In challenging times, when things are most turbulent, the right information strategies can make the most difference. In fact, they can determine an enterprise's ultimate survival. Initially, the focus is on getting the proper information into the right hands, but the question that remains to be asked is, "Now what?"
Where companies have faced major challenges to success, closer examination revealed that data actually existed and analytics could have saved the day. But instead, decision-makers said, "We don't have the right information at our fingertips, so it must not be available." Information often ends up locked in data vaults, and companies still face difficulties in breaking the code and getting the right content to the right business users.Business thinking around data access has been one of our great long-standing challenges. Yet, unlike business intelligence (which has been about historical analysis with an emphasis on why an event happened), predictive performance analytics involves looking forward, looking deeper and extrapolating projections out of data in real time. It's about working like the human brain does, figuring out how to survive in an often hostile and competitive environment. It is about reacting to that environment rather than wondering how your heartbeat or respiration rate compare to historical rates.
How are profit- and cost-sensitive businesses justifying the expenditure in performance analytics? Well before economies stalled, the rationalization began in success stories from companies like Harrah's and Netflix and how they used analytics to drive business models. That is what intrigued and continues to drive the analytic message to CEOs. The trap arrives when an executive thinks, "We've faced challenges before and trusted our instincts, and that's always worked." As Ronald Reagan said, "Trust, but verify." Companies that set themselves apart have looked at the horizon and realized the need for instinct combined with advanced analytics to prosper even in challenging times. CEOs face information-based decisions in disciplines from supply chain to customer relationship management in the context of understanding their business and their markets. But without analytics, prediction is fragmented, difficult and uninformed.
How do companies increase their use of performance analytics? The answer depends on where they reside on the maturity curve of existing BI and data management program usage. For those with existing infrastructure and working programs in place, the next level can be reached comparatively easily, provided they ask the right questions and have sufficient analytic talent. For companies early into information management adoption that are looking to dive into widespread, thorough use of analytics, there is a bigger investment profile. That route can be a struggle because of data quality issues, overlapping legacy systems and a need to change the company's entire operating model. It is a pioneer's undertaking, though for some, the struggle will ultimately be rewarding. Management must be prepared to embrace analytics as a part of decision-making. If decision-makers revert to gut instinct or politics, it doesn't make sense to pour money into a program. From another angle, CIOs who have been most successful in this space are a different breed focused on more than server farms and network analysis. Turning data into a business asset unquestionably takes a different skill level.
There is little utility investing in analytics if the company doesn't know the questions it wants answered. Organizations that have success in the early stages are not those companies that dive into systemic reform, but those that consider where to use specific information in the business and where it will make a difference. The goal of any journey has to be an established point. If you're simply starting off in a particular direction, you're a lot more likely to get lost!The biggest failure rate Accenture confronts in business analytics is where data quality fails, causing people to lose faith in it. In such cases, organizations reapproach with the wrong philosophy, spending time arguing about issues rather than deciding what they need to do. The next biggest hurdle, depending on the industry, is an inability to combine analytics with access to unstructured data, whether it's voice, video or documents. This is important in areas like patient care and credit risk analysis. Because this is a "look before you leap" analytic proposition, businesses need to know what sort of questions they want answered before paying a provider to deliver it.
Analytics is difficult and complex, and it brings a whole new possibility of scientific thinking into the world of management. But even fields traditionally thought to be the domain of qualitative decision-making rather than quantitative data analysis are beginning to show the fruits of information management. Who uses the information and what they use it for is key, as businesses need to be very critical about using analytics to drive their decision-making. Decision-makers who understand and manage with this in mind will make much better and more timely decisions at a point when it can make the greatest difference. Tough economic times mean solid business practices and efficient use of data are simply much more important today. The margin of error may be smaller, but that does not mean it's not time to act.
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