From the monthly archives: September 2011
The US Government’s March 2009 paper, ‘A Tradecraft Primer: Structured Analytic Techniques for Improving Intelligence Analysis’, which forms Week 5, Day 2 of the CORTEX MBAnalytics program, nicely complements Harvard Business Review’s recent essay, ‘The Big Idea: Before You Make That Big Decision…’, by Daniel Kahneman, Dan Lovallo, and Oliver Sibony (requires registration, but well worth it). Both pieces set out to counteract cognitive biases—the primer in the context of sense making and analysis, and the HBR essay in the context of decision making. Each provides practical strategies for systemising skepticism.
Business cases for analytics often focus on applications which automate decisions by delegating them to algorithms. One of Analyst First’s consistent contentions has been that, whilst analytics certainly can automate low level operational decisions, it makes others harder. Analytics enables higher value decisions to be made. As the Tradecraft Primer puts it:
This primer highlights structured analytic techniques—some widely used in the private sector and academia, some unique to the intelligence profession. It is not a comprehensive overview of how intelligence officers conduct analysis. Rather, the primer highlights how structured analytic techniques can help one challenge judgments, identify mental mindsets, stimulate creativity, and manage uncertainty. In short, incorporating regular use of techniques such as these can enable one to structure thinking for wrestling with difficult questions.
The techniques covered fall into three groups:
Diagnostic techniques suited for “making analytic arguments, assumptions, or intelligence gaps more transparent”:
- Key Assumptions Check
- Quality of Information Check
- Indicators or Signposts of Change
- Analysis of Competing Hypotheses (ACH)
Contrarian techniques designed to challenge status quo thinking:
- Devil’s Advocacy
- Team A/Team B
- High-Impact/Low-Probability Analysis
- “What If?” Analysis
Imaginative thinking techniques aimed at “developing new insights, different perspectives and/or develop alternative outcomes”:
- Brainstorming
- Outside-In Thinking
- Red Team Analysis
- Alternative Futures Analysis
Each of these are practically described and illustrated with case studies. The final section, ‘Strategies For Using Structured Analytic Techniques’, locates them along a stylised analytic project timeline:
A Tradecraft Primer: Structured Analytic Techniques for Improving Intelligence Analysis, prepared by the US Government, March 2009, p.38
In ‘The Big Idea: Before You Make That Big Decision…’, Kahneman et al. address “decisions that are both important and recurring, and so justify a formal process”, in other words, strategic decisions. The typical case involves an executive making a decision on the basis of recommendations provided by a subordinate team. Overcoming cognitive biases in this context, the paper reports, has been shown to pay off in the form of better decisions. But although we may each be aware that we are prone to cognitive biases, this knowledge alone is not helpful, because as individuals we are unable to neutralise our own biases. In the organisational context, however, there is strength in numbers:
[M]ost decisions are influenced by many people, and… decision makers can turn their ability to spot biases in others’ thinking to their own advantage. We may not be able to control our own intuition, but we can apply rational thought to detect others’ faulty intuition and improve their judgment.
To do this, Kahneman et al. propose a “systematic review of the recommendation process” consisting of a twelve point checklist of questions, each designed to counteract specific cognitive biases. Executives are encouraged to ask:
- Is there any reason to suspect motivated errors, or errors driven by the self-interest of the recommending team? (Self-interested Biases)
- Have the people making the recommendation fallen in love with it? (Affect Heuristic)
- Were there dissenting opinions within the recommending team? (Groupthink)
- Could the diagnosis of the situation be overly influenced by salient analogies? (Saliency Bias)
- Have credible alternatives been considered? (Confirmation Bias)
- If you had to make this decision again in a year, what information would you want, and can you get more of it now? (Availability Bias)
- Do you know where the numbers came from? (Anchoring Bias)
- Can you see a halo effect? (Halo Effect)
- Are the people making the recommendation overly attached to past decisions? (Sunk-Cost Fallacy, Endowment Effect)
- Is the base case overly optimistic? (Overconfidence, Planning Fallacy, Optimistic Biases, Competitor Neglect)
- Is the worst case bad enough? (Disaster Neglect)
- Is the recommending team overly cautious? (Loss Aversion)
The thinking behind each of these is elaborated and case study examples are provided.
Both papers are recommended in full. Analysts and decision makers may be accustomed to being data driven, but being rigorously and systematically skeptical is a broader discipline—welcoming a diversity of opinions, actively seeking and valuing disconfirmation, and being prepared to challenge accepted organisational wisdom.
Related Analyst First posts:
All Analytics – The Community for Data Management, Business Intelligence, & Analytics – has invited Analyst First to argue the case for open source software in analytics as part of their Point/Counterpoint series. Our point post, ‘The Case for Commodity & Open-Source Analytics’ is here. The counterpoint post, ‘Downsides Dampen Open-Source Analytics’, by Ajay Ohri, is here. Beth Schultz’s introduction is here.
I encourage our readers to explore the All Analytics site and to comment on the debate (which necessitates free registration).
Pattern-driven Performance: Should You Start with Tools—or with Talent? That’s one of the questions addressed by Deloitte at Real Analytics:
Companies everywhere are catching onto the wisdom of mining information for patterns of performance. Using a combination of advanced statistical tools and good, old-fashioned experience, they’re discovering and dissecting hidden patterns that can help guide their choices in operations, talent, technology, financial strategy, you name it. For those looking to drive performance in this way, there are two paths forward: start by investing in tools or start by investing in talent.
In Analyst First terms it’s a contest between human and electronic infrastructures. Deloitte frames it as a debate, presenting a set of rhetorical, stylised point / counterpoints to which a panel of its Directors and Principals respond. The case for tools first cites scale, automation, efficiency, transparency, and some degree of insulation from undesirable human subjectivity. It also talks up the scarcity of good people and talks down the difficulty of analytics. The case for talent first argues for the importance of business knowledge, an appreciation of context and nuance, interpretative skill, big picture understanding, the ability to ask the right questions, and the soft skills required to build cross-functional communication, coordination, trust and support networks.
Three out of the four Deloitte contributors prioritise talent over tools; the fourth elects both. As Janet Foutty, National Managing Director, Technology, Deloitte Consulting LLP puts it:
[T]here’s a big problem with the “buy technology first” approach: What if you’re not asking the right questions. I know it might sound strange coming from a person who leads Deloitte’s IT services, but I’m “talent first” all the way.
One of Analyst First’s key principles is our advocation of investing in the human over the electronic infrastructure. Simply recommending “both” is appealing, but the reality is that investment decisions are always taken at some margin at which a trade-off is being made, so “both” is never a real choice. A decision to spend any amount of money on commercial software is always a decision to not spend that money on alternative uses—such as hiring more or superior analysts. In comparing the marginal utility of commercial tools with alternative investments, the following should be added to the case put by the Deloitte panel, which further strengthen it:
- Although some instantiations of analytics are process-based, analytics itself is not a process.
- Most of the analytical tools any organisation will require, especially at the outset of its exploration of analytics, are readily available. Much can be done—and is being done—with the commodity tools already available on analysts’ desktops (e.g. Excel and SQL) and with open source tools such as RapidMiner and R.
- Many different tools exist—commercial, commodity, and open source—and sensibly choosing between them means becoming an educated buyer. This entails leveraging experimentation and experience—either in-house in the form of trial and error, or that of outside help.
- Prominent expenditure on commerical tools—while it may perversely benefit individuals—erects a number of barriers to organisational success.
- Tools without sufficient expertise are not harmless. In fact, they may act as risk multipliers.
This does not mean that the marginal utility of commercial software is always less than the marginal utility of analysts. This is of course possible. However, it is empirically the case far less than outsiders to Business Analytics—and many insiders—intuitively expect.
Related Analyst First posts:
We’re building new information delivery systems for a future that isn’t there. Our state-of-the-art environments are already becoming obsolete because our view is distorted by the lens of the past, showing us the future as it was years ago. That world of scarce computing resources and limited data is gone.
That’s Mark Madsen at TDWI, arguing that many of the key assumptions driving our construction of analytic systems—decision support systems, data warehouses, and business intelligence—are wrong. The first wrong assumption is of scarcity. Processor cycles, memory, and storage used to be expensive. They aren’t any longer, but we’re still batch processing our ETL, prematurely archiving, summarising and normalising our data, and limiting our storage of derived information.
His second target is the tabula rasa impulse:
Most data warehouse and BI methodologies assume that you start with no analysis systems in place. The methodologies were created at a time when information delivery meant reports from OLTP applications.
The reality today is that analytics projects don’t start with a clean slate. Reporting and BI applications are common in different parts of the organization.
Third is the assumption of stability. Build-from-scratch methodologies made sense the first time around, but:
By not focusing on evolution, the methodologies miss a key element about analytics: they often focus on decisions that change business processes. Process change means the business works differently and new data will be needed. When someone solves a problem, they move on to a new problem. The work is never done because an organization is constantly adapting to changing market conditions.
One of Analyst First’s key principles is that:
Analytics is not a linear process, like most engineering projects. Its end product is discovery: you cannot determine what will be discovered ahead of time. Thus the outcomes of analytics, and the decisions based on them, cannot be made before the analysis has been carried out.
Consequently we advocate the primacy and ongoing centrality of strategic analytics over operational analytics. The more analytics is conceived of as a set of activities which only adds value at existing operational margins, the more it is unnecessarily constrained and the less it is able to change the game. Echoing yesterday’s Analyst First post on analysis as a read-write activity, Madsen continues that:
Business intelligence methods and architecture assume that what’s being built is a single system to meet all data needs. We still think of analytics as giving reports to users. This ignores what they really want: information in the context of their work process and in support of their goals. Sometimes reports are sufficient; sometimes more is needed.
He goes on to confirm that big data is both challenging status quo electronic infrastructures and driving demand for higher value advanced analytics:
The interaction model for BI delivery is that a user asks a question and gets an answer. This only works if they know what they are looking for. Higher data volumes, more sophisticated business needs, and high-performance platforms require that BI be extended to include advanced analytics. These answer “why” questions that can’t be answered by the simple sums and sorts of BI.
As I’ve argued before, the assumption-heaviness and manual intensiveness of standard BI technologies such as OLAP can’t compete, at scale, with the automated exploration that machine learning methods make possible. Madsen concludes that the data warehouse should be conceived of as a platform rather than an application. His closing four paragraphs are worth quoting in full:
The data warehouse has evolved to the point where it needs to provide data infrastructure, and needs to support information delivery by other applications rather than trying to do both. Data infrastructure requires a focus on longer planning horizons, stability where it matters, and standardized services. Information delivery requires meeting specific needs and use cases.
Design methods today seldom address the need to separate data infrastructure from delivery applications. Designs focus on data management and fitting the database to the delivery tools. This leads to IT efforts to standardize on one set of user tools for everything, much like Henry Ford tried to limit the color of his cars to black.
The new needs and analysis concepts go against the idea that a data warehouse is a read-only repository with one point of entry. They do not fit with established ideas, tools, and methodologies.
Today, the tight coupling of data, models and tools via a single SQL-based access layer prevent us from delivering what both business users and application developers need. The data warehouse must be split into data management infrastructure that can meet high-performance storage, processing, and retrieval needs, and an application layer that is decoupled from this infrastructure. This separation of storage and retrieval from delivery and use is a key concept required by data warehouse architectures as business and technology move forward.
Related Analyst First posts:
Week 3, Day 2 of the CORTEX MBAnalytics program includes TDWI’s best practices report, ‘Strategies for Managing Spreadmarts – Migrating to a Managed BI Environment’, by Wayne W. Eckerson and Richard P. Sherman, from 2008 and based in part on a survey conducted in 2007. It’s an excellent document—one of the best articulations of a problem with which Business Analytics practitioners and interested parties ought to be familiar: the nature and causes of ‘spreadmarts’, their strengths, weaknesses and limitations, what to do about them, and the risks involved. Given how well the report covers these topics, I commend it in full. But I’m also going to address where it falls short. It’s right in its reasoning and its conclusions, but potentially misleading in its emphasis and what it omits.
By way of definition:
A spreadmart is a reporting or analysis system running on a desktop database (e.g., spreadsheet, Access database, or dashboard) that is created and maintained by an individual or group that performs all the tasks normally done by a data mart or data warehouse, such as extracting, transforming, and formatting data as well as defining metrics, submitting queries, and formatting and publishing reports to others. Also known as data shadow systems, human data warehouses, or IT shadow systems.
Unsurprisingly:
Finance generates the most spreadmarts by a wide margin, followed by marketing, operations, and sales… Finance departments are particularly vulnerable to spreadmarts because they must create complex financial reports for internal and external reporting as well as develop detailed financial plans, budgets, and forecasts on an ad hoc basis. As a result, they are savvy users of spreadsheets, which excel at this kind of analysis.
Spreadmarts are categorised into three types:
- One-off reports. Business people use spreadsheets to filter and transform data, create graphs, and format them into reports that they present to their management, customers, suppliers, or partners. With this type of spreadmart, people are using data they already have and the power of Excel to present it. There’s no business justification—or even time—for IT to get involved.
- Ad hoc analysis. Business analysts create spreadmarts to perform exploratory, ad hoc analysis for which they don’t have a standard report. For instance, they may want to explore how new business conditions might affect product sales or perform what-if scenarios for potential business changes. They use the spreadmart to probe around, not even sure what they’re looking for, and they often bring in supplemental data that may not be available in the data warehouse. This exploration can also be time-sensitive and urgent.
- Business systems. Most spreadmarts start out as one-off reports or ad hoc analysis, then morph into full-fledged business systems to support ongoing processes like budgeting, planning, and forecasting. It’s usually not the goal to create such a system, but after a power user creates the first one, she’s asked by the business to keep producing the report until, eventually, it becomes an application itself. This type of spreadmart is called a “data shadow system.”
Of these, it’s ‘business systems’ which are the report’s focus. The ‘one-off report’ and ‘ad hoc analysis’ categories of spreadmart, the report fittingly concludes, are inappropriate for systemisation.
But defining spreadmarts in terms of them being ‘shadow systems performing all the tasks normally done by a data mart of data warehouse’ gets things somewhat backwards. Presupposing that all spreadmarts are appropriate for systemsation—in effect viewing them as ‘data marts in waiting’—is misleading when their one-off and ad hoc uses are recognised and their wide proliferation and coverage taken into consideration. One might more accurately define data marts and data warehouses as ‘scaled-up systems which perform some of the tasks normally done by a spreadmart’.
The report’s framing of spreadmarts is a product of its BI/DW view of the world. In that worldview, data lives in source systems and needs to be ETL-ed into a relational repository in order to be published en masse to business users whose analysis requirements consist of read-only slice and dice. These needs do of course exist, but analysis entails much more. This narrower BI/DW view is reflected in the TDWI survey’s design. For example, respondents are asked to rank the top five reasons why spreadmarts exist in their group. “Quick fix to integrating data” ranks second overall, “Inability of the BI team to move quickly” third, ”This is the way it’s always been done” fifth, and “Desire to protect one’s turf” seventh. These options are hardly worded neutrally. They’re phrased so as to norm systemisation and to cast ad hoc and one-off uses of spreadmarts as the products of sloppiness, frustration, ignorance, and narrow self-interest. The section on the benefits of spreadsheets is similarly biased in its framing. “Ideal for one-time analysis” and “Good for prototyping a permanent system” are listed, but not ‘ideal for exploring data, creating scenarios, capturing assumptions, and enriching existing data’. Interpretation also suffers. For example:
Ironically, organizations with a “low” degree of [BI] standardization have the lowest median number of spreadmarts (17.5), and only 31% haven’t counted them. The proper conclusion here is that standardization increases awareness of spreadmarts.
It may be a convenient conclusion, but it certainly isn’t the only one. Perhaps the 70 to 80 percent failure rate of BI standardisation projects is driving business users back to spreadsheets. Excel integration with BI platforms is also filtered through a read-only lens:
BI vendors are starting to offer more robust integration between their platforms and Microsoft Office tools. Today, the best integration occurs between Excel and OLAP databases, where users get all the benefits of Excel without compromising data integrity or consistency, since data and logic are stored centrally.
This tends to be how BI vendors understand Excel integration. They recognise that users enjoy Excel as a query and reporting interface, but they understate its importance as a data and logic creation tool. The BI/DW worldview understands all data as the product of business processes which write it to ‘source systems’. (The one exception to this is the sub-discipline of enterprise budgeting and planning, which is in effect BI with people as the source systems.) What is underappreciated is that analysis itself is a business process—one which can’t help but create data.
These biases are understandable. The TDWI report is sponsored by a collection of BI software vendors, and 60% of those surveyed are IT professionals. What’s missing, then, is a more nuanced understanding of what analysis entails. Such an understanding needs to recognise:
- The centrality of data transformation and enrichment by individual analysts
- The value of tacit data
- The importance of presentation
Simply put, analysis is a read-write activity. Routine analytical tasks I find myself doing, for example, include:
- Entering hitherto tacit data
- Codifying business knowledge
- Finding and synthesising data from outside sources
- Creating dummy and randomised data
- Capturing novel assumptions
- Imposing new categories on existing categorical data
- Enriching existing data by deriving or devising on-the-fly metadata
- Building scenarios and constructing counterfactuals
- Drafting and adding commentary, interpretation, and notes
- Formalising and detailing new questions and follow-on analyses
All of these activities involve me creating new data, and I would submit that neither I nor any BI/DW requirement gathering cycle would be able to anticipate that data ahead of time. These are creative, reflective, results-contingent activities. As the report puts it:
[M]ost BI vendors have recognized that a large portion of customers are using their BI tools simply to extract data from corporate servers and dump them into Excel, Word, or PowerPoint, where they perform their “real work.”
If we additionally overlay the question of data value, one of Analyst First’s key contentions is that the most valuable information in organisations lives in people’s heads. It’s tacit, and spreadsheets are one of the best tools for eliciting it and making it explicit:
Spreadsheets are the most pervasive and effective decision support tools. No organisation doesn’t use them, and it’s a safe bet that this will always be the case. No amount of data warehousing will ever be able to provide decision makers with all the information they need. To the extent that it can, those decisions can be automated. Decisions invariably require new data. That new data will be either unanticipatable, or tacit, or both. Spreadsheets are unbeatable for ad hoc data analysis and turning tacit data into explicit data.
But spreadsheets aren’t the only tools available for tacit data mining. Nor, for some types of data, are they the best tools. As the ‘The Economics of Data – Analytics is… Investing in Data‘ post argued:
The most interesting, readily available, strategically relevant and poorly understood form of data is tacit data: the information contained in the brains of staff, board, shareholders and anyone else who would see the organisation do well… How is tacit data mined? The most effective and powerful way is by use of collective intelligence and forecasting techniques, such as prediction markets.
Finally—analysis process, ad hoc, and one-off needs aside—decision support systems need to be more than portals for publishing structured data as tables, charts, and indicators. As the TDWI survey picks up:
While Excel is the most popular tool for building spreadmarts, business analysts also use Microsoft Access, PowerPoint, and SAS statistical tools.
SAS and PowerPoint are telling inclusions: SAS contains a great deal of statistical and modelling functionality that BI stacks don’t (or certainly didn’t in 2007); PowerPoint is able to flexibly integrate unstructured commentary with the more structured outputs of BI platforms—as is Word. The TDWI report itself is an example of this: most of it is unstructured text, then there are graphics and other design elements, and finally the charts and tables. Very few high value analyses don’t contain narrative, diagrams, and other unstructured presentation elements.
The TDWI report does in fact acknowledge all of the above:
[T]here is often no acceptable alternative to spreadmarts. For example, the data that people need to do their jobs might not exist in a data warehouse or data mart, so individuals need to source, enter, and combine the data themselves to get the information. The organization’s BI tools may not support the types of complex analysis, forecasting, or modeling that business analysts need to perform, or they may not display data in the format that executives desire.
The report’s biases are in its view of analysis as fundamentally amendable to structure, and spreadmarts (as one of its enablers) as precursors of systems. Scalability, repeatability and automation certainly have their place, but a more realistic view would recognise that the analytical activities which a data warehouse can viably support are a subset. This has important implications for BI/DW practices now that the Business Analytics domain incorporates them alongside advanced analytics and the emerging field of big data.
Related Analyst First posts:
Bloomberg Businessweek reports that US government intelligence agencies are putting big money into social media and big data analytics in order to track people’s online behaviour. Such agencies don’t have customers, per se, but they do have adversaries.
In the decade since the 9/11 attacks, various attempts to leverage analytics in the service of national security have been reported. See, for example, this January 2007 post from Jeff Jonas, summarising a US Senate Judiciary Committee hearing on the subject. Jonas was (and to my knowledge remains) sceptical about the viability of predictive modelling to predict outcomes such as terrorist incidents given the lack of historical data. He does however note consensus as to the value of methods such as link analysis (one of his own specialties) and ‘predicate-based analytics’ (which could certaintly include predictive modelling). Along with others, he’s concerned about the privacy and civil liberties implications of Big Brother’s forays into big data mining. But his central concern is that—at a Senate Judiciary Committee hearing entitled “Balancing Privacy and Security: The Privacy Implications of Government Data Mining Programs”—the meaning of ‘data mining’ is so variable:
[A]ny policy that emerges that regulates data mining or mandates reporting better define it. Because under one definition of data mining even something as simple as using a computer to lookup your name on a reservation list (e.g., at the hotel during check-in) is considered data mining.
More recently, David Skillicorn—an expert in adversarial data mining—reported a similar diversity of definitions and aims:
I was at a meeting last week whose focus was on social media. It quickly became clear that there were two kinds of interests. One group wanted to build high-level systems that would revolutionize business and government (somehow) leveraging social media; another group were building or wanted to build tools that would provide some kind of meta-view of social media content and activity.
The topic that was missing from all of the discussion was what social media was, and why it is the way it is; and so I came away feeling like the entire discussion, and quite a lot of work, was dancing on clouds. There seem to be a number of things that “everybody knows” about social media, but for which there seems to be little or no evidence. The Arab Spring was driven by social media! Well, maybe, but (a) was it and how much, (b) which parts were important and which were irrelevant?
There are two ways to process this confluence of intention and imprecision. One is that Big Brother’s data mining efforts are well advanced, in which case press releases and conference presentations on social media analytics are examples of well-executed counter-intelligence. On the other hand, it may well be that—like so many other organisations—our security agencies are still struggling with the small data basics, in which case the hype serves other purposes.
Related Analyst First posts
I presented on Business Analytics and Analyst First to 80+ people at the Corruption Prevention Network‘s 2011 Annual Full Day Forum in Sydney yesterday. The first half of the presentation covered the role of advanced analytic methods in audit and investigation contexts, illustrating with some anonymised examples of link analysis, digital analysis, clustering and predictive modelling. The second half addressed ‘how to get started’ with analytics the Analyst First way. The slides are available on our Resources page.
UPDATE: All papers from the forum (including previous years) are available here.
So far from making us more profligate with information, perhaps the Goddess of ‘big data’ will spur us to be smarter in data selection, and ensure more intelligence is embedded within our data extraction, transformation and reporting processes.
Greg Taylor‘s comments on the ‘Knowing what you’re missing‘ post are spot on. One of the clear implications of the big data explosion—technical challenges aside—is that manual analysis methods simply can’t scale to the volume and velocity at which potentially relevant data is being generated. As such, analytics (particularly of the machine learning variety) is ever more vital. One of my rules of thumb in consulting is that any OLAP cube is a standing business case for predictive modelling. As I put it in the ‘Advanced analytics and OLAP‘ post:
OLAP makes multidimensional data exploration about as fast and intuitive as it can be when a human is doing the driving. This means being able to arrange on screen, in two dimensions (perhaps taking advantage of colour and shape to visualise a third and fourth), relatively small subsets and arithmetical summaries of data. Advanced analytics, however, automates exploration. Only data mining methods can look at all dimensions simultaneously, at all levels, in combination. And they can do this in unsupervised (looking for natural structure in the data) or supervised (inferring input-outcome relationships) modes.
Greg elaborates:
- So as Analysts search more broadly for relevant data to meet the decision making requirements of management, perhaps they need to increasingly ask themselves: how will this piece of information fit within the network of predictive functions which explains the business?
- How might Analysts apply Occam’s razor to ensure only information which contributes predictive understanding is included, given the exponential growth in the potential data sources that could be used? One logical approach is for Analysts to undertake more experimental testing of variables (and transformations) for their explanatory power with respect to business outcomes.
As the earlier post reported, status quo electronic infrastructures aren’t ready for big data, and new technologies and disciplines are evolving rapidly to close the gap. But even more substantive changes are required of organisations’ human infrastructures. The key transition that business users of data need to make in the big data context is from the default of consuming more data to the practice of consuming data of higher value. This means becoming analytically literate and learning how to trust and leverage analysts. The key role that analysts must play in supporting decision makers is to understand what constitutes higher value, and to seek it out and communicate it. The key role for IT functions and BI managers is to enable analysts to enable decision makers.
Related Analyst First posts:
My interpretation of Tom Davenport’s recent work is that he’s addressing the relative reluctance of knowledge workers in established businesses to adopt and compete on analytics. In a recent LinkedIn discussion of the former post on TDWI’s Business Intelligence and Data Warehousing Discussion Group, Vincent Granville observed that:
Many industries still have little exposure to business analytics: government, military, education, transportation (except airlines), security, health care, entertainment, mining, etc. Some still stick to “old analytics”: insurances, banks, pharmaceuticals.
In response, two other discussants–Dorothy Hewitt-Sanchez and Phil Bailey–reflected on the difficulty of achieving business change in such environments.
All organisational change ultimately comes from ‘within’ in the sense that a business either changes itself or dies. But the impetus for change can come from either the inside or the outside. The outside impetus for change is some form of existential threat, typically from competition–e.g. “we’re losing market share; we need to change”. Note that all of the examples cited by Vincent are sectors with significant capital overheads, or lots of regulation, or both. These mean high barriers to entry and a fair amount of insulation from competition–that is, a buffer against having to change. Also note how many of the prominent competing on analytics success stories are, by contrast, in the online sector (Amazon and Netflix, for example).
Change can alternatively come from the inside–e.g. “we could do things better; let’s try to change”. Arguably that’s what performance management is about. But as Davenport acknowledges–by focusing on the prospect of knowledge worker resistance to additional structure–most of us, most of the time, don’t relish disruptions to the status quo. The fragility of inside change comes from the fact that the imperative is inherently weaker and less clear. The status quo is not demonstratably problematic. Proactivity, compared to reactivity, is a luxury response.
As legislated monopolies, government agencies are an interesting midpoint between inside and outside change. Unlike businesses, their existence is for all intents and purposes guaranteed, and they don’t have competitors. But they are subject to various forms of outside change. New policies force organisational change. Those agencies responsible for law and order, security, revenue collection, and other fundamental conditions of sovereignty have adversaries–who, like competitors, are a type of threat. That said, most change in the public sector is the product of internal initiative.
Vendors, consultants, and other service providers are adept at seeing the ‘need’ for Business Analytics. However, not all need expresses itself as demand. Demand is need that has been recognised, prioritised over other needs, and accordingly resourced. Outside change propels need into demand more powerfully than inside change.
Related Analyst First posts:
I’ve argued before that most businesses continue to struggle with query and reporting, and that the increasingly common invocation of big data and social media in business cases for Business Analytics is a way of leveraging hype in order to fund second and third chances at getting these basics right. Beth Stackpole, writing at SearchBusinessAnalytics.com, quotes Brian Hopkins of Forrester on a related aspect of this phenomenon:
“Over the last 20 to 25 years, companies have been focused on leveraging maybe up to 5% of the information available to them,” said Brian Hopkins, a principal analyst at Forrester Research Inc. in Cambridge, Mass. “Everything we didn’t know what to do with hit the floor and fell through the cracks. In order to compete well, companies are looking to dip into the rest of the 95% of the data swimming around them that can make them better than anyone else.”
Stackpole’s thrust is that big data presents fundamental technical challenges to established data management practices:
This whole notion of extreme data management has put a strain on traditional data warehouse and BI systems, which are not well-suited to handle the massive volume and velocity requirements of so-called big data applications, both economically and in terms of performance.
Sources of big data identified in the piece include:
[T]he constant stream of chatter on social media venues like Facebook and Twitter, daily Web log activity, Global Positioning System location data and machine-generated data produced by barcode readers, radio frequency identification scans and sensors.
Social media, the Web, and GPS are data sources we’re conscious of interacting with everyday. It’s transparent to us as users of social media sites and smartphones that we’re consuming and generating digital information. The social processes which generate big data are more obvious and more novel to us than the business processes which generate ‘small data’.
Part of the significance of social media from a Business Analytics perspective, then, is that it has made business consumers of information far more aware of how much data they’re not able to get their hands on. The 95% that Hopkins characterises as having fallen through the cracks in the past did so less visibly. In the age of the smartphone, however, it’s much clearer how little data makes it into warehouses relative to what’s out there.
Seen in this way, big data is a two-edged sword for BI managers. It increases awareness and demand for information, and can breath new life into data management initiatives, but it also increases the dissatisfaction of business consumers because they’re now more conscious, and more frequently reminded, of what they’re missing.
Related Analyst First posts:
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