Increasing Your Data AUM

Data Governance for Asset Management

Every company needs some kind of data governance strategy. They might not call it that. Depending on their size and business model, they might not even need a Chief Data Officer. (Gasp!) But to keep a business running, you have to keep your ducks in a row. Naming ducks and keeping them organized is the essence of data governance.

For asset management companies, good data governance and engineering are especially critical. The unique mix of data use cases in asset management magnifies the risks and opportunities of applied data science. Other companies might be able to pick and choose their data governance battles. Asset management has it all: performance analytics and forecasting; home-grown and acquired data sets; upline and downline channel partners — not to mention multi-agency regulatory oversight and a demand for ever-faster and transparent decisions. Asset management companies risk falling seriously behind their peers and rivals if they don’t have at least a long-term strategy that checks all the boxes.

Your Requirements = My Requirements

For mid-level asset management companies, in particular, one principle is absolutely critical: You must accept your customers’ and partners’ requirements as your own. A lot of service delivery companies tell their clients, “Your success defines our success.” In asset management, this is simply objectively true. Investment firms and RIAs don’t demand insight into performance details just to test you. They do it because their ownclients and regulators are demanding it of them. To succeed and grow, you have to enable and support their success. Investment is a close-knit industry, and the best way to expand your client base is to inspire trust and loyalty. This means prioritizing your clients’ user experience alongside your own operational needs, keeping in mind that transparency is currency.

The good news is that getting your clients’ uses and views right will drive out other requirements and opportunities, making your whole operation more client‑focused and more effective. In asset management, for instance, information about the buying and selling patterns of fund wholesalers is publicly available. Many large firms are extremely predictable, making the same kinds of sales and purchases at the same time each year; others respond in measurable ways to leading market indicators. Of course, aggregating, analyzing, and visualizing take effort. But the intelligence to be able to anticipate your clients’ trading patterns—and redirect your sales teams accordingly—can yield dramatic savings in the first year, as well as ongoing efficiencies as those predictive algorithms get refined.

A similar “bucket-sharing” approach is important for internal data requirements, too. McKinsey touches on this in their article, “Achieving Digital Alpha in Asset Management,” when they note that successful firms have “erased the traditional boundaries between their operations and technology groups, combining their budgets and development strategies,” and “made their operations and technology capabilities central to their competitive strategies.”

A Single Data Cosmos

Uniting operational and analytic data requirements within a single data cosmos requires leadership that thoroughly understands the company’s revenue mechanisms and can assess and foresee the profit potential of new data wells and analytic tools. Ideally, this is a single person. (See “Chief Data Officer: Rule Maker or Rain Maker?”) Practically speaking, though, it can also be achieved by making sure your data stakeholders include people who are directly responsible for revenue—and that these people are tightly integrated into the data governance mission, not just spectators. When all of this works well, data stewardship will emerge simultaneously, and nearly organically, from both the technical and revenue‑making sides of the business.

It’s also important to seek out, bring onboard, and keep onboard “minor” stakeholders who will personally reap the benefits of the “unified data cosmos” in their work. Engaging these people early ensures that their processes and requirements are accurately reflected in the analytical models. Keeping them engaged, through rapid iteration and consistent messaging, helps assure the long-term success of data changes by building their skills, allaying their uncertainty, and giving them a view of their success in the new data cosmos. Practically speaking, it also helps the company recognize and allocate budget for data changes across the full breadth of the organization.

It boils down to this. In asset management — as in other industries — implementing good data governance isn’t about making and enforcing rules. It’s about creating leverage and unleashing organizational lift.

A final thought. Where in this picture is compliance? In the highly regulated financial sector, it is tempting to make compliance the chief driver for data governance, especially if the organization has been sanctioned in the past. However, it’s extremely difficult to track measurable ROI against a sanction that has been avoided—and the more successful the changes are, the harder it gets. But in a “data cosmos” focused on organizational lift, good governance begets good data—and, as a result, regulatory compliance falls out almost free of charge.

Chief Data Officer: Rule maker or rain maker?

In his book Infonomics (a nice conference giveaway at last summer’s MITCDOIQ Symposium, by the way), Gartner VP Doug Laney makes some important claims about how companies should regard their data.

Notably, he states that all companies should be actively seeking to monetize their data, especially the “dark” data that resides in their systems but isn’t used or needed to meet core business objectives. This is the precious dross that everyone is in search of: the data “slag” that serves no purpose for the organizations that produce them, but which can be incredibly useful to others. Laney suggests that a natural home for this monetizing activity is in a company’s Analytics function, which makes sense: why not let the people who already massage data for business use take the lead on finding new ways to use it?

At the same time, Laney confirms — or at least he doesn’t contradict — the view of many senior data practitioners that most companies need someone in the role of Chief Data Officer to make sure the company’s data is protected. This view coincides with the common understanding that the CDO’s main responsibility is to shepherd their company through the culture shift of transforming into a data-centric or “data-driven” organization.

Mind the Gap

Hanging between these two positions is something of a gap. Analytics for monetization demands an aggressive, forward-looking stance. In contrast, the stance typically described for the CDO “data shepherd” is protective, governance-oriented, and compliance-focused. So the question is: what should the relationship between these two positions be?

One view is that the Chief Data Officer should be the rule maker, setting the standards and boundaries for collecting and storing data across the company, and optimizing data-sharing to meet the business needs of the company. This puts the CDO in good company with other members of the C-suite who fulfill this kind of protective, provisioning role — CFOs, CIOs, General Counsel, etc. The monetizing team, in contrast, are the “rain makers“. Their work is not limited to current business processes. Instead, they have permission to comb through all the company’s data looking for new patterns. They are also expected to look beyond the current interests of the company and query the market — much like traders on the stock market floor — for new ways to use otherwise stale data.

Does this model sound familiar? Depending on the size and maturity of your company, it might. There’s an inherent problem with this structure, though. Can you see it? Again, depending again on the size and maturity of your company, I’ll bet you do.

The problem this structure poses is that it effectively pits the rainmakers against the rule‑makers. Their respective responsibilities give them both “jurisdiction” over the same company asset — data — but with radically differing priorities. Over time, no matter how friendly and collaborative individuals try to be, the structural tension between revenue and rules can easily devolve into the old familiar rift between profit center and cost center. And this is serious — because when that happens, the cost center is always at a disadvantage.

The bottom line is this: a Chief Data Officer whose responsibilities are tied only to strategic goals of compliance and efficiency is already compromised.

Yes, and…

Bearing all this in mind, the alternative might seem obvious. To head off a conflict between the goals of compliance/efficiency and the goals of future profit, the CDO’s role simply has to encompass both. It’s an ambitious stance, though, and more challenging than it might look.

One challenge in bringing these goals together lies in the different backgrounds of today’s business data experts, who already cross a range of disciplinary lines including data science, information governance, business analysis, systems development, program implementation, and more. A more serious challenge lies in setting the right corporate expectations about what the CDO should do. This includes not only the functions and capabilities under their control, but also how they relate to the CEO and the Board.

If the Chief Data Officer is to create value for the enterprise, their counterpart in the C-Suite cannot be the CFO, CIO, or General Counsel. Instead, they must be expected to behave like the Head of R&D (in science & technology) or Upstream Exploration (in oil & gas): actively involved in prospecting, testing, and growing new sources of revenue, while also protecting the safety and integrity of the company’s data.

Now What?

So, CDOs (and CDOs-in-waiting), ask yourself this: “How does my company view my role?” If you find that your goals are being consistently construed as protecting your business, then I encourage you to push to expand that view — for the good of your company and your own career. The skills involved in finding and creating value in data will persist long into the future, even as the technology continues to evolve.

What makes data science different?

Reflections on the 2017 MIT CDO/IQ Symposium


This was one of my favorite moments at the MIT Chief Data Officer and Information Quality Symposium this summer. The organizer, Dr. Richard Wang, called all of these people up to the front and said pointedly to the room: “Now, these are vendors. But they’re also my friends. So be nice to them.”

There was laughter. But you know what? Everyone was. Nice. Really nice. Vendors, CDOs, CIOs, and other panel participants readily shared their own experiences, and just as readily admitted their weaknesses. It was refreshing to hear people say, “You know what, I’ve never really done that. You should talk to that person over there—they have a lot of expertise in that.” Even when “that person over there” was a vendor from a different company.

The same collaborative atmosphere was present at the Open Data Science Conference East earlier in the summer. Vendors, old hands, newbies, and fellow travelers all together in a friendly, collaborative environment sharing, critiquing, and suggesting.

Perhaps I should not be so impressed by this level of cooperation. But to someone who has seen their share of professional conferences and vendor expos—not to mention vendor selection committees—the atmosphere was striking. Turf battles? Defensive hedges about capabilities? Didn’t see it. Instead, there was a pervading sense of everyone being on the same side. One person’s success didn’t diminish another’s.

I’m making a big deal about this because folks coming up in data science now may take it for granted. The collaborative values of open source and open data seem natural to those who pursue it academically. But to many corporations, they are still alien.

There are two important lessons that data professionals, executives, and thought leaders should take from this.

First, be sensitive to the impact of your company’s culture on your internal clients. We know that data takes on new power when it is shared, aggregated, and shared some more. But it’s hard for a Chief Data Officer to promote a culture of openness, collaboration, and collective business benefits if other leaders are pitting directors or managers against each other for project funds or operating budget. A great CDO is defined by the ability to persuade others that data serves their best interests as well as the company’s. A prerequisite for this is listening and understanding what those interests really are.

Second, leverage the values of collaboration in meeting your own business needs. Is it really necessary to pit vendors against each other? Can we bring some flexibility to the way we analyze our own business needs, so that we can invite collaboration and get strengthen complementary benefits? (I’ve written more about this in a previous post, “Core, Collaborate, Delegate.”)

And while we’re at it, let’s all be nice to each other. How about it?

Leading digital transformation — Imagine, build, and run to the future

 Information assets. More and more companies are using this term and recognizing the tremendous revenue potential inherent in the information they collect, both intentionally and accidentally. I call this a company’s “information on deposit”. (I’ll expand on that term in a later post.)

As information is folded into the revenue-generating capabilities of a firm, interest in—and more importantly budgets for—information solutions and technology are swiftly moving to business units, rather staying in IT hands.

Note that this is not the same as that older, observed phenomenon of IT siloing, where business divisions responded to a vacuum in enterprise guidance by investing in their own IT teams and solutions. Now, the expansion of exceptionally user-friendly cloud, SaaS, and consultant-led solutions—along with the elevation of more a tech-confident generation used to consumer-driven, single-use tech solutions—has put direct control of information solutions squarely in the hands of “non-expert” experts. These are the folks we at the enterprise level have been accustomed to calling “the business” or “subject matter experts” or even “our internal clients”.

Regardless of where information is being harnessed and managed, information leaders and CxOs alike need to make every effort to lead by example. The easier the tools become to use, the more critical it is to emphasize that information leadership is a team sport, requiring regular collaboration across teams and disciplines. The challenging work at hand is to describe, classify, integrate, share, and govern (that is, control and manage) information assets in a way that creates business value, regardless of the application or solution.

The easier the tools become to use, the more critical it is to emphasize that information leadership is a team sport, requiring regular collaboration across teams and disciplines.

Here are four points to consider:

1. In our increasingly digital world, the focus is on the customer — and all data matters.

One major key to unlocking revenue is to take a broad, holistic view of the business’s customer. Customer relationships are not just about filling product demand; loyalty comes from being a trusted advisor. Good long-term customer relationships are built on good problem-solving experiences, at the organizational or individual level—optimized to the issue of the moment, but also coherent over time. Consultants know that every experience matters. With larger organizations, cultivating this kind of customer-centric view begins with the recognition that no data is without value. This means acquiring, keeping, managing, and using data that would formerly have been discarded as the byproduct of operations or manufacturing processes, and making that data a key input for services and products. (For business units going their own way with single-use applications, this means a serious conversation with vendors about obtaining data and understanding how the vendor uses the data for its own part.)

2. Operations and analytics are no longer separate: business intelligence (BI) is fundamental.

Analytics is no longer an afterthought to transactional systems—it’s the heart of our future information infrastructure. The mountains of structured, semi-structured, and unstructured data that we’re now storing will likely be retained for 10 or even 25 years into the future. Powerful tools already exist to perform advanced (e.g., predictive and prescriptive) analytics, supporting insights into where business is headed. The next generation of information infrastructures will combine legacy content, sensor data, transactional data, and conventional analytic data (i.e., “big data”) into a single, focused solution-agnostic set of data services or information capabilities. These capabilities will enable people, processes and technologies to leverage information assets in support of organizational goals, drive better decisions, and create value. Developing and deploying these capabilities takes dedication and effort, but the potential is nearly limitless.

3. Embrace the Internet of Things.

From business tools like geolocation in vehicle fleets to internet hotspots and devices like remote door locks or thermostats, the IoT generates actual data about things that we previously understood only through manual-entry logs, periodic checks, interviews, and assumptions. Yes, the data needs to be filtered through and analyzed, but it’s an unbeatable source of intelligence about actual use and ways of working. Properly harnessed, it has the potential to bring businesses greater efficiency and process improvements—as well as other benefits such as improvements to product design. And these benefits accrue up and down the supply chain, as businesses share or sell their data assets. For example, McKinsey & Company has estimated that data and mobility services related to the “connected car” could generate as much as $1.5 trillion in revenue by 2030, as automakers and their partners buy and sell vehicle-specific system, trip, advertising, and other data.

The IoT generates actual data about things we previously understood only through manual-entry logs, periodic checks, interviews, and assumptions.

4. Quality through governance

As technological capabilities grow more powerful, it is more important than ever to pay attention to perennial obstacles impeding business intelligence and analytics. I’m referring to the challenge of integrating multiple, disparate sets of data, so that they are fit for use. Data sets that can’t be used—or trusted—are nearly worthless. Governance capabilities and solutions are the key to reliable integration between sensor data, traditional analytic systems, unstructured data, and transaction systems. Data and information governance strategies help define the controls and monitoring structures that ensure access, utility, and quality. A sound data governance strategy is the foundation that ensures that information assets are accessible, usable, and of the highest quality and reliability—in other words “fit for use.” (Once again, this should spawn a serious conversation within the business before adopting a single-use, problem-solver app, to assess the requirements and cost of integration as part of adoption process.)

What have I missed? Add a comment below to share your perspective.

Photo by Julia Raasch on Unsplash

Calling Bullshit on Big Data?

After nearly 20 years in the public discourse, the term “Big Data” is still teetering on the cusp (or maybe stumbling over the threshold?) of true ubiquity. Yes, you see the term everywhere. But how well is it understood? Has the era of “Big Data” finally arrived? Or is it over? What’s the “big” deal anyway?

The headline and photo caption for Michelle Nijhuis’s piece in The New Yorker, “How to Call B.S. on Big Data: A Practical Guide,” are tantalizingly dismissive: we’re overwhelmed and “information-addled” and “Big Data” is routinely used to confuse and deceive the public. Nijhuis’s actual article is a bit more nuanced, and the University of Washington course that she profiles is even more so: bullshit is pervasive and nothing new; “Big Data” just provides some new tools for crafting and expressing it, potentially more convincing than others because of their supposed objectivity. As Nijhuis summarizes: “While data can be used to tell remarkably deep and memorable stories, Bergstrom told me, its apparent sophistication and precision can effectively disguise a great deal of bullshit.”

The course that Carl Bergstrom and Jevin West designed for the University of Washington Information School is actually meant to address the phenomenon of bullshit “in the age of Big Data.” Their explicit goal is to equip students to “Remain vigilant for bullshit contaminating your information diet,” and “Recognize said bullshit whenever and wherever you encounter it.” But Bergstrom and West acknowledge that data can illuminate as well as obscure meaning. They expect their students to be able to meet data scientists on their own terms—knowledgeably and critically.

So what can a business leader take away from this discussion? Is Big Data just a load of B.S.? Here are some key points to keep in mind:

  1. It’s not easy.
    Peter Drucker, as insightful as he was, spent three decades trying to understand the role of information workers, and went to his grave still searching for ways to grasp and refine these new ways of working. If harnessing information for business value were easy, Drucker or someone like him would have solved it already.
  2. Just because we haven’t solved it yet doesn’t mean it’s not worth doing.
    Rome figured out how to deliver water effectively. Boston delivers water effectively. Some of our technology is remarkably similar to the Romans’; some isn’t. In a spirit of progressive elaboration, we continue to tackle this very old problem, and every little investment pays benefits down the road.
  3. Don’t get hung up on names: focus on the goal of unlocking value.
    The phrase “Big Data” isn’t really a description of a particular size or kind of data. It’s a narrative device, telling us that we’re looking at a new dimension of opportunity. Think of it as a hook to help us as business leaders envision the possibilities embedded in the information we have on deposit. The goal is to keep focusing on the goals of value and alignment.
  4. Healthy skepticism is good…
    …and it’s important for colleges and universities to emphasize that. We need to be able to tell the difference between snake oil and real medicine—and we assuredly want to pass that skill along to our kids. At the same time, though, don’t let skepticism turn into a Jedi Mind Trick—the droids you’re looking for are really there. Call bullshit on the bullshitters, but remain vigilant for real value.
  5. B.S. is, was, and always will be.
    Where there is information, where there is communication, there will be bullshit. But guess what? There is real value in information as well—always has been, and always will be. And our job is to find it.

The bottom line is this: in business we don’t yet have a good solution for extracting the value of information on deposit. Terms like “Big Data” describe our current understanding of that information, and they can help galvanize us for future. Yes, that information can be aggregated in ways that are deceptive and/or deliberately confusing, but that doesn’t mean it’s worthless. Don’t lose patience; don’t lose nerve—true value is out there!



PIER Working Paper 12-037, “On the Origin(s) and Development of the Term ‘Big Data,'” by Francis X. Diebold, Penn Institute for Economic Research, September 2012.

“I’m A Data Guy And I Don’t Get Why Everyone’s Obsessed With Data” by Alex Kirk on LinkedIn Pulse, December 2016.

“How to Call B.S. on Big Data: A Practical Guide” by Michelle Nijhuis in The New Yorker, June 2017.

“Calling Bullshit in the Age of Big Data” by Carl Bergstrom and Jevin West, 2017.

“Calling Bullshit in the Age of Big Data: Syllabus with links to readings” by Carl T. Bergstrom and Jevin West, University of Washington, Spring 2017.


Photo credit:
Adam Sherez

Core. Collaborate. Delegate.

Is your enterprise data strategy keeping up with the changing digital economy?

Pop quiz: A researcher—or maybe an analyst or a paralegal—approaches your department with 2 TB of data they need to house for immediate access. What do you do?

We’ve all been there. The options are often not pretty.

House it inside? …On whose budget?

Outsource it? …With what security guarantees?

The solution is almost always more complicated than they expect. One thing is sure, though: between cheap storage and free cloud solutions, if you don’t work with them, they will go on without you.

The role of enterprise data strategy is to unlock the value of information for an enterprise. It’s the underpinning for a framework of capabilities that, when executed together, let a company acquire and maintain accurate, consistent data to meet their business requirements—and put roles, principles, and tools in place to increase the value of the information.

As a discipline, enterprise data strategy encompasses policy and governance, master data management, data and system architecture, security and access controls, lifecycle and archiving. That’s a lot of moving parts. The key to a successful enterprise data strategy is to not only protect the company’s interests but also to create business value through deliberate strategic alignment with corporate objectives.

For instance, the basic corporate mandate to increase shareholder value could drive a goal of better performance on strategic investments. We know better forecasting supports better investment decisions. What supports better forecasting? How about cleaner and more timely data? A good enterprise data strategy ensures that even the humblest data quality project aligns with strategic corporate goals such as investment, customer satisfaction, and operational improvement by providing measurable improvements in key business indicators such as forecast accuracy, client churn, transaction costs, or cycle times.

The key to a successful enterprise data strategy is to not only protect the company’s interests but also to create business value through deliberate strategic alignment with corporate objectives.

So how can you ensure that your enterprise data strategy is aligned with your company’s best interests? And how can you make practical decisions in the moment that preserve your alignment while still meeting the immediate needs of the business?

Strategic alignment can be obtained by following a 3-part mantra:


  • Core: Identify your central competencies—the things you can control that contribute to the strategic goals of your company.
  • Collaborate across the organization to make sure your core competency is contributing to the organization’s success.
  • Delegate, either to another part of the organization or to a vendor, things that are not part of your core competency.


Your core competency is the unique set of capabilities you have that deliver strategic value to your company and its customers.

Note that this concept is completely scalable. Executives and board members are responsible for making sure a company stays focused on its core value proposition. Directors and managers have a similar responsibility to understand their areas’ core value to the company and its customers, and to make sure that their teams or processes are actively contributing to the organization’s success.

Think about this question: “What do I (or we) do on a regular basis that contributes measurably to the overall success of the company?”

Think broadly: where do you see the effects of your work? What strategic aims does it support?

Think creatively: you’re not necessarily limited to your what you already do. What strategic aims could you impact, if you were able to?

Think practically and realistically: just because you can (or could) do something doesn’t mean you should. “Capability” means what you have the power to accomplish. If there are limitations on your capabilities, where are they coming from, and what would it take to remove them? If it’s too hard to remove them, then those capabilities are probably not part of your core competency.

It’s important to make sure that everything you define as part of your “core” contributes clearly to one or more strategic goals of the company. This will form the basis of your strategic corporate alignment. If a function or capability can’t be aligned, then it may be superfluous. If a particular activity or responsibility actually interferes with your ability to perform core functions, then it’s a good candidate for collaboration or delegation.

It’s important to make sure that everything you define as part of your core competency contributes clearly to one or more strategic goals of the company.

In the case of a strategic enterprise data capability, the core competency might include

  • Policy — Principles for enterprise data governance
  • Processes — Guidelines for implementing policies so that actual use meets governance principles
  • Technology — Scalable tools to enable capabilities
  • Taxonomy/Dictionary — Standardization and enhancement of data descriptions
  • Metrics — Monitoring and measurement of performance/impact


If “core” describes what you do for your organization, then “collaborate” defines how you do it. Collaboration is what you do to ensure that your work is actually contributing to the ongoing success of the organization.

With enterprise data strategy, collaboration might focus initially on clarifying how data are used and the controls needed to maintain high data quality. From there, it could extend to determining key data elements, codifying business roles, defining standards, and performing root-cause process analysis to identify strategic improvements.

Collaboration is what you do to ensure that your work is actually contributing to the ongoing success of the organization.

It’s important to note that collaboration doesn’t come “after” you’ve developed all the tools to support your core competency. Creation and deployment of a successful strategic enterprise data framework depend on harnessing independent expertise from across the organization through cross-functional collaboration. Workgroups wanting guidance on data governance don’t need to cultivate their own expertise in data quality root-cause analysis—any more than data analysts need to be able to read x-rays or climb communication towers. But the respective teams do need to know how to talk to each other and agree to work together for the good of the organization.

Ultimately, the goal of collaboration is to establish an efficient “fit” between different capabilities and competencies within an organization. Doing this well sustains all parties and can even lay the groundwork for new competencies to emerge down the road.


Here is where things get interesting. The thing to remember is that delegation is not just a matter of passing something off to someone else. Successful delegation entails specifying a desired outcome to a responsible person or group, which is then tasked with delivering that outcome. For delegation to work well, you need to establish controls, identify limits on the scope of work, provide sufficient support, and stay up to date on progress.

We’ve already discussed ways to recognize possible activities for delegation. If there are limitations on success that you just can’t remove, then that activity could be a good one to delegate. If it’s in line with the same strategic goals you support, but carrying it out impedes your ability to do other work, then it’s an excellent candidate for delegation.

The key is to focus on results rather than procedures. In delegating, you’re drawing on someone else’s core competency to reach your desired outcomes. You also need to pay careful attention to how and when those outcomes plug back into your strategic framework, so that everyone moves together toward the same goal.

In delegating, you’re drawing on someone else’s core competency to reach your desired outcomes.

Delegation can be internal—for example, calling on business process owners within your organization to create processes in harmony with established guidelines and standards. In other cases, outsourcing can allow you hit a sweet spot of meeting your company’s mission and enabling business growth by keeping your internal resources focused on core strategic goals and leaving everything else in the hands of a vendor or strategic partner. If you do outsource, it’s important to do so dependably and judiciously, keeping your eye on the outcomes and the controls you set around them.

*  *  *

So let’s return to that researcher/analyst/paralegal with all the data…

Knowing your area’s core competency, and, conversely, understanding which activities are right for collaboration or delegation can allow you to suggest a path forward that is already aligned with your processes, compliance principles, and strategic goals. It can also allow you to speak meaningfully and realistically about the controls, costs, and service levels that would accompany that course of action. In the long run, being able to respond quickly and reasonably to this kind of request makes it less likely that groups within your organization will go their own way when faced with data needs. This makes compliance more likely, mitigation less necessary, and processes more efficient, and strategies more effective.

Core. Collaborate. Delegate. Succeed. Grow. Repeat.

Going Beyond

What do you think when you hear the phrase “Beyond the Table”? What kind of table are we talking about?

A data table? A conference table? Maybe a kitchen table? A banquet table? How about a poker table? What exactly is a table, anyway?

At its essence, a table is a space where things get done.

prep_table  You prepare.

You consume.  catering_table

poker_table  You gamble.

You decide.  conference_table

data_table  You organize.

Other qualities might come into play when we think about tables in a metaphorical sense. For instance, tables are flat. Tables are also finite: there are only a limited number of spaces at a table, or seats around it. And often they come with rules—sometimes very complicated ones governed by generations of etiquette—about who belongs where and how everyone is supposed to behave.

conference_tableIn business, “the table” represents decision-making authority. It’s where we as executives meet to set strategy. “A seat at the table” represents power. Having a seat at the table means participating in or influencing the decisions, direction, and values of a company.

Yet recently it seems we’ve begun to see more and more cracks in the traditional decision-makers’ table. In the face of new remote work habits, personal devices, social media, and the general democratization of information sources, the authority that once stemmed from the board room doesn’t seem to carry the weight it once did.

Many businesses—most recently and dramatically United Airlines—have learned that social media and instant connectedness are a double-edged sword at best. As John Bailey has observed, it typically takes a business 21 hours to generate crisis communications—during which time Twitter and the web of media outlets are exclusively in control of the message. If you believe the only thing standing between your company and financial ruin is the quick-witted intern handling your company’s official Twitter account, then strategic planning could start to feel like a luxury. (Much more on this in a later post!)

Repeatedly, business leaders are encouraged to respond to these pressures by “expanding the table,” “inviting more [or different] people to the table,” or “bringing new ideas to the table.” Likewise, managers and functional experts are urged to “earn” or even “demand” their “seat at the table.” Some have even suggested changing the purpose of the table from strategic decision-making to listening, curating ideas, or performing analysis.

A few years back, Polly LaBarre summarized a number of the pressures on the traditional decision-makers’ table in business, ending with a challenge to managers and executives to “re-set your own table.” That message stayed with me—but in the years since it came out, it seems the role of “the table” as a source of quality, strategic leadership has continued to erode. Why is this?

To explore this question, let’s go back and look at a different kind of table.


Tables are meant to contain it. Name it. And, most importantly, allow it to be correlated with other data points, in order to arrive at meaningful conclusions. Data tables are everywhere in business (not to mention everywhere else), embedded in every tool we use to try to make sense of the business world: ERP, CRM, SCM, BPM, DMS, ECM.

Yet in the case of data, too, tables are less useful in business than they used to be. Why?

The thing about data tables is that within databases, and every relational system that uses them, you have to plan your structure first, then set up your tables, and then populate them with data. This means you have to either know in advance, or make some sweeping assumptions about, the kind of information you have—so you can name it, format it, set some basic parameters, and associate it with the right metadata. As with a banquet table or board table, everything in a data table has a specific place, title, and role to play, defined well in advance.

But what if you don’t know exactly what data you’ve got? Or what if you have so much that you can’t process it or match it up with other data points quickly enough?

For more than a decade now, businesses have been living with an explosion of “unstructured data”: juicy qualitative insights buried in digital assets. Electronic documents, email and messaging communications, images, schematics, videos. This list goes on. As leaders, we sense that these unstructured assets are where “the good stuff” is. But how can it be reached? And what can we do with it?

What if you don’t know exactly what data you’ve got? Or what if you have so much that you can’t process it or match it up with other data points quickly enough?

Even with structured data such as transactions, the volume and pace of data production are far higher than anyone can reasonably keep up with. But businesses have to keep up—because even the best ERP system doesn’t make strategic decisions. The best it can do is spit out reports based on people’s best guesses about what data they have, and what it means.

Semi-structured data, such flows of sensor data, pose an even greater challenge. These sweeping rivers of data afford a near-real-time view of how equipment and processes are working. Is it possible store, track, and comprehend such huge volumes of information, without ripping the data out of its real-time context and subjecting it to conventional analysis?

The bottom line is this: businesses are drowning in information. And the volume and kind of data we have can’t be contained in traditional structures such as tables. And—here’s the thing—it shouldn’t be. Because to really make the most of this kind of information, you need to be able to make qualitative inferences, not just static relational reports. Systems can be taught to make qualitative distinctions and learn from their own experience. But systems that learn can’t rely only on relational tables.

So in the world of data management, “going beyond the table” is not just a metaphorical idea. It is a necessity. The structures needed to turn masses of data into action and profit are not flat, or stable, or filled with named articles. In fact, they might not even be “structures” at all—they might be approaches, or algorithms, or guided interactions, or perhaps something else entirely.

Now let’s look again at the decision-makers’ table.

We know that we need to “bring more to the table” to accommodate rapidly changing customer needs, work culture, and technology infrastructure. For the past few years, the answer for what to bring to the table has resoundingly been, “DATA”.

As the volume of data blossoms (or explodes), leaders seeking innovation continue to lean hard into the promise of data-driven decisions. But it can be frustrating—especially when the some of the biggest successes seem to come from leaders who just skim the data and then rely on “gut instinct”. (The flip side, which we don’t hear about, is how many have tried that approach and failed.) I do believe we’re on the cusp of realizing genuine, comprehensive, data-driven decision-making. However, I understand why, at this moment, some can feel frustrated by masses of unintelligible data that, on their own, don’t “do” anything obvious to drive the business forward.

But what if they did? What if information was able to flow, not into giant databases for reporting, but directly to the people and tools that know what it means and how to use it? What new strategic directions might open up? Are you ready?

Leadership is on a journey; data are the map. To complete this leg of the journey and really make the most of data—in all its chaotic glory—we can’t just “bring it to the table.” We have to change the way the information is made, shared, and used for strategic purposes. Deep down, this means also changing the way strategic, directional business decisions are made. The decision-makers’ table—where everyone has a known, proscribed role to fill—isn’t big enough, or flexible enough, or nimble enough, or visionary enough to look forward into the flowing current of data, rather than backward at analytics. Adding more seats or changing the faces at the table won’t change that fact. We’re not talking about re-setting the table, or rearranging the chairs. We’re talking about getting rid of the table and building something new in its place.

So here in this forum, when we talk about “Going Beyond the Table,” we really do mean it in every sense. Stay tuned!



Easy to Criticise—Harder to Get It Right,” by John Bailey at Ketchum Blog on April 21, 2017 (

Expand The Table,” by Maren Hogan at Recruiter Today on September 13, 2012 (

Who Gets a Seat at the Table?” by Polly LaBarre at Harvard Business Review on December 13, 2011 (

‘Everybody Bring Data to the Table’, Teradata 2013,” by Nicole Giannopoulos at RIS News on October 22, 2013 (