Understanding augmented analytics
Traditionally, the intricacies of data analytics belonged to data professionals. They had the know-how, expertise, and software required to execute key processes in the data analytics lifecycle, which includes data exploration and preparation, model design and development, and insights generation and dissemination. Often manual and tedious, the work could take days, weeks, or longer. Business teams waited on the sidelines for information to guide their decisions and actions.
However, given the speed with which companies must now operate in highly competitive digital environments, decision makers simply can’t wait. They need deeper insights—and more of them—quicker than ever. Yet most data science teams can’t scale their operations fast enough to keep up with demands for data analyses, a challenge compounded by big data and other large, complex data stores.
By using artificial intelligence (AI) and related technologies, augmented analytics helps transform how companies generate, consume, and share business intelligence (BI) and business analytics (BA).
Three key components comprise augmented analytics:
- Machine learning (ML). A type of AI, ML uses algorithms to rapidly search historical data, identify patterns, spot deviations, and generate insights and recommendations. ML models thrive on big data and continuously learn from new structured and unstructured data—without human intervention. ML models underlie most augmented analytics capabilities.
- Natural language technologies. Humans and computers can more easily talk with one another through natural language processing (NLP), which interprets human language for computers, and natural language generation (NLG), which translates computer code into human language. As a result, businesspeople can engage with machines in back-and-forth, question-and-answer sessions using familiar domain and industry terms.
- Automation. ML-driven technologies automate routine manual tasks across the data analytics lifecycle. This significantly reduces the time needed to build, train, and deploy ML models. For example, aided by automatically generated prompts, technical and nontechnical individuals more quickly discover and prepare raw data. Near the end of the lifecycle, text-based reports—automatically created and distributed with user-specified frequency—speed insight sharing.
True to its name, augmented analytics doesn’t replace but rather augments human intelligence, intuition, and curiosity. Taking contextual and behavioral cues gathered over time from users, ML models assess human intent and preferences and offer appropriate insights, guidance, and recommendations through natural language. They leave the actual decision making to people.
The benefits of augmented analytics and augmented analytics tools
Your company’s augmented analytics journey may just be beginning, but it’s one well worth taking. Consider the advantages of using augmented BI tools:
- Improved decision making. Augmented analytics helps businesspeople take greater ownership of data analytics and generate actionable insights. Consolidating specific metrics, key performance indicators (KPIs), and other information into customized data dashboards and reports makes complex data more comprehensible. In addition, data storytelling enables natural-language narratives that further contextualize the data with graphs and charts.
- Data democratization. When more people from across departments get involved in data analytics, data literacy increases. Over time, the organizational culture shifts. More teams become comfortable working with data and collaborating to create business value with it.
- Faster data preparation. The process of creating data sets needed to build, test, and train ML models is streamlined through augmented data preparation. Prompted by recommendations tailored to their project requirements, users can choose and consolidate data sets; clean, format, and enrich data sets; and find new data sets to further optimize ML models.
- Reduced analytical bias. Bias caused by incomplete data sets, faulty assumptions, and lack of context leads to inaccurate, untrustworthy results. ML algorithms that analyze huge data volumes—and automated workflows that reduce manual errors—minimize bias.
- Time and cost savings. With fewer manual processes, data science teams can be more productive and commit more resources to higher-level analytics initiatives. Also, as business teams increase their data literacy, they can take on simpler analytics projects, further freeing data scientists for more complex tasks.
Challenges of augmented analytics tools
Integral to many enterprise BI and BA solutions, augmented analytics tools take advantage of AI technologies while respecting human intelligence.
Still, your company should be prepared to address common obstacles that slow adoption. Potential challenges include the following:
- Misconceptions of AI. Some employees might fear that AI technologies will replace them. Communicate openly and help them understand that AI has limitations. Augmented analytics relies on human initiative and domain expertise to deliver value.
- Poor data literacy. Hold workshops and provide mentors to help business teams approach analytics with confidence. Teach users key data concepts and phrases and how to think about data in ways that help their team and business. Highlight successful augmented analytics projects.
- Ineffective data and model management. Train ML models using comprehensive, current data that’s free of errors and bias, and regularly update algorithms to handle evolving data assets. With high-quality data and robust models, your users will trust the tools to help them generate timely, accurate insights.
- Irrelevant results. Show users how to generate information that’s meaningful to their roles and responsibilities. Otherwise, they will grow frustrated and waste time filtering out irrelevant results.
- Insufficient computing power and scalability. Depending on your IT capabilities, an increase in information volumes and processing requirements could affect response times.
Integrating augmented analysis for business success
Rapid “time to insight” is crucial to your company’s ability to establish and maintain a competitive advantage. Rapid “time to meaningful insight” is even more crucial. Augmented analytics tools can help the right people generate the right information so it’s ready when needed.
With augmented BI, businesspeople engage with data through personalized, conversational interactions that enable them to consider data from fresh angles and generate insights pertinent to their roles. In addition, by using augmented analytics capabilities with business analytics tools, they can gain clearer insight into events and trends and then proactively plan for rather than simply react to them.
By quickly and accurately analyzing data, organizations make better business decisions and formulate more effective strategies. Over time, they can drive greater growth and higher revenues.