Innovation in Parallel: Improving Business Outcomes by Separating Analytics and Data Strategies

Extracting Critical Insights Before, During, and After Data Is Moved

For many years, finding insights in data using analytics—and data collection and storage strategy—have been considered a package deal. This is because operations data is often siloed, has limited analytics options, and is in difficult-to-reach places. As a result, some companies embark on huge data strategy and migration efforts, often lasting months or even years. They believe data must be centralized to begin extracting value with advanced analytics technologies. And in the meantime, companies miss out on valuable insights that can optimize assets and processes, minimize waste, and prevent incidents.

However, the idea that analytics and data strategy must be joined is simply not true. Data doesn’t have to be moved to be valuable, and data consolidation is not necessary to access advanced analytics capabilities and gain insights. While data migration strategies can be extremely beneficial to organizations, moving a database doesn’t enable new use cases or derive new insights, it simply puts the data in another location. However, by decoupling analytics from data strategy, organizations can innovate in parallel, enabling users to extract operational insights and improve outcomes before, during, and after a data migration or strategy initiative.

Seeq's Approach to Data Analytics and Data Strategy

Seeq’s approach enables organizations to use data where it is, how it exists today. Whether data is located on-premise or in the cloud, in silos or data warehouses, in data historians or open-source time-series databases, Seeq enables immediate access to advanced analytics functionality. Instead of waiting for data to be moved, Seeq enables engineers, subject matter experts, and other users to quickly identify the most critical areas for return on investment. They can easily find insights in the enormous amounts of operations data that is collected every day, regardless of where the data is collected and stored. This means operations teams can create value prior to any investments or actions in a data migration program.

Seeq informs immediate decision-making wherever data is located, and when data is moved or consolidated, Seeq users can continue to extract insights, regardless of the data’s final location. This means data can be moved in pieces, with users still realizing immediate value.

Making Data Management Decisions Based on Value

For example, organizations realize rapid value by targeting a subset of data, contextualizing it in Seeq, and then migrating the data set. By making migration decisions based on value and not on where data is stored, organizations can make more strategic data migration and long-term data management decisions. This accelerates time-to-value, while removing the turbulence traditionally caused by data migrations.

Enhancing Economic Impact

Beyond data strategy decisions related to data lakes, cloud services, and more, separating data analytics initiatives from data strategy drives economic value because companies aren’t locked into paired migration/analytics offerings. Companies can switch out data storage solutions, which can be an expensive component, from analytics decisions and strategies. Because all of this can be done at the administrator level, Seeq abstracts the complexity of data location and movement from end-users so they can continue to experiment with operations data to drive value and make informed decisions.

Finally, by enabling faster insights on data where it is today, Seeq empowers organizations to make faster improvements in production and more quickly reach overarching business goals in the near term, while informing data strategy efforts for the long term.

Enabling Faster Insights with Root Cause Analysis

For example, a manufacturer was facing compressor failure issues due to vibrations in a bearing. Root cause analysis in Seeq revealed maintenance was required on the asset, but identifying the predictive data triggers proved to be difficult. The issue was presented to the data science team, and they identified a targeted and contextualized data set for migration to aid in building a training set for a machine learning model. Using Seeq, the customer achieved near-term benefits while using those same insights to inform future data migration requirements.

Decoupling data and analytics strategies enables companies to use operations data faster. By extracting critical insights before, during, and after data is moved, manufacturers can optimize asset performance and the production process today and tomorrow, wherever data may reside.

Seeq’s advanced analytics are ready to lead your team to rapid, actionable insights. Schedule a demo of the technology today.