How to Improve Business Continuity Processes with Machine Learning

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How to Improve Business Continuity Processes with Machine Learning

Most of us wouldn’t make it through the day without technology. Given that it keeps us connected by relaying information in real-time 24/7, it’s no surprise that as technology evolves, so does the role it plays in influencing the direction of business transformation at a global scale.

Machine learning, or ‘training’ an inanimate tool to understand and translate thoughts to actions is making waves, especially in disaster recovery programs. After all, no single person can be held accountable for detecting and resolving every threat infiltrating the organization. Using a combination of behavioral modeling and pattern recognition, however, machine learning stands a better chance at improving business continuity than the humans who created the system!

Project management essentially involves juggling technical, environmental, market and human elements. Since the rights hands ensure that your enterprise runs like clockwork, you’d exercise more caution as a project manager with different teams who report to you. After all, you wouldn’t settle for second best if you’re handed the choice to tap into true potential from within. Machine learning aids you in your efforts to rope in the right people by simplifying the resource requisitioning process. What’s more, it lets you proactively identify resource usage and in turn, maintain schedules with optimal resource allocations. It’s safe to say that the brains behind predictive systems aim to make machine learning people-centric. So,

Which 4 Benefits Fit into Your Business Continuity Plan?

Machine learning predicts risks coming in from different quarters. It lessens the time you’d spend catching and profiling risks by their impact on individual project phases. For instance, it alerts you to sudden member absences when the project’s underway, helping you allocate secondary resource types who are familiar with the line of work their predecessors were working on. This measure allows existing team members to distribute tasks evenly amongst themselves, ensuring no one’s biting off more than they can chew. Other key benefits include:

#1 Transparency over Financial Regulatory Procedures

A fully-trained system memorizes rules and predicts those areas likely to overrun costs. Consequently, you receive a 360° heatmap that lets you investigate the cause behind budgeting excesses. The resulting portfolio collates only those projects that prove to offer business-value in line with your enterprise’s objectives.

More importantly, machine learning detects fraudulent activity with an underlying algorithm that works by blocking attempts to override a set of permissible permutations. Simply put, it prevents unauthorized transactions from taking place with a user-verification system thus protecting consumer as well as company data.

#2 Segmenting Resource Capacity To Demand Curves

Once you know the type of work likely to fall down the chute, you’ll be better prepared for the onslaught and in turn give your teams sufficient time to reorient themselves around new and/or changing requirements. Thanks to the amount of data Machine Learning can consume and store indefinitely, you can create a capacity powerhouse that stores a skills report encompassing relevant skills, experience, qualification and bodies of knowledge.

You can periodically review your existing capacity to determine if it needs downsizing or expansion, in line with the scale and complexity of forecasted work. Unused expenses are lying around, you can revise your budget plan and relocate costs onto other aspects of the project activity while staying within the proposed ceiling. Added to which, you’ll know which way the demand curve bends with predictive algorithms that identifies newer lines of work. Your staff can then be placed on training programs that ready them to take up market opportunities ahead of the competition.

Once you have the requisite resource capacity comprising of several employment types, the next step is to match numbers i.e. their availability against projected demands. This way, you won’t cram extra hands on a simple task that requires fewer people nor assign complex tasks to inexperienced hires. Those projects with a higher likelihood for conversion can be pushed up the queue, with staff who are prepared to work on it. You can then cost your projects such that quality service is billed at affordable rates.

#3 Simplified Product Marketing Versus Customer Outreach

The speed at which machine learning analyzes and reconciles consumer data lets you interpret existing product buying and usage patterns, plus deviations in expected behavior. For example, it predicts areas of poor engagement, thus letting you expedite an appropriate marketing strategy to retain users.

What’s more, users who are segregated by their historic purchases avail product recommendations that narrow down their efforts in finding things that interest them. Consequently, their hours shopping online are spent productively which increases primary conversions from site traffic.

As visibility over customized offers widen, a larger volume of users receive real-time push notifications whenever a product is back in stock or an alternative is up for grabs within a certain timeframe.

#4 Efficient Reporting Functionalities Through Repository Maintenance

While data sits at the core of all operations, its accuracy, cleanliness and relevance is a key differentiator when retrieving reports. With a digitized record covering the entire enterprise, you get to reduce labor and time-intensive paperwork. You can even control who accesses information to prevent it from being changed up too often which would otherwise send ripples of confusion down the line.

Since it frees up more time, your skilled resources can focus their energies on actual BAU activity all the while having a single-point access to their profile including timesheets, scheduled billable hours, task logs and approved absences. A complete data set contains rules that systematically orders calculations and graphs. This means that your numbers are mathematically verified , standardizing information without repetitive rows.

Cutting to the chase, machine learning automates several different processes at once, ultimately creating an environment that always uses the right data. It informs you of your shortcomings as well as capabilities, letting your teams play to their strengths on the right project opportunities.

Although risk gets its rap sheet owing to situations that are forcibly altered, the right predictive tools lets you counter them proactively. In other words, machine learning keeps your business strategy nimble such that you can alter portions of it rather than throw out the entire business model that otherwise functions well. It’s worth remembering that while you jostle against several competitors per industry, you wouldn’t center your enterprise’s value proposition on the same business blueprint. With the right resource management product suite, you’ll enhance your business continuity with an optimized workforce ready to maximize every project and program delivery!

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