With IoT paving way for connected devices, how is data analytics making businesses more efficient?
October 27th, 2017
More data is always an advantage when coupled with the means to use it. Over the last decade, web-based platforms have exponentially improved the way data can be used to one’s advantage over its life cycle. Real-time analytics, cloud networks, and cross-device compatibility have been great achievements. However, they bring us closer to a future that spells or at least seems to spell, u.t.o.p.i.a. for business intelligence.
The latest IT implementations have set the ball rolling for organizations to maximize access to data alongside ‘miniaturizing’ the workstation. The hybrid architecture concept leads the way to organizations perceiving dependency on globally connected devices, internal devices, and external sources of raw data, correlations, etc.
In such an environment, organization workers may be connected through personal devices to sensor-based utility devices, smart machines, stock markets, global weather systems, smart homes, and smart offices. A telling sign that they will, is increasing adoption of web-based platforms to ensure seamless data flow and robust analytics, which communicate via personal devices as well.
Factors currently eating into costs
Loopholes in data sourcing, processing, and management lead to setbacks such as poor accuracy in communication. This alone can lead to low operational efficiency and poor customer satisfaction. While organizations are moving from desktops to personal mobile devices, people-to-people connectivity has already gone cloud based. At the same time, companies need to be well versed in their IT provisions for security and authentication protocols. While appification leads to great cost cuts, the transition may not have been smooth without the right strategy for digitization, leading to slow adoption and competitive losses.
How close to IoT are organizations
The key strategies for a business to run efficiently include automation of analysis, multi-device and cross-compatible infrastructure, and a flexible application-development model for multiple job roles. With most of the analytics being machine driven and devices generating crucial data through sensors, a global data communication system must include workers, decision makers, partners, and customers. Besides that, cloud-based data sources need tapping continuously. That is why IT transformations focused on wide-scale cross compatibility are becoming more popular. The interplay between operating systems becomes seamless, while organizations can choose relevant, web-based, and real-time working platforms. That puts today’s business environment only a step away from industrial IoT.
To what extent are machines taking over from people?
In order to make sense of growing volumes of data, use of software is inevitable. As data grows, so does dependency on software. Data for an organization may come from external sources, internal processes, or by getting generated from every day devices. Analytics to perform on such diverse data has to be flexibly deployed and intelligence capabilities must be built into applications that provide data-driven notifications for job roles. While humans can interact with interfaces to drive and execute decisions, actions at the ground level are increasingly becoming dependent on suggestions generated by software. This is possible only if the entire business ecosystem, including partners and customers share a platform.
Competitive advantage among early adopters
The drive today is geared towards less time spent on analyzing data, and as a result swifter decisions and more actions. Cost is an obvious advantage of adopting technology before competitors do. Global digitization strategies are driven by the need to reduce technology maintenance costs, and operational costs in the long run. Appification of the enterprise, or brands has led to a significant cost advantage for business ecosystems, be it in consumer retail, finance, manufacturing, or travel & entertainment. However, being better connected to data is a still continuing drive—for being able to create customer experiences competitively.
Business technology is designed to centralize or consolidate data, and distribute the burden of data-driven decisions accurately. Besides empowering the worker in a more transparent environment, technology must link OEMs and suppliers, manufacturers and retailers, customers and customer-service personnel and enable people with the use of accurate data, which is constantly being generated globally.
Capturing the contextual nuances has always been the key ingredient for making businesses more efficient. Often, in traditional systems, it isn’t possible to capture and analyze this context at the desired level of granularity. This has made personalization difficult, be it marketing campaigns, personalized insurances, ecommerce recommendations, manufacturing batch, etc.
However with devices becoming ubiquitous and ever more connected, it is now possible to capture granular context about everything, every interaction, location, etc. including but not limited to consumers, machines, transactions, etc.
Irrespective of the domain, be it retail, marketing, industrial, healthcare, or automotive leveraging devices to capture real-time data and perform streaming analytics on petabytes of data and cross-referencing the same with demographics, past preferences, asset management history, etc. is what is making businesses more efficient.
At Sasken our differentiated expertise in chip to cognition positions us uniquely to help our customers IoTize their dark assets (devices) and leverage AI/ML to build out analytical models which can provide insights into making businesses more efficient.
Author: Ashish Mital, AVP-Digital Platform, Digital Transformation Services