Younger Workers Need a Career Narrative

Two senior management consultants are chatting with each other between meetings:

"I heard we managed to staff the new project in New Jersey. Sounds like a good team — I don't know the junior guy, do you?" says the first.

"I don't know Greg yet either," says the second. "But I'm relieved we were able to secure somebody, given how short-staffed we are. I know he's got lots of experience doing this type of assignment, but I'm not sure if it's something he loves or if he's just in a rut. I'm hoping it's the first, of course — it could be like pulling teeth otherwise, since this is going to be such a tough engagement. We'll see, I guess."

Senior executives in professional firms aspire to match the right people to the right work, but here, these senior executives don't have enough to go on. Facing pressure to staff a project quickly, there's little to stop them from assuming that Greg wants his next assignment to be just like projects he's done in the past.

But what if that's not so? How would they know? They have no idea how his past achievements relate to his future interests and development goals. Greg has not done a good enough job telling his own story in this company and so he's allowed other people to define him.

Greg, in short, lacks a career narrative.

This is not surprising. In recent years, much has been written about the importance of career narratives for mid-career and senior professionals, particularly those making a career transition. But, we'd argue, they're even more important for younger professionals who don't yet have a multipage CV or a high-powered headhunter in their corner. What, then, makes for an effective narrative?

First, it should be easy to remember and retell. The whole point is to give your colleagues a narrative that quickly comes to mind whenever they're asked about you, preventing them from making assumptions and drawing conclusions on their own. Two or four sentences, maximum.

Second, it should meaningfully link your past successes to your near and long-term development needs and suggest the kinds of assignments that would help to achieve those objectives. Those goals might certainly be developmental (to test a particular skill; gain experience with a certain tool or methodology; explore a specific industry). But they can also be more personal (limit travel to spend time with family, for instance).Think of it as a "sound-bite resume" — on hearing it, senior professionals should have two reactions. First, they should be interested in working with you. Second, they should know if it makes sense for you to work with them.

Third, your narrative needs to hang together with the right combination of honesty, humility, and personal flavor. Doing so creates an authentic and compelling career narrative. Narratives that just articulate a string of successes are not credible and are not likely to be repeated. Similarly, boilerplate chronicles without any personal flair rarely get traction.

An example of a poor narrative would be this:

"I wanted to work in biotech, so that's why I joined the firm. I've had a lot of experience in labs, and I'm still considering graduate school."

While it has the virtue of being short, this narrative doesn't connect this person's experiences to any goals that could be furthered by work within the firm. He mentions his initial interest in "biotech" but doesn't explain how that interest is evolving and what additional experience he's therefore seeking. As a result, it fails to make clear what type of assignment he would ideally be staffed on.

Make a few tweaks, though, and we have a powerful career narrative:

"I worked in labs through college and entered the firm with a strong interest in health care clients. I've had the opportunity to develop my quantitative financial skills in the comfortable context of health care. Now I'd like to test those skills with other commercial clients to determine what industry most interests me over the long term. That said, given my wife's and my family commitments, I'd really like to work locally if possible."

The narrative now clearly demonstrates a string of successes (experience in labs, completing several project assignments, a growing family) which lead to a development goal ("determine what industry most interests me over the long term") and a need ("I'd really like to work locally"). And all this in four sentences!

Fourth, once you've crafted your narrative, the next step is to share it, in the course of meeting and getting to know your colleagues. Seek out informal opportunities to tell your story. A reflective moment sitting in the airport with a senior colleague or sharing a cab with a teammate are occasions when people might ask, "How's everything going?" And when they do, they'll be giving you a perfect opportunity to share your career narrative.

As you talk to more and more people, it will become increasingly likely that the kinds of conversations they'll be having about you when you're not in the room will work in your favor. Perhaps a senior exec will hear your story from one of his juniors and identify a development opportunity that perfectly fits your needs. Maybe a senior partner will hear about your set of skills and geographic constraints from one of your managers and offer you a perfect, local project assignment.

Finally, once developed, your narrative should never be set in stone. It needs to be regularly updated, as you achieve more and your needs change. Ensuring that senior staff understands the logic behind your career aspirations — and that they are not surprised by them — will go a long way toward maintaining top executives' respect and support.

Sharing your personal narrative isn't just good for you — it's good for the people who hear it, too. As Heidi Gardner's research on teams shows, an important factor that can depress team performance is a phenomenon known as "expertise dissensus" — that's when team members who actually have markedly different views nevertheless think they all agree because they've made unwarranted assumptions about one another. By disseminating your story, you can give people a far more accurate view of who you are, which can avert potentially crippling coordination challenges, interpersonal friction, and misunderstandings down the road.

Too often, junior professionals rely on formal mechanisms for getting the word out about their achievements and needs — in particular, formal staffing processes, periodic performance reviews, and professional social networks. While these are all excellent resources in career development, they are rarely a match for the informal chatter that's already going on in hallways, rental cars, and restaurants. By inserting your career narrative into those conversations, you can help ensure that when people talk about you — and they will talk about you — they're saying the right things.

via feeds.harvardbusiness.org

Rethinking the Dimensions of Data Quality

A few months ago, I wrote a column asking if the dimensions of data quality, such as accuracy, consistency and timeliness, are real. I pointed out that there are no generally accepted definitions for the dimensions, no generally accepted exhaustive list of them and no generally accepted methodologies for measuring each one.

Since the column was published, I have been "encouraged" to say something a little more positive on this topic – something that will help practitioners deal with the daunting problems of data quality. I agree that being negative is not that helpful, although it is refreshing to have a frank conversation about what really underlies terms that are often thrown about our industry.

The Road to Abstraction

One argument in favor of having dimensions of data quality is that data quality is such an enormous space that we cannot deal with it effectively unless we break it down into subareas. I think it is better to say that data quality represents a large, complex set of issues and that we need to tease out individual types of issues, each with its own specific problems and requiring its own specific methods to deal with it. This is more of a bottom-up approach.

However, it seems to me that the first view prevails. The top-down approach is repeatedly taken, with attempts made to break data quality up into different dimensions. Here I am talking from experience: I have seen this approach first-hand at a number of conferences and industry initiatives.

What this top-down approach focuses on are abstractions, and we need to understand abstractions to appreciate what is going on. There are many different classes of abstraction, but the one involved here is the process of turning a property into an object. This is a source of argument among philosophers, going back to Plato, who believed that such abstractions really exist in some part of the universe, just as much as material objects do.

This form of abstraction is illustrated in the following example. Imagine I am represented by a customer record in a database of Enterprise X. This record holds my date of birth. If the value is my actual date of birth, we can agree it is completely accurate. If the month and year of this value are correct, but not the day, then we can say it is reasonably accurate. If the year is correct, but not the month and day, we can say it is moderately accurate. If day, month and year are incorrect, we can say it is not accurate at all. We are using the term "accurate" to describe the quality of the relationship between the data value and the reality it is trying to represent. 

Human beings then make the leap from using "accurate" as an adjective to using "accuracy" as a noun. Our language allows us to do that, but that does not mean reality has to go along with us. We have created a type of abstraction and this type of abstraction (a) is not instantiated, (b) does not bear properties and (c) cannot enter into causal relationships. The concept "dog" is instantiated in my pet Leo, who weighs about eight pounds and knows perfectly well how to manipulate me into feeding him. The concept "accuracy" is not instantiated anywhere. We do not see "accuracies" lying around in the universe, or having attributes like color or weight, and “accuracy” does not enter into causal relationships. We can say that the birth date example above "represents an instance of accuracy," but this is a bewitchment of our language. Just because we can say it does not make it so. It’s better to say the example "has the property of being accurate."

But We Need the Dimensions of Data Quality

So far, this is still of little help to the practitioner. We know that data quality is large and complex, and it is our duty to improve it as much as we can for the enterprises we work for.

Here, I think the bottom-up approach is better. This does not view a dimension like "accuracy" as an abstracted object with a single definition. Rather, each dimension is a complex area that has its own structure, problems and methods.

If I think about accuracy, I can ask a series of questions, such as:

• Is the thing being represented covered by the definition used for the entity/table in the database?
• Is the attribute of the thing being represented covered by the definition used for the attribute/column in the database?
• Does the thing represented by a record in the table in the database actually exist?
• Does the value held in the column of the record in the table in the database objectively represent the expression of the attribute of the thing?
• Does the value held in the column of the record in the table in the database subjectively represent the expression of the attribute of the thing to the extent needed to meet business requirements?

There are very likely even more questions pertaining to accuracy that can be asked. This shows that accuracy is a complex of many concepts, not a single concept. If you object that each question needs to be broken out as a different dimension, then you are going to end up with an awful lot of dimensions, as I have many such questions for each of the traditional dimensions. The traditional dimensions give the illusion that each is only answering one question, because somehow each has a single definition.

The questions cover the structure and problems of accuracy. Methods are another facet that needs to be addressed. Some methods include:

• Testing the entity/table definitions with data producers to see if they correctly classify instances to the entity/table.
• Checking that the attribute/column has not been deliberately repurposed by operational staff to hold something other than what the official definition describes.
• Sampling records and auditing that these represent real-world instances.
• Sampling data values and independently measuring the attributes of the things they represent.
• Sampling data values, independently measuring the attributes of the things they represent and comparing these to the tolerances allowed in each business use case.

via information-management.com

The first sample data set is always the hardest, but it can roughly tell you how good the data capture process is.

How to Allocate Your Time, and Your Effort

How does he find time to meet with 10 customers a week and make his yearly quota in the first quarter?, a salesman wonders about his top producing coworker. I can barely find time to have five appointments a week and get all my paperwork done correctly and turned in on time.

How does she manage to champion strategic initiatives, network with executives, and only work 40 hours a week?, a manager ponders about his colleague on the corporate fast track. After a day full of project meetings, the best I can do is reactively respond to e-mail at night instead of proactively developing my department.

Here's the secret: Your colleagues that zoom ahead of you with seemingly less effort have learned to recognize and excel in what really counts — and to aim for less than perfect in everything else.

Most likely the highest producing salesman on your team spends less than half the amount of time that you do on filling out paperwork. Yes, it may be sloppy, but no one really cares because he's skyrocketing the revenue numbers. The manager who has caught the eye of upper management may send e-mails with imperfect grammatical structure and decline invites to tactical meetings. But when a project or meeting really matters, she outshines everyone.

If you're shocked and feel like this seems completely unfair, I'm guessing that you probably performed very well in school where perfectionism is encouraged.

I know. I was a straight-A student from sixth grade through college graduation who did whatever it took to produce work at a level that would please my professors. Admittedly, this strategy paid off as a student. My perfect GPA signified an exceptional level of achievement, and I was fortunate that in my case, it was rewarded with scholarships and job offers.

The rules changed when I started my own business over seven years ago. I realized that doing A-work in everything limited my success. At that point I realized that I needed to focus more on my strengths. As Tom Rath wisely explains in his StrengthsFinder books, you can achieve more success by fully leveraging your strengths instead of constantly trying to shore up your weaknesses. Realizing the importance of purposely deciding where I will invest more time and energy to produce stellar quality work and where less-than-perfect execution has a bigger payoff has had a profound impact on my own approach to success and my ability to empower clients who feel overwhelmed.

As I talk with time coaching clients struggling with overwhelm whether they be professors, executives, or lawyers, a common theme comes up — they can't find time to do everything. And, they're right: no one has time for everything. Given the pace of work and the level of input in modern society, time management is dead. You can no longer fit everything in — no matter how efficient you become. (This conundrum is what inspired me to write a book on time investment).

In my time investment philosophy, I encourage individuals to see time as the limited resource it is and to allocate it in alignment with their personal definition of success. That leads to a number of practical ramifications:

• Decide where you will not spend time: Given that you have a limited time budget, you will not have the ability to do everything you would like to do regardless of your efficiency. The moment you embrace that truth, you instantly reduce your stress and feelings of inadequacy. For example, professionally this could look like reducing your involvement in committees, and personally this could look like hiring someone else to do lawn maintenance or finish up a house project.
• Strategically allocate your time: Boundaries on how and when you invest time in work and in your personal life help to ensure that you have the proper investment in each category. As a time coach, I see one of the most compelling reasons for not working extremely long hours is that this investment of time resources leaves you with insufficient funds for activities like exercise, sleep, and relationships.
• Set up automatic time investment: Just like you set up automatic financial investment to mutual funds in your retirement account, your daily and weekly routines should make your time investment close to automatic. For example, at work you could have a recurring appointment with yourself two afternoons a week to move forward on key projects, and outside of work you could sign up for a fitness boot camp where you would feel bad if you didn't show up and sweat three times a week.
• Aim for a consistently balanced time budget: Given the ebbs and flows of life, you can't expect that you will have a constantly balanced time budget but you can aim for having a consistently balanced one. Over the course of a one- to two-week period, your time investment should reflect your priorities.

Once you have allocated your time properly, you also need to approach the work within each category differently. As I explained above, trying to "get As in everything" keeps you from investing the maximum amount of time in what will bring the highest return on your investment. That's why I developed the INO Technique to help overcome perfectionism and misallocation of your 24/7. Here's how it works:

When you approach a to-do item, you want to consider whether it is an investment, neutral, or optimize activity. Investment activities are areas where an increased amount of time and a higher quality of work can lead to an exponential payoff. For instance, strategic planning is an investment activity; so is spending time, device-free, with the people you love. Aim for A-level work in these areas. Neutral activities just need to get done adequately; more time doesn't necessarily mean a significantly larger payoff. An example might be attending project meetings or going to the gym. These things need to get done, but you can aim for B-level work. Optimize activities are those for which additional time spent leads to no added value and keeps you from doing other, more valuable activities. Aim for C-level work in these — the faster you get them done, the better. Most basic administrative paperwork and errands fit into this category.

The overall goal is to minimize the time spent on optimize activities so that you can maximize your time spent on investment activities. I've found that this technique allows you to overcome perfectionist tendencies and invest in more of what actually matters so you can increase your effectiveness personally and professionally.

On a tactical level, here are a few tips on how you can put the INO Technique into action:

• At the start of each week, clearly define the most important investment activities and block out time on your calendar to complete them early in the week and early in your days. This will naturally force you to do everything else in the time that remains.


• When you look over your daily to-do list, put an "I," "N," or "O" beside each item and then allocate your time budget accordingly, such as four hours for the "I" activity, three hours for the "N" activities, and one hour for the "O" activities.


• If you start working on something and realize that it's taking longer than expected, ask yourself, "What's the value and/or opportunity cost in spending more time on this task?" If it's an I activity and the value is high, keep at it and take time away from your N and O activities. If it falls into the N category and there's little added value or the O category and spending more time keeps you from doing more important items, either get it done to the minimum level, delegate it, or stop and finish it later when you have more spare time.


• If you keep a time diary or mark the time you spent on your calendar, you can also look back over each week and determine if you allocated your time correctly to maximize the payoff on your time investment.

via feeds.harvardbusiness.org

Such true..the overall goal is to minimize the time spent on optimize activities so that you can maximize your time spent on investment activities.

The Power of Business Rules Management

There is a growing interest in business rules and business rules management systems or business rules engines. Major software vendors, from IBM, Oracle and SAP to RedHat and SAS, have or are developing business rules management systems.

The use of these systems to support the practice of decision management to automate and manage high-volume, transactional decisions is growing rapidly. A new standard, called the Decision Model and Notation standard, is under development that will bring consistency of representation to the industry. Yet there is still a sense that this is a niche technology, and it is somewhat poorly understood outside of its traditional areas of strength. So what is a BRMS and how does it support decision management?

What is Decision Management?

First we need to discuss decision management. Decision management is a business approach that explicitly focuses on the management and automation of business decisions, especially the day-to-day operations that must be made to complete an operational process or handle a specific transaction. This approach brings together business rules and various analytic techniques and is widely used to effectively apply BRMSs. While there are many other things that you can do with business rules (e.g., improve data quality, manage user interfaces, etc.), the use of business rules to manage decisions is what makes a BRMS compelling.

If you want to know more about decision management, you can check out my columns, entitled "Building Agile, Analytic and Adaptive Systems" and "Four Principles of Decision Management Systems."

Business Rules Management Systems

A BRMS is a complete set of software components for the creation, testing, management, deployment and ongoing maintenance of business rules or decision logic in a production operational environment. These systems used to be, and sometimes still are, called business rule engines. However not all BRMSs use a BRE at all (some generate code) and even when a BRMS includes a BRE, it is just one part of a complete system — an important part, but one that deals only with execution. A BRE determines which rules need to be executed in what order. A BRMS is concerned with a lot more, including:

• The development and testing of the rules
• Linking business rules to data sources
• Deploying business rules to decision services in different computing environments
• Identifying rule conflicts and quality issues
• Enabling business user rule maintenance
• Measuring and reporting rule effectiveness

To deliver on all this, a BRMS needs a robust set of capabilities for managing decision logic, such as those documented in my Decision Management Systems Platform Technologies Report and shown in Figure 1 (click here or see left for image), The Elements of Decision Logic Management Supported by a Typical BRMS. Specifically, you need:

• An enterprise-class rule repository with audit trails and versioning.
• Technical rule management tools that allow technical users (e.g., developers and architects) to integrate business rules with the rest of the environment and edit/manage technical rules.
• Non-technical rule management tools that allow business analysts and even business users to routinely change and manage business rules (see below).
• Verification and validation tools, usable by both technical and business users, that take advantage of the nature of business rules to make sure they are correct and complete.
• Testing and debugging tools to confirm that you get the decisions you were expecting.
• Deployment tools supporting multiple platforms that allow the logic you have specified to be deployed into a decision service (see below).
• Data management capabilities to bring real enterprise data into the environment so rules can be written against it.
• Impact analysis tools so that a user can see what the impact of a change will be before he or she makes it.
• Either a high-performance BRE to which the rules can be deployed or an ability to generate code that can be deployed.
• An ability to support the logging of rule execution, so you can tell exactly how a particular decision was made and which rules were executed.

It should be noted that a modern BRMS is likely to support the management of rules derived from optimization and analytic tools as well as rules specified explicitly by a user.

Decision Services

Decision services are the link from our BRMS, to a focus on managing decisions, to decision management. These are sometimes called transparent decision services, agile decision services or even decision agents. Decision services are the key technical deliverable from the combination of a BRMS and the decision management approach. A decision service is a self-contained, callable service or agent with a view of all the information, conditions and actions that need to be considered to make an operational business decision. Deployed on a service-oriented infrastructure and available to other services, to service-enabled applications and to business processes managed using a business process management system, decision services package up all the rules (and any analytics) that go into making a decision.

Decisions can often be thought in terms of a question for which there is a known allowed set of answers. For instance, a decision about routing an insurance claim might be thought of as the question, “How should we handle this claim?” Allowed answers include auto pay, fast track, refer to claims adjuster or refer to fraud investigation group. A decision service handling claims routing would take the information about the claim and then return one of the allowed answers to a calling process or service. In other words, a decision service answers a business question for other services.

via information-management.com

One of they main focus these days.

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Here's a Better Way to Remember Things

A group of Brazilian entrepreneurs who have come north for a week's worth of ideas on growing their ventures, are leaving a class, when one of them breaks from pack toward the coffee maker, where I'm heading too. He works the machine first, reciting something again and again in Portuguese as he watches his cup fill.

"Excuse me?," I say, unsure he's talking to me.

"Sorry, I am repeating what the lecturer said," he explains, "so I remember later."

Remembering new information is an underappreciated skill. The fact that most of us have never evolved our technique beyond the rudimentary and ad hoc approaches we used as middle schoolers suggests this. It is required for any sort of professional growth, since the need to learn is high, and can separate the exceptional performances from the mediocre ones. After all, would you prefer to hire the consultant who presented using cue cards or the one who pitched from memory?

Fortunately for us, insights from cognitive psychology have vastly improved our understanding of how we remember. Many of these are accepted wisdom in the neurological and psychological realms. But it hasn't been easy to transfer that knowledge to actual tools for individuals. Until recently, anyway. Easy-to-use auto-analytic tools that exploit our understanding of memory can now help you treat remembering as the skill it is, and improve it the same way you improve any professional skill, like public speaking. Here's how to get started.

First, focus on the right unit of measure. Yes, your objective is to remember better, but you'll get the best results by focusing on forgetting as your base unit of analysis.

Experimental psychologist Hermann Ebbinghaus's pioneering discovery of the forgetting curve shows that we forget the majority of newly learned information within hours or days, unless we review it again and again. This alone won't be a shock to many of us. But Ebbinghaus demonstrated how systematic forgetting. It occurs exponentially on a predictable curve — researchers call this "exponential decay."

Different things you're trying to remember will have different curves. For instance, that piece of operations data that you remember clearly, since you prepped and presented it to your team, has a flatter downward curve (you'll remember longer) than that the now hazy sales figure a colleague mentioned during the same team meeting. Evenso, each curve is predictable.

Practice remembering at the right time. Think about how you really use your memory for things that matter to you and your career, like in preparing for a speech. Maybe you're a crammer who tries to prime your memory by doing as many dry-runs as possible the night before. Or perhaps you've committed to ploddingly rehearsing your lines each afternoon for a month from 3 pm to 4 pm. Or maybe you're an improviser who finds time here and there, rehearsing what you'll say at random moments between meetings.
The forgetting curve suggests you should follow a very different memorization process than any of these entail. It shows that there's a precise moment that's best for practicing your lines. That moment is just before you are about to forget them.

So sessions aimed at learning new content should happen at "about-to-forget" moments, with spaces between practice sessions increasing as you approach mastery. This learning process is called spaced repetition, and can help us avoid the inefficiencies and risks of ad hoc memorization methods like cramming.

Incorporate auto-analytics tools. OK, so you get the idea that you should try to commit things to memory only when you are just about to forget them. But how do you know when that critical moment is about to happen? How do you know what your forgetting curve looks like?

Almost like your fingerprint, your forgetting curve is very different from anyone else's. But a type of auto-analytics tool called "Spaced Repetition Software" or "SRS" can learn the idiosyncrasies of your memory, and then ping you to practice at the optimal time.

These mobile and desktop tools are like automated flashcards, though you work through your "pile" according to your personal algorithm and the rules of spaced repetition.
They fine-tune your algorithm using a straightforward rating system. Let's say you're a newly appointed manager learning some finance for the first time, and you're trying to improve your recall of many new terms. When the term "Leverage" appears you recall its meaning effortlessly and assign it an A. But when "Arbitrage" appears you assign it a D since you must labor to recall its basic meaning, and even then it remains fuzzy.

The tool continually hones its prompts based on your input. No doubt you'll see "Arbitrage" sooner than "Leverage," as practice sessions for the second concept would be scheduled later and less frequently to maximize efficient memorization.

Map your practice to your priorities. Finally, be very selective when choosing what you want to get better at remembering. In theory, you could work on mastering numerous new domains at once, but experimental research and case studies suggest this isn't practical for full time workers.

Focus instead on a single development opportunity integral to your career. (See the accompany chart for examples of cases where you could use SRS.) Does this opportunity require learning new terms, concepts, or narratives? If yes, then it makes sense to focus on hacking your memory with these computing tools to pursue it.

In short, when you're on a steep learning curve, remember the forgetting curve, and then beat it.

via feeds.harvardbusiness.org

very useful, especially the tips to improve one's memory

What a Big-Data Business Model Looks Like

The rise of big data is an exciting — if in some cases scary — development for business. Together with the complementary technology forces of social, mobile, the cloud, and unified communications, big data brings countless new opportunities for learning about customers and their wants and needs. It also brings the potential for disruption, and realignment. Organizations that truly embrace big data can create new opportunities for strategic differentiation in this era of engagement. Those that don't fully engage, or that misunderstand the opportunities, can lose out.

There are a number of new business models emerging in the big data world. In my research, I see three main approaches standing out. The first focuses on using data to create differentiated offerings. The second involves brokering this information. The third is about building networks to deliver data where it's needed, when it's needed.

Differentiation creates new experiences. For a decade or so now, we've seen technology and data bring new levels of personalization and relevance. Google's AdSense delivers advertising that's actually related to what users are looking for. Online retailers are able to offer — via FedEx, UPS, and even the U.S. Postal Service — up to the minute tracking of where your packages are. Map services from Google, Microsoft, Yahoo!, and now Apple provide information linked to where you are.

Big data offers opportunities for many more service offerings that will improve customer satisfaction and provide contextual relevance. Imagine package tracking that allows you to change the delivery address as you head from home to office. Or map-based services that link your fuel supply to availability of fueling stations. If you were low on fuel and your car spoke to your maps app, you could not only find the nearest open gas stations within a 10-mile radius, but also receive the price per gallon. I'd personally pay a few dollars a month for a contextual service that delivers the peace of mind of never running out of fuel on the road.

Brokering augments the value of information. Companies such as Bloomberg, Experian, Dun & Bradstreet already sell raw information, provide benchmarking services, and deliver analysis and insights with structured data sources. In a big data world, though, these propriety systems may struggle to keep up. Opportunities will arise for new forms of information brokering and new types of brokers that address new unstructured, often open data sources such as social media, chat streams, and video. Organizations will mash up data to create new revenue streams.

The permutations of available data will explode, leading to sub-sub specialized streams that can tell you the number of left-handed Toyota drivers who drink four cups of coffee every day but are vegan and seek a car wash during their lunch break. New players will emerge to bring these insights together and repackage them to provide relevancy and context.

For example, retailers like Amazon could sell raw information on the hottest purchase categories. Additional data on weather patterns and payment volumes from other partners could help suppliers pinpoint demand signals even more closely. These new analysis and insight streams could be created and maintained by information brokers who could sort by age, location, interest, and other categories. With endless permutations, brokers' business models would align by industries, geographies, and user roles.

Delivery networks enable the monetization of data. To be truly valuable, all this information has to be delivered into the hands of those who can use it, when they can use it. Content creators — the information providers and brokers — will seek placement and distribution in as many ways as possible.

This means, first, ample opportunities for the arms dealers — the suppliers of the technologies that make all this gathering and exchange of data possible. It also suggests a role for new marketplaces that facilitate the spot trading of insight, and deal room services that allow for private information brokering.

The most intriguing opportunities, though, may be in the creation of delivery networks where information is aggregated, exchanged, and reconstituted into newer and cleaner insight streams. Similar to the cable TV model for content delivery, these delivery networks will be the essential funnel through which information-based offerings will find their markets and be monetized.

Few organizations will have the capital to create end-to-end content delivery networks that can go from cloud to devices. Today, Amazon, Apple, Bloomberg, Google, and Microsoft show such potential, as they own the distribution chain from cloud to device and some starter content. Telecom giants such as AT&T, Verizon, Comcast, and BT have an opportunity to also provide infrastructure, however, we haven't seen significant movement to move beyond voice and data services. Big data could be their opportunity.

Meanwhile, content creators — the information providers and brokers — will likely seek placement and distribution in as many delivery networks as possible. Content relevancy will emerge as a strategic competency in delivering offers in ad networks based on the context by role, relationship, product ownership, location, time, sentiment, and even intent. For example, large wireless carriers can map traffic flows down to the cell tower. Using this data, carriers could work with display advertisers to optimize advertising rates for the most popular routes on football game days based on digital foot traffic.

There are many possible paths to monetize the big data revolution ahead. What's crucial is to have an idea of which one you want to follow. Only by understanding which business model (or models) suits your organization best can you make smart decisions on how to build, partner, or acquire your way into the next wave.

via feeds.harvardbusiness.org

Interesting usage of Big data