have already realized the growth potential of developing and operationalizing analytics in order to drive new value through data. Cloudera customers like Opower, an energy analytics provider, have seen this shift first hand. They provide consumers descriptive and predictive analytics to reduce energy consumption; and through these analytics they have already saved over $500 million dollars. Using Cloudera as the central platform, they not only built these external facing analytics but also internal facing analytics that help business users do their job better. But the world of the enterprise hasn’t yet caught on to exposing more of their business to data and analytics; they’re holding back game-changing insights and innovations because they don’t think they are ready. The interaction between data insights and those in charge of making key decisions hasn’t yet reached a level of seamlessness.
Reaching a pervasive analytic state is not an easy task.
The way traditional architectures and processes have been constructed has made it extremely difficult to implement a pervasive analytic strategy that can grow with your business. The reason this is so difficult is because data silos make it challenging to access the data needed; enterprise don’t have the right frameworks for data processing and analytics; and fragmented security and administration takes significant IT resources to manage.
These barriers lead enterprises to make costly upgrades and build one-off analytic pipelines that take months to move into production and don’t always perform as expected. In order for organizations to overcome these common challenges, they must prepare their enterprise architecture and people in order to deploy a pervasive analytics strategy. Preparation comes in the form of extending your architecture to capture larger volumes of structured and unstructured data, providing an environment to foster analytic innovation, and finally, empowering individuals by operationalizing analytics and embedding analytics into end users’ workflows.
Extend. Innovate. Empower.
Can your architecture handle large volumes of structured and unstructured data? This is the first element that any organization must address when they are preparing themselves for analytics everywhere. This includes the ability to keep all data online, the ability to bring in structured and unstructured data sources, and the ability to process data in either batch or stream depending on the business needs.
The next element that you need to think about is analytic innovation. Enterprises need to bring more applications and users to the data so that these data savvy employees can access and interrogate this information to build the proper analytics needed. These employees are using multiple data sources and analysis techniques in order to build a report, model, or rule that they then want to put into production. This process requires organizations to enable employees to discover enterprise data and interrogate it using a variety of techniques ranging from simple search, to SQL, to machine learning.
Once the decision-oriented analytic has been created, the enterprise needs to put the analytic into production. Single discovery-oriented analytics have their place in an enterprise, but should not be the end goal. The end goal is to empower individuals by operationalizing analytics and embedding these decision-oriented analytics into the end users’ workflow, whether that be your customer or employees. If this data sits in a silo or doesn’t reach the end user in time, then the process of collecting, storing, and analyzing the data will be all for nothing.
When the analytic is put into production, the enterprise will start to see the significant data returns by bringing analytics to the masses. If the operational analytic is not moving the KPI it is suppose to be influencing, then the analyst has to immediately remove that analytic from production and optimize that it. This means that they have to either include additional data or change the analysis technique in order to optimize the analytic.
Vendor Solution - Example
Cloudera’s implementation of an enterprise data hub, powered by Apache Hadoop, provides an underlying platform that provides the analytic hub needed in order to achieve a pervasive analytic state. Cloudera’s enterprise data hub provides unified data, multiple processing and analytic frameworks, and the security and administration needed in order to deploy analytics across the enterprise.
Unified Data: Today, only structured data is operationalized across the organization and it is stored in siloed systems that make it difficult to discover and interrogate. But with an EDH, imagine that data is not just a way to support strategic decisions – it drives strategic decisions. By unifying larger volumes of structured and unstructured data with the power of Hadoop, it becomes easier for data to be discovered and new insights to be built. Hadoop provides the underlying technology needed in order to scale processing and storage of any type or volume of data.
Multiple Frameworks: At your company you have limited, scattered frameworks in order to support the analytic lifecycle that usually stops at one-off, ad-hoc reports. An EDH and our partner ecosystem enable you to support all of the different frameworks that are needed to turn raw data into decision-oriented operational analytics. This includes the ability to process data, explore and analyze data, train and test models, and respond to data in near real-time.
Enterprise-Ready Security & Administration: Today, IT is likely juggling multiple data systems that expose their enterprise to security risks and that are time-consuming to manage. With an EDH, you have unified management and security across all of your frameworks and data. This includes role-based authentication, encryption, and key management.
We are in the middle of a data rush. When you are right in the center of a storm, it can seem overwhelming. Where should I start? What do I need to think about? What is the best long-term bet? But don’t forget that more data should mean great news. More data should mean more insight, more guidance, and more strategic direction. However, more data doesn’t automatically rally your entire business around common goals and insights. You need a platform and architecture that can support a thriving, analytic-driven business culture that embraces a pervasive analytics strategy. Cloudera has made pervasive analytics a reality for hundreds of businesses. Allow us to make it happen for yours.