Genetic information is a perfect example of big data – a single sequencer can generate terabytes of data per week. How can researchers use it to speed the development of personalized medicine that promises to optimize health outcomes based on genetic information for each patient? By applying high-performance analytics.

High-performance analytics offer the possibility to analyze what treatment options are best for a particular patient based on their personal genetic makeup, rather than just comparisons to other patients similar to them. The ability to analyze big data extremely quickly means high-performance analytics could move personalized medicine research from the lab to the clinic so it could be used to drive individual treatments.

Stay tuned to hear how hotels and casinos can benefit from high-performance analytics in the next article in this series. Or read more now in this white paper.

In this video interview, Franks expands on analytics, video analytics and his upcoming book, Taming the Big Data Tidal Wave.

However, with the volume, velocity, and variety of data skyrocketing, these three marketing imperatives become even more daunting, and make it even more critical to get timely insights so that marketers can make quicker decisions in an ever-shrinking window of opportunity. It is not just about taking action on complex issues, but taking better actions faster. This is where high-performance analytics (HPA) start to pay off. Technologies that co-locate the data and analytics will become even more indispensable in achieving marketing speed, efficiency, and accuracy.

Here are the top five ways high-performance analytics can provide immediate marketing benefits:

1. Faster, more sophisticated, effective segmentation – With HPA, you can look at all of the data instead of just a sample. In segmenting for campaigns, control or sample groups can be eliminated and segmentation tests can be run against the entire populations in order to determine the best campaign interaction methods. Currently, marketing analysts refer to a sample and derive the key attributes that potentially define a group or segment. Samples then undergo A/B testing to determine which ones provided the greatest lift when making offers.
 
With HPA you can skip the process of extracting a sample and create segments based on the entire data population. HPA gives the ability to extract more robust, precise segments from the start. While further A/B testing could be done on smaller group, marketers would have saved a huge step (extracting a sample) and improved the accuracy of their segmentation process.

2. Real-time, relevant next-best customer actions or offers – Using HPA, much higher numbers of offers and interaction decisions can be processed and determined at one point in time. These offers/decisions can then be queued appropriately and sent instantly to inbound or outbound offer channels such as the call center, customer service agents, campaigns, etc. This results in a more relevant offer or customer interaction surfacing at the “point of need” in real-time.

3. Instant deployment and management of marketing models that give you a sustainable advantage – Marketing models (such as pricing, markdowns, offers, special subscriptions, marketing mix, segmentation, etc.) are constantly being changed and updated due to differences in variance, thresholds, and limits companies place on customer segments prior to campaign execution.With HPA, marketers can not only manage multiple models, but also deploy them with greater speed and accuracy. This allows companies to quickly and efficiently update their numerous models without submitting a slow overnight batch update process. The results: uncover hidden customer opportunities faster, beat the competition, and ensure brand differentiation.

4. 1:1 real-time experiences to bolster brand connections – With HPA, massive amounts of Web behavior data is collected, integrated and analyzed much more quickly, and thus presented to the offer or interaction engine more speedily. The outcome is more precise, real-time interactions with consumers at the “point of need.”

5. Optimized marketing for broader business impact – When the optimal offer or campaign for a customer is determined via marketing optimization – an algorithm is executed using analytics. With HPA’s parallel processing speeds, this optimization computation can be done more quickly and efficiently than in the past. Now businesses can not only determine the customer and financial impacts of their campaigns faster but also adapt instantaneously to market, competitive and customer changes.

At the end, the two key elements that drive all the scenarios I mentioned is that HPA enables businesses to execute their marketing FASTER and with more PRECISION. In today’s world, marketers must be fast to confidently seize new opportunities; to identify, manage, mitigate risks, and to deliver high-value, real-time engagements that matter to their customers. Marketers must also be precise in order to garner higher brand affinity and mind share of customer; create highly targeted, relevant, unique next actions and offers, and attain stronger customer loyalty and advocacy.

For examples of how high-performance analytics is being used in different industries, download this white paper.

Tradeshow opportunities are evaluated at SAS using a standard process we’ve developed, and even so we’ve found that some of the most promising opportunities have not produced the hoped-for results. That said, tradeshows have a place in a well-balanced marketing plan, so one way to mitigate any downside potential is to focus on ways to improve the results with “ongoing impact.” Here are five of my favorites:

1. Look for pre- and post-show touches

Admittedly, there is nothing new or whiz-bang about pre- and post-show touches, but I’d suggest this as one of your minimum requirements when considering any tradeshow opportunity. If the show won’t provide the pre-registration list and the actual attendee list, try to find the value with the 4 suggestions below. Since these events are gatherings of your target market, use your interactions to try to get your audience to opt-in to something, such as your blog or a resource center. Doing that extends your interactions well after the show.

2. Engage with Social Media – before, during and after the show

This is low-hanging fruit when your audience is professional marketers, but there are some industries where social media adoption is just now starting to grow. Whatever the case, use available social media channels because doing that can be more impactful than pre-and-post show postcards or email blasts for three important reasons. First, by engaging in the social media aspects of the show, you are associating your name and that of your company with the show and its “hot topics.” Second, your target market will self-identify and self-qualify themselves simply by following you or “liking” you when you emerge in this context. Third, you can build your relationships with these people over time by engaging with them on their terms and in their preferred medium.

How to do it? Look in same places where you are most active, such as Twitter, Facebook and LinkedIn:

• On Twitter, look for the show’s hashtag and use it, such as #DMA2011, #FMF11 and #NCDM. Before the show, share your plans and also the parts of the upcoming event you’re most excited about. During the show, tweet during sessions and share the points you find most interesting. After the show, ask for input and provide suggestions for ways to learn more about some of the major topics at the show.
• On Facebook, look for the show’s page and “like it.” Comment on posts and share what you’re most interested in or details about your plans at the show by focusing on how it will enhance the show experience for attendees.
• On LinkedIn, share your plans for the show with your own followers, and consider creating an event page for any side-activities you’ve planned during the show. Look for the show’s presence in any LinkedIn groups and join them. As on Facebook, comment in the group’s discussions on topics that are raised and relate it back to your enthusiasm for the show. Highlight the ways your plans for the show will enhance the experience for attendees.

3. Provide speakers for a session or two

Research the process for submitting abstracts for speaking opportunities and submit multiple on as many topics as is relevant. Ask about the show’s themes and what the major topics are for which show organizers need content. Try to engage one of your customers whose experience will be most interesting for the audience, or find a thought leader to speak if your budget allows it. A noted author with a recently published book is a good choice because you can include a drawing for signed copies of the book in the session.

During the session, introduce the speaker to the audience in a way that explains the connection between what your company does and why it made sense to sponsor the speaker. Keep it short and to the point (60 – 90 seconds) and do not do a sales pitch – it’s an explanation why the sponsorship makes sense to the audience. Close the session with an invitation to subscribe to your blog or a link to visit and learn more – make it easy for the audience to opt-in to ongoing impact.

4. Throw a party

Unless your target market has restrictions on hospitality (such as governments), a reception or executive dinner is a great way to enhance your target market’s experience at the show. Even if they are unable to accept, you’ll have made an impression simply by extending the invitation. Plan it so that it supports the content in any speaking sessions at the show you’re sponsoring, and/or include your speakers as the featured speaker. This is yet another opportunity for customers to self-qualify by accepting your invitation, and then it gives you the chance to have a more in-depth conversation away from the hustle & bustle of an exhibition hall. Make sure you have enough people from your company so that each attendee at the hospitality event gets some attention and boosts the inclination to have an ongoing relationship.

5. Preserve the content and share it

My perspective is that video recording your speaking session and having the rights to promote the content is the best way to drive ongoing impact at a tradeshow . There are many, many reasons why I believe that and here are some of them:

• Doing that is a great way to engage the folks who may have wanted to attend the event, but somehow could not be there.
• You can offer the video as a post-show “thank you” gesture. One nice example was detailed by Justin Huntsman in his post which includes an embedded YouTube link about our session at the 2011 eMetrics Optimization Summit in San Francisco.
• A video recording can also be transcripted and the content can be re-cast as a summary paper, which is downloadable or printable at will to read even when not online. These can be wonderfully engaging documents. even on potentially dry topics. A great example is from that same session at eMetricsm which we was turned into a paper titled, “How to Stop Annoying Your Customers.”
• You can also take the audio feed of the session and turn it into a podcast – some people prefer this medium. As an example, here is the link to a podcast series from a session we hosted at the 2011 SAS Global Forum Executive Conference.
• Having the fresh content gives you a good reason to reach out to your contacts to share it, and you can amplify that impact by tying it to a lead nurturing campaign. Or you can even use it to spark interest in your support of the show the following year.
• Doing all of the above gives you lots of searchable content – so find all the ways you can make it searchable and you’ll really have ongoing impact.

On the topic of search, consider this – YouTube is the second largest search engine after Google, but it is a separate search “ecosystem” than Internet search. Knowing that, you can surface your content in both worlds by posting the video recording both to YouTube and on your Website. With relevant tagging, you’ve put a toe-hold for your company in both search ecosystems on that topic, and you’ve also tied it to that large tradeshow in both places. Bada-bing!

Here’s an example of how we did that with David Meerman Scott’s Thought Leadership session at DMA:2011, which we posted to both YouTube and to sas.com. What’s even nicer is that David included the recording in a blog post of his own and our friends at the DMA have included the asset on the DMA Knowledge Center. We’re also crafting a whitepaper from this content and it’s nearly finished so check back and I’ll post the link in a comment below.

In the meantime, I’d love to hear your thoughts on this, and especially your ideas. Let’s add to this list with other examples of what you’ve done or may have seen others do well. As always, thank you for following!

Cross posted from the SAS Customer Analytics blog

To begin, consider the banking and financial management sector. Here is an excerpt from a recently released white paper on the topic, focusing on customer relationship dynamic pricing.

Many banks today are hoping to grow consumer and small business revenues through cross-sell and up-sell techniques that increase the number of products per customer and also create “sticky” relationships that reduce attrition. The challenge in selling additional products to each customer is there may be no way to modify the price of new products under consideration based on the current value of the relationship and how this could change with the addition of other new products. With high-performance analytics, the bank representative could assess the customer’s current use of existing bank products and services along with associated profitability and combine that information with in-house propensity, credit scores and external data (such as outstanding loans and other financial relationships) to gain a view of the customer’s potential lifetime value. The overall value to the bank through the addition of high-performance analytics is that every customer interaction can be based on optimizing the price of new products for each customer in a way that increases retention, grows revenue and improves the bank’s profits while providing the optimal customer experience for each individual consumer or business client.

Read the full white paper and follow this series for more industry examples.

The smart meter not only allows consumers to have a better understanding of their energy consumption, but also allows energy companies to understand the detailed consumption patterns of their consumers and plan more accurately for energy needs. This new strategy will benefit the entire energy supply chain, including energy retailers, distributors and generators. It is also a UK government-backed initiative with a full rollout of smart meters to residential and small business customers planned to be completed by 2019.

Ten thousand times the data

Today in the UK, meters are typically read on a monthly basis, since most electricity meters are situated inside and thus meter reading is reliant on someone being home to provide access. Instead, estimates are often relied on for billing and consumer budgeting. Smart meters can record data on a more granular level, providing, for example, half-hourly consumption information to the energy supplier via wireless data networks.

With approximately 30 million residential houses in the UK, getting half-hourly readings would equate to 1.6 trillion smart readings a year. This ten-thousand-times increase in readings means BIG data! With big data there’s great potential to mine for insights into customers’ behaviors.

Customer segmentation to find your best customers

For energy providers, the most costly consumers to serve are those who demand energy during a daily peak period, which typically occurs in the late afternoon or early evening. If utilities do not generate or contract enough energy to meet peak demand, they are forced to buy short-term contracts through the wholesale energy market. The wholesale cost of energy is highest during peak, so with current single-rate costs, energy companies are likely to be making little or no profit – maybe even a loss – during this peak period. To prevent this operating loss, energy suppliers want to identify those consumers who are lower than average users during this peak period, since they have the potential to be the most profitable customers. They may also want to identify heavy peak users to modify their usage patterns or avoid acquiring them as customers.

In a recent project, we applied segmentation analysis to half-hourly usage data, identifying the various types of usage patterns, for both “peaky” and “nonpeaky” customers. Since suppliers want to smooth out the demand made by peaky customers, they can offer them financial incentives to move their consumption to off-peak hours using time-of-use guidelines with lower rates during off-peak times. This could entice consumers, for example, to set their dishwashers and dryers to run during off-peak hours or set heaters in the winter to come on before arriving home from work.

Stress relief: Dual-purpose data

Another purpose of the smart meter is to reduce the strain on the system during heavy usage periods; as electricity cannot be economically stored, it has to be generated on demand – this means the more peaky the demand, the more unused (or wasted) generation capacity there is for other times of the day. Once the customer energy usage segments have been identified, other data such as socio-demographic information (e.g., household size, location, household income) regarding consumers can then be overlaid onto these segments to identify which consumer groups display similar consumption patterns.

An example segment (Figure 2), displays the standard usage curve (dotted line) and the consumption pattern identified in the segment (solid line). The standard usage curve is used throughout the industry as a representation of average usage across the population. It shows a slight peak in energy usage around 8-9 a.m. and a more extensive peak in the early evening. In the example segment, we identified that usage in the weekday evening period was 40 percent higher than the standard usage across the entire population. This clearly identifies a segment which is “peakier” than the normal peak, and maybe a customer group an energy supplier would want to target in order to try and spread energy usage throughout the day.

Consumers benefit from this segmentation through greater understanding of their own energy consumption, allowing them to better manage cost of their consumption. It also allows them to understand which energy rate may suit their consumption needs more cost-effectively. Suppliers benefit by meeting energy demand without having to build expensive power-generating capacity, improving efficiencies in load management and smoothing out demand by offering consumers more appropriate rates. Everyone benefits by better understanding and managing energy demand, so that we can ensure sustainability of energy supply for future generations. Analytics can also be used in forecasting future demand to better plan for strain on the network and determine when and where new generators need to be built.

There is a magnitude of potential within the field of smart meter analytics, and this is just the beginning of what can be achieved when analytical techniques are applied to mine a rich new stream of information.

Folks, there’s no bubble here. 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.

Maybe the term “big data” 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 analyzing large volumes of data more efficiently.

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 it 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, HPA will 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 optimizing markdowns for millions of SKUs at hundreds of retail locations, you’ll be able to optimize those prices more quickly.

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, it’s still going to be looking at the past when you get it in a shorter timeframe. 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.

Only predictive analytics like forecasting and optimization will bring you out of the past and into the future. When you use high-performance analytics to predict things like risk, customer satisfaction or marketing optimization, you’re getting your predictions sooner than before, and you can react more quickly. When you’re computing forward-looking results, the speed really can make a difference.

At its most basic level, high-performance analytics reduces the time dimension. You have to decide what types of answers you want more quickly: standard reports or predictive analytics.

Once you know that, you can start asking questions and making decisions that could change your business. Or your world. I don’t think that’s hype. It’s as real as you can get, and the organizations that get it first are going to be the ones that make it further into the future.

NOTE: originally published on The Corner Office.

Felicia Ramsey, SAS Field Marketing, Financial Services

As a marketer, I always take an interest in the Super Bowl commercials.  The $3.5M spent per spot to reach such a broad cross-section of the population astounds me. Now, I am not denying the entertainment value, but is this approach the “magic bullet” of marketing? Whether you prefer the return of Ferris Bueller or the Clint Eastwood ad (or were partial to the David Beckham spot, like me) you have to wonder whether these advertisers see the ROI on these mega-millions. Can such a mass-marketing approach have the same kind of impact as using customer analytics for smart, targeted and customer-centered campaigns?

There has been tremendous progress in recent years in how we can segment and target customers using analytics.  The ability to accurately predict customer behavior in real-time is intriguing and happening today.  Analytics is the key to understanding customers. Using analytics, we can leverage customer data to create highly defined segments, and we can tailor offers that specifically cater to those individual needs.

While the concept of customer segmentation is not new to banking, there is a shift towards becoming more customer-centric. In the past, banks were far more concerned with products and offerings, and banking is now at a pivotal point where the priorities have shifted. An increased focus on the customer and the customer experience is becoming fundamental to the way banks operate. A new report from BAI Research highlights the importance of understanding customer needs and conveys ideas on how to address them.  Banks are using analytics to optimize their marketing investment so that they can do more with less, at the right time, in the right channel with the most appropriate customers.

To learn more on this topic, tune in for a Webinar with BAI on February 14, 2012 at 2:00pm ET. This Webinar will highlight the banking segmentation research completed by BAI and will include the perspective of Leroy Abrahams, EVP of Suntrust Bank. The panelists will share the insights and practical information necessary to develop customer segmentation strategies, and will describe how analytics is the key to finding the best opportunities for exponential revenue growth and greater ROI — and rather than $3.5M commercials, isn’t that the real marketing “magic bullet”?

Cross-posted from the Customer Analytics Blog.

Kishore S. Swaminathan, Accenture

Contributed by Kishore S. Swaminathan, Chief Scientist and Global Director, Accenture Technology Labs’ Systems Integration Research 

Case study after case study confirms the value of analytics across a wide range of business functions, including pricing, demand prediction, targeted marketing, supply chain optimization, customer relationship management and HR. In my view, analytics is something much more than a technology with an ROI; it’s a transformational phenomenon that will fundamentally change how business discourse will be conducted and decisions made. Here are five key areas to focus on:

1 – HIGH ANALYTICAL LITERACY

Data is a double-edged sword. When properly used, it can lead to sound, well-informed decisions. When improperly used, the same data can lead not only to poor decisions but to poor decisions made with high confidence that, in turn, could lead to erroneous and expensive actions. Let’s consider some specific examples.

When one has access to real-time data, it’s tempting to make real-time decisions. For instance, if you are a retailer and you have real-time access to sales data from cash registers from all of your stores and the inventory in your warehouse, you could be tempted to run sales promotions on the fly and manage your supply chain in tandem to support your real-time promotions.

This is unlikely to work because three types of events – your decisions, the ensuing customer behavior and supply chain events – operate in different time frames, so making decisions faster than the slowest-moving event could be useless at best and dangerous at worst.

Another problem with data and analytics is that they give you very fine-grained visibility into your business processes, and you could be tempted to over-optimize the processes. Highly optimized processes – just-in-time inventory as an example – are very fragile because circumstances beyond your control could arise, and there is little room for error.

A third problem is known as “oversteering,” or making a decision when none is needed. For example, your data could tell you that a project is behind schedule, which, in turn, may lead you to berate the project manager or tell your stakeholders that the project will be delayed. Neither of these actions may be necessary if the project has contingency built in, if the status update has a different frequency from your sampling frequency, or if perhaps the employees who are aware of the project delay will put in more work time to get the project back on schedule.

2 -VOLATILITY

Businesses thrive on stability and repeatability. Stable and repeatable processes justify large-scale capital expenses and large-scale employee training. That stability also reduces cognitive overhead because those processes and decisions do not change, hence their rationale does not have to be explained repeatedly. By contrast, an analytically based enterprise of the future will have to be designed around volatility rather than repeatability.

When you have fine-grained visibility into your processes, customers, suppliers and competitors, you have the ability to make fine-grained decisions. In fact, your decision rules can capture subtleties such as “stock more beer on Sunday nights in locations where the home football team is on a winning streak.” Such decisions are highly context-sensitive and can change as rapidly as the fortunes of the football team.

Volatility – or rapidly changing decisions that are context- and time-sensitive – will be a big challenge for enterprises. Decisions are no longer easily explainable and capital investments cannot be based on mass repeatability, but must cater to endemic volatility.

3 – INTEGRATED AWARENESS

Today’s enterprises have more information than they can act upon because the information is siloed in so many ways: technologically (data in different systems that cannot be brought together), organizationally (data in different governance units that cannot be brought together) or by ownership (inside versus outside the enterprise). The enterprise of the future will be (or will be forced to be) conscious in the sense that it will know that it must integrate everything it has access to.

As an extreme example of integrated awareness, let’s consider the pharmaceutical industry, which has traditionally relied on clinical trials data to establish the efficacy and side effects of a drug.

A pharmaceutical company today can legally and morally claim immunity from adverse effects of a drug that were not revealed during clinical trials – in other words, any information that it did not explicitly collect as part of a clinical trial protocol. But in a world of blogs and social networks, where people share this information unprompted and in public, it will become both a responsibility and an obligation of pharmaceutical companies to monitor public sources and integrate the public information with their own clinical data.

“I should have known” (either for regulatory or competitive reasons) will be the new normal, replacing the “I did not know” or “I could not have known” approach to awareness and information integration.

4 – THE END OF ANALYSIS PARALYSIS

In the future, businesses will likely be run by managers and leaders who are no-nonsense empiricists; they won’t move a finger until all of the relevant data has been gathered and analyzed. A recipe for organizational analysis paralysis? This is not an unreasonable fear. Though it may seem counterintuitive, an empirical enterprise with high analytical literacy is less likely to fall prey to this malady than today’s enterprises.

There are three very distinct ways that organizations can fall into the analysis-paralysis trap. One is a managerial tendency to “over-fit the curve” – a statistical term that refers to the diminishing value of additional data once a pattern (or curve, in the graphic sense) has been found. Data collection has a price, inaction has a price, and an analytically literate organization will clearly understand the cost of over-fitting.

The second cause of analysis paralysis is waiting for data that simply does not exist, which reflects an inability to design experiments to generate the needed data. An analytically literate organization will be characterized by a clear understanding of data gaps and the value of experimentation to break the logjam.

The third cause of analysis paralysis is that most companies do not know their risk tolerance and are much more likely to penalize failed action than inaction. As a result, many managers do not act unless there is enough data to assure them of successful outcomes. An analytically literate organization will have a firm grasp of its risk tolerance. With guidelines and models for action under uncertainty, it will restore the symmetry between how it treats failed action and inaction.

5 – INTUITION’S NEW PULPIT

Empiricism and analytics sound a death knell for such vaunted business traits as intuition, gut feeling, killer instinct and so forth, right?

Wrong.

Science is purely empirical and dispassionate, but scientists are not. Science is objective and mechanical, but it also values scientists who are creative, intuitive and can take a leap of faith.

Data, by itself, can be interpreted in many ways. Imagine a physical or business phenomenon that produces the following sequence of data: 1, 2, 6, 24, 33. Perhaps it’s a factorial sequence with 33 as noise or a sequence where every fourth term is twice the multiple of the previous three. Or perhaps every fifth term is the sum of the previous four.

All are correct. To prove or disprove any theory, you need the next several terms of the sequence. A good scientist knows when there is enough data to warrant a theory, when there isn’t, what new data to gather and how to design an experiment to gather the right data.

The late Steve Jobs, Apple’s former CEO, was known to explicitly discount the value of surveys and focus groups for designing new products. How do you explain his apparent anti-empiricism?

One explanation is that, much like a creative scientist, people like Jobs recognize when there is not enough data or the right kind of data to form a theory. They recognize that, for completely new lines of products that will change a user’s experience or behavior, the only useful data is experiential data, not commentary and reactions from those who have never used the product.

Jobs and people like him are akin to scientists who recognize what type of data is needed to support a theory (in this case, whether a product will succeed), recognize that such data cannot be gathered through focus groups (one type of experiment) and boldly design new types of experiments (release the product and gather experiential data).

It should be noted that some products – in Apple’s case, it was the Newton – do not succeed and are terminated. Intuition, creative leaps and clever experimentation are not incompatible with empiricism; in fact, the value of these traits will be even better understood in the future enterprise by analogy to theoretical and experimental scientists.

The enterprise of the future, based on empiricism and analytical decision making, will indeed be considerably different from today’s enterprise.

NOTE: Originally published in sascom magazine.

Those are ultimately the levers that every business has, but as businesses innovate and compete, by default they must take on more risk. So the only way for them to succeed in that high innovation environment is by having better risk management capabilities.

Consequently, across industries there is a movement of risk management activities and focus from the back office to the front office, from compliance and cost control to differentiation. This movement of risk management into the mainstream helps the organization play offense more frequently. In other words, businesses are beginning to do things with risk that add value rather than simply limiting the business or protecting the business from “risk”.

Recently, one Chief Risk Officer (CRO) made a striking analogy involving a high level tightrope walker and a safety net. He said that risk management is about providing that net, enabling business executives with the confidence to execute their vision but that ultimately the walker should never have to use the net.

Knowing that a robust risk management system is in place in this way turns a potentially defensive stance into an offensive confidence to move the business forward and take the steps necessary to push ahead even when the path is complicated or unproven. Taking risks are part of everyday business, taking unmanaged risks is where troubles arise.

Here are 8 business and technology trends that we believe will shape risk management in 2012:

1.  CROs and CFOs. Prior to the recent financial crisis, there was often a lack of consistency between CROs and Chief Financial Officers (CFOs) across process, systems and data. In many FIs (financial institutions) the two sides were talking a different language and unintentionally creating challenges for the organization with conflicting priorities and messages. Today, some aspects of the crisis have helped to push the agendas together and the trend is toward tighter alignment and collaboration between the CRO and CFO and as a result more consistency across finance and risk.
2. Culture. Many organizations leave risk appetite as a concept and fail to translate this into limits and specific direction. A risk culture is a set of guidelines and expectations from the top down on appropriate risk taking and decision making. Both the CRO and Chief Human Resource Officer should determine the proper training and discussions needed throughout the FI to ensure that all employees understand their role in managing risk in the organization.
3. Complexity. To manage the business going forward firms need risk management capabilities to support scenario planning and risk mitigation, and they need information based on more than just a finance or process perspective. They need to be able to look at different markets, customers and product lines in a more sophisticated manner.
4. Geographical shift. Another change that has taken place is that risk management best practice is no longer concentrated in the usual regional centres. Accenture’s 2011 Global Risk Management research shows that more than 90 percent of Latin American firms have existing ERM programs in place, compared to only 52 percent of European companies and 60 percent of North American ones. Also, 90 percent of Latin American firms foresee significant or moderate increases in risk management pending, compared to only 82 percent of companies in North America and Asia Pacific.
5. Technology.A significant challenge facing virtually all FIs is the need to integrate, align and harness the technologies in a way which will better serve the business and deliver the outputs and insights required to outpace the competition. Over the years, data volumes along with regulatory and business requirements have driven FIs to take a leading position in technology, developing new features, new automated solutions, more sophistication and complexity covering more countries and more business activities. However, these requirements have also progressively contributed to a dramatic increase in IT costs as demands have become more complex and the resulting systems more diverse.

   One important technological change designed to meet the increasing demands is in the way that the application architecture is built. Traditionally, the application landscape was built layer upon horizontal layer. So each layer carried a function and interfaced with the other layers through transformational rules, lead times, short cuts, complex reporting rules, and so on. However, that horizontal structure is changing to something much more vertical. Consequently, individual pieces of information will be able to be gathered from the bottom up, elevating the reporting lines and data governance where necessary.

1. Partnerships. This is beyond the traditional in-house versus outsourcing debate. Given the levels of complexity faced by FIs, firms are likely to have little choice than to partner in new ways, including traditional competitors and peers, as well as, IT and change specialists. Given the recent level of investment in risk management, it will be critical for CROs to demonstrate the benefits – and tie the outcomes – from risk management projects more directly to business outcomes and tangible cost reductions. To achieve this, many organizations are more actively seeking collaboration and reusing some aspects in a “utility sense.”
2. Regulatory. Compliance and regulation have always been key drivers in terms of defining budgets. However, this has resulted in many organizations making significant investments in projects for specific regulations only to have an ROI based on ticking the regulatory box. Having done that for four or five regulatory waves, it’s now time to implement an integrated risk management framework more suited to meeting the strategic business needs.
3. Harmonization. Many FIs are looking to consolidate vendor applications, particularly in areas such as data warehousing, the applications themselves (e.g., the risk calculation engines) and reporting or business intelligence environments.

Pressures on margins, the high cost of technology and burgeoning regulation mean that firms are searching for competitive differentiation by moving from compliance to performance and adopting more effective and efficient risk management practices. Technology is playing a key role as an enabler for this transformation driving demand for new architectures and high-performance computing. However, technology alone is not going to deliver the desired outcomes. Culture and collaboration are also critical success factors.

Ultimately, successful organizations will look beyond regulation and cost-reduction and view risk management as a strategic element of their value chain, delivering sustainable growth and innovation.

Read the entire conclusions paper for a deeper look into these trends. Nearly 400 firms from across the globe were surveyed to develop this information – is your firm going to be making changes to its risk management processes or culture? Do you agree with these findings? 

*Other contributors to this article include Christophe Mouille, the Managing Director of the Accenture SAP Practice for EMEA and Latin America, and Karsten Ebersbach, Senior Director, Accenture Technology, Risk Management, for Austria, Switzerland and Germany.

NOTE: Originally published by Accenture in 2011. Copyright 2011 Accenture. All rights reserved. Reprinted by permission. For more information about Accenture’s Risk Management group please visit www.accenture.com/riskmanagement.