Seeking A Competitive Data Analytics Edge With Edge Computing
By Prerna Lal, Faculty, International Management Institute, New Delhi
With the advent of computing systems and its adoption by businesses, enterprises and organizations both in the private and public sector, data stores based around databases and ancillary applications has increased exponentially. In the current Web-based and digital era, even more data hoards and data stores are being created. It’s a given that much of data stored, hoarded or archived loses its value over time. Analysis is the key to keep data useful and relevant for all business-related processes and operations and core organizational activities.
Data in today’s inter-connected times is generated daily from multiple of millions of installed machine sensors, thermostats, mobile data networks, cloud services, intelligent machines and other sensor-embedded devices. Data is generated from computer systems of billions of cars and other vehicles on a daily basis. Then there is streaming data from Web and Internet feeds in real-time from millions of sources-making it almost a nightmare for retailers, manufacturers and marketers, which need to keep track of millions of consumers and potential customers and their data and/or engagement and transactions-related activities. The data deluge and data tsunamis are beginning to overwhelm organizations of all sizes and in almost all sectors. The intersections of data hordes and data points in the larger Web ecosystem only adds to the complication. Such data needs to be analyzed but can be too much for traditional data analytics tools and systems.
Edge Computing has emerged as the panacea or solution to make sense of such data stores and to help channelize data and analyze them in a timely and quickly manner. Organizations and businesses are looking to the promise and potential and dynamism of Edge Computing. Defined as per conventional terms, Edge Computing pushes computing power to the edges of networks and systems. Also referred to at times as mesh computing, Edge Computing pushes computing applications, services and data away from centralized nodes to the extremes of networks.
The ability to carry out deep analytics at the source of data and data stores in real-time is what distinguishes edge computing from other plain-vanilla data analytics suites. Stand-alone components of networks and systems can do self-intelligent analysis of streaming data and communicate with each other to accomplish tasks instead of having to rely or dial back centralized computing systems or from networked back-end data stores and data centers. This gives a lot of power to Edge Computing and has certainly revolutionized analytics and its applications for businesses and organizations. Research studies and certain industry-specific best practices from early adopters showcase the many benefits of edge computing; mainly facilitating autonomous working and delivery of intelligent information and data insights to business and line managers and other key organization decision makers quickly and far more efficiently.
The adoption of Edge Computing and its widespread usage cannot have been timelier in today’s era. The digitization of businesses and the always-on and on-demand nature of most businesses due to hyper-connected consumer segments and audiences have necessitated the need for Edge Computing. Businesses in this hyper-competitive era now well understand that time, and timely insights on customer intelligence is indeed money and that Edge Computing can be the answer to myriad and complex data challenges and issues and how to manage them. It is the time value of data, which makes Edge Computing so important to critical decision making processes. The proliferation of the Internet of Thing (IOT) sensors, social media, mass media Web feed and other streaming data has compelled organizations to channel the force-multiplying effects of Edge Computing. The key for them is to get real-time analytics, which is sharp-edged and insightful and help impact the bottom-line and also do pre-emptive disaster or crisis management, if needed.
Businesses across sectors and segments have cottoned onto the need to integrate their Edge Computing solutions encompassing cloud services, ubiquitous mobile platforms and distributed storage across on-site data centers and offsite locations too. Service providers thus need to adapt to the evolving needs of businesses and configure Edge Computing solutions accordingly by offering the near-customized suite of software, technology and services, which would give a cutting-edge to data analysis to address dynamic and fluid data-analytics related challenges. Analytics at the edge or source can simplify and quite exponentially speed analysis in real -time and pare costs over the medium to long-term. Enterprises and businesses in sectors ranging from oil and energy, banking and financial services, real estate, retail, healthcare, transportation and logistics, industrial automation, microelectronics manufacturing and others can weigh the insights generated from such speedy, on-source analysis to derive sharper-edged intelligence and configure offerings and services for their customers.
Critical use cases and time sensitized businesses and sectors now contend that Edge Computing has really helped and has proven to be a great fit as even small time lapses can be prohibitively costly for any time lag in offering solutions or addressing customer-centric issues. Information gained quickly due to local processing power of parsing vast troves of source data for quick and speedy analysis is really helping such sectors as mobile healthcare, sharing economy sectors, oil and gas industry, transportation and logistics. For more detailed analysis and to fine-tune macro business leveraging of opportunities, the data from preliminary Edge Commuting analysis can be meshed with deep Big Data Analytics. This facilitates quick turnarounds needed to keep businesses running day-to-day and then manage large-scale business operations and macro strategy–led business decisions based on trends analysis thru Big Data over the medium-to-long term.
Edge Computing is here to stay and will be relevant for the foreseeable future. The very data-centric nature of businesses and the ubiquity of IOT have changed the business rules of the game. Organizational thinking for those late-on–the-draw indeed needs to change as the benefits far outweigh the transition pangs of adopting Edge Computing. The chance to scale back large, unwieldy budgets of central data analytics-based networks and infrastructures is promising and paves the way for more time-sensitized and efficient data analytics and for long-term impact on organization IT budget allocations. The additional benefits of avoiding latency and gaining near-time intelligent insights again and again cannot be overstated.
Those early adopters and pioneers as always are showing the way forward for many businesses, and lessons can be drawn cross-sectorally. Edge Computing solutions adopted well can suitably address the latent computing and data-centric challenges of today.