Enterprises and organizations always work to improve and mitigate how they respond to challenges and make their businesses agile at the center of every business organization that aims to remain relevant. Big data is transforming how enterprises are viewing and responding to problems they face daily. Consequently, big data plays a critical role in shaping businesses’ strategic policy for the short term and future. 

Business entities, like a company that sells glass barn doors, have already embraced big data. The capabilities this technology brings to the table get better positioned to make well-informed decisions, allowing such organizations to gain a competitive edge in the challenging market. On average, businesses have recorded improved performance and their bottom-line performance has been boosted.

I’m sure currently, you might be wondering what big data for business refers to. In short, we can define big data as a set of digital information and data that organizations and businesses entities use in the analysis. 

Big data provide such organizations with patterns, trends, and associations that directly relate to the customers and products they work with. The data reveals behavior and interactions your organization has with those sets of data. Big data is not intuition-based, but rather a fact-based method backed up with evidence organizations can use to transform and digitize the process. Gaining insights on ascertain targets is the goal of any business organization and having an improved performance is the end goal that big data offer.

Big data is playing a critical role in strengthening business processes. Organizations are now better positioned to access data sets from multiple sources such as mobile devices, websites, and social media. The data collected is critical in assisting businesses in making critical decisions to improve and better customer engagements through real-time data interactions. Consequently, big data is essential in enhancing efficiency and sealing loopholes in business operations. 

Similarly, big data integration in business processes has a ripple effect on the cost of business investments. Big data reduces the cost and resources required to perform the same job it does with efficiency.

For instance, one department that is critical to any organization is the customer service department, and organizations achieve tremendous results when this department is fully optimized. Integrating big data in real-time in this department’s operations have an overall positive effect on achieving the overall organization objectives. Big data allows organizations to personalize each client and offer a personalized service that targets each client’s needs.

Sale is a fundamental aspect of any business entity. Planning on a sales strategy revolves around combining several factors and processes that enhance a successful result. Big data plays a vital role in sales and marketing. Business analytics has proved to be an effective ingredient in achieving the desired results in a short period. Also, enhancing sales and marketing efficiency had improved tremendously when big data gets used to power these processes.

Organizations should strive to develop a better roadmap for compilation and data collection. Consequently, analyzing this data should be done professionally to allow companies to reap the desired results out of this gold mine. Improving brand value, better customer engagement, and delivering the impossible have been made easy by integrating big data into this process.

The process of analyzing big data for business decision-making requires the organizations to follow the laid down procedure that include these steps.

1. Goal Identification

Any aspiring organization to integrate big data in their processes must first identify why they need to have this technology integrated. Organizations must lay down their business goals and operations and related decisions that they make out of these operations. Big data requires that the analytics techniques that will get employed follow the laid business processes for each enterprise. 

2. Creation and Improvisation

Business organizations are working to improve their organizational goals and their performance metric as well. Avoiding non-significant data or any other non-related data sets in your collection and analysis will save time and resources.

During the staging period, organizations must eliminate any non-related data. It allows business entities to plan and stay focused on the goal as outlined in their business plan and objective of integrating big data in their processes. Data cleaning allows quicker and easier goal optimization to take place, and it should be data before the big data analysis process gets started. Once creation and improvisation are in place, organizations can now kick start the data collection process.organizations

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Artificial intelligence (AI) and machine learning can help fleets solve long-running problems and improve safety, if deployed effectively.

That was a message from the Drive 2021 Summit held virtually by AI company Nauto. Stefan Heck, CEO and founder of Nauto said fleets targeting risky behaviors such as hard braking may be focusing on the wrong areas. It’s actually driver distraction that’s a greater risk than hard braking, Heck pointed out.

“Ninety-eight per cent of distractions have no hard braking associated with them,” he said. In fact, he added, three-quarters of the time a driver runs a red light they didn’t even realize it. Artificial intelligence provides insights into the four or five seconds leading up to an incident and “fills in the missing gap.”

Not only does it identify potential risks, it can warn the driver. Traditional technologies only detect a potential incident upon impact, Heck noted.

Neil Cawse, CEO of Geotab, spoke of how machine learning can address issues fleets are experiencing in the field. He gave an example of PepsiCo, which was having a high rate of battery failures on route and resorted to replacing batteries every three months just to reduce roadside service calls.

Pepsi submitted its work orders to Geotab every time a truck needed a battery replaced, which Geotab used to label machine learning data. The telematics provider began monitoring battery voltage at start-up, which allowed it to predict about 80% of battery failures before they happened.

Not yet satisfied, Geotab realized it should also monitor oil temperature to determine if a greater voltage drop at start-up was reflecting a battery in poor condition or cold outside temperatures.

“By giving the machine learning algorithm not just the battery voltage at crank, but also the oil temperature and adding that into the data, it gave a far superior outcome,” Cawse said, noting Geotab was then able to predict 99.8% of imminent battery failures.  

That is typical of how machine learning solutions are rolled out by Geotab, Cawse said. It will initially work with a fleet that’s seeing a real-world problem and after solving it, will roll it out at large to its customers.

Telematics can also be used to improve fleet performance. Cawse said Geotab worked with a logistics fleet to benchmark its fuel consumption against a broader population of similar vehicles in the same duty cycles. It found the customer was getting 7% worse fuel economy than the industry average, costing it tens of millions of dollars annually. It drilled down into the data and discovered one make of vehicle in the fleet was to blame for the poor fleetwide fuel economy.

Other ways Geotab is, or plans to, leverage machine learning include: monitoring electrical systems; identifying the severity of an impact; fuel and driver performance benchmarking; detecting u-turns; identifying the vehicles within a fleet that are best suited for electrification; predicting DPF maintenance requirements; and calculating wait times at ports and border crossings.

It is even working to differentiate between minor damage events and unavoidable jolts caused by things like railway crossings.

Artificial intelligence also allows fleets to focus their training efforts in the right areas. A focus on speeding alone, for example, overlooks the fact aggressive driving is a more reliable predictor of future crashes. To fully exploit the benefits of artificial intelligence, Cawse said fleets should add a camera system to their existing telematics.

“So much more can be done when you add the camera,” he said.

Chris Richards, former executive vice-president and general counsel at FedEx, noted technology has completely transformed driver training.

“The way we used to do it was check rides,” she said of her early days in the industry. “You’d sit in the jump seat, go out and watch the driver every couple of months.”

In driver training, she added, “The human element was the hardest to deal with and that is where I really see artificial intelligence and traditional data being helpful.”

Richard Peretz, former CFO of UPS, added, “I’d argue that artificial intelligence, machine learning and deep learning are going to add much more value to the industry than autonomous vehicles.”

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In March 2021, Google announced that it was ending support for third-party cookies, and moving to “a more privacy first web.” Even though the move was expected within the industry and by academics, there is still confusion about the new model, and cynicism about whether it truly constitutes the kind of revolution in online privacy that Google claims.

To assess this, we need to understand this new model and what is changing. The current advertising technology (adtech) approach is one in which platform corporations give us a “free” service in exchange for our data. The data is collected via third-party cookies downloaded to our devices, that allow a browser to record our internet activity. This is used to create profiles and predict our susceptibility to specific ad campaigns.

Recent advances have allowed digital advertisers to use deep learning, a form of artificial intelligence (AI) wherein humans do not set the parameters. Although more powerful, this is still consistent with the old model, relying on collecting and storing our data to train models and make predictions. Google’s plans go further still.

Patents and plans

All corporations have their secret sauce, and Google is more secretive than most. However, patents can reveal some of what they’re up to. After an exploration of Google patents, we found U.S. patent US10885549B1, “Targeted advertising using temporal analysis of user-specific data”: a patent for a system that predicts the effectiveness of ads based on a user’s “temporal data,” snapshots of what a user is doing at a specific point instead of indiscriminate mass data collection over a longer time period.

We can also make inferences by examining work from other organizations. Research funded by adtech company Bidtellect demonstrated that long-term historical user data is not necessary to generate accurate predictions. They used deep learning to model users’ interests from temporal data.

Alongside contextual advertising — which displays ads based on the content of the website on which they appear — this could lead to more privacy-conscious advertising. And without storing personally identifiable information, this approach would be compliant with progressive laws like the European Union’s General Data Protection Regulation (GDPR).

Google has also released some information through the Google Privacy Sandbox (GPS), a set of public proposals to restructure adtech. At its core are Federated Learning Cohorts (FLoCs), a decentralized AI system deployed by the latest browsers. As the Google AI blog explains, federated learning differs from traditional machine learning techniques that collect and process data centrally. Instead, a deep learning model is downloaded temporarily onto a device, where it trains on our data, before returning to the server as an updated model to be combined with others.

With FLoCs, the deep learning model will be downloaded to Google Chrome browsers, and analyze local browser data. It then sorts the user into a “cohort,” a group of a few thousand users sharing a set of traits identified by the model. It makes an encrypted copy of itself, deletes the original and sends the encrypted copy back to Google, leaving behind only a cohort number. Since each cohort contains thousands of users, Google maintains that the individual becomes virtually unidentifiable.

Cohorts and concerns

In this new model, advertisers don’t select individual characteristics to target, but instead advertise to a given cohort, as Google’s Github page explains. Although FLoCs may sound less effective than collecting our individual data, Google claims they realize “95 per cent of the conversions per dollar spent when compared with cookie-based advertising.”

The bidding process for ads will also take place on the browser, using another system codenamed “Turtledove.” Soon, Google adtech will all work this way, contained on a web browser, making constant ad predictions based on our most recent actions, without collecting or storing personally identifiable information.

We see three key concerns. First, this is only part of a much larger AI picture Google is building across the internet. Through Google Analytics, for example, Google continues to use data gained from individual website-based first-person cookies to train machine learning models and potentially build individual profiles.

Secondly, does it matter how an organization comes to “know” us? Or is it the fact that it knows? Google is giving us back legally acceptable individual data privacy, however it is intensifying its ability to know us and commodify our online activity. Is privacy the right to control our individual data, or for the essence of ourselves to remain unknown without consent?

The final issue concerns AI. The limitations, biases and injustice around AI are now a matter of widespread debate. We need to understand how deep learning tools in FLoCs group us into cohorts, attribute qualities to cohorts and what those qualities represent. Otherwise, like every previous marketing system, FLoCs could further entrench socio-economic inequalities and divisions.

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