A Look at an Analytics Solutions Cloud Marketplace

The medium size business always needs help with choices.  They need easy to make choices that are as inexpensive as possible, and link directly to their current infrastructure.  That's what makes the choices less expensive and effective for them.  Analytics makes these same choices essential for making the modern business efficient.

" ... This week IBM announced a number of new offerings on the cloud, one of which being the IBM Cloud Marketplace. It was designed to be an easy access, technical shopping mall for all users (business through IT and development) to access solutions and technologies on demand. ... " 

Empowering the modern business.  Much more about that here.  I have started to look at the cloud market place.. Its a good start, but will need some additional work.  In particular a more direct link between specific problems and solutions.  That's the effectiveness part.  The small to medium size business does not have the resources required to do a great deal of tailoring of solutions.  The solution need to be quickly adaptable to their infrastructure, without large changes in what they do today.

Having a store to go to is a good start. It also includes some short explanatory pieces about the technology involved, making this store a good place to browse.  I would further suggest a glossary of terms that may also not be familiar to the browser.   Finally some broad statements of value by industries, likely via surveys, to show a company what their competitors are doing.  Giving incentive for browsing the offerings.

This post was written as part of the IBM for Midsize Business program, which provides midsize businesses with the tools, expertise and solutions they need to become engines of a smarter planet. I’ve been compensated to contribute to this program, but the opinions expressed in this post are my own and don't necessarily represent IBM's positions, strategies or opinions.  #MidsizeIBM

Smart Look at Hadoop and Big Data

Good piece by Tina Groves.   I have discovered that many technologists, especially in the small to medium sized businesses, do not understand the value Hadoop or Big Data.  Or how the methods work together with predictive analytics.  Here is a perspective from IBM, using their suite of tools.  Not universal, but useful to review.

It is also key to understand that analytics methods are most purposefully applied against decision processes. They do not always  need complex methodologies, but start with the simplest techniques, like visualizing data that is created by your methods.    Groves writes:

" .. For many people, big data is synonymous with Hadoop. Certainly, the ability to store and processing vast amounts of data on commodity hardware has fueled a new generation of applications. The synergies of Hadoop, fast and reliable internet and the increasing love for all things mobile have channeled heady investments in software as a service (SaaS) startups.

IBM understands the opportunity with Hadoop and addressing big data challenges. Early and continued investment in this area is why IBM has been consistently rated as a leader in numerous analyst reports, including the recent Forrester Wave on Big Data Hadoop Solutions. ... 

In the big data realm "data” means obtaining data as close as possible at the source, and then analyzing it for immediate action. IBM® InfoSphere Streams enables tapping into streaming data or data in motion where the throughput rates are from the thousands to millions per second. Whether that data originates from medical devices to monitor neonatal infants or from sensors to predict manufacturing yields, InfoSphere Streams has opened the door to exciting new applications of analytics. ... " 

This post was written as part of the IBM for Midsize Business program, which provides midsize businesses with the tools, expertise and solutions they need to become engines of a smarter planet. I’ve been compensated to contribute to this program, but the opinions expressed in this post are my own and don't necessarily represent IBM's positions, strategies or opinions.  #MidsizeIBM

Countering Fraud and Financial Crime

I was invited to a meeting on Fraud and Financial crime last week, but due to a number of issues could not make it.  I believe the detection of detecting fraud and how to counter it is an important one and is in particular a place where classic big data analytics can be used to address the problem. I have been involved in several such efforts long before there was the depth of data available today.   We have that data, but where do we start?

Addressing fraud is also an area where any sized company can get benefit.  The large company can employ expertise to address this problem and use the solutions attached to specific problems. Smaller companies often cannot make the same investments.  Its natural that they could leverage automated methods to discover and then address ninety percent of the typical fraud issues that arise.

The problems here are related to generalized compliance activity as it connects to regulations. It can be addressed by constructing a portfolio of most important and likely fraud issues, then taking them in the order of their possible risk and return. Also understanding the costs involved to detect and remedy each component.  An agility also of value to the smaller company.

Below are some documents that cover the meetings, and some comments I provide.   First the NYTimes article: IBM Launches New Software and Consulting Services to Help Organizations Tackle $3.5 Trillion Lost Annually to Fraud and Financial Crime

The meetings can be traced on Twitter with #counterfraud:  https://twitter.com/#Counterfraud

 Read the blog post: http://bit.ly/1eSljbP

 And the related video: http://bit.ly/1qQ0OEV

 Also a supporting infographic: http://bit.ly/1mg0UFg

And the supporting press release: http://ibm.co/Oy94dH  Excerpt:

" ...  IBM launched its “Smarter counter fraud” initiative, drawing on the expertise and innovation from more than 500 fraud consulting experts, 290 fraud-related research patents and $24 billion invested in IBM’s Big Data and Analytics software and services capabilities since 2005. The initiative extends IBM’s leadership in Big Data and Analytics and Cloud to help public and private organizations prevent, identify and investigate fraudulent activities.    ... " 

Continuing to follow the threads above.  Will have more comments in this thread as I digest the approaches mentioned.

This post was written as part of the IBM for Midsize Business program, which provides midsize businesses with the tools, expertise and solutions they need to become engines of a smarter planet. I’ve been compensated to contribute to this program, but the opinions expressed in this post are my own and don't necessarily represent IBM's positions, strategies or opinions.  #MidsizeIBM

Strategies for Businesses Using Data Visualization

Specifically the article covers Small and Medium Sized Businesses. (SMBs)   But the recommendations are good for any new and emerging technology. And a large business has probably dabbled here before, these are mostly about making quick, efficient and correct decisions. With that in mind, good quick read for anyone.

What's Watson for Midsize Companies?

Back on January 9th I attended the IBM announcement of its group for Watson applications.  I tweeted the announcement at @FranzD #IBMWatson.  Full announcement here.  The Watson group ecosystem site. It had been expected for some time, but I was surprised by its scale.  A billion dollars, a  location in NYC, a couple of thousand people. Partners in a number of big contexts, like health care. A very big investment.  I have some former colleagues there and hope to get an insider 'tour'.

At the big enterprise we were not unaware of their progress in this area.  In the late 1980s we had our own artificial intelligence / expert systems efforts underway.  How do we leverage massive enterprise expertise?  While Baby boomers retire?  Premature, but producing very real value.  At that time we talked to IBM and got the impression that they had made good  progress.  The science was already there, pioneered by academics at MIT, Stanford and Carnegie Mellon.   The science was there but the connection to real computing systems and data had just begun to mature.  IBM made that happen in the coming years.  Watson provided a demonstration.  We waited.

So now it seems, based on this investment, that we are ready to proceed.  But the midsize company is not an enterprise, and cannot spend millions of dollars promoting these approaches.   But these methods, anchored in the cloud, help companies because it can allow access to both the methods and the large and volatile databases required to drive these systems.  The systems also need access to unstructured language models (ontologies), volatile big data sets, and publicly available data bases to drive the models that will form the bedrock of any intelligence.  Sounds like Big Data.  Without this data you cannot leverage intelligence.

IBM is setting the stage with this ecosystem. making the right steps to move forward.  I look forward to being involved in making this available to the midsized company.  I will follow with more impressions in this space as this work evolves.

More on Watson in this blog.


This post was written as part of the IBM for Midsize Business program, which provides midsize businesses with the tools, expertise and solutions they need to become engines  of a smarter planet. More here.  

Staying Secure in the Cloud

As the owner and operator of a consulting business I am acutely aware of the need to readily exchange information between people and multiple devices to improve communications.  For a long time we were forced to exchange information via email, but as email use has grown, keeping track of all these exchanges became difficult.  Also, we need to be able to exchange value and services in discrete quantities to clients.  This has led to more software as a service, and also to access to remote, growing and volatile  databases used to solve analytic problems.

Bottom line, the sharing of data and services in a what is now called a Cloud has become essential for getting business done.  Especially for the small to medium sized business.

But as part of this growth,  the danger of security has become crucial.  It is another means to lose data, to give up control, to not be sure of the integrity of what we share.   This is not only an issue of keeping our business working, but also has serious regulatory implications in some industries.

I was pointed to a Slideshare recently provided by Midmarket IBM that addresses this topic: Staying Secure in the Cloud: Four Tips For Midsize Businesses   A short 8 slides that is worth a look for any sized business.

The overview is simple.  Clouds DELIVER with agility and speed.  but opening your system up in this way is by its nature risky.  So what do we do?

The recommendations come down to key and very straightforward, largely common sense recommendations that help you protect your cloud environment.   I think the initial slides, addressing the basic issues, do a good of of setting the stage.  Not every mid size business will need to go further.   I like the method of positioning a problem simply first.

The slide show ends with the ability to download a white paper Integrated IT Security for Midsized Businesses.   If you need more information.

This post was written as part of the IBM for Midsize Business program, which provides midsize businesses with the tools, expertise and solutions they need to become engines  of a smarter planet. More here.  

Real time Cloud Retail Data Analysis

This site was recently brought to my attention.  I am currently in the process of helping a retail marketing operation better understand how well it is doing when doing promotions under varying conditions.  IBM Digital Analytics Benchmark Hub: Your source for real-time cloud-based online retail data and analysis.  As they describe it further:

IBM Digital Analytics Benchmark
View performance benchmarks for your peers and competitors to help you uncover opportunities to grow your digital marketing and web properties. ... 

In general, the small to medium size business has a more difficult time in getting the data to understand how their business is operating.   In the enterprise we had  many sources of data and expertise. In the small to medium sized firms you track your own sales, but suppose you are trying to understand how well your promotional activities are working.  What promotions should you use in the future, what are your competitors doing, what are the demographics of your buyers? All key questions to ask.  New analytics exist to measure each of this questions, and the site includes tools and articles about them.   They point me to a study:

Read the study: How Marketing is Taking Charge: Leading the Customer Experience
Learn what leading marketers are doing to differentiate themselves in a perpetually shifting omnichannel world. .... 

Which highlights the fact that we are heading toward a very multi channel world.  Also a world where it is not about which channel we use, but how many channels we use to support a purchase and even what order we use them in.

This is all about leveraging data and analytics.   In rapidly changing conditions.   Benchmarking the results against your business sector, and helping you plan for the future.

An interesting place to start.  I plan to give it a try.

This post was written as part of the IBM for Midsize Business program, which provides midsize businesses with the tools, expertise and solutions they need to become engines of a smarter planet. I’ve been compensated to contribute to this program, but the opinions expressed in this post are my own and don't necessarily represent IBM's positions, strategies or opinions.  #MidsizeIBM

Questions for the C-Suite

The earliest experiences I had with the global enterprise were working directly with the CEO.   He had built a system that ran on an early desktop, I helped with its expansion.   In that early work I learned that even back then the C-Suite cared about the direct use of computing power to help their decision making.  They also much cared about the data that would drive these systems.  There was a big gap in time before executives cared as much again.

IBM, as part of their Institute for Business Value asked a lot of executives about how they interacted with technologies. And also with each other about technology. And where they thought the opportunities and challenges existed.   This was the first attempt to study the entire C-Suite, and it was interesting to see how this fragmented with C Suite responsibilities. That had changed much since my first work with them.     ....  The scale was impressive:

" ... We now have data from more than 23,000 interviews stretching back to 2003.

Our latest study draws on input from:
Chief Executive Officers (CEOs) 884 Chief Finance Officers (CFOs) 576 Chief Human Resources Officers (CHROs) 342 Chief Information Officers (CIOs) 1,656 Chief Marketing Officers (CMOs) 524 Chief Supply Chain Officers (CSCOs) 201 ... " 

In an interesting aside, IBM's Watson AI system was used to analyze the interview data.  I look forward to hearing more about that.

It is a remarkable study.  Which also emphasizes the current opinions and their forecasts for the near future.  While it probably means most for the complexity of the large enterprise, it gives lessons to Mid Market firms as well.  In one paragraph they provide a good summary:

" ... How do you view the world?

CEOs consider technology the single most important external force shaping their organizations. Other CxOs also see it as one of the top three factors. The members of  the C-suite are likewise united in believing that an entirely new set of dynamics is emerging. 

Customers and citizens expect to be treated as individuals, which means knowing what makes each of us “tick”:  our values, beliefs, habits and quirks. That, in turn, requires much closer collaboration between organizations and  the people they serve.

Most CxOs recognize that what applies to customers  and citizens applies to employees and partners, too.  They envisage that organizational boundaries will become far more porous, enabling greater collaboration with employees and partners to accelerate innovation. They  also anticipate sourcing more of that innovation from outside. Where once an enterprise could go it alone, and  be successful doing so, it must now collaborate. .... " 

There is much detail beyond that, each using a small pie chart to indicate the changes over time.   I don't think the pies where necessary, but they were often joined by bar and time charts that indicated change that had occurred.

I attended the preview of the study, a good overview,  it would have been better to add a few case studies about C-Suite communications of data and responsibilities.   I was also surprised that little was said about funding, is it more of an issue in a world where we are automating more interactions?   How do changes in the economy change investment in digitization?   A nod to investment, which could have helped understanding how different size organizations would behave.

In particular I liked the statement about the future of these interactions:

" ... Pioneer digital-physical innovation:

The emergence of social, mobile and digital networks has played a big part in democratizing the relationship between organizations and their customers. It’s also forcing them to rethink how they work. Some 60 percent of CxOs now look to partners who will have an equal hand in creating business value (see Figure 4, foldout). And almost half are sourcing innovation from the outside .... "

Overall a good study.  provocative read.  You can get to the whole study (with registration) here.  

 Also , see their Android, IOS App: IBM IBV which covers the study and on Twitter:  @IBMIBV  I have found their stream quite interesting.

This post was written as part of the IBM for Midsize Business program, which provides midsize businesses with the tools, expertise and solutions they need to become engines of a smarter planet. I’ve been compensated to contribute to this program, but the opinions expressed in this post are my own and don't necessarily represent IBM's positions, strategies or opinions.  #MidsizeIBM

Focusing Effort with Data, Leveraging it with Analytics

In preparation now for a talk to local executives on the use of analytics in the enterprise I reviewed some of the work done with Business Sphere.   Its been some thirty years since I arrived at a global soap company, degree and government experience in hand,  to see how I could help them with improving their manufacturing and supply chain.  I finished my direct work there thirty years later, with a three year hiatus in a startup.

Analytics then was called Operations Research, and we applied the mathematics and the principles of the approach in many places.  Early on we were introduced to the concept of focus, and how systems could provide that ability at every level of the company.  One of the first examples we explored was how the top executives used computing power.  At the time it was unheard of for executives to have computers available directly to them, it was their admin people than ran their email, made sure they got the right reports, and were altered to key issues by phone call.

In those first efforts, we worked with the CEO to develop a method by which he would get information he needed for specific tasks.  We had not yet reached a world where we could organize information to make it easily and directly usable by the decision maker.  Make it as easy as possible to do their job with the right data.

Then, years later, as software and hardware became pervasive and mobile, a key extension was understood.  People work as individuals, and in multiple groups.  So why not give every person, every group, of any size, from new hire to CEO, the right data to do their job?  This gives you the advantage of doing their job, with the right data, as efficiently as possible.  It was also discovered that this would also focus their jobs in new ways.   Data can drive the work.  Analytics can be added to sculpt and leverage  the data.   The work is not done, it continues to move on under new names and implementations.  Stay tuned for new progress.

For more about this see my previous writings about what is now called Business Sphere.

This post was written as part of the IBM for Midsize Business program, which provides midsize businesses with the tools, expertise and solutions they need to become engines of a smarter planet. I’ve been compensated to contribute to this program, but the opinions expressed in this post are my own and don't necessarily represent IBM's positions, strategies or opinions.

Big Data Myths and Misconceptions

I am in the midst of preparing an executive presentation about the use of analytics for companies in our region.    Our group consists of experienced practitioners from academia and industry.  As part of our preparation we have been gathering examples from dozens of years of applications.   I have noticed something interesting, all of the people involved are avoiding the term 'Big Data'.    We all preferred to talk about the improvement of business, then about the data, big or small,  that will make that possible.   Analytics.

But you can't avoid the term, its heavily hyped.    And it makes sense.  If you are gathering huge quantities of volatile data, it makes sense to mine it to find leverage points for your business.  But you have to understand your business process first, or else you are finding solutions for problems you do not have.

The small and medium sized business has a particular dilemma.  They don't have the funding to experiment with their data.   So they have to be focused before they start.   Expensive consultants to carve through the hype is not an option.

I recently heard a 16 minute podcast by Tom Deutsch ( @TomDeutsch) , program director of big data and analytics at IBM.   He also writes in  IBM's IBM Data Mag   In this podcast he addresses recently emerging myths.

I liked his presentation, well done and nontechnical.  I like the content of the talk, and like him I am also a skeptic on hype driven claims for Big Data.   Rather than state the myths, below are the contrary facts as I would state them.

- Although much Big Data work has been on unstructured data like text, video, etc.  It can certainly be used with structured data as well.  Consider it a test bed for trying analytics ideas on any data.

- Big Data does not inherently have quality problems.  Any data must be verified to determine if it is the required quality.  Garbage in, Garbage out.

- Machine learning, often a goal of Big Data efforts, does not eliminate human bias.  Humans still select data,  implement the results of the analytics, etc.  So human bias remains.

- Machine learning does not occur in real time.  These kinds of analytics need to be iterated on, adjusted and ultimately implemented.  Its possible that the results may be implemented in real time systems, when verified.

It seems all of these hyped claims from misunderstanding marketing types.  It also stems from the term 'Big Data' itself, which has magical capabilities attached to it.  That can sell consulting,  I will state it again, if we are completely honest, we should change the term used and call it Analytics.   Analytics that can now be done more efficiently for larger and more varied kinds of data.  Driven by improvements in hardware, software and connectivity.

I further like his statement, always good advice, even when talking to big consulting companies:
"Anyone who makes assertions and is unwilling to engage in a discussion or provide evidence for what they say, is probably someone who doesn't really know what they're talking about. Be very skeptical."

This post was written as part of the IBM for Midsize Business program, which provides midsize businesses with the tools, expertise and solutions they need to become engines of a smarter planet. I’ve been compensated to contribute to this program, but the opinions expressed in this post are my own and don't necessarily represent IBM's positions, strategies or opinions.

Interview on Big Data and Analytics for MidMarket

Friend Paul Gillin runs a Big Data Google Plus Hangout on the use of Big Data methods for Midmarket and beyond.  The 37 minute stream brings together current users of these ideas.    As a practitioner myself it has always been intriguing to see how this is now playing out at all levels of use.  In the past it was companies like Procter & Gamble and IBM, who could make these solutions work, now the possibilities have increased considerably.

The interview brings together three practitioners  of the use of analytics for improving business. Two areas of often mid market applications are discussed.  Biometric data in hospitals and marketing data to improve sales operations.

I mostly deal with is commonly called advanced analytics, so it was good to see the examples discussed here that use relatively simple, often just visual methods to get to solutions.   And emphasize the understanding of how companies make decisions.   Again I emphasize starting with the most simple methods.

Another topic discussed was how the Internet of Things is starting to create large amounts of data, in particular in health systems, and leading to big and complex data to be mined and leveraged.  In the case of hospital systems, the Internet of Things can include patients, monitors and diagnostic devices and a complex array of data types including imagery and quantitative measures.   Part of this system would be unstructured data such as text, such as doctor's observations that need to be assembled and attached to more structural data.

Once the data has been captured and simply presented  you can start to consider specific mining techniques to look for deeper points of value.  Every business has value opportunities.

In the final part emphasis is made of getting away from the need for data specialists.   Instead using tailored systems for particular industries, and then selling the analysis as a service.  Even suggesting that intelligence systems like Watson could drive the analysis,

Good piece to listen to to see what is happening in the field today that is important to the small and mid market company.  With pointers to the future.    This blog also covers the topic.

This post was written as part of the IBM for Midsize Business program, which provides midsize businesses with the tools, expertise and solutions they need to become engines of a smarter planet. I’ve been compensated to contribute to this program, but the opinions expressed in this post are my own and don't necessarily represent IBM's positions, strategies or opinions.  #MidsizeIBM

Cloud, Data, Analytics and Process for SMB

Brought to my attention,  In The Financial Express, from Jyothi Satyanathan, director Mid-market & Inside Sales, IBM India/South Asia.  I much agree with his opening statement:

" ... Small and mid-size businesses (SMB) are some of the fastest adopters of IT, as they clearly see the immediate benefits in growing their business and becoming more efficient. IT solutions providers recognise the opportunity in the market to accommodate SMBs in a big way among customers. Today the world is experiencing another sweeping change—powered by big data analytics, mobile, social and cloud. These foundational technology shifts are inspiring forward-thinking leaders from smaller businesses to drive change in how decisions are made, redesign how their teams work, and reassess how to serve the individual consumer. ... " 

He goes on to say that big data and analytical techniques are now available to Small and Medium sized Businesses (SMB).   Yes, this is revolutionary.  The Cloud allows the use of these technologies for any sized business.  Adding a strong flexibility capability.   In the past SMB's could not spend what was needed to buy the servers, license the software, and deliver the results to its people.   Now they can also pay for only what they need. The maintenance and security can be outsourced.  All of these capabilities are there, at a bargain.

A good example:  In a recent project we were asked to deliver analytical methods to tablet based systems.  The Small business was not ready to license multiple seats for visualzation software, or the analyical packages needed to test their efficiency ideas.  But the use of R, a free open source system that readily links to databases, Big Data and other platforms, could be readily be used to test out ideas, and then migrate these ideas to their core systems.

He goes on to discuss the key element of efficiency and some good examples:   " ... the use of cloud technologies is enhancing efficiency manifold as it enables the placing of marketing assets such as digital files of campaigns, videos in the cloud so that all involved parties can create, modify, use and share as needed. Marketing data on the cloud makes it easier for marketing staff to access information anytime and anywhere on the field. ... " 

And ultimately the use of Big Data.  Here I think there is need for some caution.  Start with small and less complex data.  Place your data in a 'sandbox' to test new ideas.  Find some real wins you know you can make happen.   Determine what kind of data you need, have or can acquire.  Make sure you really understand the business decisions being improved ...   Make sure the small scale experiments you complete provide real value, and then scale up.   Along the way,  set up the architecture to repeat this, and then do repeat it!    I agree there is a big contribution potential for Big Data here, as he says:

" ... The major contribution cloud makes to the SMBs ecosystem is big data analytics. Like larger enterprises, SMBs stand to gain from understanding the importance of big data. Big data can arm small businesses with insights and capabilities by analysing data surrounding their business, creating new ways to operate more efficiently, find new customers and more importantly improve bottom-line results. ... "

This post was written as part of the IBM for Midsize Business program, which provides midsize businesses with the tools, expertise and solutions they need to become engines of a smarter planet. I’ve been compensated to contribute to this program, but the opinions expressed in this post are my own and don't necessarily represent IBM's positions, strategies or opinions.

Going Global Faster with Cloud and Mobile

I have been in the midst of looking at how companies use mobile, focused data, cloud and ultimately analytical support.  So have developed interest in how all these elements work together.

An article by IBM Midmarket General Manager John Mason recently published “When Small Businesses Use Cloud and Mobile to Go Global” on IBM’s Smarter Planet Blog was pointed out to me.  It suggests that the Cloud and Mobile were causing company globalization at a faster rate than every before.

I knew that focusing data could speed agility, and that it was natural to deliver it with mobile solutions,  Especially when data was changing rapidly due to context or domain volatility.  Added analytics with the right cloud access can result in value added solutions.

But I had never considered that cloud and mobile would lead to faster globalization.  That was a provocative thought I had to consider.   Agility is the key.  Managers traveling into new areas, gathering new data, but constantly needing information and analytical methods to support their
needs.

I had seen an example where a retail compliance system linked images taken on site could be quickly transmitted to experts at headquarters, analyzed and questions sent back to the aisle.   In another example presentations based on demand volumes were developed by artists and shipped to people on site.  Agility and the ability to transfer analytical solutions to the field based on data from the field.   In another proposed project, construction of local buildings by contractors was checked by distant engineers.  International compliance examples are common and ache for this integration of methods.  This is agility and speed by linking distant expertise.   Available now to all sizes of businesses.

Besides the software provided, the social constructs are also invaluable.  To quickly determine the answer to key questions, local needs and communications methods.   I find this mobile agility useful when traveling even for my personal needs.  Imagine its value when focused for a business.

So I much like the idea and if we prepare for global and agile integration using cloud, mobile and analytics methods, we can progress in the right direction.    Faster and with fewer errors. I will think of  this for future applications.

This post was written as part of the IBM for Midsize Business program, which provides midsize businesses with the tools, expertise and solutions they need to become engines of a smarter planet. I’ve been compensated to contribute to this program, but the opinions expressed in this post are my own and don't necessarily represent IBM's positions, strategies or opinions.

Customer Engagement Analytics for Cultural Venues

Its all about engaging the customer.   All companies, large, small and midsize need better ways to engage with and retain  their customers.  And as the context of engagement varies over time, how they engage will have to be adapted to new customers and their needs.

I was recently sent some information from the IBM Midsize smarter commerce group that addresses exactly these issues.   How do millennials use and engage with cultural venues, typically midsize enterprises?    Two excellent examples here: The Point Defiance NY Zoo and Aquarium, and  The Colorado History Center.   In each link there are embedded videos which describe their experiences using big data.  These examples are also good general useful case studies of the general use of data mining of experience in data.    Recall also the example  I wrote  about the Cincinnati Zoo.  Above an infographic expressing the relationship beween Big Data and Cultural Venues for Millennials.

This post was written as part of the IBM for Midsize Business program, which provides midsize businesses with the tools, expertise and solutions they need to become engines of a smarter planet. I’ve been compensated to contribute to this program, but the opinions expressed in this post are my own and don't necessarily represent IBM's positions, strategies or opinions.

Big Data at The Speed of Business

I was just informed of this online broadcast, done twice today,  4/30. I plan to attend and tweet.

Follow with hashtags #bigdatamgmt and #ibmsmb .... 

 " ... Today, there are exciting opportuniies to take advantage of all data — including data that was once considered too "big" — for faster insights and agile application development, all at reduced costs. Join us on April 30 for a one hour online broadcast as IBM unveils a new generation of data solutions ..... 

11AM – 12PM EST, April 30, 2013 or  9PM – 10PM EST, April 30, 2013
Hangout URL: http://goo.gl/2252N

Bob Picciano - General Manager, IBM Information Management Software
Inhi Cho Suh - Vice President of Product Management & Strategy, IBM Information Management Software
Martin Wildberger - Vice President of Development, IBM Information Management Software
Kent Collins - Database Solutions Architect, BNSF
John Schlesinger - Chief Enterprise Architect, Temenos
Michael Kowolenko, PHD - Principal Research Scholar, North Carolina State University

Additional Background information around IBM and Big Data:  http://goo.gl/aP6Sj ... "

Owning Social Media in the Enterprise and Beyond

Who owns an employee social media account?  In an excellent blog post Debbie Laskey brings up an excellent question that leads me to a number of related questions.  Some years ago our enterprise talked to IBM about how they were developing 'social' methods to improve communications within their company.   Their method took it beyond the organization white pages we had computerized as documents to be searched to find fellow employees with similar ideas and address skill needs.  We had developed an in house advanced email and collaboration system called Confer in the late 1970s, so you could communicate with people, but it was still difficult to find the right people outside of our own network.

At that time Twitter was mostly being made fun of, but we were experimenting with social media like blogs and Wikis, but had not thought about how to tie that all together into the structure of the organization.    Everyone agreed that Email should be informal, but it was usually not so.  When we tried to include personal and informal communications, it was criticized by some as wasting time.

So there was a big surprise when social media like Twitter took off.  It was a new kind of communication;  short and mostly to the point.  It was akin to the 'one page memo', in that it sought  to be as concise as possible.   But often did not include all the points that needed to be made in communication.

We were also surprised when the social media extended over the border of the enterprise and directly to the consumer.  This was unheard of.  There were several issues that did come up based on bridging this frontier.  There was no plan to manage it at first.  People started communicating internally, with suppliers and even directly with consumers.  As Debbie suggests, there should be a plan, even in an informal world.   Who are the stakeholders, who owns the communications, and what do we when communications create problems?  Here I would further recommend Paul Gillins's book: Attack of the Customers ...  for anticipating and answering issues that extend beyond the borders of the organization.  This is even more important for the small and medium sized company.

This post was written as part of the IBM for Midsize Business program, which provides midsize businesses with the tools, expertise and solutions they need to become engines of a smarter planet. I’ve been compensated to contribute to this program, but the opinions expressed in this post are my own and don't necessarily represent IBM's positions, strategies or opinions.

Cincinnati Zoo is a MidMarket Engine

In an interesting development our local Cincinnati Zoo has been declared IBM midmarket engine of  the week.   I have been there many times, its a haven for things zoological and also botanical.  Both interests of mine.  It is always fascinating to see how the backroom of any operation works.  And Zoos are no different than others.  They are complex in many different ways.  They are a business, an enterprise with customers and assets, a research facility and scientific repository.

So it is natural that they have to deal with complex data challenges, and have a desire to utilize analytics to make predictions about their systems.  They would also like to be able to market to their loyal customers by tailoring services to them to ensure that the community will continue to visit and support.  You don't typically think of a zoo this way, but in these times all enterprises need to be smarter.

It is a great example of the midmarket enterprise.   All of which have similar challenges.    IBM has worked with Brightstar Partners to build a data warehouse and utilize that data to perform predictive analytic  operations to better solve their problems.  This has led to a 35% increase in food and retail sales.   And an expected increase of 50,000 visitors a year.  Very good for a midmarket operation that has been around since 1873.  I look forward to visiting it again.

This post was written as part of the IBM for Midsize Business program, which provides midsize businesses with the tools, expertise and solutions they need to become engines of a smarter planet. I’ve been compensated to contribute to this program, but the opinions expressed in this post are my own and don't necessarily represent IBM's positions, strategies or opinions.

Thinking Through the Forecast

I just caught this article in the Insiders Group blog and thought I would comment about our demand forecasting experiences and how they can be applied to the midsize business.

About the sales forecast.  Or for that matter any forecast.  In my own experience this has yet to be solved generally.   We worked for years aiming to perfect the methods used.  It is often not so much about the analytical technology you can apply, but the number of contextual influences you can include.   Can you include the influence of promotion, of the economy, of competitor activity?  Depending on the industry cyclical and the changes in fashion are also important.  The article states:

" ... Instead of relying on gut feelings and hope when forecasting , top-performing companies in Aberdeen's research are 46% more likely than all others to perform regular sales pipeline modeling and simulation exercises.

On a tactical level, predictive analytics can cut down on the end-of-cycle demands for C-level support to close deals that their reps claim are "THIS close to the goal line!" In reality, there are only so many opportunities that merit high-level help, volume discounting, and the other forms of late-stage motivators.

Accurate forecasts have benefits across the business. For example, the folks who run purchasing, inventory, logistics, supply chain, operations, and even human capital management, can dramatically benefit from realistic sales forecasts that helps them more efficiently plan for their own activities post-sale. .. ." 

Good thoughts,  and I will add a few.  First is that you need a single set of forecasts, so that all the company is being driven from the same numbers.  You need to frequently calibrate the numbers, as the context of your markets change.  You also need either a corporate economist, or access to good econometric models.  This last point has changed radically in the last few decades.  When I arrived at the enterprise we had a room full of corporate economists.  When I left we had none, and had outsourced the entire econometric process.  This created several problems in our ability to deal with  changes proactively.

What does this mean for the small to midsize company?  The forecast is very important to the Midsize, even more important because minor changes in forecasts can severely hurt the small business.  A close linking between true business process and forecast is also important.  A forecast should be a key part of the business process model, so it can help direct next steps and cautions.  Make sure there is a business model, and you know where it links to sales and demand forecasts. Make the forecasts count, and continually re calibrate them.  Any technology choices should support this process and be clear to executive using the results.

This post was written as part of the IBM for Midsize Business program, which provides midsize businesses with the tools, expertise and solutions they need to become engines of a smarter planet. I’ve been compensated to contribute to this program, but the opinions expressed in this post are my own and don't necessarily represent IBM's positions, strategies or opinions.

Compost Analytics for the City

This is a classic example of vehicle routing using analytics.  I have worked on problems as disparate as ambulance routing in semi rural counties and routing log trucks to pulp mills, to minimizing the travel of fork lifts in product warehouses.  We called these methods 'operations research'.  They are about doing things smarter, by being faster and cheaper.

Examples where the profit margin of an operation is small to begin are particularly interesting. This video outlines the example of gathering trimmings and cast offs from San Francisco restaurants and efficiently getting the resulting compost to vineyards to use for fertilizer.  How should I route trucks though complex city streets, do pickups and then get the result to far away vineyards while minimizing costs? It is all about becoming smarter using analytical methods.

The company doing the work to take trimmings from recycle bins to composting center to vineyard is done by Recology Inc  with data and analytics help from IBM Partner Key Info Systems. More on that collaboration.

Problems like routing are combinations of data, timing, resource and goal management.  With the data often changing based on the dynamics of streets, labor and schedule.  All needed to ultimately feed the smarter city.  Mining data for doing something effectively is not unlike mining minerals. Or mining compost for its value in agriculture.

More about the analytics dimensions of these problems.  Also this blog often covers the topic of analytics for any size company.  Posts about that here.   #Midsize

This post was written as part of the IBM for Midsize Business program, which provides midsize businesses with the tools, expertise and solutions they need to become engines of a smarter planet. I’ve been compensated to contribute to this program, but the opinions expressed in this post are my own and don't necessarily represent IBM's positions, strategies or opinions.

Cheesecake Factory Restaurant Analytics

The Cheesecake Factory delivers an exceptional brand experience with IBM Big Data analytics http://goo.gl/Mfn5c #IBMPWLC

I had yet to see a restaurant application of this type, so it was fun to see the linked to video.  I have eaten at the Cheesecake Factory a number of times, and when in a business I always look for indications of data gathering and process applications. It is always interesting to see how this can be linked to midsize business needs.

Cheesecake Factory is a global company that serves 80 million guests a year, with over 200 menu items, hardly a small company.  How do they deliver a unique brand experience that meets customer expectations?  In  my own visits I was singularly impressed by their process, though I could not see how they used data to achieve it.  Clearly they had dound out how to deliver quality consistently. This short video and provided a look at the data back of the house.   A chef myself, I wanted to see how big data contributed to the experience.



As every chef knows, its all about the quality assurance of what goes into the food.   They also seek to make the experience as consistent as possible, regardless of the location of the restaurant.  IBM and partner N2N Global  have put together a strategy that provides " ... ERP, Tracebility, Quality and Food Safety, and Business Intelligence Software integrated in a single solution for the Food Supply Chain. ... " 

This is by its nature a big data problem. Multiple sources of supplier information about key ingredients that is being updated constantly. Volatile data because it needs to address such questions as traceability and food safety.  Usage information in each region to assure the highest quality ingredients to meet restaurant specifications.  Feedback from the users of the ingredients to assure continued quality.   Consistent  analysis of costs to assure profits.  That's lots of data, and lots of places where that data can be used to streamline the process. Analytics.

This post was written as part of the IBM for Midsize Business program, which provides midsize businesses with the tools, expertise and solutions they need to become engines of a smarter planet. I’ve been compensated to contribute to this program, but the opinions expressed in this post are my own and don't necessarily represent IBM's positions, strategies or opinions.