Doug Laney

The research note, Emerging Role of the Data Scientist and the Art of Data Science, I authored with colleague Lisa Kart just hit the Gartner wires this week. Since most of the data scientist role dissenters  we come across seem to believe that the role’s title is is nothing more than a pretentious moniker for a statistician or business intelligence (BI) analyst, we decided to take an…er…scientific approach to making that determination. We thought it would be entirely fitting to perform text analysis of hundreds of job descriptions for “data scientist,” “statistician,” and “BI analyst” to learn what the commonalities and differences are according to those actually hiring for the the role.

Data Scientist Job Description Wordcloud

I’d like to believe that these findings led us to more clearly define and distinguish the role of the data scientist, without speculation, than anyone else to-date. Through our research we learned that data scientists are expected to work more in teams, have a comfort and experience with “big data” sets, and are skilled at communication. They also frequently require experience in machine learning, computing and algorithms, and are required to have a PhD nearly twice as often as statisticians. Even the technology requirements for each role differed, with data scientist job descriptions more frequently mentioning Hadoop, Pig, Python and Java among others.

The piece then goes on to define and describe the three core data science skills: data management, analytics modeling and business analysis. But beyond these, there’s an art to data science. We detail several soft skills that our research showed are also critical to success, i.e., communication, collaboration, leadership, creativity, discipline and passion (for information and truth).

With the need for data scientists growing at about 3x those for statisticians and BI analysts, and an anticipated 100,000+ person analytic talent shortage through 2020, we also included a listing of university programs around the world offering degrees in advanced analytics.

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Category: Uncategorized     Tags: analytics, big data, busienss intelligence, data science, data scientist, statistician

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Yesterday during the on-air buildup to the Super Bowl a reporter mentioned that over one billion people were expected to watch this year’s big game. It occurred to me how few of these individuals, including some Americans, fully understand what the Super Bowl really means.  The next news story was about Super PACs (a new form of political action committee), and it occurred to me how, despite Stephen Colbert’s best efforts, even fewer people understand what a Super PAC is. So for both fun and education I created a little side-by-side comparison of the Super Bowl (and American football) versus a Super PAC (and the American elections).

Ultimately the larger story for both the Super Bowl and Super PACs is about corporate influence. Super Bowl ads may be expensive, but the cost per second per viewer is on par with any other TV show. Moreover, due to social media these Super Bowl ads often take on a life in the Twittersphere, on YouTube and in Facebook after (and even before) they air, thereby enabling a business to reach a much larger audience than those viewing the ad when it aired. Many businesses also use the power of social media to actively engage potential customers by drawing them to their website or Facebook page. Think: Danica Patrick. Similarly, US elections are expensive, and reaching voters today also requires a social multichannel approach. Super PACs now provide the unbounded means for individuals and corporations from anywhere on the planet to influence US elections. So if your business wants to and has the financial means to reach a large swath of both consumers and voters, the Super Bowl and the Super PAC have got you covered.

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Category: Uncategorized     Tags: pac, politics, super bowl, superbowl, superpac

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Today the Gartner Information Management and Analytics Community held its weekly Twitter Chat, (Tweetchat, Tweetjam, TweetUp, whichever you prefer) to discuss concepts around big data, the role of the data scientist, and data quality. Over a half dozen Gartner analysts shared their ideas and research. (Where else can you get access to that many Gartner analysts in one place at the same time?) And dozens more individuals from other organizations also shared their perspectives and questions.

Big Data—Hey What’s the Big Idea?

First we discussed whether “Big Data” is an animal, vegetable or mineral, concluding that it has become very much a marketing term. Gartner analyst Andy Bitterer (@bitterer) jabbed, “Is Big Data nothing but a marketing play, since many organizations had ‘big data’ for a long time?’ Tim Elliott (@timoelliott) concurred, stating that “new terms arise because of new technology, not new business problems.” Esteban Kolsky (@ekolsky) thought the term was a more specific “marketing word used to describe the incredible volume coming out of social [networks].”

Yves de Montcheuil (@ydemontcheuil) suggested that organizations “have had Big Data all along but couldn’t get value out of it, except with lots of $$$,” and Gartner analyst Doug Laney (@doug_laney) agreed with a quip about Big Data being relative: “Big Data is merely data that’s an order of magnitude greater than data you’re accustomed to…Grasshopper.”

Hadoop was mentioned more than a few times as both an enabler and also a driver of big data, with Mark Troester (@mtroester) summing it up that the “hype of Hadoop is driving pressure on people to keep everything.” Some suggested archiving or even unloading data that is unused, but John Haddad (@JohnM_Haddad) and Martin Schneider (@mschneider718) both reminded everyone that data retention may depend on industry regulations and government mandates.

Some inquired about how to finding value in data so Doug Laney offered that there are two sides to that equation: 1) “looking beyond basic BI to advance analytics” and 2) “quantifying data’s potential and actual value.” Doug also summarized one of Gartner’s strategic planning assumptions for 2012: “Through 2015, >90% of business leaders say info is a strategic asset, yet <10% will quantify its economic value.” Gartner analyst Merv Adrian (@merv) admittedly had some fun with the notion of hidden value in data, asking, “Would it be a bad thing for organizations to say ‘Maybe there is value in the dark fiber of our information fabric?’”

The Art of Data Science

This led into a discussion about data science and the realization of data value. Gartner analyst Ted Friedman (@ted_friedman) wrote that it’s “good that analytics roles are becoming key, but ‘data scientist’ is a little bit elitist IMO.” Esteban disagreed contending that the term “scientist is not elitist, it defines a specific role.” Gartner analyst Carol Rozwell (@CRozwell) responded by suggesting, “But shouldn’t the average person be able to derive value from data?…[even though] some people refuse to see the truth in data.”

Nenshad Bardoliwalla (@nenshad) contended that the need for data scientists may be overblown. He believes that “Purpose-built apps can democratize making sense of Big Data for business folks without the need for data scientists (in some domains).” @Brett2point0 agreed, offering that “ideally end users should be empowered to explore their own data, seek their own insights through self-service.”

Gartner’s Doug Laney shared his analysis of current job descriptions for “data scientist” versus those for “BI analyst”. Key words in the “data scientist” job title include: design, knowledge, research, complex, learning, machine, models, problems, and performance; whereas top words used in “BI scientist” job descriptions are reporting/reports, company, technical, industry, user, sql, applications, and metrics. Tony Baer (@TonyBaer) and Doug agreed that communication is the skill that differs theoretical from applied science.

Mark Troester argued that someone needs to have “real intelligence to identify relevance and rationalize data,” and Jill Hulme (@jill_hulme) chimed that “a data scientist needs skills in math, engineering, writing, and a healthy dose of skepticism.” Adrian Bowles (@ajbowles) philosophized that a data scientist is like “a sculptor, finding a figure in material,” and that “Science is discovery, but not all who discover are scientists.”

Mopping Up with Data Quality

Finally we wrapped up with some thoughts on data quality in a Big Data context. Esteban claimed that “Big Data has compounded the [data quality] problem” and that now 40% of the data he sees now is bad. Seth Grimes (@SethGrimes) similarly lamented that “questionable data is the rule rather than the exception in my specialization areas: text and sentiment analysis.”

Yves thinks that “data volumes make it hard for traditional data quality architectures to keep up with big data.” However, Gartner’s Ted Friedman offered up another perspective that “data quality problems can be eased by big volumes in that individual flaws may have less impact when the data set is bigger.”

Mark Troester turned the idea of analytics on its head, recommending, “We shouldn’t just apply data quality for analytics, we should use analytics to help with quality.” He said he’s also “seen people so aggressive about cleansing that they cleanse away insight.”

When some participants suggested that data should ideally be cleansed at the source or when received, Doug Laney cautioned that “you can’t always cleanse data before storing it because of performance and the need to integrate and analyze it first.” Ted Friedman added that data quality is a “harder problem when organizations wish to use data they didn’t produce or don’t own it. The greater competency is assessing data quality…but that depending upon the usage and type of data, some you will still have to get nearly perfect.”

———-

Thanks again to the following individuals and organizations for their participation:
@ajbowles @arbeiza @berkson0 @bgassman @bikespoke @bitterer @Brett2point0 @briellenikaido @chirag_mehta @cpreston64 @cpydimuk @CRozwell @datachick @DataIntegrate @DavideCamera @decisionmgt @DivineParty @donloden @doug_laney @eIQnetworks @ekolsky @erao @EventCloudPro @furukama @howarddresner @iam_joshd @infanteAL @InformaticaCorp @jamet123 @JayMOza @jessewilkins @jill_hulme @johndavidstutts @johnlmyers44 @JohnM_Haddad @JSussin @juliebhunt @loranstefani @marciamarcia @merv @mschneider718 @mtroester @Natasha_D_G @NeilRaden @NekkidTech @nenshad @OhThisBloodyPC @pishabh @RobertsPaige @RomanStanek @rqtaylor @ryanprociuk @s_pritchard @seamuswalsh @SethGrimes @SocialMediaJeff @StacyLeidwinger @stevesarsfield @Tanvi_MR @techguerilla @ted_friedman @timoelliott @TonyBaer @userevents @ValaAfshar @Vivisimo_Inc @wiseanalytics @XeroxDocuShare
@ydemontcheuil

Please join or follow Gartner’s BI, analytics and information management analysts each Friday at 12:00pm ET on Twitter at #GartnerChat.

Note: Some tweets have been edited slightly in this blog to improve their comprehension and/or enhance context.

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Category: Uncategorized     Tags: analytics, BI, big data, business intelligence, data quality, data science, data scientist, hadoop, information management

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A major Wall Street securities ratings firm ignores the recommendations of a consultant report it paid for on rating collateralized debt obligations (CDOs)–contributing to the collapse of the mortgage industry, near-collapse of the banking industry and a multi-year global recession requiring $trillions in government (tax payer) dollars to avoid a full-blown Depression.

A major video game maker has millions of user IDs and credit card numbers pilfered, and spends many times more than was actually lost in revenue on bolstering its online security.

Thousands of credit cards belonging to Israeli citizens are exposed resulting in an actual military build-up in response.

A major retailer gets slammed by a Twitter and Facebook barrage then decides to implement a social media program.

A shipping line suffers numerous attacks by pirates off the Somali coast. They spend millions paying ransom, beefing security and reconfiguring routes.

The US Post Office continues to borrow from government coffers to run at a financial loss without making changes to its business model. Raising postage rates only exacerbates the problem.

And a an online shoe retailer announced yesterday the potential exposure of account information for as many as 24 million customers. What level of investment will they have to make to prevent this kind of event, let alone to identify and tie-up other loose ends?

True, major snafus are a part of business life, but knee-jerk budgeting in their immediate aftermath to prevent similar future incidents shouldn’t be.  In a recent online discussion of the topic I referred to this kind of behavior as “blunderfunding.” So let’s make it official:

blunderfunding [BLUHND-er-FUHND-ing]
verb

1. basing the level of investment in a business initiative upon the amount of loss incurred from a recent mistake or mishap
2. making a hasty outlay for a project to deflect or cover up for those responsible for a mistake
3. allocating monies or budget to fix a problem symptom rather than its actual cause

Origin:
Tweet by Gartner analyst Doug Laney on 13 Jan 2012

Etymology:
“blunder”: n. a mistake, v. to make a mistake
“funding”: [fund] n. a collection of money for a specific purpose, v. to allocate money for a specific purpose

While examples of enterprise-scale blunderfunding make regular headlines, it is also pervasive throughout lower levels of most organizations.  E.g. Buying “caution cones” to place when recently washed floors may be slippery–only after a hurried person or two did a back-side plant, or the overhaul of server farm air conditioning after overheating resulted in degraded online customer response times.

Some of these blunderfunded investments may be perfectly justified. That is the outlay is less than the risk-adjusted cost of their re-occurrence, and addresses the actual cause. In other cases the risk-adjusted loss (financial loss X the probability of re-occurrence) is much lower than the budget allocated to prevent any such problems in the future. Worse, and perhaps more frequent, money is allocated to fix, repair or even hide the symptom rather than resolve the root cause of the problem.

Organizations tend to compound the damage by neglecting to:

• calculate the actual economic loss
• estimate the likelihood of re-occurrence
• identify similar possible incidents
• compute the risk-based loss potential of future incidents
• discover the factors that led to this incident
• deal directly with the root cause(s), and avoid funding their resolution

What we’ve got here is also a recipe to avoid blunderfunding.

So why is it that we tend to see most blunderfunding is related to information mishandling, misappropriation and misuse? I believe this is because information asset are more easily accessed, more often in-movement, more easily transported. In addition, since information “theft” or “usage” almost never actually involves its depletion in any way (I.e. it’s merely copied not deleted), instances of information breach are that much harder to recognize. Finally, because information assets are not regularly covered by property rights laws, perpetrators if caught can get off easier than if they’d stolen actual “balance sheet” assets.

Just imagine, if you’re a criminal, what kind of loot would be better to heist than one in which:

• You steal it by sitting at your desk rather than scaling walls, dealing with armed guards or blowing up safes
• After you steal it, it still remains in place (as if nothing happened)
• You don’t need a fast truck to carry it off
• It is the kind of asset that increasingly makes up a large part of a company’s overall valuation
• Companies don’t measure its economic value, so they typically fail to manage or secure it with the same discipline as their traditional assets
• You can sell it multiple times to multiple black-market buyers (even on Amazon-like marketplaces)
• The courts only sometimes consider it to be covered under property laws

I’m not advocating cyber crime, just merely stating why organizations need to be proactive rather than reactive in securing their information assets, and to do so based on these assets’ actual computed value. The alternative is blunderfunding…and potentially more unwelcome headlines.

You can follow Doug on Twitter @doug_laney

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Category: Uncategorized     Tags: asset management, data security, funding, infonomics, information assets, information security, investment

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In the late 1990s, while a META Group analyst (Note: META is now part of Gartner), it was becoming evident that our clients increasingly were encumbered by their data assets.  While many analysts were talking about, many clients were lamenting, and many vendors were seizing the opportunity of these fast-growing data stores, I also realized that something else was going on. Sea changes in the speed at which data was flowing mainly due to electronic commerce, along with the increasing breadth of data sources, structures and formats due to the post Y2K-ERP application boom were as or more challenging to data management teams than was the increasing quantity of data.

In an attempt to help our clients get a handle on how to recognize, and more importantly, deal with these challenges I began first speaking at industry conferences on this 3-dimensional data challenge of increasing data volume, velocity and variety.  Then in late 2000 I drafted a research note published in February 2001 entitled 3-D Data Management: Controlling Data Volume, Velocity and Variety.

Fast forward to today:  The “3V’s” framework for understanding and dealing with “big data” has now become ubiquitous.  In fact, other research firms, major vendors and consulting firms have even posited the 3Vs (or an unmistakable variant) as their own concept.  Since the original piece is no longer available in Gartner archives but is in increasing demand, I wanted to make it available here for anyone to reference and attribute:

Original Research Note PDF: 3-D Data Management: Controlling Data Volume, Velocity and Variety

Date: 6 February 2001     Author: Doug Laney

3-D Data Management: Controlling Data Volume, Velocity and Variety. Current business conditions and mediums are pushing traditional data management principles to their limits, giving rise to novel and more formalized approaches.

META Trend: During 2001/02, leading enterprises will increasingly use a centralized data warehouse to define a common business vocabulary that improves internal and external collaboration. Through 2003/04, data quality and integration woes will be tempered by data profiling technologies (for generating metadata, consolidated schemas, and integration logic) and information logistics agents. By 2005/06, data, document, and knowledge management will coalesce, driven by schema-agnostic indexing strategies and portal maturity.

The effect of the e-commerce surge, a rise in merger & acquisition activity, increased collaboration, and the drive for harnessing information as a competitive catalyst is driving enterprises to higher levels of consciousness about how data is managed at its most basic level.  In 2001-02, historical, integrated databases (e.g. data warehouses, operational data stores, data marts), will be leveraged not only for intended analytical purposes, but increasingly for intra-enterprise consistency and coordination. By 2003-04, these structures (including their associated metadata) will be on par with application portfolios, organization charts and procedure manuals for defining a business to its employees and affiliates.

Data records, data structures, and definitions commonly accepted throughout an enterprise reduce fiefdoms pulling against each other due to differences in the way each perceives where the enterprise has been, is presently, and is headed.  Readily accessible current and historical records of transactions, affiliates (partners, employees, customers, suppliers), business processes (or rules), along with definitional and navigational metadata (see ADS Delta 896, 21st Century Metadata: Mapping the Enterprise Genome, 7 Aug 2000) enable employees to paddle in the same direction.  Conversely, application-specific data stores (e.g. accounts receivable versus order status), geographic-specific data stores (e.g. North American sales vs. International sales), offer conflicting, or insular views of the enterprise, that while important for feeding transactional systems, provide no “single version of the truth,” giving rise to inconsistency in the way enterprise factions function.

While enterprises struggle to consolidate systems and collapse redundant databases to enable greater operational, analytical, and collaborative consistencies, changing economic conditions have made this job more difficult.  E-commerce, in particular, has exploded data management challenges along three dimensions: volumes, velocity and variety.  In 2001/02, IT organizations must compile a variety of approaches to have at their disposal for dealing with each.

Data Volume

E-commerce channels increase the depth and breadth of data available about a transaction (or any point of interaction). The lower cost of e-channels enables and enterprise to offer its goods or services to more individuals or trading partners, and up to 10x the quantity of data about an individual transaction may be collected—thereby increasing the overall volume of data to be managed.  Furthermore, as enterprises come to see information as a tangible asset, they become reluctant to discard it.

Typically, increases in data volume are handled by purchasing additional online storage.  However as data volume increases, the relative value of each data point decreases proportionately—resulting in a poor financial justification for merely incrementing online storage. Viable alternates and supplements to hanging new disk include:

• Implementing tiered storage systems (see SIS Delta 860, 19 Apr 2000) that cost effectively balance levels of data utility with data availability using a variety of media.
• Limiting data collected to that which will be leveraged by current or imminent business processes
• Limiting certain analytic structures to a percentage of statistically valid sample data.
• Profiling data sources to identify and subsequently eliminate redundancies
• Monitoring data usage to determine “cold spots” of unused data that can be eliminated or offloaded to tape (e.g. Ambeo, BEZ Systems, Teleran)
• Outsourcing data management altogether (e.g. EDS, IBM)

Data Velocity

E-commerce has also increased point-of-interaction (POI) speed, and consequently the pace data used to support interactions and generated by interactions. As POI performance is increasingly perceived as a competitive differentiator (e.g. Web site response, inventory availability analysis, transaction execution, order tracking update, product/service delivery, etc.) so too is an organization’s ability to manage data velocity.  Recognizing that data velocity management is much more than a physical bandwidth and protocol issue, enterprises are implementing architectural solutions such as:

• Operational data stores (ODSs) that periodically extract, integrate and re-organize production data for operational inquiry or tactical analysis
• Caches that provide instant access to transaction data while buffering back-end systems from additional load and performance degradation. (Unlike ODSs, caches are updated according to adaptive business rules and have schemas that mimic the back-end source.)
• Point-to-point (P2P) data routing between databases and applications (e.g. D2K, DataMirror) that circumvents high-latency hub-and-spoke models that are more appropriate for strategic analysis
• Designing architectures that balance data latency with application data requirements and decision cycles, without assuming the entire information supply chain must be near real-time.

Data Variety

Through 2003/04, no greater barrier to effective data management will exist than the variety of incompatible data formats, non-aligned data structures, and inconsistent data semantics.  By this time, interchange and translation mechanisms will be built into most DBMSs. But until then, application portfolio sprawl (particularly when based on a “strategy” of autonomous software implementations due to e-commerce solution immaturity), increased partnerships, and M&A activity intensifies data variety challenges. Attempts to resolve data variety issues must be approached as an ongoing endeavor encompassing the following techniques:

• Data profiling (e.g. Data Mentors, Metagenix) to discover hidden relationships and resolve inconsistencies across multiple data sources (see ADS898)
• XML-based data format “universal translators” that import data into standard XML documents for export into another data format (e.g. infoShark, XML Solutions)
• Enterprise application integration (EAI) predefined adapters (e.g. NEON, Tibco, Mercator) for acquiring and delivering data between known applications via message queues, or EAI development kits for building custom adapters.
• Data access middleware (e.g. Information Builders’ EDA/SQL, SAS Access, OLE DB, ODBC) for direct connectivity between applications and databases
• Distributed query management (DQM) software (e.g. Enth, InfoRay, Metagon) that adds a data routing and integration intelligence layer above “dumb” data access middleware
• Metadata management solutions (i.e. repositories and schema standards) to capture and make available definitional metadata that can help provide contextual consistency to enterprise data
• Advanced indexing techniques for relating (if not physically integrating) data of various incompatible types (e.g. multimedia, documents, structured data, business rules).

As with any sufficiently fashionable technology, users should expect the data management market place ebb-and-flow to yield solutions that consolidate multiple techniques and solutions that are increasingly application/environment specific. (See Figure 1 – Data Management Solutions) In selecting a technique or technology, enterprises should first perform an information audit assessing the status of their information supply chain to identify and prioritize particular data management issues.

Business Impact: Attention to data management, particularly in a climate of e-commerce and greater need for collaboration, can enable enterprises to achieve greater returns on their information assets.

Bottom Line: In 2001/02, IT organizations must look beyond traditional direct brute force physical approaches to data management.  Through 2003/04, practices for resolving e-commerce accelerated data volume, velocity and variety issues will become more formalized and diverse.  Increasingly, these techniques involve trade-offs and architectural solutions that involve and impact application portfolios and business strategy decisions.

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Over the past decade, Gartner analysts including Regina Casonato, Anne Lapkin, Mark A. Beyer, Yvonne Genovese, and Ted Friedman have continued to expand our research on this topic, identifying and refining other “big data” and “extreme data” concepts. In September 2011 they published the tremendous research note Information Management in the 21st Century.  Only time will tell how long it takes for other organizations to seize upon these great new ideas as their own!

Follow Doug on Twitter: @Doug_Laney

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Category: Uncategorized     Tags: big data, data management

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Yesterday the Gartner Analytics Community held our first Twitter Chat or Tweet-Up (if you prefer) to discuss ideas around collaborative analytics, BI for “big data” and defining & aligning enterprise metrics. Over a half dozen Gartner analysts participated. Hey, where else can you get access to that many Gartner analysts in one place at the same time?  Many individuals from other organizations also shared their perspectives and questions.

Metrics Development and Alignment

First we discussed the notion that organizations need to do a better job at defining and aligning metrics throughout their various BI/analytic initiatives. We shared one of Gartner’s strategic planning assumptions (SPAs) for 2012, that through 2014 fewer than 30% of enterprises will align metrics completely with enterprise business drivers. Some departments may focus on growth-oriented metrics, while others on profit-oriented or customer service-oriented.

Rowing in the same direction is important, but analyst Bill Gassman (@BGassman) noted that there may be metrics specific to a business unit that help it optimize, but are not necessarily tied directly to overall business drivers. Bill also advised that organizations should learn the difference between metrics, measures and KPIs.

Other participants suggested that metrics developers should “read the annual report and start there [for] clarity and focus,” (analyst @AdamSarner) and cautioned that “metrics alignment drive behavior, and usually does [but] not only in expected ways.” (Bill Gassman).  Participant @Bob_Hetu warned about too many metrics–”focus should be on those particularly those not tied to driving revenue, profit and corporate strategies,” and participant @lorita observed that “BI sometimes builds walls.” Analyst Carol Rozwell (@CRozwell) noted that people sometimes go beyond or against established metrics if they think it’s the right thing to do. “Culture rules” she wrote. Analysts @IanABertram and @Doug_Laney advising that instituting a BI Competency Center is one way to help achieve collaboration, sharing and common understanding.

BI Challenges and Opportunities for Big Data

Next we chatted about the convergence of analytics and big data–how each one is both an enabler and limiting factor of the other.

Participant @Brett2point0 wrote that the biggest opportunity is “drilling down to unforseen insights, the ‘unknown unknowns.’” Bill Gassman observed that big data techniques are important and “at the leading edge, but still a long way to go with analyzing the data we have.” @Kalido_CEO suggested a particular technology, but Doug Laney countered, “Are tools the answer? We’re told by clients that finding people to model business probs is the bigger issue,” after which the conversation side-tracked a bit into the need for and role of the “data scientist.”

Analyst Jenny Sussin (@JSussin) observed that the big problem with analyzing big data is dealing with real-time data, and Ian Bertram questioned “Doesn’t it really depend on the speed of decision making within an org?” Doug Laney agreed, sharing that he’s “seen some orgs want data feeds an order of magnitude more frequent than they or their systems make decisions!”  Adam Sarner encapsulated it by writing, “Real time can be elusive. However, just in time or right time can be much more obtainable, relevant and effective.”

Analyst Rita Sallam (@rsallam) brought up that data bigness is about more than volume: “I think the biggest opportunities come from being able to apply analytics to diverse data.”

Participant @My_AYons highlighted the potential of RFID to “reflect price for a consumer in real time based on the basket.” Others agreed that retail is a fertile ground for big data analytics, and @pstonier suggested that it’s “possible to effectively make offers in real time…personalized in-store promos could be very effective.” Bill Gassman advised that real-time offers “don’t have to be perfect, just move the needle.”

Collaborative Analytics

Finally we chatted about what collaborative analytics means and what to look for in technologies and implementations.

Bill Gassman tweeted about benefits first: “A collaborative analytic process encourages transparency and provides an audit trail for how decisions were made,” and Carol Rozwell noted that collective intelligence is one promise of collaborative analytics, and a way to “fight low employee engagement.”

Doug Laney mentioned that he’d seen some BI tools that simply allow for report/chart annotation and sharing via email or shared workspaces, but that “other tools recommend users to collaborate with based on their role and analytic history” while you’re analyzing.

Rita Sallam advised that collaborative decision making (CDM) works “when there is a specific high-value line-of-business decision, e.g. portfolio optimization, vendor selection, collaborative forecasting.”  And seeing Rita’s perspective Doug added that “collaborative analysis (technology) isn’t the same as collaborative decision-making (process).”

@Armennajarian agreed with Rita that “the next wave of analytics will include an embedded social layer.” And regarding enabling collaborative analytics, Doug recommended that “Collaborative decision making needs a ‘peer review’ kind of mentality/culture/process.”

Finally, analyst Jenny Sussin concluded, “I feel sick…it’s either this #GartnerChat or the bag of sour gummy worms I have eaten during it.”

Looking forward to having you join our next #GartnerChat on BI/analytics. BYO gummies.

Follow Doug on Twitter: @Doug_Laney

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After a seven year hiatus from the analyst profession, it’s really good to be back. I can’t imagine any other job that allows, encourages and provides such a well-honed platform for one to learn, synthesize, create and influence.

Not that I’ve been ignoring BI/analytics and information management doings while I was away, but rather more fixated on particulars. Returning to the analyst ranks, however, I really had to “get my sponge on” again–soaking up everything, or as much as I can about the market. From new/updated technologies to new/acquired vendors to new/evolved techniques, it’s quite the mind-exploding effort. (This is where I plug Evernote.) Sometimes what’s new is new (e.g. mobile BI, NOSQL), but just as often what’s new is old (e.g. the 3Vs’ of “big data” I first wrote about at Meta Group over 10 years ago seemingly have become in vogue this year).

Then there’s the task of making sense of it all. What does it mean? Why is it happening? Which happenings are most important? What should be happening that isn’t? What will be happening that organizations should prepare for?  Thinking time is at a premium in this job. Thank goodness for (?) increasingly protracted air travel and a pre-teen kid who decreasingly want to be seen with me!  After this comes the fun part: developing ways to articulate and illustrate this analysis. I’m a bit geeked on frameworks, hierarchies, flows, metaphors, maturity models, etc. Powerpoint and lately Prezi are my best mates, but staring at a blank sheet of paper that needs a couple thousand words can be as unsettling as ever.

As for the influence part, I know we’re called “influencers” (I have already been invited to five vendor Influencer Summits in just two months.), but I’m perhaps a bit more altruistic about the term. The greatest part about being an analyst isn’t influencing buying decisions, it’s influencing client success–whether it’s technological, architectural or organizational advice for an enterprise client, or whether it’s product, marketing or marketplace strategy for a vendor client. Doing my little part to help our clients win, advance the marketplace as a whole and maybe nudge the US and global economies in the process makes this a feel-good job.

Finally, the chance to work again with some of the most brilliant minds in the IT industry is frosting on the cake. Nearly 800 analysts–each a top thought-leader in his/her respective slice of IT.  And daily I get to read what they write, hear what they say, and collaborate with them on research. Awesome!

It’s good great to be back.

Follow Doug on Twitter: @Doug_Laney

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