Is Big Data becoming a Dirty Word?

If you’re 40+, you may remember 15-20 years ago when data warehousing was the THING. Every company that could afford to build one was either in the process, or thinking about it. According to the pundits at the time, the data warehouse (DW) would revolutionize business. It would give you quicker, deeper, more actionable insights and allow you to have foresight as well, so that you could make predictions about your future and better decisions on how to leverage that future to your advantage.

Unfortunately, even though there were many DW success stories (think Wal Mart and Amazon) there were also some major horror stories. Many companies that tried to build enterprise DWs fell into a quagmire of bungled requirements, dirty data, and technology overload, and their DWs crashed and burned spectacularly. So, even though many companies now have DWs as the stalwart of their IT infrastructures, the concept has a really bad name and is sometimes used as metaphor for failure.

Big data may be on the cusp of going the way of the DW. Today’s pundits are shouting virtually the same accolades at big data analytics as they were about DWs. Harnessing big data can theoretically give you the power to extract better, actionable insights and provide both predictive and prescriptive capabilities. However, big data is also causing headaches at many companies because they simply don’t understand how to effectively exploit its massive potential.

StatsThreeThe numbers don’t lie. Despite the fact that large companies are on an analytics spending binge—IDC predicts that by 2019, companies will spend $187 billion per year on analytics initiatives—these projects are often riddled with complications[1]. Indeed, according to one Gartner analyst, upwards of 85% of big data projects will experience some degree of failure.[2] What’s more, 75% of CEOs feel that they’re doing the best they can with their data, but in reality, only about 43% are actually positioned for success.[3]

How Smart Companies Avoid Failure

There are companies that are doing big data right. Approximately 57% of companies in North America are doing a decent job of wringing valuable insights from their big data analytics projects. What are these companies doing that their competitors aren’t?

They are Data Driven

Companies that are gleaning accurate, actionable insights from the mountains of data they’re wrangling on a daily basis have made the commitment to move away from trusting their gut for decision-making toward relying on the data. It’s hard—especially when those in the C-suite have decades of experience and their gut is telling them one thing and the numbers say another. However, if—and this is a big if—you have analysts who have deep domain experience, you can trust them to interpret the numbers correctly and provide insights that your gut can’t.

They Get the Tools They Need

Analytics RightThere are myriad tools on the market, and every vendor out there will tell you that their tool is the right one for you. It’s not. The tool that’s right for you is the one that will enable you to get the answers you seek. And that’s the catch. To get the right tools, you have to ask the right questions. So, really, it’s not a technical issue, it’s a knowledge—and leadership—issue.

Everyone can contribute to asking the right questions. The C-suite provides strategic direction, and knowledge workers provide deep industry knowledge and business questions. Those questions—and the technology you need to answer them—will determine your infrastructure.

They are on the Same Page—Enterprise Wide

Fully 88% of companies that are doing well with big data say they have a handle on how information flows through the organization. [4] They’re on the same page as to how information affects their job—and how it affects their co-workers’ jobs. That’s important, because an understanding of information flows leads to an understanding of what information you need, and where the gaps in that information lie. When you know the gaps, you can begin to fill them. Those gaps will give you the insight you need to answer the questions you have and glean value from your analytics.

Big Data Doesn’t Have to be a Dirty Word

It’s clear that many companies have a lot of work left to do to realize the potential of big data. Those that are doing it right are reaping the benefits. Those that aren’t are running into some serious headwinds and are feeling the burn of failure. If big data is to live up to its promise, today’s companies will have to tread on the shoulders of those who failed with their data warehouses and learn from their mistakes: become data driven, get the right tools to answer their critical questions, and understand what information they need, and how it flows through the enterprise.

I’d love to hear what you think. Please comment here, connect with me on Linkedin and Twitter, or email me at


nalytics-spending-to-hit-187-billion.html. Retrieved May 16, 2018

[2] Retrieved May 15, 2018

[3] Retrieved May 15, 2018

[4] ibid

Disaster-Proofing Your Supply Chain

Imagine having to shut down the production line of your most popular–and profitable–product because of a disastrous fire at one of your crucial suppliers. Such a scenario has the potential to be catastrophic because of the slice of revenue it represents. Sure, you can move quickly to reduce the impact of the incident, but the temporary shutdown will haunt you for months—or years—to come.

This type of disaster is certainly not predictable, but the consequences unfortunately are. How can you ensure that you’re prepared for future disruptions such as this? And, even if there are no disruptions, how can you make your supply-chain analytics better? The answers lie in how you implement analytics, manage the process, and who you listen to for answers.

Go Digital

Even small companies globally source parts these days, and their complex production processes reflect that. In order to manage such a complex, tightly-orchestrated sourcing and production environment, many companies are implementing digital supply networks (DSN). With a DSN you create an ecosystem where information flows from suppliers, to producers, to logistics partners seamlessly. Information flow is on-demand, near real-time. That flow creates almost instantaneous insights that are accessible to all interested and authorized parties.

To be sure, a catastrophic fire is almost impossible to predict. However, more predictable disruptions—e.g., labor unrest, economic uncertainty, shipping delays or bottlenecks, and global political issues—can be better managed when information flows more quickly, and insights can be acted on in near-real-time to anticipate and solve problems.

Perhaps even your supplier’s catastrophic fire could have been less disastrous if your management had had better insights and realized that the supplier was a potential bottleneck, due to its unique function within the network, and had more effectively spread the sourcing of these unique parts.

Manage from the Top

The C-Suite must drive the process to embed supply chain analytics into the fabric of the organization. One-off analytics initiatives on a department-by-department basis won’t cut it. Your information—and thus your supply chain—will be fragmented. Moreover, the chain will remain just that—a chain in which links are easily broken and disruptions become disasters.

It’s absolutely critical that efforts to use analytics to build better supply chains (DSNs, if you want my humble opinion) be a fundamental objective of the strategic plan, and that executive performance be measured—in part—on the company’s ability to improve its supply-chain performance. Otherwise, the C-suite will be focused on other issues and supply-chain analytics will take a back burner. This will leave you under-prepared to predict and/or deal with eventual disruptions.

Listen to the People who Know your Business

Most investment bankers likely know very little about designing cars, and I probably wouldn’t trust the VP of production at auto company to underwrite an IPO. What I’m getting at here is that industry knowledge is important. This is true for analytics implementation—especially when you’re talking supply-chain analytics.

You can hire all the quant jocks you want, but if they don’t have some industry knowledge, they won’t be able to produce many helpful analytical insights, because they don’t really understand how the whole process works—and how information flows, and needs to flow.

They may focus on the wrong data, or they may miss crucial pieces of information that someone with industry knowledge wouldn’t. So whether you build in-house, or you outsource to consultants, a critical requirement for implementing supply chain analytics is to make sure that the data scientists and analysts you hire are steeped in your industry enough to understand what the data is telling them.

Further, they must be willing to engage with, and listen to, people within the organization that do have that knowledge. Otherwise, you’re just getting a lot of fun facts and numbers that may or may not tell you the entire story. And with the potential for disruptions to turn into disasters, you don’t need that. You need actionable information that drives quicker reactions to problems and more effective decision-making.

Nothing is Certain

The connections in your business environment are more fragile than you think. Catastrophes such as this one are rare and unpredictable, but others, not so much. You can be better prepared to handle disruptions if your analytics strategy is sound. My advice is simple: go digital as much as possible to derive faster, better insights; lead from the top to drive coordinated implementation; and listen to the people who know your business best so that you get better answers and make better decisions.

I’d love to hear what you think. Please comment here, connect with me on Linkedin and Twitter, or email me at

Artificial Intelligence–Optimization, Redefined

Many people I talk to—especially those in the C-suite—are excited about artificial intelligence (AI), and machine learning (ML) in particular. However, they’re a little fuzzy on exactly how it can help them. There’s a lot of information flying around about AI and ML, but much of it is contradictory, and it’s hard to discern the truth from the hype.

AI and ML will revolutionize the way you do business in the future—in ways we can’t imagine now. In this blog, we’ll take a look at some potential uses of AI/ML in finance, retail, and manufacturing/supply chain. My guess is you’ll find something that applies to you, and my hope is that it will incent you to get started with your own journey to optimize your own business.

The Future of AI and ML in Finance and Insurance

Soon, instead of chatting with a customer service representative, you’ll chat with a bot that has natural language processing capabilities and is armed with tons of customer specific interaction data—and that will mimic the human-to-human chatting capabilities available today. You’ll be able to ask the bot questions such as, “How much did my 401k gain/lose last year?” or “What’s the outstanding balance on my credit card?” Bots won’t replace humans, but humans will be freed up to handle more complex cases.

AI and ML will also enhance security and market analysis capabilities. Biometric data such as face, voice, or even retinal recognition will become the norm for security measures. And, investment banks and hedge funds will use AI and ML to perform deep analysis to better understand the human and social factors that influence markets and use that sentiment analysis to optimize decision-making.

AI and ML in Retail

In the next half decade, retailers will use AI and ML to make predictions about inventory needs and adjust levels in real time. These systems will make suggestions to store managers about which items to order, and in some cases, companies may enable them to make purchases without human intervention.

Product placement will also get a boost from AI. Gaze detection technologies will be used to analyze customer interest and place products in ways that optimize foot traffic patterns and visual attention. These systems will also analyze that foot traffic and direct product placement based on not just seasonal, but demographic trends such as age, gender, etc. Think: older women tend to shop on Thursday, so we’ll put products of interest to them in high-traffic areas.

Another obvious trend for AI/ML augmentation will be in loss prevention. Detection of customers (and employees) exhibiting suspicious behavior will be made easier by algorithms that leverage big data to better understand behavior patterns. Much care is needed here, however, to scrupulously avoid profiling behavior at all costs.

AI and ML in Manufacturing/Supply Chain

Over the next five to ten years, AI and ML will also revolutionize the business for large logistics companies and manufacturers. AI and ML will be used to eliminate many manual processes now handled by humans: invoice exception tracking, responses to inquiries, purchase order corrections, etc. Think of how much this could free up employees to engage in productive tasks.

Other leading companies will use chat bots (see finance above) to engage with suppliers in routine communications, place purchase orders, monitor regulatory compliance vis-à-vis materials, and to keep up with the voluminous documentation that often bedevils—and slows down—logistics operations.

Another obvious application of AI/ML (and one that syncs well with retail) that will become prevalent is inventory forecasting—correlating supply with demand and optimizing decision-making processes with intelligent algorithms and ML augmented analysis of big data.

The Future is Now

Many AI/ML applications I’ve discussed are only one to five years out on the horizon—the blink of an eye. Get ready, because the future is coming. Humans won’t be replaced, but our capabilities will be greatly enhanced by AI/ML. Companies that use AI/ML to enhance their processes will improve their top and bottom-line revenues and grow at rates that outperform the competition. Those that don’t, well…

Digital Reality: It’s Here and You Want It

My kid wanted a pair of virtual reality (VR) goggles. He wanted to walk with dinosaurs. It’s a harmless pursuit, so I bought him a pair. We won’t talk about how I use them more than he does now, though. What I will talk about is how the VR craze—and digital reality (DR) in general—is moving from a consumer tool to spice up your life to a way for companies to improve the way they do business.

Over the past decade, several factors have made widespread use of DR technologies possible. Mobile bandwidth has increased, device batteries last longer, and application environments support more integration. Also, true to Moore’s law, as the use of technology has increased, prices have come down, and availability is nearly ubiquitous.

So what exactly is DR? Digital reality is the umbrella term for technologies such as augmented reality (AR), virtual reality, mixed reality (MR), and other immersive, multi-sensory experiences. All these technologies incorporate varying degrees of reality augmentation or replacement, but the bottom line is that they are built to enrich our day-to-day, human experience and give us new, and uniquely personalized, ways to view and interact with our surroundings.

The Business Side of DR

Digital reality isn’t just for play. Sure, you can walk with dinosaurs and experience an auto race near first-hand, but more and more, forward-thinking companies are using this confluence to leverage DR technologies to change the way they do business. Indeed, DR technologies are changing the game for companies and providing opportunities for knowledge workers to be integrally involved in technology deployment in ways heretofore unheard of.

DR will upend the current worker-technology model. It will change the way we collaborate; it will change how we share and incorporate knowledge; and it will change how we deal with technology itself.


We’ve all used tools to have conference and video-conference calls. It brings us closer together and makes flying to and fro not the necessity it once was. However, DR technologies make these capabilities look positively stone-age. With DR, you can actually “see” and “feel” what your non-co-located workers are experiencing. For example, an engineer for a pipeline company could use DR technologies and see what her workers are grappling with as they struggle to fix a burst pipe. She can direct their every move to get the repair done quickly and more effectively than with a conference call where someone juggles a camera-equipped mobile phone.


Conceptualization is often the most opaque part of the product development phase. Until you’ve built a model, it’s difficult to see how all the moving parts will work. With DR technologies, conceptualization comes to life. For example, wearing DR glasses, aerospace engineers can see—and manipulate—three-dimensional models of an aircraft or satellite’s design and observe simulated testing scenarios to spot potential problems and deal with them before expensive models are built. This saves time and money and lends greater efficiency and accuracy to the development process.


Widespread adoption of DR technologies will bring about a sea-change in the way we store data and integrate applications. DR technologies are data hogs—there’s no way around it. Current VR glasses—with their 360-degree view—take up probably 20 or 30 times the data storage space of a standard video file. As DR technologies morph and grow, that figure will increase exponentially. The solution? The cloud. If you haven’t made the move to the cloud before, you’ll need to if you implement any type of DR tech. You won’t be able to afford and maintain the storage necessary for extensive DR adoption.

DR adoption will also force manufacturers to play nice with each other. Companies wishing to implement DR technologies won’t stand for being told that every time they need to purchase new goggles or other new technology components, that they’ll have to stick with a single manufacturer. Can you imagine if you bought one manufacturer’s PC, only to be told that no other manufacturer’s peripherals would function with it? That’s my point. With future DR technology and system design, DR components will be pan-functional with most ERP, CRM, and other mission-critical systems and databases. It won’t work otherwise.

The Future’s So Bright…

There’s so much that’s possible with DR technology, most of it still years away, but some of it within our current grasp. The bottom line is that the DR implementation curve is trending upward, and it’s safe to say that with its power and powerful—nearly unlimited future—DR technologies are an excellent investment in the future that you can make right now, if you do your research and invest judiciously. Are you in?

I’d love to hear what you think. Please comment here, connect with me on Linkedin and Twitter, or email me at

Failing Forward

I spend a lot of time talking to clients who’ve failed. They’ve either tried to implement a project that didn’t go as planned, or they’ve made the wrong choice of tools, technologies, people, or process. There are a thousand possible reasons for failure. Some could have been foreseen and avoided; some were awful surprises.

We all fail—if you haven’t, you will. Failure is not the end of the road; it’s the beginning of a learning opportunity. If I sound like a motivational speaker, I don’t mean to. I’m being realistic. You don’t learn much from success; you learn from failure. And, the most important lesson you should learn from failure is how to fail.

Failing properly sounds like an oxymoron. But really, failing properly—especially when we’re talking about IT projects—can open a path forward and make the eventual outcome better than the original outcome would have been if not for the failure. I call this phenomenon failing forward. So how do you fail forward? I’m glad you asked.

Perform the Autopsy—With an Eye on the Future

It’s only natural after you’ve declared defeat, to take a closer look at what happened to precipitate it. For example, if you’ve implemented an analytics initiative that’s proven less than successful, you’re going to ask what went wrong. Was it not the right tool? Were people not trained extensively enough to use it? Was the supporting architecture insufficient?

Don’t spend too much time finding fault and laying blame. Instead, focus on what you need to move forward. I know that sounds trite and obvious, but you’d be surprised how many companies, after they experience a failure, analyze the failure ad nauseam to the point where they become paralyzed and unable to move forward. Don’t do that. Use every issue you find as an opportunity to learn, move forward, and make your next project better.

Focus on Delivering Value

One reason many IT initiatives—especially analytics initiatives—fail is that they don’t deliver sufficient value for the money spent. It’s that fuzzy cost-benefit concept from that managerial accounting class you slept through. Unfortunately, that concept is very real and very dangerous to those who fail to heed its dictum: whatever you build must return more than you invested in it, or you will fail.

To focus on delivering value going forward—and avoiding future failure, it’s key to answer three questions:

  1. Do we have a solid business case that can withstand the assault of X number of other projects competing for funding?
  2. Do we have the sponsorship who’ll commit the appropriate support and resources to this project?
  3. Have the requirements been fully defined so that everyone is on the same page as to what needs to be done, and what the timeline is for completing the project?

Obviously, if the answer to all these is not a resounding yes, you need to rethink the project. Maybe it’s not right for your organization, maybe it’s just not the right time, or maybe you just need to work harder—or smarter to turn the nos to yeses. If you learn anything from a failure, learn this: without being able to deliver projects with a perceived or actual value greater than their cost, you will keep delivering failure, not value.

Quantify and Measure Success

Yes, we’re talking about failure, and how to fail smarter, but in order to keep from failing again—or at least so many times—you need to understand what success looks like. Surprisingly, many companies don’t define success at the outset of a project, other than to declare that it will provide the “answers” that everyone’s been clamoring for.

That’s not enough. What you must do is, from the beginning, define what success will look like, in quantifiable terms. Will your logistics costs be reduced? By how much? Will your customer churn be reduced? By how much? Will your cross-sell and up-sell revenues increase? By how much? Of course, you can’t deliver an exact number, but you can create a target, aim for that target, and measure how close you get. Just make sure that your targets are realistic, achievable, and scrupulously communicated.

Fail Forward

No one will escape failure—it happens to all of us at one time or another. What we can do is learn from it and move forward, taking those lessons and putting them into practice so that our next project is the better for it.

I’d love to hear what you think. Please comment here, connect with me on Linkedin and Twitter, or email me at

Transform your business with a DSN

Last week we talked about some of the benefits of building a digital supply network (DSN), as well as some of the challenges that companies face when shifting from traditional supply chain operations to DSNs. This week, we’re going look at what has made the rise of DSNs possible over the past couple of decades, then we’ll look at a real-world scenario that illustrates the concrete benefits of a DSN.

I’m not going to go into an exhaustive discussion of the history of IT; it’s not my bailiwick, and you don’t want to hear it. What I will say, however, is that there are three converging trends that have given rise to DSNs, and digitization in general: a reduction in computing costs, an increase in bandwidth and speed, and an explosion in data—both in volume and variety.

Drivers of DSNsAs big data overtook traditional data structures, companies realized that if they could find a way to harness all that data, they could enhance their knowledge and collaboration capabilities to not just enhance their traditional supply chains, but to create entirely new, digitized, interconnected supply networks.

Early adopters created proto-DSNs as far back as 2001. Now true DSNs are is becoming widespread, with fully 90% of manufacturers either implementing or considering a DSN. The results have been phenomenal for those companies who’ve made the leap to digitization. Companies that create DSNs are faster, smarter, and more flexible than ever—and both they and their end customers reap the benefits.

As an example, let’s take the case of a consumer products manufacturer that was facing serious, existential challenges with quality and costs. This manufacturer was under increased regulatory scrutiny because they’d had to issue several safety recalls over a five-year span. Their products tended to either not function correctly or, in some cases, to spontaneously ignite. Not a good look.

Because of lack of supply chain integration, they were forced into a reactive mode to solve problems. It took them too long to identify issues and to fix the problems, and they couldn’t track how effective their fixes were in the long term. Their warranty expenses were astronomical, and their reputation was sinking fast.

Their solution was to create a DSN. They built a technical infrastructure that enabled them to use cutting-edge technologies like embedded sensors on parts to capture real-time performance data. They also integrated data from all their suppliers to form a complete picture of their parts’ chain of custody. This data was coupled with textual quality and safety event data and stored in a data lake to facilitate quick retrieval and integration.

Next, they used advanced analytics tools such as probability analysis and clustering to explore their data. They used a simplified GUI and dashboards to enable users to see the data they needed, when they needed it—to quickly deliver actionable insights.

The result? To be sure, they were able to catch production problems more quickly, but the real bonus was that they were able to integrate their supply network and leverage big data and advanced analytics to improve quality and prevent those problems in the first place. They had insight into every facet of their production process to ensure that every step in that process was tracked, monitored, and tweaked to deliver peak performance.

Benefits of Better InsightsEveryone, from suppliers, to logistics partners, to each production facility, had access to the same data set and could identify issues before they became explosive problems. The company’s warranty expense decreased over time as quality increased, the government regulators eased off, and their reputation was salvaged—creating an environment where they could increase top line revenue and long-term growth.

Will your DSN implementation turn out this well? There’s no guarantee, but I will promise you that your competition is thinking about digitizing. And if they do, and you don’t, they’ll have the ability to gain those insights that you won’t. Where will that leave you?

I’d love to hear what you think. Please comment here, connect with me on Linkedin and Twitter, or email me at


Let’s Get Digital–With Your Supply Network

Everyone’s going digital with their supply networks, aren’t they? Well, no, they aren’t. They should be, but in my conversations with clients and colleagues, it’s my estimation that only about 30% of companies are in the process of implementing a digital supply network (DSN). Probably another 60% plan—or want—to, but they don’t know how to get started, or they can’t secure buy-in. What’s the big deal? Do DSNs live up to the hype? Yes, if you do it right.

This week and next, I’m going to discuss the benefits and key challenges of building a DSN, and we’ll take a look at some real-world examples of how companies are improving their operations by taking on the challenge to leave the traditional supply chain behind and move to an interconnected network that helps you create a strategic advantage and grow your bottom line.

DSNs Defined

When you create a DSN, you do truly leave the old supply chain mentality behind. Information no longer flows linearly. Instead, you create an ecosystem where information flows to and from who and where its needed, when its needed, to maximize efficiency throughout the network. Information latency is no longer an issue, because on-demand, near real-time information flow creates almost instantaneous insights that are accessible to all interested and authorized parties.

DSNs—What They Can do for You

The benefits of these freed-up, on-demand information flows are enormous. First, more timely information can help you reduce costs by improving efficiency. The back-and-forth data flows can also help you gain the information you need to make product improvements more quickly, which results in more satisfied customers. More satisfied customers leads to a better reputation, and with that reputation and better information in the hands of your management and salespeople, your sales effectiveness can soar.

On a macro level, increased information flow speed gives you the insights you need to develop more effective business strategies, which leads to more business opportunities and helps you create new strategic advantages.

It’s all about the flow—and the speed. If you know more things more quickly than your competitors, you can make quicker decisions and meet market demands faster, which makes you flexible enough to change with the markets—and you know they don’t ever sit still.

DSN Challenges

Benefits Challenges DSNAs with anything worth having though, there are some challenges to building a true DSN. They’re daunting, but not insurmountable, and it’ll be worth it in the end to meet them.

Talent will be your first obstacle. Advanced analytics capabilities are the heart of any DSN. And, if you haven’t been hiding under a rock for the past five years, you know that the demand for analytics skills is currently outpacing supply. Fortunately that’s changing. More universities are turning out more quant jocks these days, and more IT people are re-skilling to meet the need.

Data management will be your next hurdle. If you don’t have clean, organized data, it will be hard to make it flow through the ecosystem. Governance is key, along with programs to remove information silos across your enterprise, and then from your supply network at large, to build a consistent, high-quality data set for your supply network—end to end.

Unfortunately, perhaps the biggest challenge you’ll have to overcome is the “yeah-buts.” Yeah, but we’re doing fine with what we have. Why should we invest millions in some supply matrix technology we don’t understand? Yeah, but can you show the business value before we spend all that money? Yeah, but do we have the buy-in from our stakeholders and partners that we need to pull this thing off? The list is endless, but you get my point.

These political challenges will represent the biggest threat to your DSN project. Do everything you can to educate all stakeholders—your Board, your C-suite colleagues, your suppliers, and your customers. Build a sound business case to show them how this thing will benefit them. There’s a litany of information out there about the benefits and value of building a DSN. Like the Caveman commercials say, “Do a little research.” Don’t just build it. They will not come. Build the business case. Show the value. Get the buy-in.

Looking Forward

No doubt, building a DSN will give you the timely information and insights to help you compete more effectively. You’ll be able to create better products, move them more efficiently, and satisfy your customers on a whole new level. Next week, we’ll discuss some real-world examples of how DSNs deliver on their promise. Stay tuned!

I’d love to hear what you think. Please comment here, connect with me on Linkedin and Twitter, or email me at

Welcome to the Now of Analytics

So, you’ve got your analytics environment set up nicely for your business. You have hindsight into what has happened, some understanding as to why it might have happened, and some pretty good predictive—and maybe even prescriptive—capabilities. Those insights are driving better decisions and helping you make your operations more efficient. But, do you have some sort of artificial intelligence (AI) capabilities built into your analytics infrastructure? If not, you’re in the same boat with most companies, but that boat is quickly sinking.

Leading companies have begun to realize that AI is the technology that will take their analytics to the next, very powerful, level and revolutionize their business. Artificial intelligence—in the way I’m using it here—can also be used synonymously with cognitive computing (CC). While there’s really no agreed-on definition of CC, I define it as hardware and/or software that simulates human thinking. AI/CC has three characteristics:

  1. It learns as information changes—even in real time.
  2. It can understand data in contextual terms, and it can help you interact—or can interact on its own—with users, using that contextual information.
  3. It is curious. It remembers previous events and asks questions, returns answers, and makes recommendations based on those events.

The Power of Intelligence

AI/CC brings tremendous, transformative power to those companies that embed it into their analytics infrastructures.

AI changes how you operate

Transform with AIWith AI/CC you can transform your traditional supply chain into a digital supply network (DSN) that is connected, smart, scalable, and flexible. You can use that intelligence to drive coordinated planning, 24/7 connections with suppliers and customers, smart production models, and dynamic logistics operations that can help you adjust quickly, as your needs change.

DSNs are highly-adaptable, interwoven—almost organic—ecosystems that help you optimize your production processes and supplier relationships. They help you maximize the value of every step in your product and work flow to deliver the right products to the right people, at the right time.

AI helps you get smarter

AI/CC uses information much like the human brain. It draws on experience (information, past and present) to make connections and form hypotheses. It can make recommendations based on that information—and it can adjust on the fly, based on new information it receives. Your decisions will be smarter and faster.

And what’s really transformative is that AI/CC-enabled-systems get smarter over time, as they couple the past with the present to create an environment for contextual learning that builds the future by continuously evolving and improving to deliver better long-term insights and decision-making capabilities.

AI helps you develop customer intimacy

Customers today—especially those ultra-valuable millennials—want a plethora of channels available for interaction. Yet, they also want a deeply personal—intimate—relationship with retailers and service providers. AI/CC hits the sweet spot. Chat bots—think Siri[1] or Alexa[2]—are revolutionizing customer service. For customer service, chat bots—even if they don’t employ voice capabilities—use AI to handle “personalized” first-line, or even secondary, contact duties, while freeing up actual humans to handle more complex customer issues.

AI/CC can also leverage previous information about customer interactions to predict current and future needs and can offer “smart” suggestions for services or offers that are individualized for particular customers. This helps you create a deeply intimate relationship with your customers that builds extreme loyalty and maximizes customer value, both now and in the future.

Welcome to Now

To be sure, AI/CC is the wave of the future, but that wave is beginning to crest. You can either ride it, or be left standing as it rolls by. I’d lay good odds that your competition is almost certainly putting AI/CC capabilities on its near-term wish list. Are you?

I’d love to hear from you! Email me at, or contact me on Linkedin or Twitter.


[1] Siri is a registered trademark of Apple Inc.

[2] Alexa is a registered trademark of, Inc.

Grow Up–Analytically That Is

If your company is like most mid-to-large size companies today, your analytics spend is high and climbing. The pressure to keep up with the newest technologies is almost crushing, especially in the face of abundant data showing that companies with mature analytics technology outperform their competitors. There’s an analytics arms race out there, and it often seems as if there’s no end in sight until every last dollar is spent.

Do you need to participate in the arms race? Yes and no. To be sure, you need sophisticated, mature analytics capabilities, but you don’t need every new analytics toy that comes on the market. Instead, you need to right-size your analytics capabilities now, and for the future.

Analytics Maturity

To get the right analytics program for your company, first you need to figure out where you are. Do you still perform analytics via spreadsheets? You’d be surprised at how many companies do. Many have climbed the maturity ladder a bit and begun to integrate their data sources and give decision-makers some data visualization capabilities. They can get hindsight, and some rudimentary insight, but their capabilities could be more robust.

Analytics Maturity Model UpdatedIncreasingly, forward-thinking companies are making the shift to implement predictive capabilities and advanced visualization technologies with real-time dashboards so management can keep its hand on the pulse of the business and achieve real insight into what’s happening. They’re moving to eliminate data silos and analyze multi-structured data to get a more holistic picture of their operations.

Cutting-edge companies go further, however. They’ve realized the power of analytics to help them leap-frog their competition. These companies have adopted an analytics mindset where analytics, not gut feel and experience, drive decision-making. They’re investing in technologies that give them the speed and flexibility to make decisions more quickly than their competition does. This is where you want to be.

Growing Up

But how do you get there? What do you need to grow up analytically? It starts with defining what you want. It sounds simple, but it’s not. It requires a 360-degree look at your business—where you are, what you want now, and what you’ll need in the future. Define those outcomes that will have the highest impact on your future. Align your analytics goals to achieving those outcomes.

Path to Analytics MaturityNext, define the low-hanging fruit. What can you implement quickly and cost-effectively to achieve some of those outcomes? Pick one high-impact area and implement a pilot or proof-of-concept that will show quick value and return.

Identify the data sources you’ll need—both for the pilot and for the future, to the extent possible. Think ahead as much as you can. It’s critical to think outside the database box and include multi-structured data that covers the customer and supplier touchpoints that show you a clear picture of how you’re operating and what your customers are saying and doing.

Once your data is set, define which analytics capabilities you need to implement the pilot—with an eye toward what you’ll need in the future. You’ll need advanced visualization and dashboard capabilities, as well as predictive and prescriptive modeling tools.

Leverage a cloud-based platform to get fast, reliable service and free up your own IT resources to focus on producing the analysis that will achieve your high-impact outcomes, rather than managing a byzantine IT infrastructure.

However, what you need most to grow up analytically is a shift in your mindset. Experience is nice, but as they say in those ubiquitous lawyer ads, past experience does not predict future performance. Data does. What you did last year is relevant, but you need to couple it with how you’re performing now, and with sophisticated modeling techniques that enable you to predict the future, AND that will tell you what your next moves should be to achieve your goals.

In short, you need to morph into a data-driven organization. Rely on those high-tech tools you purchased and trust their output. If your input is right—meaning that if you’ve fed the analysis tools with clean, reliable data—they should do their jobs. Thus, data should drive every decision you make. If the numbers aren’t there, don’t pull the trigger, or pull a different one.

Just do it

Achieving analytics maturity won’t happen overnight, but you can do it. Scope it; pilot it; get the data; get the tools; and develop the mindset. Pick a good technology partner and make the investment. You won’t regret it.

I’d love to hear what you think. Please comment here, connect with me on Linkedin and Twitter, or email me at

Meet your Challenges Head-On to Achieve High-Impact Outcomes

Your business environment is more challenging than ever. Globalization has led to increased operational complexity. New technology and automation have increased fraud and compliance risk, at a time when customers are demanding innovative new products and services, and shareholders are demanding that companies unlock the greatest value from all their physical, intellectual, and digital assets. There’s no break—no time to breathe before the next problem hits.

The challenges are exacerbated by fragmented analytics capabilities and architectures. Decision-making is hobbled by poor insight into customer behavior and operations. Innovation is stifled and customer satisfaction plummets. Losses from compliance violations and fraud mount, and supply chains become sluggish. This lethal mix leaves many companies foundering and unable to satisfy anyone—shareholders, customers, or regulatory agencies.

Achieving High-Impact Outcomes

To solve your problems, and gain the deeper insights to create the high-impact, improved outcomes critical to thrive in today’s digitally transformed marketplace, you need three things: the right data, a technical architecture design that stresses flexibility and scalability, and the right environment to store your massive data workloads and provide access when and where it’s needed. Simple, right? No; but it is doable.

The Right Data

Keys to HIOsCustomer demands to innovate and provide better, more personalized, customer service—coupled with shareholder demands to increase share value and speed their ROI—have created a critical need for companies to better leverage data for decision-making. However, amidst the inundation of big data, it’s often difficult to tease out valuable data from noise.

To get the data you need, you first have to understand your business needs. What data do you need to drive value and create those high-impact outcomes that will help you thrive? How can you use that data to improve your business processes and customer service? How can you use it to innovate? To avoid and mitigate risk? How can you use it to drive operational excellence? To optimize the value of your assets? Ask these questions, and get the right framework to identify the data you’ll need. But one caveat: do it quickly, so that you can move on to the next step: designing the architecture you need to leverage that data.

The Right Architecture

Once you’ve identified the data you need, it’s crucial to ensure that you have the right architecture to access it, store it, and manipulate it. That architecture design should have three key components: it should have the ability to accommodate multi-structured data; it should be flexible to incorporate new technologies as they arise, and as your needs change; and it should be scalable to grow as you do.

It’s also imperative to be honest with yourself about where you are, and where you can realistically go over multiple time horizons. Construct an infrastructure and analytics maturity model. If your capabilities are relatively immature, don’t expect to have infused analytics and a genius technical infrastructure in six months. Assess where you are, and where you want to be in six months, a year, etc., then build an achievable roadmap to get there. Remember, though—it’s important to show value quickly both from a competitive standpoint and to satisfy C-suite demands and access the money you need to build your optimal state.

The Right Environment

Most companies expend enormous human and capital resources each year just to keep pace with the technological changes needed to meet data and market demands. What’s more, the pace of change is growing exponentially, and many organizations struggle to keep up.

It doesn’t have to be that way. More and more CIOs are taking advantage of digitization and leveraging the transformative value of the cloud By moving your analytics applications and technical infrastructure to the cloud, you can gain a flexible, scalable platform that helps you implement your desired technical architecture—now and in the future—and achieve cost-effectiveness and certainty. When you don’t have to wrangle with applications and infrastructure management, you free up resources to focus on proving the business value of your efforts by achieving those high-impact outcomes that drive success.

Take the Leap

The pace of business change is faster than ever. Digitalization and globalization are upending the market for all businesses. Those who keep up will keep their competitive edge in a world where that edge is knife thin. Those who don’t will…well, you know.

I’d love to hear what you think. Please comment here, connect with me on Linkedin and Twitter, or email me at