Like Tinker to Evers to Chance: BI to BA to KDD

It’s spring training time in sunny Florida, as well as other areas of the country with mild weather and baseball.  For those of you new to the allusion, it comes from a poem by Franklin Pierce Adams and is also known as “Baseball’s Sad Lexicon”.  Tinker, Evers, and Chance were the double play combination of the 1910 Chicago Cubs (shortstop, second base, and first base).  Because of their effectiveness on the field these Cubs players were worthy opponents of the old New York Giants, for whom Adams was a fan, and who were the kings of baseball during most of the first fifth of a century of the modern era (1901-1922).  That is, until they were suddenly overtaken by their crosstown rivals, the Yankees, who came to dominate baseball for the next 40 years, beginning with the arrival of Babe Ruth.

The analogy here is that the Cubs infielders, while individuals, didn’t think of their roles as completely separate.  They had common goals and, in order to win on the field, needed to act as a unit.  In the case of executing the double play, they were a very effective unit.  So why do we have these dichotomies in information management when the goals are the same?

Much has been written both academically and commercially about Business Intelligence, Business Analytics, and Knowledge Discovery in Databases.  I’ve surveyed the literature and for good and bad, and what I find is that these terms are thrown around, mostly by commercial firms in either information technology or consulting, all with the purpose of attempting to provide a discriminator for their technology or service.  Many times the concepts are used interchangeably, or one is set up as a strawman to push an agenda or product.  Thus, it seems some hard definitions are in order.

According to Technopedia:

Business Intelligence (BI) is the use of computing technologies for the identification, discovery and analysis of business data – like sales revenue, products, costs and incomes.

Business analytics (BA) refers to all the methods and techniques that are used by an organization to measure performance. Business analytics are made up of statistical methods that can be applied to a specific project, process or product. Business analytics can also be used to evaluate an entire company.

Knowledge Discover in Databases (KDD) is the process of discovering useful knowledge from a collection of data. This widely used data mining technique is a process that includes data preparation and selection, data cleansing, incorporating prior knowledge on data sets and interpreting accurate solutions from the observed results.

As with much of computing in its first phases, these functions were seen to be separate.

The perception of BI, based largely on the manner in which it has been implemented in its first incarnations, is viewed as a means of gathering data into relational data warehouses or data marts and then building out decision support systems.  These methods have usually involved a great deal of overhead in both computing and personnel, since practical elements of gathering, sorting, and delivering data involved additional coding and highly structured user interfaces.  The advantage of BI is its emphasis on integration.  The disadvantage from the enterprise perspective, is that the method and mode of implementation is phlegmatic at best.

BA is BI’s younger cousin.  Applications were developed and sold as “analytical tools” focused on a niche of data within the enterprise’s requirements.  In this manner decision makers could avoid having to wait for the overarching and ponderous BI system to get to their needs, if ever.  This led many companies to knit together specialized tools in so-called “best-of-breed” configurations to achieve some measure of integration across domains.  Of course, given the plethora of innovative tools, much data import and reconciliation has had to be inserted into the process.  Thus, the advantages of BA in the market have been to reward innovation and focus on the needs of the domain subject matter expert (SME).  The disadvantages are the insertion of manual intervention in an automated process due to lack of integration, which is further exacerbated by so-called SMEs in data reconciliation–a form of rent seeking behavior that only rewards body shop consulting, unnecessarily driving up overhead.  The panacea applied to this last disadvantage has been the adoption of non-proprietary XML schemas across entire industries that reduce both the overhead and data silos found in the BA market.

KDD is our both our oldster and youngster–grandpa and the grandson hanging out.  It is a term that describes a necessary function of insight–allowing one to determine what the data tells us are needed for analytics rather than relying on a “canned” solution to determine how to approach a particular set of data.  But it does so, oftentimes, using an older approach that predates BI, known as data mining.  You will often find KDD linked to arguments in favor of flat file schemas, NoSQL (meaning flat non-relational databases), and free use of the term Big Data, which is becoming more meaningless each year that it is used, given Moore’s Law.  The advantage of KDD is that it allows for surveying across datasets to pick up patterns and interrelationships within our systems that are otherwise unknown, particularly given the way in which the human mind can fool itself into reifying an invalid assumption.  The disadvantage, of course, is that KDD will have us go backward in terms of identifying and categorizing data by employing Data Mining, which is an older concept from early in computing in which a team of data scientists and data managers develop solutions to identify, categorize, and use that data–manually doing what automation was designed to do.  Understanding these limitations, companies focused on KDD have developed heuristics (cognitive computing) that identify patterns and possible linkages, removing a portion of the overhead associated with Data Mining.

Keep in mind that you never get anything for nothing–the Second Law of Thermodynamics ensures that energy must be borrowed from somewhere in order to produce something–and its corollaries place limits on expected efficiencies.  While computing itself comes as close to providing us with Maxwell’s Demon as any technology, even in this case entropy is being realized elsewhere (in the software developer and the hardware manufacturing process), even though it is not fully apparent in the observed data processing.

Thus, manual effort must be expended somewhere along the way.  In any sense, all of these methods are addressing the same problem–the conversion of data into information.  It is information that people can consume, understand, place into context, and act upon.

As my colleague Dave Gordon has pointed out to me several times that there are also additional methods that have been developed across all of these methods to make our use of data more effective.  These include more powerful APIs, the aforementioned cognitive computing, and searching based on the anticipated questions of the user as is used by search engines.

Technology, however, is moving very rapidly and so the lines between BI, BA and KDD are becoming blurred.  Fourth generation technology that leverages API libraries to be agnostic to underlying data, and flexible and adaptive UI technology can provide a  comprehensive systemic solution to bring together the goals of these approaches to data. With the ability to leverage internal relational database tools and flat schemas for non-relational databases, the application layer, which is oftentimes a barrier to delivery of information, becomes open as well, putting the SME back in the driver’s seat.  Being able to integrate data across domain silos provide insight into systems behavior and performance not previously available with “canned” applications written to handle and display data a particular way, opening up knowledge discovery in the data.

What this means practically is that those organizations that are sensitive to these changes will understand the practical application of sunk cost when it comes to aging systems being provided by ponderous behemoths that lack agility in their ability to introduce more flexible, less costly, and lower overhead software technologies.  It means that information management can be democratized within the organization among the essential consumers and decision makers.

Productivity and effectiveness are the goals.

Takin’ Care of Business — Information Economics in Project Management

Neoclassical economics abhors inefficiency, and yet inefficiencies exist.  Among the core issues that create inefficiencies is the asymmetrical nature of information.  Asymmetry is an accepted cornerstone of economics that leads to inefficiency.  We can see in our daily lives and employment the effects of one party in a transaction having more information than the other:  knowing whether the used car you are buying is a lemon, measuring risk in the purchase of an investment and, apropos to this post, identifying how our information systems allow us to manage complex projects.

Regarding this last proposition we can peel this onion down through its various levels: the asymmetry in the information between the customer and the supplier, the asymmetry in information between the board and stockholders, the asymmetry in information between management and labor, the asymmetry in information between individual SMEs and the project team, etc.–it’s elephants all the way down.

This asymmetry, which drives inefficiency, is exacerbated in markets that are dominated by monopoly, monopsony, and oligopoly power.  When informed by the work of Hart and Holmström regarding contract theory, which recently garnered the Nobel in economics, we have a basis for understanding the internal dynamics of projects in seeking efficiency and productivity.  What is interesting about contract theory is that it incorporates the concept of asymmetrical information (labeled as adverse selection), but expands this concept in human transactions at the microeconomic level to include considerations of moral hazard and the utility of signalling.

The state of asymmetry and inefficiency is exacerbated by the patchwork quilt of “tools”–software applications that are designed to address only a very restricted portion of the total contract and project management system–that are currently deployed as the state of the art.  These tend to require the insertion of a new class of SME to manage data by essentially reversing the efficiencies in automation, involving direct effort to reconcile differences in data from differing tools. This is a sub-optimized system.  It discourages optimization of information across the project, reinforces asymmetry, and is economically and practically unsustainable.

The key in all of this is ensuring that sub-optimal behavior is discouraged, and that those activities and behaviors that are supportive of more transparent sharing of information and, therefore, contribute to greater efficiency and productivity are rewarded.  It should be noted that more transparent organizations tend to be more sustainable, healthier, and with a higher degree of employee commitment.

The path forward where there is monopsony power, where there is a dominant buyer, is to impose the conditions for normative behavior that would otherwise be leveraged through practice in a more open market.  For open markets not dominated by one player as either supplier or seller, instituting practices that reward behavior that reduces the effects of asymmetrical information, and contracting disincentives in business transactions on the open market is the key.

In the information management market as a whole the trends that are working against asymmetry and inefficiency involve the reduction of data streams, the construction of cross-domain data repositories (or reservoirs) that allow for the satisfaction of multiple business stakeholders, and the introduction of systems that are more open and adaptable to the needs of the project system in lieu of a limited portion of the project team.  These solutions exist, yet their adoption is hindered because of the long-term infrastructure that is put in place in complex project management.  This infrastructure is supported by incumbents that are reinforcing to the status quo.  Because of this, from the time a market innovation is introduced to the time that it is adopted in project-focused organizations usually involves the expenditure of several years.

This argues for establishing an environment that is more nimble.  This involves the adoption of a series of approaches to achieve the goals of broader information symmetry and efficiency in the project organization.  These are:

a. Instituting contractual relationships, both internally and externally, that encourage project personnel to identify risk.  This would include incentives to kill efforts that have breached their framing assumptions, or to consolidate progress that the project has achieved to date–sending it as it is to production–while killing further effort that would breach framing assumptions.

b. Institute policy and incentives on the data supply end to reduce the number of data streams.  Toward this end both acquisition and contracting practices should move to discourage proprietary data dead ends by encouraging normalized and rationalized data schemas that describe the environment using a common or, at least, compatible lexicon.  This reduces the inefficiency derived from opaqueness as it relates to software and data.

c.  Institute policy and incentives on the data consumer end to leverage the economies derived from the increased computing power from Moore’s Law by scaling data to construct interrelated datasets across multiple domains that will provide a more cohesive and expansive view of project performance.  This involves the warehousing of data into a common repository or reduced set of repositories.  The goal is to satisfy multiple project stakeholders from multiple domains using as few streams as necessary and encourage KDD (Knowledge Discovery in Databases).  This reduces the inefficiency derived from data opaqueness, but also from the traditional line-and-staff organization that has tended to stovepipe expertise and information.

d.  Institute acquisition and market incentives that encourage software manufacturers to engage in positive signalling behavior that reduces the opaqueness of the solutions being offered to the marketplace.

In summary, the current state of project data is one that is characterized by “best-of-breed” patchwork quilt solutions that tend to increase direct labor, reduces and limits productivity, and drives up cost.  At the end of the day the ability of the project to handle risk and adapt to technical challenges rests on the reliability and efficiency of its information systems.  A patchwork system fails to meet the needs of the organization as a whole and at the end of the day is not “takin’ care of business.”

River Deep, Mountain High — A Matrix of Project Data

Been attending conferences and meetings of late and came upon a discussion of the means of reducing data streams while leveraging Moore’s Law to provide more, better data.  During a discussion with colleagues over lunch they asked if asking for more detailed data would provide greater insight.  This led to a discussion of the qualitative differences in data depending on what information is being sought.  My response to more detailed data was to respond: “well there has to be a pony in there somewhere.”  This was greeted by laughter, but then I finished the point: more detailed data doesn’t necessarily yield greater insight (though it could and only actually looking at it will tell you that, particularly in applying the principle of KDD).  But more detailed data that is based on a hierarchical structure will, at the least, provide greater reliability and pinpoint areas of intersection to detect areas of risk manifestation that is otherwise averaged out–and therefore hidden–at the summary levels.

Not to steal the thunder of new studies that are due out in the area of data later this spring but, for example, I am aware after having actually achieved lowest level integration for extremely complex projects through my day job, that there is little (though not zero) insight gained in predictive power between say, the control account level of a WBS and the work package level.  Going further down to element of cost may, in the words of the character in the movie Still Alice, where “You may say that this falls into the great academic tradition of knowing more and more about less and less until we know everything about nothing.”  But while that may be true for project management, that isn’t necessarily so when collecting parametrics and auditing the validity of financial information.

Rolling up data from individually detailed elements of a hierarchy is the proper way to ensure credibility.  Since we are at the point where a TB of data has virtually the same marginal cost of a GB of data (which is vanishingly small to begin with), then the more the merrier in eliminating the abuse associated with human-readable summary reporting.  Furthermore, I have long proposed through this blog and elsewhere, that the emphasis should be away from people, process, and tools, to people, process, and data.  This rightly establishes the feedback loop necessary for proper development and project management.  More importantly, the same data available through project management processes satisfy the different purposes of domains both within the organization, and of multiple external stakeholders.

This then leads us to the concept of integrated project management (IPM), which has become little more than a buzz-phrase, and receives a lot of hand waves, mostly by technology companies that want to push their tools–which are quickly becoming obsolete–while appearing forward leaning.  This tool-centric approach is nothing more than marketing–focusing on what the software manufacturer would have us believe is important based on the functionality baked into their applications.  One can see where this could be a successful approach, given the emphasis on tools in the PM triad.  But, of course, it is self-limiting in a self-interested sort of way.  The emphasis needs to be on the qualitative and informative attributes of available data–not of tool functionality–that meet the requirements of different data consumers while minimizing, to the extent possible, the number of data streams.

Thus, there are at least two main aspects of data that are important in understanding the utility of project management: early warning/predictiveness and credibility/traceability/fidelity.  The chart attached below gives a rough back-of-the-envelope outline of this point, with some proposed elements, though this list is not intended to be exhaustive.

PM Data Matrix

PM Data Matrix

In order to capture data across the essential elements of project management, our data must demonstrate both a breadth and depth that allows for the discovery of intersections of the different elements.  The weakness in the two-dimensional model above is that it treats each indicator by itself.  But, when we combine, for example, IMS consecutive slips with other elements listed, the informational power of the data becomes many times greater.  This tells us that the weakness in our present systems is that we treat the data as a continuity between autonomous elements.  But we know that the project consists of discontinuities where the next level of achievement/progress is a function of risk.  Thus, when we talk about IPM, the secret is in focusing on data that informs us what our systems are doing.  This will require more sophisticated types of modeling.

Don’t Know Much…–Knowledge Discovery in Data

A short while ago I found myself in an odd venue where a question was posed about my being an educated individual, as if it were an accusation.  Yes, I replied, but then, after giving it some thought, I made some qualifications to my response.  Educated regarding what?

It seems that, despite a little more than a century of public education and widespread advanced education having been adopted in the United States, along with the resulting advent of widespread literacy, that we haven’t entirely come to grips with what it means.  For the question of being an “educated person” has its roots in an outmoded concept–an artifact of the 18th and 19th century–where education was delineated, and availability determined, by class and profession.  Perhaps this is the basis for the large strain of anti-intellectualism and science denial in the society at large.

Virtually everyone today is educated in some way.  Being “educated” means nothing–it is a throwaway question, an affectation.  The question is whether the relevant education meets the needs of the subject being addressed.  An interesting discussion about this very topic is explored at Sam Harris’ blog in the discussion he held with amateur historian Dan Carlin.

In reviewing my own education, it is obvious that there are large holes in what I understand about the world around me, some of them ridiculously (and frustratingly) prosaic.  This shouldn’t be surprising.  For even the most well-read person is ignorant about–well–virtually everything in some manner.  Wisdom is reached, I think, when you accept that there are a few things that you know for certain (or have a high probability and level of confidence in knowing), and that there are a host of things that constitute the entire library of knowledge encompassing anything from a particular domain to that of the entire universe, which you don’t know.

To sort out a well read dilettante from someone who can largely be depended upon to speak with some authority on a topic, educational institutions, trade associations, trade unions, trade schools, governmental organizations, and professional organizations have established a system of credentials.  No system is entirely perfect and I am reminded (even discounting fraud and incompetence) that half of all doctors and lawyers–two professions that have effectively insulated themselves from rigorous scrutiny and accountability to the level of almost being a protected class–graduate in the bottom half of their class.  Still, we can sort out a real brain surgeon from someone who once took a course in brain physiology when we need medical care (to borrow an example from Sam Harris in the same link above).

Furthermore, in the less potentially life-threatening disciplines we find more variation.  There are credentialed individuals who constantly get things wrong.  Among economists, for example, I am more likely to follow those who got the last financial crisis and housing market crash right (Joe Stiglitz, Dean Baker, Paul Krugman, and others), and those who have adjusted their models based on that experience (Brad DeLong, Mark Thoma, etc.), than those who have maintained an ideological conformity and continuity despite evidence.  Science–both what are called the hard and soft sciences–demands careful analysis and corroborating evidence to be tied to any assertions in their most formalized contexts.  Even well accepted theories among a profession are contingent–open to new information and discovery that may modify, append, or displace them.  Furthermore, we can find polymaths and self-taught individuals who have equaled or exceeded credentialed peers.  In the end the proof is in the pudding.

My point here is threefold.  First, in most cases we don’t know what we don’t know.  Second, complete certainty is not something that exists in this universe, except perhaps at death.  Third, we are now entering a world where new technologies allow us to discover new insights in accessing previously unavailable or previously opaque data.

One must look back at the revolution in information over the last fifty years and its resulting effect on knowledge to see what this means in our day-to-day existence.  When I was a small boy in school we largely relied on the published written word.  Books and periodicals were the major means of imparting information, aside from collocated collaborative working environments, the spoken word, and the old media of magazines, radio, and television.  Information was hard to come by–libraries were limited in their collections and there were centers of particular domain knowledge segmented by geography.   Furthermore, after the introduction of television, society had developed  trusted sources and gatekeepers to keep the cranks and flimflam out.

Today, new media–including all forms of digitized information–has expanded and accelerated the means of transmitting information.  Unlike old media, books, and social networking, there are also fewer gatekeepers in new media: editors, fact checkers, domain experts, credentialed trusted sources, etc. that ensure quality control, reliability, fidelity of the information, and provide context.  It’s the wild west of information and those wooed by the voodoo of self-organization contribute to the high risk associated with relying on information provided through these sources.  Thus, organizations and individuals who wish to stay within the fact-based community have had to sort out reliable, trusted sources and, even in these cases, develop–for lack of a better shorthand–BS detectors.  There are two purposes to this exercise: to expand the use of the available data and leverage the speed afforded by new media, and to ensure that the data is reliable and can reliably tell us something important about our subject of interest.

At the level of the enterprise, the sector, or the project management organization, we similarly are faced with the situation in which the scope of data that can be converted into information is rapidly expanding.  Unlike the larger information market, this data on the microeconomic level is more controlled.  Given that data at this level suffers from significance because it records isolated events, or small sample sizes, the challenge has been to derive importance from data where sometimes significance is minimal.

Furthermore, our business systems, because of the limitations of the selected technology, have been self-limiting.  I come across organizations all the time who cannot imagine the incorporation and integration of additional data sets largely because the limitations of their chosen software solution has inculcated that approach–that belief–into the larger corporate culture.  We do not know what we do not know.

Unfortunately, it’s what you do not know that, more often than not, will play a significant role in your organization’s destiny, just as an individual that is more self-aware is better prepared to deal with the challenges that manifest themselves as risk and its resultant probabilities.  Organizations must become more aware and look at things differently, especially since so many of the more conventional means of determining risk and opportunities seems to be failing to keep up with the times, which is governed by the capabilities of new media.

This is the imperative of applying knowledge discovery in data at the organizational and enterprise level–and in shifting one’s worldview from focusing on the limitations of “tools”: how they paint a screen, whether data is displayed across the x or y axis, what shade of blue indicates good performance, how many keystrokes does it take to perform an operation, and all manner of glorified PowerPoint minutia–to a focus on data:  the ability of solutions to incorporate more data, more efficiently, more quickly, from a wider range of sources, and processed in a more effective manner, so that it is converted into information to be able to be used to inform decision making at the most decisive moment.

I Can See Clearly Now — Knowledge Discovery in Databases, Data Scalability, and Data Relevance

I recently returned from a travel and much of the discussion revolved around the issues of scalability and the use of data.  What is clear is that the conversation at the project manager level is shifting from a long-running focus on reports and metrics to one focused on data and what can be learned from it.  As with any technology, information technology exploits what is presented before it.  Most recently, accelerated improvements in hardware and communications technology has allowed us to begin to collect and use ever larger sets of data.

The phrase “actionable” has been thrown around quite a bit in marketing materials, but what does this term really mean?  Can data be actionable?  No.  Can intelligence derived from that data be actionable?  Yes.  But is all data that is transformed into intelligence actionable?  No.  Does it need to be?  No.

There are also kinds and levels of intelligence, particularly as it relates to organizations and business enterprises.  Here is a short list:

a. Competitive intelligence.  This is intelligence derived from data that informs decision makers about how their organization fits into the external environment, further informing the development of strategic direction.

b. Business intelligence.  This is intelligence derived from data that informs decision makers about the internal effectiveness of their organization both in the past and into the future.

c. Business analytics.  The transformation of historical and trending enterprise data used to provide insight into future performance.  This includes identifying any underlying drivers of performance, and any emerging trends that will manifest into risk.  The purpose is to provide sufficient early warning to allow risk to be handled before it fully manifests, therefore keeping the effort being measured consistent with the goals of the organization.

Note, especially among those of you who may have a military background, that what I’ve outlined is a hierarchy of information and intelligence that addresses each level of an organization’s operations:  strategic, operational, and tactical.  For many decision makers, translating tactical level intelligence into strategic positioning through the operational layer presents the greatest challenge.  The reason for this is that, historically, there often has been a break in the continuity between data collected at the tactical level and that being used at the strategic level.

The culprit is the operational layer, which has always been problematic for organizations and those individuals who find themselves there.  We see this difficulty reflected in the attrition rate at this level.  Some individuals cannot successfully make this transition in thinking. For example, in the U.S. Army command structure when advancing from the battalion to the brigade level, in the U.S. Navy command structure when advancing from Department Head/Staff/sea command to organizational or fleet command (depending on line or staff corps), and in business for those just below the C level.

Another way to look at this is through the traditional hierarchical pyramid, in which data represents the wider floor upon which each subsequent, and slightly reduced, level is built.  In the past (and to a certain extent this condition still exists in many places today) each level has constructed its own data stream, with the break most often coming at the operational level.  This discontinuity is then reflected in the inconsistency between bottom-up and top-down decision making.

Information technology is influencing and changing this dynamic by addressing the main reason for the discontinuity existing–limitations in data and intelligence capabilities.  These limitations also established a mindset that relied on limited, summarized, and human-readable reporting that often was “scrubbed” (especially at the operational level) as it made its way to the senior decision maker.  Since data streams were discontinuous, there were different versions of reality.  When aspects of the human equation are added, such as selection bias, the intelligence will not match what the data would otherwise indicate.

As I’ve written about previously in this blog, the application of Moore’s Law in physical computing performance and storage has pushed software to greater needs in scaling in dealing with ever increasing datasets.  What is defined as big data today will not be big data tomorrow.

Organizations, in reaction to this condition, have in many cases tended to simply look at all of the data they collect and throw it together into one giant pool.  Not fully understanding what the data may say, a number of ad hoc approaches have been taken.  In some cases this has caused old labor-intensive data mining and rationalization efforts to once again rise from the ashes to which they were rightly consigned in the past.  On the opposite end, this has caused a reliance on pre-defined data queries or hard-coded software solutions, oftentimes based on what had been provided using human-readable reporting.  Both approaches are self-limiting and, to a large extent, self-defeating.  In the first case because the effort and time to construct the system will outlive the needs of the organization for intelligence, and in the second case, because no value (or additional insight) is added to the process.

When dealing with large, disparate sources of data, value is derived through that additional knowledge discovered through the proper use of the data.  This is the basis of the concept of what is known as KDD.  Given that organizations know the source and type of data that is being collected, it is not necessary to reinvent the wheel in approaching data as if it is a repository of Babel.  No doubt the euphemisms, semantics, and lexicon used by software publishers differs, but quite often, especially where data underlies a profession or a business discipline, these elements can be rationalized and/or normalized given that the appropriate business cross-domain knowledge is possessed by those doing the rationalization or normalization.

This leads to identifying the characteristics* of data that is necessary to achieve a continuity from the tactical to the strategic level that will achieve some additional necessary qualitative traits such as fidelity, credibility, consistency, and accuracy.  These are:

  1. Tangible.  Data must exist and the elements of data should record something that correspondingly exists.
  2. Measurable.  What exists in data must be something that is in a form that can be recorded and is measurable.
  3. Sufficient.  Data must be sufficient to derive significance.  This includes not only depth in data but also, especially in the case of marking trends, across time-phasing.
  4. Significant.  Data must be able, once processed, to contribute tangible information to the user.  This goes beyond statistical significance noted in the prior characteristic, in that the intelligence must actually contribute to some understanding of the system.
  5. Timely.  Data must be timely so that it is being delivered within its useful life.  The source of the data must also be consistently provided over consistent periodicity.
  6. Relevant.  Data must be relevant to the needs of the organization at each level.  This not only is a measure to test what is being measured, but also will identify what should be but is not being measured.
  7. Reliable.  The sources of the data be reliable, contributing to adherence to the traits already listed.

This is the shorthand that I currently use in assessing a data requirements and the list is not intended to be exhaustive.  But it points to two further considerations when delivering a solution.

First, at what point does the person cease to be the computer?  Business analytics–the tactical level of enterprise data optimization–oftentimes are stuck in providing users with a choice of chart or graph to use in representing such data.  And as noted by many writers, such as this one, no doubt the proper manner of representing data will influence its interpretation.  But in this case the person is still the computer after the brute force computing is completed digitally.  There is a need for more effective significance-testing and modeling of data, with built-in controls for selection bias.

Second, how should data be summarized to the operational and strategic levels so that “signatures” can be identified that inform information?  Furthermore, it is important to understand what kind of data must supplement the tactical level data at those other levels.  Thus, data streams are not only minimized to eliminate redundancy, but also properly aligned to the level of data intelligence.

*Note that there are other aspects of data characteristics noted by other sources here, here, and here.  Most of these concern themselves with data quality and what I would consider to be baseline data traits, which need to be separately assessed and tested, as opposed to antecedent characteristics.

 

Do You Believe in Magic? — Big Data, Buzz Phrases, and Keeping Feet Planted Firmly on the Ground

My alternative title for this post was “Money for Nothing,” which is along the same lines.  I have been engaged in discussions regarding Big Data, which has become a bit of a buzz phrase of late in both business and government.  Under the current drive to maximize the value of existing data, every data source, stream, lake, and repository (and the list goes on) has been subsumed by this concept.  So, at the risk of being a killjoy, let me point out that not all large collections of data is “Big Data.”  Furthermore, once a category of data gets tagged as Big Data, the further one seems to depart from the world of reality in determining how to approach and use the data.  So for of you who find yourself in this situation, let’s take a collective deep breath and engage our critical thinking skills.

So what exactly is Big Data?  Quite simply, as noted by this article in Forbes by Gil Press, term is a relative one, but generally means from a McKinsey study, “datasets whose size is beyond the ability of typical database software tools to capture, store, manage, and analyze.”  This subjective definition is a purposeful one, since Moore’s Law tends to change what is viewed as simply digital data as opposed to big data.  I would add some characteristics to assist in defining the term based on present challenges.  Big data at first approach tends to be unstructured, variable in format, and does not adhere to a schema.  Thus, not only is size a criteria for the definition, but also the chaotic nature of the data that makes it hard to approach.  For once we find a standard means of normalizing, rationalizing, or converting digital data, it no longer is beyond the ability of standard database tools to effectively use it.  Furthermore, the very process of taming it thereby renders it non-big data, or perhaps, if a exceedingly large dataset, perhaps “small big data.”

Thus, having defined our terms and the attributes of the challenge we are engaging, we now can eliminate many of the suppositions that are floating around in organizations.  For example, there is a meme that I have come upon that asserts that disparate application file data can simply be broken down into its elements and placed into database tables for easy access by analytical solutions to derive useful metrics.  This is true in some ways but both wrong and dangerous in its apparent simplicity.  For there are many steps missing in this process.

Let’s take, for example, the least complex example in the use of structured data submitted as proprietary files.  On its surface this is an easy challenge to solve.  Once someone begins breaking the data into its constituent parts, however, greater complexity is found, since the indexing inherent to data interrelationships and structures are necessary for its effective use.  Furthermore, there will be corruption and non-standard use of user-defined and custom fields, especially in data that has not undergone domain scrutiny.  The originating third-party software is pre-wired to be able to extract this data properly.  Absent having to use and learn multiple proprietary applications with their concomitant idiosyncrasies, issues of sustainability, and overhead, such a multivariate approach defeats the goal of establishing a data repository in the first place by keeping the data in silos, preventing integration.  The indexing across, say, financial systems or planning systems are different.  So how do we solve this issue?

In approaching big data, or small big data, or datasets from disparate sources, the core concept in realizing return on investment and finding new insights, is known as Knowledge Discovery in Databases or KDD.  This was all the rage about 20 years ago, but its tenets are solid and proven and have evolved with advances in technology.  Back then, the means of extracting KDD from existing databases was the use of data mining.

The necessary first step in the data mining approach is pre-processing of data.  That is, once you get the data into tables it is all flat.  Every piece of data is the same–it is all noise.  We must add significance and structure to that data.  Keep in mind that we live in this universe, so there is a cost to every effort known as entropy.  Computing is as close as you’ll get to defeating entropy, but only because it has shifted the burden somewhere else.  For large datasets it is pushed to pre-processing, either manual or automated.  In the brute force world of data mining, we hire data scientists to pre-process the data, find commonalities, and index it.  So let’s review this “automated” process.  We take a lot of data and then add a labor-intensive manual effort to it in order to derive KDD.  Hmmm..  There may be ROI there, or there may not be.

But twenty years is a long time and we do have alternatives, especially in using Fourth Generation software that is focused on data usage without the limitations of hard-coded “tools.”  These alternatives apply when using data on existing databases, even disparate databases, or file data structured under a schema with well-defined data exchange instructions that allow for a consistent manner of posting that data to database tables. The approach in this case is to use APIs.  The API, like OLE DB or the older ODBC, can be used to read and leverage the relative indexing of the data.  It will still require some code to point it in the right place and “tell” the solution how to use and structure the data, and its interrelationship to everything else.  But at least we have a means for reducing the cost associated with pre-processing.  Note that we are, in effect, still pre-processing data.  We just let the CPU do the grunt work for us, oftentimes very quickly, while giving us control over the decision of relative significance.

So now let’s take the meme that I described above and add greater complexity to it.  You have all kinds of data coming into the stream in all kinds of formats including specialized XML, open, black-boxed data, and closed proprietary files.  This data is non-structured.  It is then processed and “dumped” into a non-relational database such as NoSQL.  How do we approach this data?  The answer has been to return to a hybrid of pre-processing, data mining, and the use of APIs.  But note that there is no silver bullet here.  These efforts are long-term and extremely labor intensive at this point.  There is no magic.  I have heard time and again from decision makers the question: “why can’t we just dump the data into a database to solve all our problems?”  No, you can’t, unless you’re ready for a significant programmatic investment in data scientists, database engineers, and other IT personnel.  At the end, what they deploy, when it gets deployed, may very well be obsolete and have wasted a good deal of money.

So, once again, what are the proper alternatives?  In my experience we need to get back to first principles.  Each business and industry has commonalities that transcend proprietary software limitations by virtue of the professions and disciplines that comprise them.  Thus, it is domain expertise to the specific business that drives the solution.  For example, in program and project management (you knew I was going to come back there) a schedule is a schedule, EVM is EVM, financial management is financial management.

Software manufacturers will, apart from issues regarding relative ease of use, scalability, flexibility, and functionality, attempt to defend their space by establishing proprietary lexicons and data structures.  Not being open, while not serving the needs of customers, helps incumbents avoid disruption from new entries.  But there often comes a time when it is apparent that these proprietary definitions are only euphemisms for a well-understood concept in a discipline or profession.  Cat = Feline.  Dog = Canine.

For a cohesive and well-defined industry the solution is to make all data within particular domains open.  This is accomplished through the acceptance and establishment of a standard schema.  For less cohesive industries, but where the data or incumbents through the use of common principles have essentially created a de facto schema, APIs are the way to extract this data for use in analytics.  This approach has been applied on a broader basis for the incorporation of machine data and signatures in social networks.  For closed or black-boxed data, the business or industry will need to execute gap analysis in order to decide if database access to such legacy data is truly essential to its business, or given specification for a more open standard from “time-now” will eventually work out suboptimization in data.

Most important of all and in the end, our results must provide metrics and visualizations that can be understood, are valid, important, material, and be right.