November 20, 2014 by Leave a Comment
Three things came across my Twitter feed the same afternoon. Consider the following and see if you think they are related: “When we depend too much on our GPS, we lose the will and skill to explore.” Tom Peters via Twitter, 20 November 2014 “A creative person is by definition inefficient. She/he is wandering along odd paths, backtracking; the life well-lived is mostly detours.” Tom Peters via Twitter, 20 November 2014 “Using analytics in decision-making requires banks to think more like musicians. If you start jamming, maybe something cool will come out, and it will sell a million records.” Yours truly as quoted by Penny Crossman, Bank Technology News, 19 November 2014 First, I must say that by including my own comments among those of Tom Peters, I am in no way suggesting that my thinking is on par with his. It is not. Rather, my Twitter feed lit up since American Banker published the article referenced above, Bank CEOs Fear the Data-Driven Decision, and Peter’s tweets seemed both humorously consistent and coincident with Penny’s article as well as my previous blog post. What in the world do they have to do with each other? A common element emerges when viewed through the lens of organizational culture. Consider the culture in which you work. Is it a get there using the shortest path every time with no wrong turns (e.g., GPS) culture? Does it tolerate taking a longer route (even occasionally) to explore and better learn one’s surroundings? Does it value the unfamiliar? Does it encourage and reward learning new music? More radically, does it celebrate creative discovery beyond established norms? Are you even permitted to improvise, or are you directed to always play from the sheet music? If so, your organization likely won’t enjoy much innovation. As it relates to becoming a data-driven organization, banks need to learn how to be good at both using the GPS and at improvising – with discernment. Each approach has value. Too many senior managers in financial institutions, however, have never experienced the kind of culture that tolerates the “test and learn” way of using analytics. Instead, it seems strange and uncomfortable. It’s not easy to do things so differently. That’s why culture is such an important element in the skilful use of data analytics specifically and innovation more broadly. Technology may be an enabler or even a disrupter, but without a culture that values and rewards new ways of doing things, investment in the best technology will disappoint. Another quote to finish today’s post: “While there are many challenges [to becoming an analytical company], the most critical one is allocating sufficient attention to managing cultural and organizational change. We’ve witnessed many organizations whose analytical aspirations were squelched by open cultural warfare between the “quant jocks” and the old guard.” Thomas H. Davenport and Jeannie G. Harris, Competing on Analytics, HBR Press, page 124
October 15, 2014 by Leave a Comment
Data analytics is not a new pursuit. SAS, for example, has been offering solutions since its inception in 1976. But owing to the inherent complexity of advanced data analytics platforms, experience with data analytics has been the domain of only the largest organizations. However, the last several years have witnessed an explosion in applications for data analytics, especially in the area of customer analytics. With the growth in applications came conveniently pre-configured software solutions that were fine-tuned for a bevy of specific applications. The combination of product evolution, specialized analytics-savvy consultants, professional services firms, and cloud computing, has brought advanced analytics swiftly down market. Now, even small banks and credit unions can foray into customer analytics with a comparatively small investment and without a legion of data scientists on staff. But are they? Well, that depends on what you mean by data analytics. Celent recently surveyed about 100 North American banks and credit unions to understand the state of analytics adoption and the drivers behind its growth. In our resulting report, “Customer Analytics Adoption in Banking: When Management Doesn’t Lead” (September 2014), we noted that about half of the financial institutions in the sample had some experience with data analytics. However most of these efforts might be considered rudimentary, such as customer profitability or web analytics applications. A third of the respondents to the survey had experience with social media sentiment monitoring, an example of advanced analytics, but inexpensively available in the cloud and easily used by non-data scientists. In contrast, usage of predictive analytics applications is far less common. Just one in five financial institutions demonstrated experience with next-best-action analytics, and one in ten showed an understanding of customer lifetime value. What gives? If customer analytics holds such great promise, why aren’t more banks and credit unions deriving value from its use? I think there are at least two reasons. First, we are seeing an immature state of data analytics at most financial institutions. Second, and perhaps more important, there appears to be a lack of interest by leadership at the top of these financial institutions in driving data-driven strategies. Is Your Organization Data-Driven? Using data to make decisions is not the same as being data-driven. An organization doesn’t become “data-driven” simply by installing an advanced data analytics application. So, what does it really mean to be a data-driven organization? Celent asserts that data-driven organizations use analytics extensively and systematically to influence and execute strategy. Practically, this takes many forms, but it begins with attitude. Organizations start by deciding to value data, develop confidence in its validity, and make decisions based upon data even when doing so is uncomfortable. In other words, being data-driven amounts to having faith in the efficacy of data and acting accordingly. It means walking the walk, not just talking the talk. How many banks are true data-driven organizations? Not many, we find. It’s probably fair to say that the concept of an organization being data-driven isn’t a binary thing. Instead of a “yes” or “no” answer, perhaps the question is best posed, “How data-driven is your institution?” and additionally; “How data-driven would your organization be if it were up to you?” The survey found that just 29% of responding financial institutions thought their organizations were highly data-driven. Nearly 90% of that same sample said their organizations would be highly data-driven if it were up to them. In other words, they wished for it. Clearly, we think that the industry wants to be data-driven, but doesn’t think it’s there yet. Source: Celent survey of North American financial institutions, July 2014, n=78 Lack of Leadership Intuitively, this suggests a leadership problem, but does the data support this conclusion? It does. We cross-tabbed the survey results by respondent roles and found significant differences in attitudes surrounding data analytics. Specifically, responses to the question “How data-driven would your organization be if it were up to you?” varied dramatically by role. It turns out that all respondents in IT/IS roles wished their organizations were highly data-driven – or would be if it were up to them. In contrast, respondents in strategy or innovation roles as well as those in marketing, showed somewhat less passion for being a data-driven organization. Perhaps a surprise, coming in last in support of data driven strategies were those in executive management; compared to those they lead, this group was the least desiring for their organizations to be data-driven. Source: Celent survey of North American financial institutions, July 2014, n=78 Although surveys aren’t the final word on any topic, the results do suggest a leadership problem, which if addressed, would go a long way towards better serving customers through skilful use of data analytics. As banks better understand the merits of being data-driven, we think that financial institution leadership will ultimately lead the march to supporting data-driven business strategies, particularly those focused on customer analytics. I will be addressing this topic in more depth in a session at American Bankers Banking Analytics Symposium in New Orleans on Thursday, October 16th.
May 22, 2014 by Leave a Comment
I had the pleasure of speaking at Fiserv Forum 2014 in Las Vegas last week, discussing “The Payoff of Turning Data into Action.” During the presentation, I offered some suggestions to financial institutions that have not yet made inroads into customer analytics. Why Here, Why Now? Quite a few community bankers have resisted implementing CRM solutions, for example, and lived to tell about it. Like big data, the promise of CRM in its early days was somewhat overblown. But, that was then. Is customer analytics the New CRM? I say “no” for at least four reasons: 1. The “new normal” in retail banking – Banks need to grow top-line revenue, but it is increasingly hard to do. Analytics applied to customer segmentation, marketing and customer experience can play a critical role. 2. The growing imperative for customer centricity – As consumers increasingly interact digitally with financial institutions, the branch channel is losing relevance and impact. In addition to improving branch channel efficiency and effectiveness, banks must learn how to engage customers digitally. Analytics is the way to do so. 3. Technological advancements – Analytics used to be the domain of data analysts and large, expensive implementations, but modern analytics applications are tailored for business users and integrated with business applications. Getting started is no longer expensive. 4. There’s money to be made – As the use cases for customer analytics multiply faster than rabbits, financial institutions are finding a growing number of ways to profit from customer analytics. In a 2013 survey of North American financial services firms, 70 percent of those having at least one year’s experience with one or more big data initiatives met or exceeded their business case. Not a bad batting average, to be sure. If you remain unconvinced, the Celent report, Customer Analytics in Retail Banking: Why Here? Why Now? may persuade. Getting Your Feet Wet How does an organization get its feet wet with customer analytics? Are there best practices for turning data into action? From interviews with a number of those in the 70 percent, as well as banks who struggled initially, I offer these getting started tips. • Begin with the end in mind – Analytics is a means to an end. Successful examples of data analytics share a common element of focused energy to achieve a limited and specific business objective. • Start small, remain focused – Like its sister topic, big data, there is really no end to customer analytics. Unlike CRM projects, one is never through with analytics – its very nature requires continual refreshing of models and their use. Analytics invites a new way of doing things as much as it invites using new technology. Get started with a single, manageable project and prove its value before moving on. • Get help – There is a steep learning curve associated with fully leveraging data analytics. A modest up-front investment in assistance from firms that specialize in analytics may hasten your project deployment and product better results. Fiserv is well positioned to help – and may already be hosting your data. • Change your culture – Benefitting from analytics requires a devotion to cultural, organization and procedural change. That’s why it is important to start small. Cultural change can and will come alongside socializing the value of early successes. Tom Davenport has authored several books that shed light on the power of making analytics more than an IT project: Analytics At Work, and Competing on Analytics. • Manage expectations – Firms like Amazon and Google make analytics look easy. It’s not. Deriving benefit from customer analytics will be more of a journey than a destination and the road will seem long at times. All the more reason to get your feet wet soon.
October 25, 2013 by 3 Comments
Now in our seventh year, Celent is seeking nominations for its Model Bank awards. Each year, we honor a hand-picked selection of financial institutions who model excellent utilization of technology in banking. Celent Model Bank 2013 winners represented 19 financial institutions across 12 countries. All received recognition at our annual Innovation and Insight Day this past February in Boston, MA. Are there excellent examples of technology innovation at your institution (or one of your clients if you’re a technology provider)? Banks and credit unions self-nominate, while technology providers may nominate a client on their behalf and with their consent. Share your story with us using our online nomination form below. A panel of analysts will review all nominations based on three criteria: 1. Degree of innovation 2. Degree of difficulty, and 3. Measurable, quantitative business results achieved Success stories are welcome across the spectrum of financial services disciplines, from: infrastructure and architecture, product development, marketing/sales, distribution / channel management, transaction processing, loan processing, customer service / support, and security and risk management. Nominations can be made online at: http://www.celentmodelbank.com. If you’d like, e-mail me and I’ll send you an excerpt from last year’s report, Celent Model Bank 2013: Case Studies of Effective Technology Usage in Banking. A PDF of previous Celent Model Bank winners is available here, and selected video case studies are available here. Bring on those nominations!
April 26, 2012 by Leave a Comment
Every duck hunter knows that in order to avoid coming home empty-handed, one must aim ahead of the bird – lead the bird as it is commonly referred. The idea is that if one aims directly at the bird, every shot will be a miss no matter how precise the aim. That’s because by the time the bird shot gets in the vicinity of the duck, it will have flown out of the shot pattern.
- How Much to Lead is the Tricky Part
June 4, 2009 by 13 Comments
Celent’s AML vendor evaluation reports have become something of a de facto standard, referenced by banks and regulators around the world. We began covering the sector in 2003, and are about to start work on our 3rd edition of the report. AML has not gone away as a concern for banks; indeed it has expanded, across both banking tiers (reaching down into community banks and credit unions in the US, for example) and across geographies (I recently spoke at an AML conference in Malaysia that drew over 500 delegates). The behavior detection technology that underpins AML software has also expanded its boundaries within the financial institution. Celent has been behind the “enterprise risk” approach, that is, consolidating AML and anti-fraud efforts, since our first AML report back in 2002. But until the last few years there were few real-life examples to point to. Recently, however, financial institutions have become increasingly concerned with fighting fraud, including fraud committed by customers as well as employee fraud. And a growing number of firms are beginning to take a wholistic approach to these issues. So this time around our report will take an enterprise risk approach as well, by including in our evaluation the anti-fraud products of the AML vendors. We’re calling it “Evaluating the Vendors of Enterprise Risk Management Solutions 2009.” We’ll be starting research on the report this month, beginning with qualifying vendors for inclusion in the report. The last edition evaluated 19 vendors and was 100 pages long. As the market has shifted, with new products emerging and others fading from sight, there may be some shuffling in order to keep the field of vendors representative of the marketplace. And although we are constantly looking at this space, we’d welcome any comments on vendors we should consider that we may have missed. As a reminder, the AML software providers evaluated in the 2006 edition of the report were: Accuity, Ace Software Solutions, ACI Worldwide, Actimize, ChoicePoint/Bridger Insight, Experian/Americas Software, Fortent/Searchspace, FircoSoft, LogicaCMG, Mantas, Metavante/Prime Associates, Fiserv/NetEconomy, Norkom Technologies, Northland Solutions, SAS Institute, Side International, STB Systems, Top Systems, Wolters Kluwer Financial Services/PCi