Category Archives: Business Impact / Benefits
I think I may have gone to too many conferences in 2014 in which the potential of big data was discussed. After a while all the stories blurred into two main themes:
- Companies have gone bankrupt at a time when demand for their core products increased.
- Data from mobile phones, cars and other machines house a gold mine of value – we should all be using it.
My main take away from 2014 conferences was that no amount of data is a substitute for poor strategy, or lack of organisational agility to adapt business processes in times of disruption. However, I still feel as an industry our stories are stuck in the phase of ‘Big Data Hype’, but most organisations are beyond the hype and need practicalities, guidance and inspiration to turn their big data projects into a success. This is possibly due to a limited number of big data projects in production, or perhaps it is too early to measure the long term results of existing projects. Another possibility is that the projects are delivering significant competitive advantage, so the stories will remain under wraps for the time being.
However, towards the end of 2014 I stumbled across a big data success story in an unexpected place. It did (literally) provide competitive advantage, and since it has been running for a number of years the results are plain to see. It started with a book recommendation from a friend. ‘Faster’ by Michael Hutchinson is written as a self-propelled investigation as to the difference between world champion and world class althletes. It promised to satisfy my slightly geeky tendency to enjoy facts, numerical details and statistics. It did this – but it really struck me as a ‘how-to’ guide for big data projects.
Mr Hutchinson’s book is an excellent read as an insight into professional cycling by a professional cyclist. It is stacked with interesting facts and well-written anecdotes, and I highly recommend the reading the book. Since the big-data aspect was a sub-plot, I will pull out the highlights without distracting from the main story.
Here are the five steps I extracted for big data project success:
1. Have a clear vision and goal for your project
The Sydney Olympics in 2000 had only produced 4 medals across all cycling disciplines for British cyclists. With a home Olympics set for 2012, British Cycling desperately wanted to improve this performance. Specific targets were clearly set across all disciplines stated in times that an athlete needed to achieve in order to win a race.
2. Determine data the required to support these goals
Unlike many big data projects which start with a data set and then wonder what to do with it, British Cycling did this the other way around. They worked out what they needed to measure in order to establish the influencers on their goal (track time) and set about gathering this information. In their case this involved gathering wind tunnel data to compare & contrast equipment, as well as physiological data from athletes and all information from cycling activities.
3. Experiment in order to establish causality
Most big data projects involve experimentation by changing the environment whilst gathering a sub-set of data points. The number of variables to adjust in cycling is large, but all were embraced. Data (including video) was gathered on the effects of small changes in each component: Bike, Clothing, Athlete (training and nutrition).
4. Guide your employees on how to use the results of the data
Like many employees, cyclists and coaches were convinced of the ‘best way’ to achieve results based on their own personal experience. Analysis of data in some cases showed that the perceived best way, was in fact not the best way. Coaching staff trusted the data, and convinced the athletes to change aspects of both training and nutrition. This was not necessarily easy to do, as it could mean fundamental changes in the athlete’s lifestyle.
5. Embrace innovation
Cycling is a very conservative sport by nature, with many of the key innovations coming from adjacent sports such as triathlon. Data however, is not steeped in tradition and does not have pre-conceived ideas as to what equipment should look like, or what constitutes an excellent recovery drink. What made British Cycling’s big data initiatives successful is that they allowed themselves to be guided by the data and put the recommendations into practice. Plastic finished skin suits are probably not the most obvious choice for clothing, but they proved to be the biggest advantage cyclist could get. Far more than tinkering with the bike. (In fact they produced so much advantage they were banned shortly after the 2008 Olympics.)
The results: British Cycling won 4 Olympic medals in 2000, one of which was gold. In 2012 they grabbed 8 gold, 2 silver and 2 bronze medals. A quick glance at their website shows that it is not just Olympic medals they are wining – but medals won across all world championship events has increased since 2000.
To me, this is one of the best big data stories, as it directly shows how to be successful using big data strategies in a completely analogue world. I think it is more insightful that the mere fact that we are producing ever-increasing volumes of data. The real value of big data is in understanding what portion of all avaiable data will constribute to you acieving your goals, and then embracing the use the results of analysis to make constructive changes in daily activities.
But then again, I may just like the story because it involves geeky facts, statistics and fast bicycles.
The thing that resonates today, in the odd context of big data, is that we may all need to look in the mirror, hold a thumb drive full of information in our hands, and concede once and for all It’s not the data… it’s us.
Many organizations have a hard time making something useful from the ever-expanding universe of big-data, but the problem doesn’t lie with the data: It’s a people problem.
The contention is that big-data is falling short of the hype because people are:
- too unwilling to create cultures that value standardized, efficient, and repeatable information, and
- too complex to be reduced to “thin data” created from digital traces.
Evan Stubbs describes poor data quality as the data analyst’s single greatest problem.
About the only satisfying thing about having bad data is the schadenfreude that goes along with it. There’s cold solace in knowing that regardless of how poor your data is, everyone else’s is equally as bad. The thing is poor quality data doesn’t just appear from the ether. It’s created. Leave the dirty dishes for long enough and you’ll end up with cockroaches and cholera. Ignore data quality and eventually you’ll have black holes of untrustworthy information. Here’s the hard truth: we’re the reason bad data exists.
I will tell you that most data teams make “large efforts” to scrub their data. Those “infrequent” big cleanups however only treat the symptom, not the cause – and ultimately lead to inefficiency, cost, and even more frustration.
It’s intuitive and natural to think that data quality is a technological problem. It’s not; it’s a cultural problem. The real answer is that you need to create a culture that values standardized, efficient, and repeatable information.
If you do that, then you’ll be able to create data that is re-usable, efficient, and high quality. Rather than trying to manage a shanty of half-baked source tables, effective teams put the effort into designing, maintaining, and documenting their data. Instead of being a one-off activity, it becomes part of business as usual, something that’s simply part of daily life.
However, even if that data is the best it can possibly be, is it even capable of delivering on the big-data promise of greater insights about things like the habits, needs, and desires of customers?
Despite the enormous growth of data and the success of a few companies like Amazon and Netflix, “the reality is that deeper insights for most organizations remain elusive,” write Mikkel Rasmussen and Christian Madsbjerg in a Bloomberg Businessweek blog post that argues “big-data gets people wrong.”
Big-data delivers thin data. In the social sciences, we distinguish between two types of human behavior data. The first – thin data – is from digital traces: He wears a size 8, has blue eyes, and drinks pinot noir. The second – rich data – delivers an understanding of how people actually experience the world: He could smell the grass after the rain, he looked at her in that special way, and the new running shoes made him look faster. Big-data focuses solely on correlation, paying no attention to causality. What good is thin “information” when there is no insight into what your consumers actually think and feel?
Accenture reported only 20 percent of the companies it profiled had found a proven causal link between “what they measure and the outcomes they are intending to drive.”
Now, I can contend they keys to transforming big-data to strategic value are critical thinking skills.
Where do we get such skills? People, it seems, are both the problem and the solution. Are we failing on two fronts: failing to create the right data-driven cultures, and failing to interpret the data we collect?
Every year, I get a replacement desk calendar to help keep all of our activities straight – and for a family of four, that is no easy task. I start with taking all of the little appointment cards the dentist, orthodontist, pediatrician and GP give to us for appointments that occur beyond the current calendar dates. I transcribe them all. Then I go through last year’s calendar to transfer any information that is relevant to this year’s calendar. And finally, I put the calendar down in the basement next to previous year calendars so I can refer back to them if I need. Last year’s calendar contains a lot of useful information, but no longer has the ability to solve my need to organize schedules for this year.
In a very loose way – this is very similar to application retirement. Many larger health plans have existing systems that were created several years (sometimes even several decades) ago. These legacy systems have been customized to reflect the health plan’s very specific business processes. They may be hosted on costly hardware, developed in antiquated software languages and rely on a few developers that are very close to retirement. The cost of supporting these (most likely) antiquated systems can be diverting valuable dollars away from innovation.
The process that I use to move appointment and contact data from one calendar to the next works for me – but is relatively small in scale. Imagine if I was trying to do this for an entire organization without losing context, detail or accuracy!
There are several methodologies for determining the best strategy for your organization to approach software modernization, including:
- Architecture Driven Modernization (ADM) is the initiative to standardize views of the existing systems in order to enable common modernization activities like code analysis and comprehension, and software transformation.
- SABA (Bennett et al., 1999) is a high-level framework for planning the evolution and migration of legacy systems, taking into account both organizational and technical issues.
- SRRT (Economic Model to Software Rewriting and Replacement Times), Chan et al. (1996), Formal model for determining optimal software rewrite and replacement timings based on versatile metrics data.
- And if all else fails: Model Driven Engineering (MDE) is being investigated as an approach for reverse engineering and then forward engineering software code
My calendar migration process evolved over time, your method for software modernization should be well planned prior to the go-live date for the new software system.
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.
Marketers, Are You Ready? The Impending Data Explosion from the New Gizmos and Gadgets Unveiled at CES
This is the first year in a very long time that I wasn’t in Las Vegas during CES. Although it’s not quite as exciting as actually being there, I love that the Twitter-verse and industry news sites kept us all up to date about the latest and greatest announcements. Now that CES2015 is all wrapped up, I find myself thinking about the potential of some very interesting announcements – from the wild to the wonderful to the leave-you-wondering! What strikes me isn’t how useful these new gizmos and gadgets will likely be to myself and my consumer counterparts, but instead what incredible new data sources they will offer to my fellow marketers.
One thing is for sure… the connected “Internet of Things” is indeed here. It’s no longer just a vision. Sure, we’re just seeing the early stages, but it’s becoming more and more main stream by the day. And as marketers, we have so much opportunity ahead of us!
I ran across an interesting video interview on the CES show floor with Jack Smith from GroupM on Adweek.com. Jack says that “data from sensors will have a bigger impact, longer term, than the Internet itself.” That is a lofty statement, and I’m not sure I’ll go quite that far yet, but I absolutely agree with his premise… this new world of connectivity is already shifting marketing, and it will almost certainly radically change the way we market in the near future.
Riding the Data Explosion (Literally)
The Connected Cycle is one of the announcements that I find intriguing as a marketer. In short, it’s a bike pedal equipped with GPS and GPRS sensors that “monitor your movements and act as a basic fitness tracker.” It’s being positioned as a way to track stolen bicycles, which is a massive problem in Europe particularly, with the side benefit of being a powerful fitness tracker. It may not be as sexy as some other announcements, but I think there is buried treasure in devices like these.
Imagine how powerful that data would be to a sporting goods retailer? What if the rider of that bicycle had opted into a program that allowed the retailer to track their activity in exchange for highly targeted offers?
Let’s say that the rider is nearing one of your stores and it’s a colder than usual day. Perhaps you could push them an offer to their smart phone for some neoprene booties. Or let’s say that, based on their activity patterns, the rider appears to be stepping up their activity and is riding more frequently suggesting they may be ready for a race you are sponsoring in a few months in the area. Perhaps you could push them an inspirational message saying how great they’re progressing and had they thought about signing up for the big race, with a special incentive of course.
The segmentation possibilities are endless, and the analytics that could be done on the data leaves the data-driven marketer salivating!
Home Automation Meets Business Automation
There were numerous announcements about the connected “house of the future”, and it’s clear that we are just beginning of the home automation wave. Several of the big dogs like Samsung, Google, and Apple are building or buying automation hub platforms, so it’s going to be easier and easier to connect appliances and other home devices to one another, and also to mobile technology and wearables. As marketers, there is incredible potential to really tap into this. Imagine the possibility of interconnecting your customers’ home automation systems with your own marketing automation systems? Marketers will soon be able literally serve up offers based upon things that are occurring in the home in real time.
Oh no, your teenage son finished off all but the last drop of milk (and put the almost-empty jug back in the fridge without a second thought)! Not to worry, you’ve linked your refrigerator’s sensor data with your favorite grocery store. An alert is sent asking if you want more milk, and oh by the way, your shopping patterns indicate you may be running out of your son’s favorite cereal too, so it offers you a special discount if you add a box to your order. Oh yeah, of course he was complaining about being out just yesterday! And whala, a gallon of milk and some Cinnamon Toast Crunch magically arrives at your door by the end of the day. Heck, it will probably arrive within an hour via a drone if Amazon has anything to say about it! No manual business processes whatsoever. It’s your appliance’s sensors talking to your customer data warehouse, which is talking to your marketing automation system, which is talking to a mobile app, which is talking to an ordering system, which is talking to a payment system, which is talking to a logistics/delivery system. That is, of course, if your internal processes are ready!
Some of the More Weird and Wacky, But There May Just Be Something…
Panasonic’s Smart Mirror allows you to analyze your skin and allows you to visualize yourself with different makeup or even a different haircut. Cosmetics and hair care companies should be all over this. Imagine the possibilities of visualizing yourself looking absolutely stunning – if only virtually – with perfect makeup and hair. Who wouldn’t want to rush right out and capture the look for real? What if a store front could virtually put the passer-byer in their products, and once the customer is inside the store, point them to the products that were featured? Take it a step further and send them a special offer the next week to come back buy the hat that just goes perfectly with the rest of the outfit. It all sounds a little bit “Minority Report-esque”, but it’s closer to becoming true every day. The power of the interconnected world is endless for the marketer.
And then there’s Belty… it’s definitely garnered a lot of news (and snarky comments too!). Belty is a smart belt that slims or expands based upon your waist size at that very moment – whether you’re sitting, standing, or just had a too-large meal. I don’t see Belty taking off, but you never know! If it does however, can’t you just see Belty sending a message to your Weight Watchers app about needing to get back on diet? Or better yet, pointing you to the Half Yearly Sale at Nordstrom because you’re getting too skinny for your pants?
The “Internet of Things” is Becoming Reality… Is Your Marketing Team Ready?
The internet of things is already changing the way consumers live, and it’s beginning to change the way marketers market. With the It is critical that marketers are thinking about how they can leverage the new devices and the data they provide. Connecting the dots between devices can become a marketer’s best friend (if they’re ready), or worst enemy (if they’re not).
Are you ready? Ask yourself these 6 questions:
- Are your existing business applications connected to one another? Do your marketing systems “talk” to your finance systems and your sales systems and your customer support systems?
- Do you have fist-class data quality and validation technology and practices in place? Real-time, automated processes will only amplify data quality problems.
- Can you connect easily to any new data source as it becomes available, no matter where it lives and no matter what format it is in? The only constant in this new world is the speed of change, so if you’re not building processes and leveraging technologies that can keep up, you’re already missing the boat!
- Are you building real time capabilities into your processes and technologies? You systems are going to have to handle real-time sensor data, and make real-time decisions based on the data they provide.
- Are your marketing analytics capabilities leading the pack or just getting out of the gate? Are they harnessing all of the rich data available within your organization today? Are you ready to analyze all of the new data sources to determine trends and segment for maximum effect?
- Are you talking to your counterparts in IT, logistics, finance, etc. about the business processes and technologies you are going to need to harness the data that the interconnected world of today, and of the near future? If not, don’t wait! Begin that conversation ASAP!
Informatica is ready to help you embark on this new and exciting data journey. For some additional perspectives from Informatica on the technologies announced at CES2015, I encourage you to read some of my colleagues’ recent blog posts:
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
It’s true. Data integration is a whole new game, compared to five years ago, or, in some organizations, five minutes ago. The right approaches to data integration continue to evolve around a few principal forces: First, the growth of cloud computing, as pointed out by Stafford. Second, the growing use of big data systems, and the emerging use of data as a strategic asset for the business.
These forces combine to drive us to the understanding that old approaches to data integration won’t provide the value that they once did. As someone who was a CTO of three different data integration companies, I’ve seen these patterns change over the time that I was building technology, and that change has accelerated in the last 7 years.
The core opportunities lie with the enterprise architect, and their ability to drive an understanding of the value of data integration, as well as drive change within their organization. After all, they, or the enterprises CTOs and CIOs (whomever makes decisions about technological approaches), are supposed to drive the organization in the right technical directions that will provide the best support for the business. While most enterprise architects follow the latest hype, such as cloud computing and big data, many have missed the underlying data integration strategies and technologies that will support these changes.
“The integration challenges of cloud adoption alone give architects and developers a once in a lifetime opportunity to retool their skillsets for a long-term, successful career, according to both analysts. With the right skills, they’ll be valued leaders as businesses transition from traditional application architectures, deployment methodologies and sourcing arrangements.”
The problem is that, while most agree that data integration is important, they typically don’t understand what it is, and the value it can bring. These days, many developers live in a world of instant updates. With emerging DevOps approaches and infrastructure, they really don’t get the need, or the mechanisms, required to share data between application or database silos. In many instances, they resort to coding interfaces between source and target systems. This leads to brittle and unreliable integration solutions, and thus hurts and does not help new cloud application and big data deployments.
The message is clear: Those charged with defining technology strategies within enterprises need to also focus on data integration approaches, methods, patterns, and technologies. Failing to do so means that the investments made in new and emerging technology, such as cloud computing and big data, will fail to provide the anticipated value. At the same time, enterprise architects need to be empowered to make such changes. Most enterprises are behind on this effort. Now it’s time to get to work.
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.