August 29, 2021

Putting AI to Work to Derive Insights from Data Analytics

It might sound dramatic, but Big Data analytics is useless without AI. According to a Gartner report, 77% of executive respondents say data science is essential to organizational success.

A 2018 McKinsey survey further revealed that half of all respondents found that Big Data and analytics have either significantly or fundamentally changed sales and marketing functions. Another third says that innovations in this space have done the same for R&D.

Artificial intelligence, automation, and machine learning are increasingly taking on a larger role when it comes to driving business growth. Perhaps more importantly, these technologies are largely responsible for making the promise of Big Data a reality, and as of late, bringing it to the masses.

In this article, we take a look at how AI-powered data analytics make Big Data a valuable business asset.

How AI Analytics Support Human Works

AI analytics are changing the game for human workers in countless ways, and not in the way that many feared. Rather than replacing us in the workplace, AI and data analytics are converging in ways that allow humans to create more value. Here’s a quick look at just some of the long list of benefits this union provides.

Reduced complexity and automated processes

Machine learning and AI tools can identify the best course of action. For example, if you try to perform root-cause analysis, manual investigations require humans to work through multiple possibilities, one step at a time. This method is too slow, particularly if you’re dealing with an urgent threat.

AI analytics automatically evaluates factors that contributed to an event. The system quickly determines a cause and recommends next steps. Platforms with automated insight generation can comb through data and surface interesting information on their own.

Improved data literacy

AI-driven analytics platforms are also becoming more accessible through self-serve reporting tools, including visualizations and explanations that make it easier to get to the point of taking action. Steps toward augmented analytics are further democratizing data analytic insights.

Guided selling

Many organizations are increasingly investing in AI to support their sales staff. AI-driven guided selling typically takes the form of machine learning generated advice offered to reps by way of their CRM or another type of SaaS tool like a sales enablement or engagement platform. In this case, the built-in AI learns by drawing on historical data, meaning it gets “smarter” over time.

Personalization

Personalization is becoming increasingly important to buyers. According to Adobe research, 67% of consumers expect brands to automatically cater to them based on the current context, while 42% say they’re annoyed when content isn’t tailored to them.

In B2B environments, consumers are willing to pay more for solutions that match their exact business needs. AI allows brands to focus on individual needs at scale. That said, CMSWire makes a critical point: content creation, design, and a deep understanding of your entire customer journey are essential prerequisites that must be in place before any algorithms are introduced to the mix.

Predictive, prescriptive and diagnostic analytics

AI analytics are becoming more accessible, with self-serve platforms, allowing users without a background in statistics or data science to tap into advanced predictive, prescriptive, and diagnostic capabilities. Additionally, Gartner points toward an emerging trend known as “explainable AI.” It aims to increase transparency around the insights generated by algorithms. Explainable AI describes a model, predicts likely behavior, and then points out strengths, weaknesses, and potential biases.

Anomaly detection

One of the big challenges of data analytics is the fact that it’s difficult to identify incoming threats or events that don’t conform to the patterns associated with past events. When AI enters the mix, organizations gain the ability to detect unusual events hiding out in their Big Data sets—events that typically aren’t possible for humans to detect on their own.

Anomalies include anything from fraud detection to catching an unknown type of malware as well as multiple failed log-in attempts that indicate there’s an internal security attack in progress. In IoT use-cases, anomaly detection extends to predicting part failures or identifying a gas leak that could hurt nearby workers.

AI at the Machine-to-Machine (M2M) Level

Arguably, the more exciting aspects of AI-enabled Big Data analytics revolve around the game-changing potential these technologies bring to human workers. The promise of knowing everything about your customer or gaining total visibility into the supply chain is likely more alluring than audit trails and data preparation.

That said, Big Data works behind the scenes to address the data problems that Harvard Business Review calls “low-hanging fruit.” This can save companies a ton of time and help them avoid the headaches associated with security breaches and regulatory non-compliance.

M2M AI data analytics use-cases include activities like data wrangling, data preparation, maintaining system security, and automatically tracking data lineage. AI can also help enforce access controls so companies can stay in compliance with increasingly complex privacy regulations. According to the CMSWire article mentioned above, AI and machine learning tools are making it possible to deploy and maintain complex data analytic systems, helping data science teams avoid falling into “technical debt” by maintaining data quality behind the scenes.

From a cybersecurity standpoint, AI tools provide full visibility into a network, allowing IT pros to detect incoming threats in real-time and identify areas that could be better optimized for performance. Additionally, AI analytics tools are making it easier to build a shared language around data—enforcing naming conventions and categories that help business users, data scientists, and IT stay on the same page, regardless of Big Data expertise.

Getting the Most Out of Your AI Analytics Solutions

Making a long story short, Big Data and AI depend on one another. Here are some tips for making the most out of your AI-enabled Big Data analytics applications.

  • Your analytics tool should include AI capabilities. Without AI, the benefits of analytics are negligible. You’ll end up wasting time trying to understand data manually, which means missing out on time-sensitive opportunities and correlations or context that only intelligent machines can provide.
  • You get out what you put in. Companies need to be strategic about data acquisition. Automating poorly-planned processes or using bad data to train ML only introduces a new set of even more complicated problems. What this means in practice is, if the goal of your AI analytics initiative is to gain a 360-degree view of the customer, you’re not necessarily going to need to analyze your security logs or fleet maintenance data to find out what your customers want from your products/services.
  • AI solutions need to be accessible to everyone in your organization. All users, whether they are tech-savvy or not, should have access to on-demand, actionable insights without calling on the IT team or a data scientist for support. Even if you do have an internal data science team, their time and in-demand skills should be applied toward something more valuable than creating reports.

Finally, it’s also worth noting that the real value of Big Data lies in its quality. If you’re working with low-quality data, the information you’re using to make business decisions, detect threats, and/or understand your customer isn’t based in reality. The point is, if you can’t trust the data analyzed by AI, the insights are worthless.

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