Category Archives: Business/IT Collaboration
Customers often inquire about the best way to get their team up to speed on the Informatica solutions. The question Informatica University hears frequently is whether a team should attend our public scheduled courses or hold a Private training event. The number of resources to be skilled on the products will help to determine which option to choose. If your team, or multiple teams within your company, has 7 or more resources that require getting up to speed on the Informatica products, then a Private training event is the recommended choice.
Seven (7) for a remote instructor and nine (9) for an onsite instructor is the break even cost per resource when determining whether to hold a private training and is the most cost efficient delivery for a team. In addition to the cost benefit, customers who have taken this option value the daily access to their team members to keep business operations humming along, and the opportunity to collaborate with key team members not attending by allowing them to provide input to project perspective.
These reserved events also provide the opportunity to be adapted to focus on a customers needs by tailoring course materials to highlight topics that will be key to a project’s implementation which provide creative options to get a team up to speed on the Informatica projects at hand.
With Informatica University’s new flexible pricing, hosting a Private Training event is easy. All it takes is:
- A conference room
- Training PC’s or laptops for participants
- Access to the Internet
- An LCD projector, screen, white board, and appropriate markers
Private training events provide the opportunity to get your resources comfortable and efficient with the Informatica Solutions and have a positive impact on the success of your projects.
To understand more about Informatica’s New Flexible Pricing, contact firstname.lastname@example.org
You Say Big Dayta, I say Big Dahta
Some say Big Data is a great challenge while others say Big Data creates new opportunities. Where do you stand? For most companies concerned with their Big Data challenges, it shouldn’t be so difficult – at least on paper. Computing costs (both hardware and software) have vastly shrunk. Databases and storage techniques have become more sophisticated and scale massively, and companies such as Informatica have made connecting and integrating all the “big” and disparate data sources much easier and have helped companies achieve a sort of “big data synchronicity”. As it is.
In the process of creating solutions to Big Data problems, humans (and the supra-species known as IT Sapiens) have a tendency to use theories based on linear thinking and the scientific method. There is data as our systems know it and data as our systems don’t. The reality, in my opinion, is that “Really Big Data” problems now and in the future will have complex correlations and unintuitive relationships that need to utilize mathematical disciplines, data models and algorithms that haven’t even been discovered or invented yet and when eventually discovered, will make current database science positively primordial.
At some point in the future, machines will be able to predict, based on big, perhaps unknown data types when someone is having a bad day or a good day, or more importantly whether a person may behave in a good or bad way. Many people do this now when they take a glance at someone across a room and infer how that person is feeling or what they will do next. They see eyes that are shiny or dull, crinkles around eyes or sides of mouths, then hear the “tone” in a voice and then their neurons put it altogether that this is a person that is having a bad day and needs a hug. Quickly. No one knows exactly how the human brain does this, but it does what it does and we go with it and we are usually right.
And some day, Big Data will be able to derive this and it will be an evolution point and it will also be a big business opportunity. Through bigger and better data ingestion and integration techniques and more sophisticated math and data models, a machine will do this fast and relatively speaking, cheaply. The vast majority won’t understand why or how it’s done, but it will work and it will be fairly accurate.
And my question to you all is this.
Do you see any other alternate scenarios regarding the future of big data? Is contextual computing an important evolution and will big data integration be more or less of a problem in the future.
PS. Oh yeah, one last thing to chew on concerning Big Data… If Big Data becomes big enough, does that spell the end of modelling as we know it?
Knowing business’s trends and needs change frequently, why is it that we plan multi-year IT-driven roadmaps?
Understandably, IT managers have honed their skills in working with the line to predict business needs. They have learned to spend money and time wisely and to have the right infrastructure in place to meet the business’ needs. Whether it is launching in a new market, implementing a new technology, or one of many other areas where IT can help its firm find a competitive advantage.
Not so long ago, IT was so complex and unwieldy that it needed specially-trained professionals to source, build, and run almost every aspect of it, and when line managers had scant understanding which technology would suit their activities best, making a plan based on long-term business goals was a good one.
Today, we talk of IT as a utility, just like electricity, you press a button, and IT turns “on.” However that is not the case, the extent to which IT has saturated the day-to-day business life means they are better placed to determine how technology should be used to achieve the company’s objectives.
In the next five years, the economic climate will change, customer preferences will shift, and new competitors will threaten the business. Innovations in technology will provide new opportunities to explore, and new leadership could send the firm in a new direction. While most organizations have long-term growth targets, their strategies constantly evolve.
This new scenario has caused those in the enterprise architecture (EA) function to ask whether long-term road mapping is still a valuable investment.
EAs admit that long-term IT-led road mapping is no longer feasible. If the business does not have a detailed and stable five-year plan, these architects argue, how can IT develop a technology roadmap to help them achieve it? At best, creating long-term roadmaps is a waste of effort, a never-ending cycle of updates and revisions.
Without a long-range vision of business technology demand, IT has started to focus purely on the supply side. These architects focus on existing systems, identifying ways to reduce redundancies or improve flexibility. However, without a clear connection to business plans, they struggle to secure funding to make their plans a reality.
IT has turned their focus to the near-term, trying to influence the small decisions made every day in their organizations. IT can have greater impact, they believe, if they serve as advisors to IT and business stakeholders, guiding them to make cost-efficient, enterprise-aligned technology decisions.
Rather than taking a top-down perspective, shaping architecture through one master plan, they work from the bottom-up, encouraging more efficient working by influencing the myriad technology decisions being made each day.
The 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!
- 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
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.
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.
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?
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.
For 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.
As 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
I 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
The 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.
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
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.
When 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
So 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
In 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.
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
Solution Brief: The Intelligent Data Platform
Author Twitter: @MylesSuer
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.