Internally, connected devices within companies will improve operational efficiency by allowing management to identify unprofitable or unproductive processes and remove or enhance them. By converting the physical world into digital data, IoT allows every step to be measured and managed.
Once IoT is integrated into a business's day-to-day process, it can become a powerful enabler for monitoring and improving operations. Manual data collection is slow and inefficient, but connected devices allow information to be continuously gathered and analyzed which enables employees to focus on more strategic, impactful tasks.
High Performance ETL
The amount of data that IoT devices and systems can gather represents a veritable treasure trove of information for organizations. Add machine learning into the mix and there’s the possibility of never-ending insights flowing through IoT devices and systems which can be put to good business use. But extracting that data requires a strategy based on business context and needs. GreenPages helps clients deploy tools and build processes that enable you to extract, transform, and load (ETL) data into smaller sets that make it easier for different groups to analyze and innovate.
One of the biggest challenges with high performance ETL however is that as data volume increases, ETL processes start to take longer to complete, stalling reporting and lengthening the time it takes to analyze results. GreenPages helps clients minimize these performance issues, whether the issue stems from poor database configuration, inadequate indexing strategies, or ETL code itself struggling under the computational load by designing scalable high performance ETL solutions from the beginning.
Automated ETL
Depending on the organization, the length of time it takes for high-performance ETL data to reach an analytics database can be too long. The number of new IoT devices on the market and multiple channels businesses use to engage with customers is also producing more data sources. And when data sets continue to grow, so do the ETL delays.
One approach GreenPages recommends is Automated ETL which speeds up the process of data ingestion, preparation, and storage by automatically mapping, matching, modeling, and merging data into a single database. GreenPages helps clients implement Automated ETL solutions to accelerate stalled or postponed analytics projects and give business the real-time data it needs to make informed business decisions.
KPI Visualization
While every organization would love to track every metric, not every metric is a KPI. Modern businesses use KPIs (Key Performance Indicators) as measurable parameters that can lead to concrete actions that help your organization achieve its goals.
We help clients define and produce the KPIs that matter to their business through workshops and analysis that help you determine what’s meaningful for your initiatives specifically. Whether it’s sales goals, financial projections, new services growth—any area of the business that has a defined goal associated with it can use KPI visualization to help drive results.
Once the KPIs are defined and measured, GreenPages can help you choose from the many different tools available to convey your results through KPI visualization. Interactive charts, graphs, gauges, etc. can all be leveraged to illustrate your findings and powerfully share important business metrics tied to goals across your organization.
Distributed Data Stores
With the new IT paradigm shift of moving applications to where the data lives, the next generation of distributed analytics technologies allows for real-time analytics, security, and extreme performance at the data sources. For IoT sources, data can be collected at the edge or at the gateway or in private or public cloud environments. The challenge for business is not just that these areas are disparate and distributed, it’s that each one has very different requirements and constraints for sending, filtering, receiving, processing, exchanging, and storing data.
GreenPages helps clients build distributed data stores (or distributed databases) where disparate IoT data is stored on more than one node on the network by means of data replication. Distributed data stores provide users with easy access to information over a large number of nodes, and can allow rich query capabilities if required.
Mesh Networks
Modern enterprises are interested in gaining contextual situational awareness of their customers and users. With IoT, there is a wealth of information that business can use to improve services and drive revenue.
Because IoT devices and systems typically communicate on wireless nodes, IT administrators don’t have a centralized access point or ISP backbone to rely on. With mesh networks, IoT devices establish connections with each other; by adding new nodes, IT can scale and build an inexpensive “network of connected things” that are self-healing, capable of keeping up in real-time, and don’t require the overhead of traditional network connections.
