Take a minute to appreciate how technology has revolutionized the experience of commuting. Based on contextual details like your home address, calendar entries, and map information, Google applications can now tell you when to leave the house in order to make it to your first meeting of the day or for less regular trips, like ensuring you catch your flight. Other innovations like Waze draw on AI to mitigate the impact of construction, accidents, or other unexpected hiccups you might encounter on your journey.
These same technologies are increasingly being applied to enterprise analytics. This new breed of analytics relies on AI and automation, connections across existing information systems, and role-based assumptions about the decisions stemming from said data and analytics. For example, automation in analytics is reducing reliance on human expertise and judgment by automatically identifying patterns and relationships in data. This can be taken a step further through smart decision-making, wherein the system then recommends what steps the user should take to address the situation identified in the automated analysis based on the data provided.
The following are just a few of the ways in which AI and automation are supercharging enterprise analytics:
The Power of Context
Data and analytics have historically been separate resources that delivered value only after being combined. However, analytics and AI applications can increasingly provide context, and Enterprise Resource Planning (ERP), Customer Relationship Management (CRM), and other key technology vendors are now baking these capabilities into their software.
In one example of automation analytics at work in the field of human resources, a human capital management system can automatically optimize the selection process by using a Natural Language Processing (NLP) of resumes and matching of terms to rank potential hires in order of best potential fit. Once the system has the contextual knowledge to connect background and skill information to job requirements, this process now becomes largely automated—freeing up HR to focus on more hands-on tasks.
Finding the right data and connecting it in order to provide context isn’t the only analytical process to be automated; as mentioned above, we’re increasingly seeing analytics itself being automated and supported by AI. This is now possible largely due to new capabilities in predictive analytics, machine learning, and NLP.
By removing some of the more significant barriers involved in the complex and often time-consuming process of data preparation and analysis, augmented analytics delivers better insights at a much faster pace. In addition, this approach makes it much easier for business users in an organization to obtain insights and apply them to their decision-making.
This new generation of business analytics is in relative infancy, but its long-term implications are promising. Perhaps one of the most critical benefits is its ability to level the playing field for small to midsize businesses that have traditionally lagged behind larger organizations due to smaller analytics budgets and resources. Now they have the opportunity to become more agile, data-driven businesses that can compete with these larger companies.
For more on what AI can do for your analytics strategy, check out this HBR article.