It is becoming increasingly possible to get a fresh perspective on the business when there is deluge of information and data sources. The need to gain deeper understanding of the data collected across the organisation to improve business decision making will only increase as the volume, variety and sheer velocity of data accelerates over time.
I spoke with Jim Davis, Senior Vice President and Chief Marketing Officer for SAS, about the pressures facing today’s corporate boardroom to become more competitive and the growing need to make informed decisions based on historical trend analysis and predictive analytics. This “IDC In Conversation” interview cuts through the hype to provide insights into how organisations should approach information management and ensure they have the competencies and skill sets to take advantage of Big Data and associated analytics technologies.
Q: What are the key drivers for organisations to rethink their information management strategies to incorporate more of an ‘enterprise analytics’ type of approach and culture?
A: It’s well recognised that organisations that embrace and leverage analytics improve processes, make better decisions and identify previously unknown insights. Organisations are in many ways facing what the consumer is facing: too much information to make an accurate decision in a timeframe that makes sense. The increasing amount of information we can and are storing is just making the problem more evident, hence the focus on Big Data. Approaches to information management to date have not addressed the entire information continuum and have tended to stop at the data management and governance phase. While initiatives in these areas are important, in order to succeed in driving better decision making across organisations, investments must be extended to support analytics, decision management as well as technologies to manage the relevant analytical models and associated processes.
Organisations should look to their information management strategy, supported by a governance framework with underlying capabilities including data management, decision management and management capabilities for the analytics environment.
Q: Are the line-of-business expectations changing in the context of business outcomes relating to analytics? If so, how?
A: Line-of-business expectations are changing daily. As we speak with our customers around the world, we are seeing an increased demand from the line of business to incorporate analytics into everything they are doing, from calculating credit risk on massive portfolios in near real-time to creating marketing offers that are relevant and timely. We have seen and are seeing something of a revolution as more business users see the value in looking forward and predicting outcomes rather than simply looking at the past for insight. The business is expecting faster response on all possible data requiring different architecture like those provided by SAS® High Performance Analytics™, they are expecting real-time performance and the ability to incorporate analytics right down into the decision making process and the systems that support them. There is a real sense coming that an analytical approach will deliver more value and that IT will need to provide robust, scalable and integrated architectures to enable the business.
As SAS continues to innovate with a new generation of high-performance analytics technologies and applications, we are excited to meet these new business challenges for both the line of business and IT executives.
Q: What do IT executives need to think about in terms of architectures, competencies and skill sets as part of an ‘analytics-led’ information management strategy?
A: As I travel and speak to a lot of business and IT leaders, I see a few simple things. Firstly, many of the architectures we have in place don’t support analytics. Secondly, IT executives need to think about how they can architect their IT environments to encourage analytical discovery as well as operational analytics capabilities. Both of these are critical, and both require an understanding of the process of creating and operationalising insights. The skills are also different. For example, being skilled in building a data warehouse or maintaining a historical reporting system requires a different view of information flows and architectures which are sometimes at odds with the very structured and rigid approach required for other systems.
An analytical information management strategy requires skills and knowledge of the traditional part of information management as well as decision management, model management (and monitoring) and analytical model deployment and integration.
Q: Is Big Data new? What will the impact be on information management architectures moving forward?
A: Certainly the term, Big Data, is new, or at least is coming to the fore, but it has existed for as long as computing has been around, and organisations have always struggled to gain meaning from the data they have. Maybe the term will change or devalue in significance, but you can’t poke holes in the concept itself, which is this: there are significant business benefits to be gained from storing and analysing large volumes of data more efficiently.
You can’t put the data back in the bottle, so to speak. Big Data – however you define it – isn’t going away and it isn’t getting smaller. It’s going to keep growing.
IT professionals will need to rethink their information management architectures not just to deal with the volumes of data but to really provide the line of business with the architectures they need to solve problems at the speed of right.
That doesn’t mean Big Data is an issue for everyone or that high-performance analytics is the answer for everyone. But there is a business case to be made for the use of high-performance analytics in many arenas, and those that see the whole idea as overhyped are looking at it too narrowly.
Here’s how I like to look at it: High-performance analytics is, simply, an enabler. Most importantly, it enables you to get answers faster than before. But – and this is important – high-performance analytics is only as good as what you’re computing. If you’re getting summary statistics about your business portfolio, high-performance analytics can give you those reports faster. However, if your system is predicting risk exposure on thousands of assets, you’re going to get those predictions faster than before. Or, if you’re optimising markdowns for millions of SKUs at hundreds of retail locations, you’ll be able to optimise those prices more quickly. That’s high performance analytics.
You see the difference?
A lot of big data proponents are promising things bigger, better and faster. But if the information you’re getting is backward-looking, you’re still only understanding the past faster than before. No matter how fast you go with summary statistics, you’re never going to get to the future.
Q: How do you see the role of IT moving forward in this context? What about the CIO?
A: The CIO is often put in the position of being taken hostage by new technologies. CIOs need to steer the course that provides a stable and well managed set of systems to run the business, or as many call it KLTO (keep the lights on). This is a key area of responsibility given that if an operational system fails you may not be able to conduct business, collect cash, pay debtors or even build a product.
However, we are seeing the increasing need for a broader approach where the CIO is not only a technology leader but a business innovator, looking to provide the business with the new and innovative capabilities to manage large and varied amounts of information and the ability to exploit those for better decision making. The emerging role of the CIO should turn to being a business enabler, a collaborator, a talent manager and IT innovator.
Q: What kind of new structures need to be put in place?
A: I don’t think it’s a matter of new structures, it’s really a matter of understanding the requirements of the business and the architectures required for effective analytics. Many IT groups have competency centre approaches to drive better alignment with business requirements. Whatever the approach you take — embed analytically savvy team members and be prepared to change the style of thinking to support analytical discovery. Create processes and analytics technology that allows the business user to be flexible in creating the key insights that analytics can deliver.
Reprinted from Industry Insights by IDC and SAS.