Posted on June 5th, 2013
This Project Management Series will highlight the top three interpersonal skills with the power to influence successful project and program management. These same interpersonal skills apply to business success. We continue the series by revealing the #2 most important interpersonal skill for Project Managers to employ. Stay tuned for the #1 most important interpersonal skill for Project Managers.
The Project Management Institute (PMI) identifies communication as one of the single biggest reasons for project success or failure. And, according to my original research, communication is the number one ranked social characteristic that impacts project success. Communication is a powerful tool in today’s global enterprise, making the difference between financial success or not.
In today’s fast paced, ever-changing and global world, project complexity contributes to miscommunication. Communication skills refer to the ability to convey ideas easily and clearly in order to ensure that the team moves to a common goal (Belzer,K. (2004) Still More Art than Science). Communication helps to navigate ambiguity, in complex project environments, to ensure project success.
Each team member, including stakeholders, has a unique communication style as well as a preferred communication mechanism. Today, we communicate face to face, in virtual only meetings, electronically and through multiple social media outlets. Communication preference can be generational. This must be taken into account when delivering a message to a project team.
To read more, please visit our Thought Leadership Page and read …
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Posted on May 27th, 2013
by Ravi Kalakota, Partner LiquidHub
Business executives face tough questions every day. Many of these questions don’t always have easy or straightforward answers: Which analytical investments and strategies really increase revenue? What pilots should I run to test data monetization ideas out? What small data or big data monetization strategies should I adopt? At the root of these queries is the one billion dollar question facing organizations everywhere: How do we monetize our data?
First, it’s critical to understand what data monetization is, that is the process of converting data (raw or aggregate data) into something useful and valuable to an organization’s bottom line. Not only can data monetization help make decisions (such as predictive maintenance) based on multiple sources of insight, it also creates opportunities for organizations with significant data volume to leverage untapped or under-tapped information and create new sources of revenue (e.g., cross-sell and upsell).
There’s just one snag. Data monetization requires a new IT clock-speed, one that most firms are struggling to keep up with. Aberdeen Research found that, with traditional BI software, it takes IT eight days on average to complete BI support requests, such as add a column to a report, and 30 days to build a new dashboard. For an individual attempting to find an answer, make a decision or solve a problem, this is an unacceptable timeline. For an organization trying to differentiate itself with information innovation or data driven decision-making, it is a major barrier to strategy execution – and it’s bad for business.
To speed up insight generation and decision-making (all elements of data monetization), business users are bypassing IT and investing in data visualization (Tableau) or data discovery platforms (Qlikview). These platforms help users ask and answer their own questions and follow their own path to insight. Unlike traditional BI, which provides dashboards, heatmaps and canned reports, these tools offer a discovery platform rather than a pre-determined path.
What’s more, companies like Marketo, which creates marketing automation software, are getting into the customer engagement and data monetization game. Their focus is to enable marketing professionals to find future customers; to build, sustain and grow relationships with those buyers over time; and to cope with the sheer pace and complexity of engaging with customers in real time across the Web, email, social media, online and offline events, video, e-commerce storefronts, mobile devices and a variety of other channels. In many companies, marketing knits these digital interactions together across multiple disconnected systems. The ability to interact seamlessly with customers across numerous fast-moving digital channels requires an engagement strategy fueled by data and analytic insights.
New Revenue Streams – Moving from Analytics to Data Monetization
Here are questions that people ask me at every meeting I attend: How do we increase revenue by leveraging analytics? What are the best practices and what are my competitors doing around this?
In every competitive industry – from retail and insurance to healthcare and financial services – companies along the value chain are racing to replace lost revenues as new regulatory environment changes, the competition heats up, and consumer choices eliminate traditional revenue sources. Rather than looking just to increase transaction volume, firms are shifting their focus to leveraging current transaction volume by mining a valuable, underleveraged asset—client/transaction data—to create new revenue streams.
The healthcare industry, for instance, is fast becoming an industry based on analytics outcomes. Within healthcare are accountable care organizations (ACOs), which are expected to connect groups of providers who are willing and able to take responsibility for improving the health status, efficiency and experience of care for a defined population. This can’t be done without a sizable investment in data, analytics and monetization capability.
Another common use case of monetization is the benefit from better inspection scheduling and preventive maintenance. The result is a huge cost savings because expensive and experienced resources are not used to respond to emergency repair calls. This was certainly the case with a large ATM manufacturer. By monitoring various assets in the ATM (cash dispensers, printers, cameras etc.) via log analysis, the manufacturer was able to substantially reduce maintenance downtimes.
As top-line growth gets tougher and cost takeout via outsourcing reaches real-world practical limits, the game is shifting to novel monetization strategies to extract new revenue from the existing customer base. Data monetization isn’t easy; it requires a sophisticated process of capturing appropriate data sources; storing and managing the data; performing analytics to identify key trends and latent themes; and presenting the insights in an accessible, easy-to-understand format.
Data Monetization analytics use cases, toolsets, skillsets and mindsets. Expanding this capability and the tools to exploit it is the new frontier. The hypothesis is that investments in technology, process and organization to build these capabilities will pay dividends not just in the ability to deliver and monetize data, but also in ancillary benefits such as faster time to market, improved organizational communication, better customer service and, ultimately, a better customer experience. The downside is that if it’s not done right, the results can hurt the company.
Data Monetization in Financial Services
So how do you monetize customer touch points via segmentation, prospect identification, campaign analysis, cross-selling and upselling, retention-lapse and lifetime value?
Here’s the thing: data monetization isn’t analytics. By themselves, analytics – predictive, descriptive or exploratory – are of limited value. Monetization comes from the downstream consumption of analytical insights to then create value. Analytics have to be consumed either by humans, machines or applications to make or facilitate decisions and create new revenue streams. The payments value chain, where issuing banks, processors and acquiring institutions all profit from data monetization, is a perfect example:
Effective data monetization enables large credit card acquirers to offer merchants more value-added services, such as analytics or report packages. Increasing the timeliness, accessibility, quality or completeness of data offered (by integrating external data, for example) can set an acquirer apart from its competitors and add important new revenue.
Interestingly, the growing trend is to arm the consumer with a lot of powerful data discovery tools. Credit card companies like American Express are providing plenty of information to help customers make the best possible decisions. They are letting the customer follow an information scent, rather than a pre-determined path, as they go off searching for the data that will help answer their business questions.
By providing cardholders with breakdowns of their card spend, alerts based on preset limits or data on what friends who bank with the same institution are buying, card issuers can use data to increase touch points with their customers—and that helps improve loyalty and stickiness. Leveraging the Internet and mobile devices further enhances the customer experience by enabling these updates to happen in real time.
Creating a Data Monetization Roadmap
Companies that want to maximize the benefit of their BI and Analytics investments should begin by evaluating their organization’s data monetization maturity level.
One good first step is a comprehensive, enterprise-wide assessment to establish a baseline. It’s important during this process to ask key questions: How is the organization monetizing its data? What data is currently being monetized? What is the business value of that monetization? How much money has been left on the table? The baseline assessment should identify not only what data is available, but also what data can be used from a practical and regulatory perspective.
Successful data monetization requires the ability to fully exploit data across organizational and application silos. A financial institution, for instance, will have data segmented and owned by different lines of business—commercial, consumer, retail banking or mortgage. Breaching these data barriers is essential to ensuring that the insights from analytics are extracted.
Companies must also improve and speed up access to their data. Having thousands of individual data stores around the company, worldwide, is a very inefficient way to store data, and it makes accessing the data for monetization purposes a challenge in itself. Collapsing, consolidating and rationalizing data stores are continuous must-do exercises.
Understanding use cases is crucial. Initiating a data monetization effort in, say, digital marketing is the perfect time to brainstorm and identify new target customers for marketing/selling data and related services as well as expanding the usages for data currently being sold to customers. Getting another perspective from outside the organization is especially important. Knowing what other institutions are doing in terms of data monetization can provide valuable insight and new ideas.
Monetizing Means Deciding
Finally, a business case or a cost-benefit analysis of the desired changes will be required for any major investment. This should include how much revenue those changes will deliver and what they will cost to implement.
Upsides and Downsides
Today a lot of emphasis is on infrastructure for managing big data. The next wave is delivering on the unique monetization opportunity that developing analytics and applications on top of the available data. While the revenue potential of data monetization can be significant, there are some considerations and potential roadblocks to note: organizational resistance, overly strict interpretations of regulatory requirements, inflexible data silos and an out-of-date infrastructure, to name a few.
Companies should compare anticipated revenue gains with the cost of bringing that revenue through the door, and adjust their expectations and scope accordingly. While some organizations will be able to justify and support a wholesale infrastructure upgrade (if required) to achieve their data monetization goals, others will not. In most cases, however, there are ways to incrementally improve on data monetization without emptying the bank account. But there’s no doubt that changing status quo isn’t for the faint-hearted.
Notes and References
Advanced analytics will automate many management decision-making processes, which will allow managers to focus more on strategy setting and business innovation. At the same time, developments in machine learning and in-memory computing will enable people to analyze masses of unstructured data. Finally, collaborative and social technologies will enable more employees to participate in decision making, thus improving the quality of management decisions. For more information see the March 27, 2012, Gartner report, “Information Innovation Will Revolutionize Decision Making.”
Aberdeen Research conducted a survey of 237 organizations on the topic of agile BI in March 2011. See the report, “Agile BI: Complementing Traditional BI to Address the Shrinking Decision-Window,” November 2011 (available to subscribers only).
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Posted on May 7th, 2013
This Project Management Series will highlight the top three interpersonal skills with the power to influence successful project and program management. These same interpersonal skills apply to business success. We continue the series by revealing the #2 most important interpersonal skill for Project Managers to employ. Stay tuned for the #1 most important interpersonal skill for Project Managers.
The next generation of Business Leaders will come from today’s project management pool. The same criteria used for C-suite success is executed daily by Project Managers. Project Managers need to be adaptable and understand the interdependence of people throughout an organization. They value communication and use it to build well-functioning teams. In fact, the title, Project Manager, is incomplete. The title should be Project Leader. A Project Leader delegates, negotiates, is politically savvy, navigates organizational complexity and leads their group to success. They utilize social characteristics to positively impact return on investment (ROI). These leaders repeatedly deliver projects on time, within budget and per scope.
For more information, visit our Thought Leadership page to read ‘Leadership and Project Work.’
Posted in Application Development & Integration, Business & Technology Strategy | No Comments »
Posted on April 11th, 2013
by Peter Classon
When it comes to technology, the evolutionary process never sleeps. Just look at Enterprise Architecture (EA). What started in corporate IT departments as a cutting-edge approach to managing an increasingly large and complex computing environment is now a prerequisite to success. Any organization serious about its future invests time and money in developing, implementing and actively managing their EA framework. Why? Because EA is the great translator. It allows all of a company’s various departments – from marketing and finance to the supply chain and risk management – to make sense of each other. It ensures that everybody is on the same page, speaking the same language. And that makes EA a critical part of any company’s strategy.
But due to competitive pressures and technological innovation, the pace of change in the global marketplace is constant, and EA must adapt and innovate accordingly. Thanks to the recent advancements in Cloud Computing, new technology form factors (think mobile), and the explosion of virtual lives online (think Social Media), technology isn’t just automating prior manual processes, it is becoming the business process. That’s where the next generation of EA comes in. Rather than relying strictly on IT to develop the architecture, business-oriented EA promotes the concept of blending business and IT to drive business goals and strategies, which enables a company to interact with its customers, employees, suppliers and commerce partners in real time across all channels with complete transparency and through numerous technology solutions. Only then can an enterprise can claim to be truly digitized.
At LiquidHub, we have built a comprehensive toolkit for business-oriented Next-Generation EA. Through our experience working in verticals, we have developed expertise across industries and business domains, with specialists who can codify business strategies. For example, a leading North American retail bank client, who was about to spend millions on sound technology strategies for its branch network, asked us to look at a subset of applications to determine if they made sense from a business strategy perspective. Were these apps well suited to the three- to five-year vision of the bank? Were the vendors who supported these apps developing the next-generation banking software? Did the cost of the apps make sense with regard to ROI and NPV for the ‘value’ provided?
The LiquidHub team applied business-oriented EA principles, visiting the vendors and creating capability reference models to show coverage of the apps and whether or not they were in synch with the emerging business strategies. What we discovered was that one app – the biggest one – was outdated technology and wouldn’t support the company’s strategic needs; the second app was supported by a vendor considered highly risky due to size and small customer base so the recommendation was for just an application upgrade; and the third didn’t make financial or strategic sense, as it was a short-term solution soon to be replaced by more comprehensive multi-channel needs facing all banks. In all three cases, we recommended that the client spend a fraction of the original amount to safeguard its current operation and instead invest the money in more long-term goals, such as effective customer engagement, multi-channel interaction technologies and innovative new-model technologies. From an IT perspective, the apps were great; from a business perspective, they were roadblocks to the company’s objectives. That’s how the business-oriented approach works.
Of course, technology is critical. We live in a technology-enabled – and increasingly dependent – world. But as business and technology evolve hand-in-hand, it’s important that companies rethink how to maximize their use of technology through a business lens. Business-oriented EA. That’s the operating model of the future.
Links:
Business-Oriented Architecture Becomes the New Focus
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Posted on April 5th, 2013
by Ravi Kalakota
Over the past few years, several trends have appeared on the scene thanks to underutilization and the complexity of managing growing data sprawl. Among those trends is Data-as-a-Service (DaaS). A relatively new concept, DaaS is a cloud-based model that allows users, customers, and consumers to access data on demand, regardless of their own location. Importantly, it represents an opportunity for improving IT efficiency and performance through centralization of resources. With the development and implementation of such technologies as data virtualization, data integration, MDM, service-oriented architecture, business-process management and Platform-as-a-Service (PaaS), DaaS strategies have increased dramatically in recent years.
As the DaaS trend continues to accelerate, so do the questions around it: How do you deliver the right data to the right place at the right time? How do you “virtualize” the data often trapped inside applications? How do you support changing business requirements (analytics, reporting, and performance management) despite ever-changing data volumes and complexity?
Enterprise DaaS strategy & Infrastructure is a core focus area for business unit and enterprise CIOs. Here’s why:
In the early years of this market, most DaaS was focused primarily on the financial services, telecom and government sectors. However, in the past two years, we have seen a big jump in the number of sectors adopting DaaS, namely healthcare, insurance, retail, manufacturing, e-commerce and media/entertainment.
What is Data-as-a-Service?
We already know that DaaS promotes the concept that data related services – aggregation, quality, cleansing and enriching data and offering it to different systems, applications or mobile users – can be provided and accessed from a centralized location. In addition, DaaS is the major enabler of the Master Data Management (MDM) concept.
Master Data is the Holy Grail of enterprise data management. Most companies focus on a single version of the truth, or Golden Source “Product,” “Customer,” “Transaction” and “Supplier” data. Why? Fragmented, inconsistent product data slows time-to-market and creates supply-chain inefficiencies, resulting in weaker-than-expected market penetration and an increased cost of compliance. Fragmented, inconsistent customer data hides revenue recognition, introduces risk, creates sales inefficiencies, and results in misguided marketing campaigns and lost customer loyalty. Fragmented and inconsistent supplier data reduces efficiency, negatively impacts spend control initiatives, and increases the risk of supplier exceptions.
Here’s where DaaS solutions come in. They provide the plumbing that enables MDM, and have the following advantages:
DaaS Use Cases
Organizations are looking to solve tough data and process-integration challenges as they start to invest in new business capabilities again. As they explore new opportunities, they also have to make choices that will help both streamline and propel the enterprise forward. DaaS is making geographic or organizational separation of provider and consumer an obsolete notion, while the emergence of Platform-as-a-Service, or PaaS, along with service-oriented architecture (SOA), is rendering the actual platform on which the data resides irrelevant as well.
DaaS has many use cases for the enterprise:
DaaS Elements
Say a client decides it’s time to take the next step. Where does that client begin to enable MDM strategy and build a data-as-a-service offering for the rest of the organization?
These are the elements a company needs in order to take that next step:
All of these capabilities come together around the data logistics chain. The last few decades have seen a dramatic shift in how companies handle data. Increasingly, they are shifting away from hierarchical, one-dimensional enterprise data-warehouses (EDW) with fixed data sources to a fragmented network of strategic partnerships with external data sources. Not surprisingly, this phenomenon causes ripple effects throughout the old data logistics network. DaaS at its core can address this problem of fragmentation.
Behavioral Politics around DaaS
In many organizations, the individuals who own the data have control. They can determine who is in the know; they can shape the “story.” One of the key benefits of DaaS is fast, low-cost access to the data. Removing barriers to data access will impact current data owner’s level of control.
So a best-practice case study informs us that a DaaS effort focused on critical enterprise data must be a joint effort between business and IT, and often requires senior executive (e.g. CEO, CTO, etc.) support to get past the potential problem of ownership issues. Senior-level engagement is typically driven by ROI business cases, and this may be part of an engagement or offering.
The challenge for DaaS may be more around organizational alignment than technical deployment. A key driver of a DaaS environment is the integration of data from multiple systems of record. Those different systems are likely to have different data definitions and hierarchies. In these types of situations, metadata management and data integration services are critical.
In addition, the market leaders want to position themselves as experts who know and understand the underlying data, largely so that everyone else in the organization doesn’t have to become one. As a result, domain expertise is a crucial component to any successful DaaS strategy.
Summary
As a combination of applications and technologies, DaaS consolidates, cleans and augments source enterprise data, and synchronizes it with all applications, business processes and analytical tools. The goal behind it is to achieve significant improvements in operational efficiency, reporting and fact-based decision making. To that end, key requirements of any DaaS strategy include domain knowledge, application knowledge, people/talent, processes and technology platforms.
Notes
1) Platform as a Service (PaaS) is being applied to Enterprise Data
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