Category Archives: Business/IT Collaboration

Garbage In, Garbage Out? Don’t Take Data for Granted in Analytics Initiatives!

Cant trust data_1The verdict is in. Data is now broadly perceived as a source of competitive advantage. We all feel the heat to deliver good data. It is no wonder organizations view Analytics initiatives as highly strategic. But the big question is, can you really trust your data? Or are you just creating pretty visualizations on top of bad data?

We also know there is a shift towards self-service Analytics. But did you know that according to Gartner, “through 2016, less than 10% of self-service BI initiatives will be governed sufficiently to prevent inconsistencies that adversely affect the business”?1 This means that you may actually show up at your next big meeting and have data that contradicts your colleague’s data.  Perhaps you are not working off of the same version of the truth. Maybe you have siloed data on different systems and they are not working in concert? Or is your definition of ‘revenue’ or ‘leads’ different from that of your colleague’s?

So are we taking our data for granted? Are we just assuming that it’s all available, clean, complete, integrated and consistent?  As we work with organizations to support their Analytics journey, we often find that the harsh realities of data are quite different from perceptions. Let’s further investigate this perception gap.

For one, people may assume they can easily access all data. In reality, if data connectivity is not managed effectively, we often need to beg borrow and steal to get the right data from the right person. If we are lucky. In less fortunate scenarios, we may need to settle for partial data or a cheap substitute for the data we really wanted. And you know what they say, the only thing worse than no data is bad data. Right?

Another common misperception is: “Our data is clean. We have no data quality issues”.  Wrong again.  When we work with organizations to profile their data, they are often quite surprised to learn that their data is full of errors and gaps.  One company recently discovered within one minute of starting their data profiling exercise, that millions of their customer records contained the company’s own address instead of the customers’ addresses… Oops.

Another myth is that all data is integrated.  In reality, your data may reside in multiple locations: in the cloud, on premise, in Hadoop and on mainframe and anything in between. Integrating data from all these disparate and heterogeneous data sources is not a trivial task, unless you have the right tools.

And here is one more consideration to mull over. Do you find yourself manually hunting down and combining data to reproduce the same ad hoc report over and over again? Perhaps you often find yourself doing this in the wee hours of the night? Why reinvent the wheel? It would be more productive to automate the process of data ingestion and integration for reusable and shareable reports and Analytics.

Simply put, you need great data for great Analytics. We are excited to host Philip Russom of TDWI in a webinar to discuss how data management best practices can enable successful Analytics initiatives. 

And how about you?  Can you trust your data?  Please join us for this webinar to learn more about building a trust-relationship with your data!

  1. Gartner Report, ‘Predicts 2015: Power Shift in Business Intelligence and Analytics Will Fuel Disruption’; Authors: Josh Parenteau, Neil Chandler, Rita L. Sallam, Douglas Laney, Alan D. Duncan; Nov 21 2014
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Posted in Architects, Business/IT Collaboration, Data Governance, Data Integration, Data Warehousing | Tagged , , , , , , | 1 Comment

Does Your Sales Team Have What They Need to Succeed in 2015?

Like me, you probably just returned from an inspiring Sales Kick Off 2015 event. You’ve invested in talented people. You’ve trained them with the skills and knowledge they need to identify, qualify, validate, negotiate and close deals. You’ve invested in world-class applications, like Salesforce Sales Cloud, to empower your sales team to sell more effectively. But does your sales team have what they need to succeed in 2015?

Gartner predicts that as early as next year, companies will compete primarily on the customer experiences they deliver. So, every customer interaction counts. Knowing your customers is key to delivering great sales experiences.

If you’re not fueling Salesforce Sales Cloud with clean, consistent and connected customer information, your sales team may be at a disadvantage against the competition.
If you’re not fueling Salesforce Sales Cloud with clean, consistent and connected customer information, your sales team may be at a disadvantage against the competition.

But, inaccurate, inconsistent and disconnected customer information may be holding your sales team back from delivering great sales experiences. If you’re not fueling Salesforce Sales Cloud (or another Sales Force Automation (SFA) application) with clean, consistent and connected customer information, your sales team may be at a disadvantage against the competition.

To successfully compete and deliver great sales experiences more efficiently, your sales team needs a complete picture of their customers. They don’t want to pull information from multiple applications and then reconcile it in spreadsheets. They want direct access to the Total Customer Relationship across channels, touch points and products within their Salesforce Sales Cloud.

Watch this short video comparing a day-in-the-life of two sales reps competing for the same business. One has access to the Total Customer Relationship in Salesforce Sales Cloud, the other does not. Watch now: Salesforce.com with Clean, Consistent and Connected Customer Information.

Salesforce and Informatica Video

Is your sales team spending time creating spreadsheets by pulling together customer information from multiple applications and then reconciling it to understand the Total Customer Relationship across channels, touch points and products? If so, how much is it costing your business? Or is your sales team engaging with customers without understanding the Total Customer Relationship? How much is that costing your business?

Many innovative sales leaders are gaining a competitive edge by better leveraging their customer data to empower their sales teams to deliver great sales experiences. They are fueling business and analytical applications, like Salesforce Sales Cloud, with clean, consistent and connected customer information.  They are arming their sales teams with direct access to richer customer profiles, which includes the Total Customer Relationship across channels, touch points and products.

What measurable results have these sales leaders acheived? Merrill Lynch boosted sales productivity by 15%, resulting in $50M in annual impact. A $60B manufacturing company improved cross-sell and up-sell success by 5%. Logitech increased across channels: online, in their retail partner’s stores and through distribution partners.

This year, I believe more sales leaders will focus on leveraging their customer information for competitive advantage. This will help them shift from sales automation to sales optimization. What do you think?

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Posted in 5 Sales Plays, Business Impact / Benefits, Business/IT Collaboration, CIO, Cloud, Cloud Computing, Cloud Data Integration, Cloud Data Management, Customer Acquisition & Retention, Data Integration, Data Quality, Enterprise Data Management, Intelligent Data Platform, Master Data Management, Operational Efficiency, SaaS, Total Customer Relationship | Tagged , , , , , , , , , , , , , , , | 1 Comment

How a Business-led Approach Displaces an IT-led Project

In my previous blog, I talked about how a business-led approach can displace technology-led projects. Historically IT-led projects have invested significant capital while returning minimal business value. It further talks about how transformation roadmap execution is sustainable because the business is driving the effort where initiative investments are directly traceable to priority business goals.

business-led approachFor example, an insurance company wants to improve the overall customer experience. Mature business architecture will perform an assessment to highlight all customer touch points. It requires a detailed capability map, fully formed, customer-triggered value streams, value stream/ capability cross-mappings and stakeholder/ value stream cross-mappings. These business blueprints allow architects and analysts to pinpoint customer trigger points, customer interaction points and participating stakeholders engaged in value delivery.

One must understand that value streams and capabilities are not tied to business unit or other structural boundaries. This means that while the analysis performed in our customer experience example may have been initiated by a given business unit, the analysis may be universally applied to all business units, product lines and customer segments. Using the business architecture to provide a representative cross-business perspective requires incorporating organization mapping into the mix.

Incorporating the application architecture into the analysis and proposed solution is simply an extension of business architecture mapping that incorporates the IT architecture. Robust business architecture is readily mapped to the application architecture, highlighting enterprise software solutions that automate various capabilities, which in turn enable value delivery. Bear in mind, however, that many of the issues highlighted through a business architecture assessment may not have corresponding software deployments since significant interactions across the business tend to be manual or desktop-enabled. This opens the door to new automation opportunities and new ways to think about business design solutions.

Building and prioritizing the transformation strategy and roadmap is dramatically simplified once all business perspectives needed to enhance customer experience are fully exposed. For example, if customer service is a top priority, then that value stream becomes the number one target, with each stage prioritized based on business value and return on investment. Stakeholder mapping further refines design approaches for optimizing stakeholder engagement, particularly where work is sub-optimized and lacks automation.

Capability mapping to underlying application systems and services provides the basis for establishing a corresponding IT deployment program, where the creation and reuse of standardized services becomes a focal point. In certain cases, a comprehensive application and data architecture transformation becomes a consideration, but in all cases, any action taken will be business and not technology driven.

Once this occurs, everyone will focus on achieving the same goals, tied to the same business perspectives, regardless of the technology involved.

Twitter @bigdatabeat

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What Should Come First: Business Processes or Analytics?

business processesAs more and more businesses become fully digitized, the instantiation of their business processes and business capabilities becomes based in software. And when businesses implement software, there are choices to be made that can impact whether these processes and capabilities become locked in time or establish themselves as a continuing basis for business differentiation.

Make sure you focus upon the business goals

business processesI want to suggest that whether the software instantiations of business process and business capabilities deliver business differentiation depends upon whether business goals and analytics are successfully embedded in a software implementation from the start. I learned this first hand several years ago. I was involved in helping a significant insurance company with their implementation of analytics software. Everyone in the management team was in favor of the analytics software purchase. However, the project lead wanted the analytics completed after an upgrade had occurred to their transactional processing software. Fortunately, the firm’s CIO had a very different perspective. This CIO understood that decisions regarding the transaction processing software implementation could determine whether critical metrics and KPIs could be measured. So instead of doing analytics as an afterthought, this CIO had the analytics done as a fore thought. In other words, he slowed down the transactional software implementation. He got his team to think first about the goals for the software implementation and the business goals for the enterprise. With these in hand, his team determined what metrics and KPIs were needed to measure success and improvement. They then required the transaction software development team to ensure that the software implemented the fields needed to measure the metrics and KPIs. In some cases, this was as simple as turning on a field or training users to enter a field as the transaction software went live.

Make the analytics part of everyday business decisions and business processes

Tom DavenportThe question is how common is this perspective because it really matters. Tom Davenport says that “if you really want to put analytics to work in an enterprise, you need to make them an integral part of everyday business decisions and business processes—the methods by which work gets done” (Analytics at Work, Thomas Davenport, Harvard Business Review Press, page 121). For many, this means turning their application development on its head like our insurance CIO. This means in particular that IT implementation teams should no longer be about just slamming in applications. They need to be more deliberate. They need to start by identifying the business problems that they want to get solved through the software instantiation of a business process. They need as well to start with how they want to improve process by the software rather than thinking about getting the analytics and data in as an afterthought.

Why does this matter so much? Davenport suggests that “embedding analytics into processes improves the ability of the organization to implement new insights. It eliminates gaps between insights, decisions, and actions” (Analytics at Work, Thomas Davenport, Harvard Business Review Press, page 121). Tom gives the example of a car rental company that embedded analytics into its reservation system and was able with the data provided to expunge long held shared beliefs. This change, however, resulted in a 2% increased fleet utilization and returned $19m to the company from just one location.

Look beyond the immediate decision to the business capability

Davenport also suggests as well that enterprises need look beyond their immediate task or decision and appreciate the whole business process or what happens upstream or downstream. This argues that analytics be focused on the enterprise capability system. Clearly, maximizing performance of the enterprise capability system requires an enterprise perspective upon analytics. As well, it should be noted that a systems perspective allows business leadership to appreciate how different parts of the business work together as a whole. Analytics, therefore, allow the business to determine how to drive better business outcomes for the entire enterprise.

At the same time, focusing upon the enterprise capabilities system in many cases will overtime lead a reengineering of overarching business processes and a revamping of their supporting information systems. This allows in turn the business to capitalize on the potential of business capability and analytics improvement. From my experience, most organizations need some time to see what a change in analytics performance means. This is why it can make sense to start by measuring baseline process performance before determining enhancements to the business process. Once completed, however, refinement to the enhanced process can be determined by continuously measuring processes performance data.

Related links

Related Blogs

Analytics Stories: A Banking Case Study
Analytics Stories: A Financial Services Case Study
Analytics Stories: A Healthcare Case Study
Who Owns Enterprise Analytics and Data?
Competing on Analytics: A Follow Up to Thomas H. Davenport’s Post in HBR
Thomas Davenport Book “Competing On Analytics”

Solution Brief: The Intelligent Data Platform

Author Twitter: @MylesSuer

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Posted in B2B, B2B Data Exchange, Business Impact / Benefits, Business/IT Collaboration, Enterprise Data Management | Tagged , , , | Leave a comment

Business-Led Transformation Is Value-Centric

Business-Led Transformation Is Value-Centric

Business-Led Transformation Is Value-Centric

Transformation roadmaps in many businesses tend to have a heavy technology focus, to the point where organizations invest millions of dollars in initiatives with no clear business value. In addition, numerous tactical projects funded each year have little understanding of how or even if, they align from a business perspective. Management often fall victim to the latest technology buzzwords, while stakeholder value, business issues, and strategic considerations take a backseat. When this happens, executives who should be focused on business scenarios to improve stakeholder value fall victim to technology’s promise of the next big thing.

I recently participated in the writing and reviewing a series of whitepapers on Business-led transformation at Informatica’s Strategic Services Group. These whitepapers discusses how executives can leverage business architecture to reclaim their ability to drive a comprehensive transformation strategy and roadmap. I will try to summarize them into this blog.

Consider the nature of most initiatives found within a corporate program office. They generally focus on enhancing one system or another, or in more extreme cases a complete rebuild. The scope of work is bounded by a given system, not by the business focal point, whether that is a particular business capability, stakeholder, or value delivery perspective. These initiatives generally originate within the IT organization, not the business, and launched in response to a specific business need quickly translated into a software enhancement, rewrite, or database project. Too often, however, these projects have myopia and lack an understanding of cross-impacts to other projects, business units, stakeholders, or products. Their scope is constrained, not by a given customer or business focus, but by technology.

Business led transformation delivers a value centric perspective and provides the underlying framework for envisioning and crafting a more comprehensive solution. In some cases, this may begin with a quick fix if that is essential, but this must be accompanied by a roadmap for a more transformative solution. It provides a more comprehensive issue analysis and planning perspective because it offers business specific, business first viewpoints that enable issue analysis and resolution through business transparency.

Twitter @bigdatabeat

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What is the Role of the CIO in Driving Enterprise Analytics?

Data AnalysisWhen you talk to CIOs today about their business priorities, the top of their list is better connecting what IT is doing to business strategy. Or put another way, it is about establishing business/IT alignment. One area where CIOs need to make sure there is better alignment is enterprise analytics. CIOs that I have talk to share openly that business users are demanding the ability to reach their apps and data anywhere and on any device. For this reason, even though CIOs say they have interest in the mechanisms of data delivery–data integration, data cleanliness, data governance, data mastering, and even metadata management — they would not take a meeting on these topics. The reason is that CIOs say they would need to involve their business partner in these meetings. CIOs these days want you have to have a business value proposition. Given this, CIOs say that they would want to hear about what the business wants to hear about.

  • Enabling new, valuable business insights out data to happen faster
  • Enabling their businesses to compete with analytics

CIOs as an analytics proponent versus the analytics customer

Tom DavenportSo if the question is about competing with analytics, what role does the CIO have in setting the agenda here? Tom Davenport says that CIOs–as I heard in my own conversations  with CIOs–have good intentions when it comes to the developing an enterprise information strategy. They can see the value of taking an enterprise versus a departmental view. Tom suggests, however, that CIOs should start by focusing upon the analytics that will matter most to the business. He says that IT organizations should, also, build an IT infrastructure capable of delivering the information and analytics that people across the enterprise need not just now but also in the future.

Tom says that IT organizations must resist the temptation to provide analytics as an add-on or a bolt-on basis for whatever transactions system have just been developed. As a product manager, I had a development team that preferred to add analytics by source rather than do the hard work of creating integrative measures that crossed sources. So I know this problem firsthand. Tom believes that IT needs to build a platform that can be standardized and integrate data from more than one source. This includes the ability to adapt as business needs and business strategies change.

Making this an Enterprise Analytics Capability

analyticsIn the early stage for analytics, IT organizations need to focus more upon a self-service approach. But as the business matures at analytics, Tom says that IT needs to shift gears and become a proactive advocate and architect of change. Tom says that IT should be a part owner of the company’s analytical capabilities. IT managers, therefore, must understand and be able to articulate the potential for analytics being created at an enterprise level. At the same time, the IT staff–which often lacks the heavy mathematical backgrounds of analysts–needs to be able to interact with the analytics pros who use and consume the information that IT creates to build models. I had this dilemma first hand where my analytics modelers were disconnected from BI product developers. They were two different communities working on our project. And although some modelers can build apps or even a BI system, what excites them most in life is building new analytical models.

Talk the language of the business

Tom Davenport says that IT managers can make their own lives easier with the business and the with analysts by instead of discussing cloud computing, service oriented architecture, or even OLAP, discussing decision making, insights, and business performance. Meanwhile, Tom feels that the enterprise analytics journey starts with good, integrated data on transactions and business processes managed through enterprise applications like ERP and CRM Systems (Analytics at Work, Thomas Davenport, Harvard Business Review Press, page 51).

Focusing on the big questions and the right problems

Clearly driving the business to focus on the big questions and the right problems is critical. IT cannot do this but they can facilitate it. Why does it matter? An Accenture Study found that “companies that derived any real value from them (their analytics) had anticipated how to leverage the information to generate new insights to improve business performance. (“Using Enterprise Systems to Gain Uncommon Competitive Advantage, Accenture, page 3). This is critical and too few organizations succeed in doing it.

With this accomplished and to achieve the second goal, IT needs to be eliminating legacy BI systems and old spaghetti code as well as silo data marts. The goal should be to replace them with an enterprise analytics capability that answers the big questions. This requires standardization around an enterprise wide approach that ensures a consistent approach to data management and provides an integrated environment complete with data repositories/data lakes, analytical tools, presentation applications, and transformational tools. This investment should be focused on improving business processes or providing data needed for system of systems products. Tom says that IT’s job is to watch out for current and future users of information systems.

Parting Thoughts

So the question is where is your IT organization at today? Clearly, it is important as well that IT measure enterprise analytic initiatives too. IT should measure adoption. IT should find out what is used or not they are used. I had a CIO once admit to me that he did not know whether currently supported data marts were being used or even still had value. It is important that we have these answers. Clearly, being close to the business customer from the start can limit what this CIO discussed.

Related Blogs and Links

Analytics Stories: A Banking Case Study

Analytics Stories: A Financial Services Case Study

Analytics Stories: A Healthcare Case Study

Who Owns Enterprise Analytics and Data?

Competing on Analytics: A Follow Up to Thomas H. Davenport’s Post in HBR

Thomas Davenport Book “Competing On Analytics”

Solution Brief: The Intelligent Data Platform

Author Twitter: @MylesSuer

 

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2015 – The Year of Data Integration?

2015, the Year of Data Integration?

2015, the Year of Data Integration?

I love the data integration coverage by Loraine Lawson in IT Business Edge,  especially in this December 12th posting  that focuses on the trends that will emerge in 2015.  The best quote from the post is: “Oddly, organizations still tended to focus on point-solutions in the cloud. As more infrastructure and data moves to the cloud, they’re experiencing similar pain points and relearning old lessons.”

The articles cites some research from Ovum, that predicts many enterprises will begin moving toward data integration, driven largely by the rise of cloud computing and big data.  However, enterprises need to invest in both modernizing the existing data management infrastructure, as well as invest in data integration technology.  “All of these new investments will push the middleware software market up 9 percent to a $16.3 billion industry, Information Management reports.”  This projection is for 2015.

I suspect that’s a bit conservative.  In my travels, I see much more interest in data integration strategies, approaches, and technology, as cloud computing continues to grow, as well as enterprises understand better the strategic use of data.  So, I would put the growth at 15 percent for 2015.

There are many factors driving this growth, beyond mere interest in cloud computing and big data.

The first consideration is that data is more strategic than initially understood.  While businesses have always considered data a huge asset, it has not been until the last few years that businesses have seen the true value of understanding what’s going on inside, and outside of their business.

Manufacturing companies want to see the current state of production, as well as production history.  Management can now use that data to predict trends to address, such as future issues around employee productivity, or even a piece of equipment that is likely to fail and the impact of that failure on revenue.  Healthcare companies are learning how to better monitor patient health, such as spotting likely health problems before they are diagnosed, or leveraging large data to understand when patterns emerge around health issues, such as areas of the country that are more prone to asthma, based upon air quality.

Second, there is the need to deal with compliance issues.  The new health care regulations, or even the new regulation around managing a publically traded company, require a great deal of data management issues, including data integration.

As these laws emerge, and are altered over time, the reporting requirements are always more complex and far reaching than they were before.  Those who want to avoid fines, or even avoid stock drops around mistakes, are paying close attention to this area.

Finally, there is an expectation from customers and employees that you will have a good handle on your data.  10 years ago you could tell a customer on the phone that you needed to check different systems to answer their question.  Those days are over.  Today’s customers and employees want immediate access to the data they need, and there is no good excuse for not being able to produce that data.  If you can’t, your competition will.

The interest in data integration will experience solid growth in 2015, around cloud and big data, for sure.  However, other factors will drive this growth, and enterprises will finally understand that data integration is core to an IT strategy, and should never be an afterthought.

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Analytics Stories: An Educational Case Study

AnalyticsAs I have shared within other posts within this series, businesses are using analytics to improve their internal and external facing business processes and to strengthen their “right to win” within the markets that they operate. At first glance, you might not think of universities needing to worry much about their right to win, but universities today are facing increasing competition for students as well as the need to increase efficiency, decrease dependence upon state funding, create new and less expensive delivery models, and drive better accountability.

George Washington University Perceives The Analytic Opportunity

AnalyticsGeorge Washington University (GWU) is no different. And for this reason their leadership determined that they needed to gain the business insight to compete for the best students, meet student diversity needs, and provide accountability to internal and external stakeholders. All of these issues turned out to have a direct impact upon GWU’s business processes—from student recruitment to financial management. At the same time university leadership determined the complexity of these challenges requires continual improvement in the University’s operational strategies and most importantly, accurate, timely, and consistent data.

Making It A Reality

processing dataGWU determined that getting after these issues required a flexible system that could provide analytics and key academic performance indicators and metrics on demand, whenever they needed them. They, also, determined that the analytics and underlying data needed to enable accurate, balanced decisions needed to be performed more quickly and more effectively than in the past.

Unfortunately, GWU’s data was buried in disparate data sources that were largely focused on supporting transactional, day-to-day business processes. This data was difficult to extract and even more difficult to integrate into a single format, owing to inherent system inconsistencies and the ownership issues surrounding them — a classic problem for collegial environments. Moreover, the university’s transaction applications did not store data in models that supported on-demand and ad hoc aggregations that GWU business users required.

To solve these issues, GWU created a data integration and business intelligence implementation dubbed the Student Data Mart (SDM). The SDM integrates raw structured and unstructured data into a unified data model to support key academic metrics.

“The SDM represents a life record of the students,” says Wolf, GWU’s Director of Business Intelligence. “It contains 10 years of recruitment, admissions, enrollment, registration, and grade-point average information for all students across all campuses”. It supports a wide-range of academic metrics around campus enrollment counts, admissions selectivity, course enrollment, student achievement, and program metrics.

These metrics are directly and systematically aligned with the academic goals for each department and with GWU’s overall overarching business goals. Wolf says, “The SDM system provides direct access to key measures of academic performance”. “By integrating data into a clean repository and disseminating information over their intranet, the SDM has given university executivesdirect access to key academic metrics. Based on these metrics, users are able to make decisions in a timely manner and with more precision than before.”

Their integration technology supports a student account system, which supplies more than 400 staff with a shared, unified view of the financial performance of students. It connects data from a series of diverse, fragmented internal sources and third-party data from employers, sponsors, and collection agencies. The goal is to answer business questions about whether students paid their fees or how much they paid for each university course.

Continual Quality Improvement

AnalyticsDuring its implementation, GWU’s data integration process exposed a number of data quality issues that were the natural outcome of a distributed data ownership. Without an enterprise approach to data and analytics, it would have been difficult to investigate the nature and extent of data quality issues from its historical fragmented business intelligence system. Taking an enterprise approach has, as well, enabled GWU to improve data quality standards and procedures.

Wolf explains, “Data quality is an inevitable problem in any higher education establishment, because you have so many different people—lecturers, students, and administration staff—all entering data. With our system, we can find hidden data problems, wherever they are, and analyze the anomalies across all data sources. This helps build our trust and confidence in the data. It also speeds up the design phase because it overcomes the need to hand query the data to see what the quality is like.”

Connecting The Dots

Dots_gameplayWolf and his team have not stopped here. As data emanating from social media has grown, they have designed their system so social data can be integrated just as easily as their traditional data sources including Oracle Financials, SunGard, SAP, and flat file data. Wolf says the SDM platform doesn’t turn its back on any type of data. By allowing the university to integrate any type of data, including social media, Wolf has been able to support key measures of academic performance, improving standards, and reducing costs. Ultimately, this is helping GWU maintain its business position as well as the University’s position especially as a magnet for the best students around the world.

In sum, the GWU analytics solution has helped it achieve the following business goals:

  • Attract the best students
  • Provide trusted reliable data for decision makers
  • Enable more timely business decisions
  • Increase achievement of academic and administrative goals
  • Deliver new business insight by combining social media with existing data sources

Related links

Related Blogs

Analytics Stories: A Banking Case Study
Analytics Stories: A Financial Services Case Study
Analytics Stories: A Healthcare Case Study
Who Owns Enterprise Analytics and Data?
Competing on Analytics: A Follow Up to Thomas H. Davenport’s Post in HBR
Thomas Davenport Book “Competing On Analytics”

Solution Brief: The Intelligent Data Platform

Author Twitter: @MylesSuer

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Building Engagement through Service and Support

This is a guest post by Tom Petrocelli, Research Director of Enterprise Social, Mobile and Cloud Applications at Neuralytix

Engagement through Service and Support

Engagement through Service and Support

A product is not just an object or the bits that comprise software or digital media; it is an entire experience. The complete customer experience is vital to the overall value a customer derives from their product and the on-going relationship between the customer and the vendor. The customer experience is enhanced through a series of engagements over a variety of digital, social, and personal channels.  Each point of contact between a vendor and customer is an opportunity for engagement. These engagements over time affect the level of satisfaction the customers with the vendor relationship.

Service and support is a critical part of this engagement strategy. Retail and consumer goods companies recognize the importance of support to the overall customer relationship. Subsequently, these companies have integrated their before and after-purchase support into their multi-channel marketing and omni-channel marketing strategies. While retail and consumer products companies have led the way on support an integral part of on-going customer engagement, B2B companies have begun to do the same. Enterprise IT companies, which are primarily B2B companies, have been expanding their service and support capabilities to create more engagement between their customers and themselves. Service offerings have expanded to include mobile tools, analytics-driven self-help, and support over social media and other digital channels. The goal of these investments has been to make interactions more productive for the customer, strengthen relationships through positive engagement, and to gather data that drives improvements in both the product and service.

A great example of an enterprise software company that understands the value in customer engagement though support is Informatica.  Known primarily for their data integration products, Informatica has been quickly expanding their portfolio of data management and data access products over the past few years. This growth in their product portfolio has introduced many new types of customers Informatica and created more complex customer relationships. For example, the new SpringBok product is aimed at making data accessible to the business user, a new type of interaction for Informatica. Informatica has responded with a collection of new service enhancements that augment and extend existing service channels and capabilities.

What these moves say to me is that Informatica has made a commitment to deeper engagement with customers. For example, Informatica has expanded the avenues from which customers can get support. By adding social media and mobile capabilities, they are creating additional points of presence that address customer issues when and where customers are. Informatica provides support on the customers’ terms instead of requiring customers to do what is convenient for Informatica. Ultimately, Informatica is creating more value by making it easier for customers to interact with them. The best support is that which solves the problem quickest with the least amount of effort. Intuitive knowledge base systems, online support, sourcing answers from peers, and other tools that help find solutions immediately are more valued than traditional phone support. This is the philosophy that drives the new self-help portal, predicative escalation, and product adoption services.

Informatica is also shifting the support focus from products to business outcomes. They are manage problems holistically and are not simply trying to create product band-aids. This shows a recognition that technical problems with data are actually business problems that have broad effects on a customer’s business.  Contrast this with the traditional approach to support that focuses fixing a technical issue but doesn’t necessarily address the wider organizational effects of those problems.

More than anything, these changes are preparation for a very different support landscape. With the launch of the Springbok data analytics tool, Informatica’s support organization is clearly positioning itself to help business analysts and similar semi-technical end-users. The expectations of these end-users have been set by consumer applications. They expect more automation and more online resources that help them to use and derive value from their software and are less enamored with fixing technical problems.

In the past, technical support was mostly charged with solving immediate technical issues.  That’s still important since the products have to work first to be useful. Now, however, support organizations has an expanded mission to be part of the overall customer experience and to enhance overall engagement. The latest enhancements to the Informatica support portfolio reflects this mission and prepares them for the next generation of non-IT Informatica customers.

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CFO Checklist to Owning Enterprise Analytics

Frank-FriedmanLast month, the CEO of Deloitte said that CFOs are “the logical choice to own analytics and put them to work to serve the organization’s needs”. In my discussions with CFOs, they have expressed similar opinions.  Given this, the question becomes what does a CFO need to do to be effective leader of their company’s analytics agenda? To answer this, I took a look at what Tom Davenport suggests in his book “Analytics at Work”. In this book, Tom suggests that an analytical leader need to do the following twelve things to be effective:

12 Ways to Be an Effective Analytics Leader

1)      Develop their people skills. This is not just about managing analytical people which has its own challenges. It is, also, about CFOs establishing the “the credibility and trust needed when analytics produce insights that effectively debunk currently accepted wisdom”.
2)      Push for fact based decision making. You need to, as a former boss of mine like to say, become the lightening rod and in this case, set the expectation that people will make decisions based upon data and analysis.
3)      Hire and retain smart people. You need to provide a stimulating and supportive work environment for analysts and give them credit when they do something great.
4)      Be the analytical example. You need to lead by example. This means you need to use data and analysis in making your own decisions
5)      Signup for improved results. You need to commit to driving improvements in a select group of business processes by using analytics. Pick something meaningful ike reducing the cost of customer acquisition or optimizing your company’s supply chain management.
6)      Teach the organization how to use analytic methods. Guide employees and other stakeholders into using more rigorous thinking and decision making.
7)      Set strategies and performance expectations. Analytics and fact-based decisions cannot happen in a vacuum. They need strategies and goals that analytics help achieve.
8)      Look for leverage points. Look for the business problems where analytics can make a real difference. Look for places where a small improvement in a process driven by analytics can make a big difference.
9)      Demonstrate persistence. Work doggedly and persistently to apply analytics to decision making, business processes, culture, and business strategy.
10)   Build an analytics ecosystem with your CIO. Build an ecosystem consisting of other business leaders, employees, external analytics suppliers, and business partners. Use them to help you institutionalize analytics at your company.
11)    Apply analytics on more than one front. No single initiative will make the company more successful—no single analytics initiative will do so either.
12)   Know the limits to analytics. Know when it is appropriate to use intuition instead of analytics. As a professor of mine once said not all elements of business strategy can be solved by using statistics or analytics. You should know where and when analytics are appropriate.

Data AnalyticsFollowing these twelve items will help strategic oriented CFOs lead the analytics agenda at their companies. As I indicated in “Who Owns the Analytics Agenda?”, CFOs already typically act as data validators at their firms, but taking this next step matters to their enterprise because “if we want to make better decisions and take the right actions, we have use analytics” (Analytics at Work, Tom Davenport, Harvard Business Review Press, page 1). Given this, CFOs really need to get analytics right. The CFOs that I have talked to say they already “rely on data and analytics and they need them to be timely and accurate”.

One CFO, in fact, said that data is potentially the only competitive advantage left for his firm”. And while implementing the data side of this depends on the CIO. It is clear from the CFOs that I have talked to that they believe a strong business relationship with their CIO is critical to the success of their business.

Enterprise DataSo the question remains are you ready as a financial leader to lead on the analytics agenda? If you are and you want to learn more about setting the analytics agenda, please consider yourself invited to webinar that I am doing with the CFO of RoseRyan in January.

The Webinar is entitled “Analytics and Data for the Strategic CFO”. And by clicking this link you can register to attend. See you there.

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Twitter: @MylesSuer

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