Business intelligence (BI) and analytics continue to be a top CIO investment priority – yet user surveys by Gartner show that only 30% of potential users in an organisation adopt CIO-sponsored analytics tools.
This appears to be changing, however, as organisations invest in making analytics “invisible,” and more consumable and accessible, to the non-traditional analytics user.
“A large organisation makes millions of decisions every day,” says Rita Sallam, research VP analyst at Gartner.
“The challenge is that companies have far more data than people have time, and the amount of data that is generated every minute keeps increasing.
“In the face of accelerating business processes and a myriad of distractions, realtime operational intelligence systems are moving from ‘nice to have’ to ‘must have for survival’. The more pervasively analytics can be deployed to business users, customers and consumers, the greater the impact will be in realtime on business activities, competitiveness, innovation and productivity.”
Gartner has identified key trends for analytics and BI professionals to consider in 2013 and recommendations on how to tackle them.
To make analytics more actionable and pervasively deployed, BI and analytics professionals must make analytics more invisible and transparent to their users – through easy natural language interfaces for exploring data and through embedded analytic applications at the point of decision or action.
As analytics moves closer to the point of action in realtime, a shift is occurring from systems that primarily aggregate and compute structured data, toward analytic systems that correlate and relate structured and unstructured data, and reason, learn and deliver prescriptive advice.
These man-machine partnerships are emerging and becoming increasingly sophisticated in ways that position the machine or application to take more natural inputs, such as written or spoken questions, extending analytics to non-traditional users.
The friendlier, more transparent and therefore more invisible the analytics are to users, the more broadly they will be adopted – particularly by users that have never used BI tools – and the greater the impact analytics can have on business activities.
Moving toward something that looks simple and invisible from the user’s perspective will require a great deal of computing power, extended capabilities and skills, and potential complexity in information management systems.
Business intelligence and analytics professionals should begin by identifying targeted data exploration and high-value decision-making opportunities where making analytics invisible, transparent, context-aware and accessible in realtime to specific constituencies can add demonstrable value.
The growing volume of realtime data and the reduced time for decision making are driving companies to implement realtime operational intelligence systems that make supervisors and operations staff more effective.
The volume of relevant, realtime data is growing, but the time available to make decisions and respond is shrinking. At the same time, virtually all the event data available to human recipients – even news feeds, e-mail, tweets and other unstructured data (content) – is now in digital form so software tools can process it. Effective operational intelligence systems offload as much work as possible from people.
Organisations should offload event data capture, filtering, mathematical calculations and pattern detection to realtime operational intelligence software, to provide better situation awareness to business people. Where the cause and sequence of events are understood, leading indicators can be used to predict situations of threat or opportunity before they occur – so that the response can be proactive. Where this is not possible, the system can be used to improve the outcome by reducing the lag time between events and responses.
Increasing competition, cost and regulatory pressures will motivate business leaders to adopt more prescriptive analytics, making business decisions smarter and more repeatable and reducing personnel costs.
Companies are under pressure to improve the quality of their decisions, while reducing their staffing and complying with ever-increasing regulation to make decisions transparent, auditable and repeatable.
These forces are motivating managers to use decision management software technologies in more places, and also to use more sophisticated forms of these technologies. Decision management software runs on-demand when a person or an application program needs computational support for making a decision. In some cases, the system can make the decision (intelligent decision automation).
In other cases, the system prepares recommendations or performs part of the analysis and presents information to a human decision maker (decision support systems).
Solutions architects should work with business analysts, subject matter experts and business managers to develop an understanding of the kinds of business decisions that will be made and let computers make decisions that are structured and repeatable to conserve people’s time and attention for the thinking and actions that computers cannot do.