There’s no denying data plays a crucial role in business decision making, but organizations today face a conundrum of a different kind. In many cases, the abundance of analytics and dashboarding can be as overwhelming as the flood of data that preceded it.
Static information that is delivered in stacks of reports, often only reviewing the prior month’s results, are limited in their usefulness. Business velocity and competition for management’s attention amid the constant “noise” have rendered traditional reports obsolete and brought new questions to the forefront: Are we examining the right data? And is it being presented in the most effective, timely and straightforward manner?
The right information the right way
Relevancy is one of the most critical qualities for analytics. Users should not have to look for information—the system should recognize that a piece of information is relevant to the user and deliver the insight on an intuitive and need-to-know basis.
Users are being equipped with increasingly “smart” discovery techniques and selective warning capabilities, fueling the augmented analyticstrend. Many analytical platforms already integrate augmented analytics, techniques that automatically evaluate data quality and offer corrective actions; detect trends and correlations in the data; and suggest analytics paths and the most appropriate format for sharing the results.
More intuitive, AI driven interaction mechanisms—for example conversational analytics—are increasingly replacing traditional, predetermined report formats. Conversational analytics enables users to ask questions, explore data, and receive and act on insights in voice or text. It also enables devices to share data and insights through natural-language text or speech.
We already see AI being embraced in B2C applications like LinkedIn, Facebook, Uber, Google and others. Amazon Echo gives us the intelligent personal assistant Alexa which responds to voice commands to provide weather forecasts, play music, set alarms, give reminders, and supply other real-time information. Through machine learning, soon we might see “her” equipped with more proactive capabilities – for example, “I see you just ordered party hats and streamers. Do you need to order paper plates? Would you like recommendations for a caterer? How about some cocktail recipes?”
From rear-view to heads-up
Applying the same conversational analytics capabilities to your organization, a decision maker could query, “Analyze my sales results for the past three months.” But here’s where it gets even more exciting: It doesn’t have to be limited to the rear-view. Self-learning of users’ relevancy triggers means the device could also proactively initiate the interaction and offer forward-looking insights and forecasts.
Imagine getting a notification saying: “I noticed a trend in the Q2 sales results that need your attention” based on past user-generated queries. Or “Here are three things you should consider implementing that could help improve your close ratio.”
It’s an exciting time for organizations that are prepared to revolutionize informed decision-making, to be sure. Wave good-bye to the same old reports and hello to interactive, intuitive, insightful analytics. Doing business the same way you have always done it just will not cut it anymore. AI is here now. Are you ready to make your next move?