Überblick
Artificial intelligence (AI)—and especially its advanced subcategory of machine learning (ML)—has majorly impacted the tech world for over a decade. Concurrently, the enterprise business world has become more focused than ever on the role of analytics in fostering greater insight into key performance metrics and crafting actionable, evidence-based strategy. It was inevitable that AI and data analytics would eventually come together to be used in tandem as AI data analytics. In fact, AI analytics have emerged to be among the most exciting data analytics trends of recent years.
In this article, we'll take a comprehensive dive into the definitions, complexities, use cases, and implementation strategies for this unique fusion of data science and the most cutting-edge tech.
What is AI data analytics?
AI data analytics involves the use of AI or ML techniques to accelerate data science and analysis projects and uncover valuable insights more efficiently. To better understand it, it's useful to define its key elements one by one.
Artificial intelligence: This umbrella term describes the technologies and processes that allow advanced analysis and logic-based techniques to support and automate various decisions and actions, or to interpret events.
Machine learning: ML builds on the foundation of AI. It uses more sophisticated algorithms that allow an automated system to "learn"—and improve its processes—based on the data it continuously retains.
Some ML systems mimic the input/output processes of the human brain, and are known as neural networks. A neural network that has more than three layers is considered to be a deep learning system.
Analytics: Data analysis that clusters, segments, scores and predicts what scenarios are most likely to happen"—e.g., predictive analytics. Data analysis of information from specific segments of the business, like sales or marketing, or of particular domains like websites and apps, is also considered analytics.
By combining AI with advanced statistical analysis to create AI analytics, enterprise users may have the opportunity to leverage data like never before. AI- and ML-driven data analytics allow organizations to analyze, classify, and process many types of data from across a wide spectrum of sources, at whatever scale is required by the use case.
AI-powered data analytics vs. traditional analytics
Computerized data analysis certainly isn't new—organizations have been feeding tables of information into mainframes that filled up whole rooms since the late 1940s. But advanced analytics that run on AI or machine learning are nothing less than a whole new universe, in terms of the breadth and depth of possibilities they can help businesses realize. It's not a matter of saying that more traditional data analytics are valueless, because that isn't true. They are simply limited in the analysis they can provide.
Gartner's analytics ascendancy model is useful in examining the gulf between traditional and AI-powered data analytics. This oft-referenced model shows the value of the four basic types of analytics rising in correlation to the increasing difficulty of actualizing each type.
Descriptive and diagnostic analytics vs. predictive and prescriptive analytics
Basic facts of an event, such as a sale, or raw key performance indicator (KPI) data, like a rep's quarterly upsell rate, are descriptive analytics. They're easy to obtain but only provide hindsight, limiting their value. At the next tier, you have diagnostic analytics, which offer some insight into the why of certain events, but not much in terms of actionability.
If there's one chief limitation of traditional analytics tools that AI-driven analytics models don't have, it's the inability to reliably and efficiently produce actionable insights. The amount of data required for predictive analytics or prescriptive analytics can't be reliably measured through legacy data processing and analysis methods, let alone strictly through strict human intelligence.
AI and ML automate data processing, analysis, and report generation, and can uncover data trends and patterns in seconds or minutes, allowing for real-time insights into critical operations. These technologies can not only predict possible outcomes of future events, but also recommend actions based on those findings— e.g., changing processes to avoid the risk of failure or keeping successful practices in place. This can help enterprises adopt the increasingly valuable discipline of decision intelligence.
ML analytics models also allow for natural language processing, so that usage of the tools aren't restricted to those with "data scientist" or "analyst" in their job titles. Moreover, the sophistication of ML algorithms mean that any analytics solution employing them is dynamic and ever-improving. By comparison, traditional, rule-based business intelligence (BI) reporting is static and inherently limited.
Last but certainly not least, an AI- or ML-powered analytics solution is capable of quickly and efficiently integrating data from disparate sources, thus providing a single source of truth. On a smaller scale, you can also unify your view of specific data sources, ranging from customer purchase histories to unit inventory across multiple facilities.
4 major use cases of AI data analytics
The following are some of the most common—and effective—applications of augmented analytics driven by AI or ML:
1. Sentiment analysis
Analyzing customer opinions supplied through website comment sections, social media outlets, surveys, calls to customer service departments, and so on provides valuable perspective for businesses. ML-based analytics tools use natural language processing to not only ascertain whether feedback is positive, negative, or in-between on a large scale, but also to determine the urgency in certain situations, such as tech support tickets.
2. Recommendation engines
In this use case, users' and subscribers' buying and browsing habits are analyzed instead of their opinions. With ML algorithms driving their analytics, enterprises can examine data points ranging from average session duration to content preferences and create customized recommendations. Amazon and Netflix are perhaps the most obvious examples of this.
3. Chatbots
Although complex consumer queries or concerns still require a ticketing system and a degree of substantive contact, ML-powered chatbots make it much easier on customer service personnel by automating responses to simple issues.
4. Fraud detection
Cases of fraud in the financial sector that might go undetected for days or longer can be discovered and prevented in real-time with AI data analytics, through advanced analysis of transaction and biometric data and the various patterns it forms. This has become extremely important as organized crime outfits and state-sponsored saboteurs have increasingly turned to fraud as one of their principal verticals.
Per Gartner's projections, 74% of organizations will not only have adopted AI but have "operationalized" it for concrete business purposes by 2024. Data analytics is all but certain to be a major part of this, which will lead to a greater diversity of AI analytics use cases. For example, federal agencies are looking into the potential of AI- and ML-driven analytics for modeling the progression of climate change.
How to implement AI analytics
Deploying enterprise AI analytics doesn't need to be difficult. The following four steps will help streamline the process while setting your analytic strategy and ecosystem up for success.
Begin with the end in mind
The first step toward deploying AI-based analytics tools within your organization is simple: You need to understand exactly what your business needs are and what outcomes you hope to achieve by putting AI analytics tools to use.
For example, if the head of the customer service team believes that agents are misreading customers' tones and replying in a way that only frustrates them further, conducting sentiment analysis should be your goal. Alternatively, if marketing techniques appear to be effective in steering prospects through the funnel but final conversions are declining, you'd need AI data analytics to conduct a sophisticated root cause analysis.
Get buy-in from stakeholders
Next, it'll be wise to foster buy-in across the organization for an AI data analytics implementation. People understand the value of both AI and analytics now more than ever, but it's still important for the data team and all relevant business stakeholders to align. Data analysts and scientists must guide non-IT personnel in the optimal use of AI analytics with clear, understandable training, and staff must be open to learning.
Assess your resource needs
Taking stock of your technical resources is also wise. Although enterprises vary so significantly that custom AI solutions are necessary, developing those tools in-house isn't always a realistic proposition for many organizations. Instead, it's best to use third-party AI tools, which are readily available from numerous vendors.
Choose the right platform for your needs
The final piece of the puzzle involves finding the right data analytics platform, one that uses advanced machine learning, is deployable in multi-cloud and hybrid cloud environments, and strikes an effective balance between performance and cost. That is exactly what Teradata Vantage brings to the table.
Vantage can boost your analytics capabilities by scaling machine learning vertically and horizontally, training its algorithmic models on millions of observations each day and programming predictive models to support hypersegmentation use cases. Through its compatibility with all major cloud providers, dynamic workload optimization, and pay-as-you-use pricing, Vantage provides the power, data integration, and flexibility enterprises need to make the most of your data and tangibly improve business performance.
To learn more about Vantage and AI, contact us today. You can also take a look at our blog to review our recommendations for developing an analytics team in an AI-driven world.
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