The face of business intelligence is changing. Why? The simple answer: almost every business now collects, produces and relies on recorded data.
Thirty years ago, that wasn’t the case. Business then was still conducted largely along relationship lines; customer relationships were created and maintained by human-to-human interactions. Now, as more consumers opt take their business online—online research, online services, online support—most B2C relationships are machine-to-human or, at the very least, system-to-human. Some B2B relationships, such as in the health care or financial industries, are completely automated: they’re system-to-system. Even small-to-midsize businesses (SMBs), under mounting pressure from data-driven competitors, are investing heavily in automation.
As such, Big Data has shifted competitive advantage away from businesses that relied on intuitive human understanding of discrete marketplaces toward corporations that can afford and field automated data systems, which can collect, store, retrieve and analyze more information than a human brain can.
Information is power. And that fact is prompting changes in companies’ BI needs.
Companies need machines that can read people.
Humans are complex characters. They don’t always say what they mean or mean what they say. In the old days, that wasn’t necessarily an impediment to effectively conducting business. A salesperson or service rep could look a customer or a co-worker in the eye and read in their face or hear in their voice the need that should be met.
Computers can’t do that. Not yet, anyway. But companies need them to be able to.
To date, business intelligence has largely been a reactive endeavor, but companies need data analysis solutions that can intuit human needs and meet them before a competitor can, before productivity grinds to halt, before a large opportunity is missed… before the company loses money.
Artificial intelligence (AI) has the potential to help businesses rise above reactive BI cycles. AI-enabled analysis solutions allow companies to re-introduce intuition and proactivity into their decision-making models, through machine learning, even as they retain (and expand) their ability to discover new opportunities through machine-enabled data collection and retrieval.
AI analytics that answer your company’s questions before they’re asked.
One of the insurmountable hurdles to businesses in speeding up the decision-making process has been the necessity of waiting on an employee to make the right query, in a way a machine could understand it. Companies were beholden to the “search” capabilities of their data management systems.
AI will change that.
BI platforms are now evolving, “to the point where enterprises are no longer required to possess sophisticated analysis skills to process and utilize raw data,” wrote Dataconomy contributor and data security consultant Ralph Tkatchuk.
By powering their data management and analysis systems with trainable, intuitive platforms, answers will begin to present themselves at the appropriate time.
Imagine a business cycle in which a system can identify (in real-time) a developing sag in sales, query the data and identify a cause, then automatically alert company stakeholders. Already, then, we’ve done away with the old tasks of building, running, distributing and reading reports — a potentially dramatic time savings.
Let’s iterate further.
What if that same system had the ability to suggest actions? What if it could suggest actions and predict their effects? In a sense, AI-powered analytic solutions could function on a business intelligence level the way a personal assistant like Siri or Alexa functions for a household.
A hypothetical example:
Sales in the Midwest are down over the last 48 hours.
Attributable to price pressure introduced by Competitor Co.’s newly-launched 25% Off Promotion.
We can reduce our price by 30% and remain marginally profitable.
Or, we could temporarily reintroduce last year’s successful 3-for-1 pricing, which resulted in a 14% rise in Midwest market share. If similar share increases occur, we would break even for the next 2 quarters as we attempt to wait Competitor Co. out.
What do you want to do, Manager Smith? Answer REDUCE PRICE, 3-FOR-1 or QUERY TEAM.
That leaves actual command-and-control functions in human hands, but drastically reduces the lag time between problem realization and solution implementation. And it decreases the all-critical time-to-value ratio.
AI-powered data solutions have already disrupted the marketplace.
So what, then, is the Next Big Trend? Mobile-based AI analytics.
Workers in the 21st Century aren’t shackled to desks, 9 to 5, Monday through Friday. They’re traveling on business. They’re working from home. They’re checking their e-mail via Bluetooth-enabled text-to-talk apps as they drive to the office or to the store. They’re as likely to be working at 7 pm on a Saturday as they are to be working at their desks during traditional business hours.
Because modern lifestyles and our Big Data-driven business environments demand it be so. And because the business cycle is now moving fast enough that companies can’t afford to wait to convene a team on Monday to tackle a problem discovered at 11 pm on a Friday.
Companies that want to create and maintain a competitive edge need democratized data to foster decision-making. They need proactive BI on which to base their decisions. And they need decision-making to take place anytime, anywhere.
Mobile-native, AI-powered solutions are now meeting those needs. They’re the critical Next Step in BI evolution. Is your business ready to evolve with them?