One of the technical concepts that intrigues me though is spinning disk or flash drives vs. in-memory as primary means of storage for data management platforms. Spinning disk is approaching a price point that should be considered “cheap”…err… financially inexpensive. In-memory is smoking fast…errr… achieves a superior level of performance. Using flash drives, “spinning disk” is getting much faster. With the falling price of memory and the increase in maximum server memory approaching 1TB per blade, in-memory is not only fast, but becoming more affordable and applicable to multiple use cases.

The ancient Greek playwright Aeschylus is credited as saying:

“Memory is the mother of all wisdom.”

It is entirely possible that in-memory databases will be that gateway to all “wisdom” as memory capacity continues to rise and memory prices continue to decline. However, with that being said, let us look at a couple of “talking points” relating to in-memory databases for technical architects and database administrators:

• Can you be in-memory and ACID compliant? One of the keys to database management systems is the ability to be ACID compliant. This is an area where strictly in-memory data stores fall down. If all information is in memory and you have a failure of electricity, processor or hardware; the information is gone and the data store fails the “durability” aspect of ACID. This is where having a level of storage on disk becomes important. While you can have a pure in-memory data store, most in-memory database management systems have made accommodation for durability by using some form of spinning disk. Speaking of disk…
• Can you be in-memory and have rotating disks? As I mentioned above, ACID compliant in-memory databases need disks for durability. However, there are other uses for disk in an in-memory environment. I prefer that architects have the facilities to manage which data “lives” in memory and which can be placed on disk. As memory capacity on individual server hardware continues to grow, architects will have less need to manage hot-warm-cold data within in-memory databases. However, until then you need some form of disk.
• Does it matter how you mix your in-memory with your spinning, and non-spinning (aka flash), disks? Solid-state drive (SSD) drives provide an interesting option for in-memory database administrators and architects. For memory “challenged” environments, SSD has the ability to serve as the “warm” bridge between in-memory and spinning disk. However, in enterprise environments SSD presents constraints (aka a nice way to say limitations) that spinning disks don’t have. Architects should be aware of those issues before putting all their eggs in the SSD basket.

What say the readers?

Are in-memory databases on your radar? If so, are you concerned about the durability of in-memory processing? Do you consider an in-memory data store to be “pure” if there are disks in the architecture?

Provide your comments below and/or ping me via twitter at @JohnLMyers44 with the hashtag #100LinesOnBIDW.

In addition, I will be participating in a webinar on March 26 on the topic of comparing and contrasting in-memory database platforms. For more information, you can register here.

Next week, I will cover Business Analytics.

Thomas Jefferson once said:

“Do you want to know who you are? Don’t ask. Act! Action will delineate and define you.”

For Business Intelligence, this is a much under served concept. For many years, Business Intelligence has focused, quite rightly, on the analysis, or the prediction, but not as much the “call to action”. The infamous cartoon implies that to provide true business value, we need to be more “explicit” in how we link Action to our Intelligence:

Here are my thoughts on the state of Actionable Intelligence in the field of Business Intelligence for 2013:

• Alerts Via Email Aren’t Really Action: Much like the above cartoon, assuming that someone is “on the other side of the line” when you send an asynchronous email is using the “miracle occurs here” approach. Many platforms and solutions have built their action on these types of alert emails. Some have developed escalation rules to be more sophisticated, but overall it is still an asych email…
• Workflow is where it is AT: When you start adding in workflow management to analytical platforms, you are starting down the road of unrelated product conglomeration ( or empire… err… technical stack building… ) for business intelligence companies. Until you realize that, you NEED to complete the feedback loop with end users and business stakeholders to provide the next level of value. I hope to see more of related product diversification activity in 2013 from the industry either via partnership or feature/function inclusion.
• Collaboration Continues: The inclusion of collaboration features in business intelligence platforms is a good step on the path to Actionable Intelligence. However, Collaboration can often be interpreted as part of the decision process vs. being the action or result of the process. In our society driven by risk avoidance and millennial workers, we might be to guide the collaboration toward action. However…
• Dynamic vs. Static: …one of the big issues in collaboration is how to handle the ‘static’ view of what the system represented (i.e. a screenshot, snapshot of data) when a collaborative comment/question is made and what the systems ‘actively’ says (i.e. real-time dashboard, current view of data) when someone comments or answers the question. Static vs. Dynamic views of analytic or predictive results in collaboration functionality needs to be addressed since the disparity between those two ‘states’ can be the difference between action and a false positive. Unfortunately, I don’t have a strong answer to what the solution should be other than “yes”…
• Business Intelligence Becomes Process Intelligence? Too often Business Intelligence is mapped to a ‘data’-based as opposed to a ‘process’-based solution. By that, I mean the data is oriented on the data than the operational process that the data came from originally. This isn’t a major change, but a change in focus. Data should be acquired, stored and presented with the metadata of its process vs. stripping that information away and attempting to rebuild it later on.
• Actionable Intelligence needs its own BI: Just as we have metadata about data and often processes, we must have metadata about the results derived from analysis. We are closing the loop in a more meaningful way with predictive models. We should now close the loop on actions and their value to an organization. Having this metadata, and ability to analyze on actions, will feed into business optimization concepts like Operational Excellence.

What say the readers?

Is adding robust workflow into business intelligence platforms folly? Do you already link your business intelligence analysis to workflow platforms? If so, do your workflow platforms feedback to your analytic solutions? Have you mastered Operational Excellence without the feedback loop?

Provide your comments below and/or ping me via twitter at @JohnLMyers44 with the hashtag #100LinesOnBIDW.

Next week, I will take a more in depth look at a Data Management topic – The wonderful world of in-memory databases: Can you be in-memory and ACID compliant? Can you be in-memory and have rotating disks? Does it matter how you mix your in-memory with your spinning, and non-spinning (aka flash), disks?

NOTE – For those unfamiliar with the song “88Lines about 44 Women” by the Nails, I highly recommend you give it a try.  At the very least, it was the inspiration for this series of blogs.

“Imagination is more important than knowledge. For knowledge is limited to all we now know and understand, while imagination embraces the entire world, and all there ever will be to know and understand.”

And I think that the wonderful world of Knowledge Delivery (aka Data Visualization, Reporting, etc.) needs to think a little more like dear old Albert… The mainstream of Knowledge Delivery has adopted some strict rules, or grammar, associated with the presentation of information in dashboards, reports, etc. Some are great ideas. Some however, are beginning to dilute the adoption of Knowledge Delivery for the sake of the “prim and proper” presentation of the data.

If one of Knowledge Delivery’s main goals is to trigger a “call to action”, why then should be make it difficult to make a decision on that action. Knowledge Delivery, in certain instanced, has evolved into to PT Barnum’s “This Way to the Egress” sign. Overly complex? Yes. Too cute by half? Almost. Let’s give people the information that they need for that “call to action. And let’s do so in the most easily digested format possible with an “Exit” sign.

This is not to say that Knowledge Delivery should be dumbed down to the lowest possible denominator. No sir… However, each audience should be considered and addressed with the proper information and presentation format.

With that stated, here are my 2013 thoughts on the world of Knowledge Delivery:

• Infographics are here: USA Today is an interesting little publication. Since its inception, it has been a brightly colored and “simply” presented paper to fight the grim and gray national papers. While the USA Today isn’t in the same editorial or journalistic class as the NY Times, Wall Street Journal or Washington Post, it has made an impact on the way that papers are presented. Infographics are the USA Today of data visualization. They are bright, brassy and simple. And they are here to stay just like USA Today.
• Dashboards need to be more “inviting”: Because infographics are here to stay, dashboards are going to be pushed to provide a more inviting or interesting presentation layer. “Grammatically” precise visualizations, while accurate, will be like the “back half” of most annual reports. The interesting information is presented up front. The accurate numbers are in the back. Dashboards will need to add elements of the infographic to maintain their place or risk being relegated to the “appendix” of the Knowledge Delivery stack.
• Mobile visualization will blossom with enterprise tablet adoption: Smartphones are great devices. However, beyond looking at a pie chart or a “stoplight” graphic, you can’t put much on the screen of the average smartphone beyond one graphic. Much more and you are either straining the eye of the user or the imagination, by scrolling around with pinches and swipes. However, the tablet has/will unleash mobile business intelligence with its larger canvas. As enterprise tablets gain a stronger foothold, we’ll see the consumption and creation of mobile business intelligence take off…. Finally!
• Reports aren’t dead… But they are close: Statics reports ( ala the infamous TPS report ) aren’t dead quite yet. However, I dare say that they are in the same place as print magazines and newspapers…. There is a limited and aging audience for static reports. With all of the advances in Knowledge Delivery over the last 3-5 years, static reports should be placed in a glass case that says, “Break only in the event of SEC or IRS request”.
• 3D pie charts aren’t bad… They are just drawn that way: 3D pie charts aren’t the evil that most people think. They are just a misunderstood graphic. Sure, they offend the “grammar” police of data visualization, like ending a sentence with a preposition or an ampersand in a business document. However, if they are bright, get adoption and move people to act, who cares what they are made of? Or, that they make the slices in the back look proportionately smaller & those in front that much bigger…. Remember this is the age of the infographic! We can let some things slide.

What say the readers?

Are infographics the knowledge graffiti of the 2010s? Will I pry your TPS reports from your cold dead fingers? Do 3D pie charts make your skin crawl like fingers on chalkboard?

Provide your comments below and/or ping me via twitter at @JohnLMyers44 with the hashtag #100LinesOnBIDW.

Next week, I will cover Actionable Intelligence…. I hope you continue the journey with me.

NOTE – For those unfamiliar with the song “88Lines about 44 Women” by the Nails, I highly recommend you give it a try. At the very least, it was the inspiration for this series of blogs.

The purpose of science is not to analyze or describe but to make useful models of the world. A model is useful if it allows us to get use out of it.

Business Analytics is very similar to this concept of science, and specifically, this quote applies to the concept of ‘advanced analytical’ models associated with predictive and descriptive analytics. Looking at the domain of Business Analytics in the EMA Business Intelligence Continuum, you can see that it is the middle component. Business Analytics builds on the data acquired from other platforms and often requires that data to be managed in some fashion.

I have used the EMA Business Analytics Pyramid to frame my comments. As you can see, the Business Analytics Pyramid builds from “unassuming” concepts such as “simple analytics” to the top of the pyramid and machine learning and natural language processing.

The following are my thoughts on the state of Business Analytics for 2013:

• It Is Not All About Predictive…: If you read about “advanced analytics”, you might just think that all analytics are predictive or machine learning. This is not the case… However, most of the ‘simple analytics’ that we are familiar with (i.e. COUNTs, SUMs, SORTs, GROUPBYs) are moving to dashboards as ‘right click’ features of other products. While you can still do your analytics with a 3D pie chart for top-ten customer percentage, soon that will be considered “old school” and using Naïve-Bayes to determine the likelihood of your potential customers to join that top-10 will be considered “nu skool”.
• Cubes Headed Out to Pasture: The wonderful OLAP cube always has been a cheat. Just as much as the spitball in baseball or holding in football. Cubes were designed and built because database technology couldn’t do its data day job AND all those aggregates. I firmly believe that between massively parallel processing, in-memory database and columnar storage technologies used separately or in various combinations, the OLAP cube will soon join the Rubik’s cube in the hall of stuff we used to use and now don’t remember why…
• Forecasting Is Not in a Slider: As we push more analytics into the category of self-service Business Intelligence, we need to give forecasting tools more imagination than a “slider”. This will only push folks back to the Excel spreadsheet where they have more control over variables and scenarios. However, forecasting can’t be so complex that the average user(s) cannot figure it out. Again, that will push more folks toward breaking with a central platform for the spreadmart.
• Advanced Analytics Are Here! As I said above, advanced analytics in the form of descriptive modeling, predictive analytics, and machine learning is here and in a form that business users can use. No longer are advanced analytics locked in a “vi editor” ( talk about old school… ) and a programming extension to the database engine. Advanced analytical features and products now have GUIs and wizards to help business analysts start down the path to data scientist-dom.
• More “Creepy” Will Accompany the “Cool”: Some of the best uses of analytics will be in those areas that sound “cool” until we think about how invasive they could be if used in the wrong hands. That’s the “creepy” factor – see Target and Benetton.  This is where ethics and the effective use of anonymization techniques will help to avoid …. err… minimize the “creepy” factors.

What say the readers?

Do you really like and want to keep your OLAP cubes? Is predicting importance of ‘advanced analytics’ like hitting water if I fall out of a boat? Do I trust companies too much with the “creepy” factors associated with privacy?

Provide your comments below and/or ping me via twitter at @JohnLMyers44 with the hashtag #100LinesOnBIDW.

Next week, I will cover Knowledge Delivery and the future of the 3D pie chart…. ( not that anyone would have strong feelings about that topic… )  I hope you continue the journey with me.

NOTE – For those unfamiliar with the song “88 Lines about 44 Women” by the Nails, I highly recommend you give it a try. At the very least, it was the inspiration for this series of blogs.

“How liars are not rewarded: even if they tell the truth, no one believes them”

If analysts like myself and others as well as the mainstream press (ie Forbes, Harvard Business Review, Economist ) continue to shout about Big Data in Data Management and no one sees Big Data change, will we run out of credibility?

For 2013, I am saying that Data Management will be the most impacted area of the EMA Business Intelligence Continuum by the “wolf” that is Big Data…. And it’s a pretty big wolf.

• Big data isn’t so big…. But, it is complex: We have all been inundated with information about Big Data and how Big it is. Well, in the EMA Study regarding Big Data last year, end users across multiple industries said that Big Data wasn’t SOOO big that it couldn’t be handled by firms not named Facebook, WalMart, and CitiGroup. What EMA did learn was that Big Data was more complex than most thought, with various data sources and data management platforms playing a role in a new ecosystem.
• Relational isn’t dead: Just as the 2010-2012 news cycle might have you believe that Big Data was the only thing and that unstructured/multi-structured data was the only data associated with Big Data, you might have gotten the impression that relational/structured data was a thing of the past. That simply isn’t true. Structured relational data will continue to have a strong role in data management. However, it will no longer be the center of the data management world. It will be a partner to multi-structured data.
• Data governance MUST be redefined: Data governance is a favorite topic of mine. It can mean many things to many people. It can be Data Quality as in proper values by row and column. It can be Data Stewardship in terms of how metadata is managed and expressed. However, with multi-structured data and NoSQL becoming such a prominent force in data management, we need to change how Data governance is defined. We need to focus on data lineage or where data came from and its freshness as opposed to the values within row, columns or records. We need to focus on understanding data as it comes into data management platforms as opposed to keeping out only the “blessed” data.
• Schemas must be flexible: With the concept that data will have variable data structures when it is created and accepted into data management platforms, the schemas that support SQL and other structured access methods will need to be flexible vs rigid. Schemas were once used as gatekeeping devices to keep unworthy data out of relational data management platforms. Now schemas must move from the gatekeeper role to the end analytical lens role. We have the ability to process data on the fly and apply schemas later and later in the process. We need to have flexibility to make that happen and speed the implementation of operational and analytical processing.
• Data modelers must get into productization and less art: With the previous two trends for data management in 2013, the teams that will have the most adaption placed on them will be our data modelers. Just as data modeling underwent (and is still undergoing) a massive upheaval in the move from 3NF operational schemas to denormalized analytical schemas, we will ask data modelers to forsake a “perfect” living model for a series of “good enough” productized models. This may take some of the art out of data modeling in trade for speed of implementation of an iterative and componentized set of data models.

What say the readers?

Has big data been cried “wolf” too many times? Would your data stewards sooner quit than allow uncleansed data in your environments? Will data modelers accept changes to their job similar to what programmers did with object oriented coding? ETL developers did with the move away from hand coding?

Provide your comments below and/or ping me via twitter at @JohnLMyers44 with the hashtag #100LinesOnBIDW.

Next week, I will cover Business Analytics…. I hope you continue the journey with me.

NOTE – For those unfamiliar with the song “88Lines about 44 Women” by the Nails, I highly recommend you give it a try. At the very least, it was the inspiration for this series of blogs.

Give me a museum and I’ll fill it.

Data Integration in 2013 seems to follow along similar lines – Give us a Platform and we will fill it with data.

This is part two of a series of blog postings on important topics for the world of business intelligence and data warehousing for 2013. While I wouldn’t call it my “predictions” for 2013, it is a listing of important topics and concepts for the domains of the EMA Business Intelligence Continuum.

This week’s installment is on Data Integration, which is the beginning of all environments – operational and analytical. And, as I stated last week – data being the new oil of business – the integration with various data sources will be the key to creating impactful and value-added business intelligence environments. Here are my thoughts for 2013 on bringing that “oil” into BI/DW.

• Data Integration is Becoming Standardized: Before the release of the Porsche Cayenne, I used to opine that “You could in fact tow a boat with a Porsche (then 911), but why would you want to?” The same applies to the world of data integration. In the past, hand coding in PERL and other languages was an option. However, I believe that for most organizations, toolsets that encapsulate hand coding from ETL professionals and others managing data integration environments should become the minimum standard.
• Let All The Data In: As we fully mature into the era of Big Data, the decision to allow only “blessed” data into our business intelligence and data warehousing environments will be a mistake. There will be too many business opportunities missed by using gating factors to keep certain data out of analytical environments. We should let all the data in and let the users, data scientists, and business analysts make the decision on the value of the data.
• Data Virtualization and ETL Need to Work Together: Lately, I have been teaching a class on Data Virtualization. Data Virtualization is nothing new, but it is making a comeback (DV vendors would probably prefer that, like LL Cool J, I don’t call it a comeback…) However, this comeback has put ETL professionals on edge. They view DV as a threat. However, these are complementary technologies that can and should be used together to maximize the value of data across the enterprise.
• Not Enough Room or Bandwidth is No Longer an Excuse: As mentioned above, all the data should be brought into our analytical environments, and not just the “blessed” versions. Since disk space and connectivity bandwidth prices have fallen and technology hurdles have been overcome, data integration teams can no longer reasonably use “disk space” or “bandwidth” as excuses to limit their activities. They should embrace the capacity they now enjoy to say “yes” rather than “no”.
• If You Thought this Crop of Big Data was Big…. : Back in the day, transactional data from the WalMart point of sale (POS) system or telecom networks was the original “Big Data”. We then linked people together with social platforms and we got “bigger” Big Data. HAH! Those two sources will be relatively small when we link the objects of our lives (i.e. cars, fridges, houses, cell phones, vans, etc.) together. Whether you call it “Internet of Things” or “Machine to Machine” (M2M), number, diversity and load from those data sources will really make data integration “fun”…errr… challenging.

What say the readers?

Have I missed something with hand coding in the age of Hadoop? Have I put too much stock on Big Data and not enough on Data Quality? Am I over-hyping the information in all that sensor data (noise vs signal)?

Provide your comments below and/or ping me via twitter at @JohnLMyers44 with the hashtag #100LinesOnBIDW.

Next week, I will cover Data Management. I hope you continue the journey with me.

NOTE – For those unfamiliar with the song “88 Lines about 44 Women” by the Nails, I highly recommend you give it a try. At the very least, it was the inspiration for this series of blogs.

These topics will follow the “domains” and flow of the EMA Business Intelligence Continuum:

• Data Acquisition: Bringing data into a business intelligence and data warehousing environment is the key to any analytical platform as I (as well as EMA’s BI/DW practice) consider data to the new oil for business. This also applies to integrating data for operational platforms such as enterprise resource planning (ERP) and customer relationship management (CRM)
• Data Management: Once you have that data in a particular environment or ecosystem, you need to manage that data. Whether it be “management” from a storage perspective (aka database management systems) or “management” from a governance perspective (aka data governance and data stewardship), this is an important area that will see growth in 2013.
• Business Analytics: Following on the “data is the new oil” concept from above, what you do with the data once you have it in your environment and under management/governance is as important as is refining oil into various petroleum products such as gasoline and jet fuel. Without that step, the data is just that…. data.
• Knowledge Delivery: While this particular “data geek” hates to admit it, you have to be oriented to a particular dataset to really get into the information. However, many folks either don’t have the time or the inclination to get invested in the tabular format of the data they are investigating. This is where the world of data visualization – reports, dashboards, etc. comes to play.
• Actionable Intelligence: You have analyzed the data. You have presented the data. Now what? If you don’t have a “call to action” or a process that can be impacted by the information in your data, have you really accomplished anything? Probably not. Moving decisions from “reading dashboard” and “send email” is an important and neglected area of BI/DW.

Now, am I going to stake the “pink slip” for my car on these predictions coming true in 2013? Probably not…. However, these are the topics that I think are important in these particular domains and should be discussed.

 

As such what do the readers think? Are these good divisions to discuss? Have I missed the “projections for 2013” bandwagon with a mid-January release?

I will start my series next week with Data Integration. Come along for the journey.

Provide your comments below and/or ping me via twitter at @JohnLMyers44 with the hashtag #100LinesOnBIDW. FYI – The lines per topic are as I put them in my text editor… Just like gas mileage, your line count results may vary….

NOTE – As I write this, I might just be writing 120 lines total including the descriptors above…. But who’s counting?

• merely a technology definition
• solely Hadoop implementations
• just petabyte and above
• only used by Data Scientists

Big Data is maturing to the point where business use cases are becoming more important than technological platforms.

In fact, if there was a single takeaway from the EMA / 9sight Big Data Research findings, it is that there is now an ecosystem of platforms serving Big Data requirements. This is as opposed to the concept of a single technology platform that can handle all of the requirements in a Big Data environment. This maturing of the marketplace and developing nature of the business requirements for Big Data initiatives have resulted in what EMA calls the Hybrid Data Ecosystem.

The technical aspects of Hybrid Data Ecosystem have been in place for some time. However, the business applications of Big Data initiatives are just beginning to develop and be adopted by a wider set of industries. It is clear that there are certain industry segments that are leading the way for Big Data implementations. Innovators include Media and PR, Retail and Leisure. The Finance and Industrial industry segments exemplify the Early Adopters. Yet, it is the Early Majority and Late Majority Adopters who will be looking not just for technical implementations, but also for specific and attainable business use cases to support their implementations.

Join Shawn Rogers, Barry Devlin and me on December 13th for more information about the Big Data research results and for a live “Q and A” session with your questions about Big Data.

Last September, Forbes Magazine published its 2011 ranking of the NFL’s Most Valuable Teams. Forbes gives detailed financial information on why NFL franchise values are “only” growing by 1.4%. The article notes that operating costs for NFL franchises are rising. This increase is due to:

• Increased costs due to stadium management and employee expenses
• Expanded marketing expense due to poor economic conditions

These increases are not massive issues for NFL teams when you look at them in isolation. However, when you link these changes in operating costs with recent drops in attendance at NFL games and associated operating revenue – two trends that negatively impact team and/or stadium operating margin emerge. For majority of NFL teams and stadium operators, this is a situation that requires attention.

Home Game vs. Games at Home

Exacerbating the operating margin problem is the trend of fans staying home to watch games. At one time, the best “game experience” was at the stadium. This is no longer the case. Three technology advances have served to “encourage” fans to stay home:

• Advances in HDTV video quality and availability
• Access to near real-time replay and scoring information via cable/satellite and internet video
• Popularity of on-demand fantasy football scoring web updates

NFL teams and stadium operators are looking for ways to combat these advances and raise stadium operating revenues. These include ticket revenue, parking and food and beverage concessions. To complicate these matters, teams and stadium operators are up against competing against some of their own league-branded offerings. For example, NFL RedZone and NFL.com Fantasy Football are two examples.

Miami’s Approach

The Miami Dolphins and Sun Life Stadium are attempting to improve their operating margins via heightening their fans’ customer experience by optimizing stadium operations. Sun Life Stadium is implementing IBM’s Intelligence Operations Center solution. The IBM IOC will enable the Dolphins and Sun Life Stadium to manage traffic congestion into and out of stadium parking lots; anticipate chokepoints at stadium entrances; and manage food and beverage distribution to make the fans’ experience worth the added time and dollar investment of coming to the stadium.

Cloud’s Silver Lining

With the operational margin and customer experience challenges, Miami has an additional issue. With the move to a new stadium and the re-branding of the Florida Marlins (now Miami Marlins), Sun Life Stadium is attempting to improve operating margins even though it has lost over 80 event dates against which to balance operational improvement investments. To combat this situation, Sun Life Stadium has chosen a cloud-based IBM IOC implementation that allows Sun Life Stadium to better manage its technology costs per event. Instead of 90+ major sporting events (10 NFL pre- and regular season / 81-83 MLB pre- and regular season), 2012 will feature only 10 scheduled NFL sporting events (not including post-season). Utilizing cloud-based operational expense models will lower fixed technology expenses. This will be critical to avoid excessive focus on implementation infrastructure costs in the form of servers and licensed software and not enough emphasis on the results of fan experience improvements.

Associated EMA IBM IOC Content:

Focus On The Forest Rather Than The Trees: IBM Smarter Cities China

Related articles

• Miami Dolphins Score With Concerted Mobile Effort (mobilemarketingwatch.com)
• Miami Dolphins Dive Into Cloud Analytics (pcworld.com)

Maturing NoSQL Product Features

However, it seems that at least for now, the NoSQL vendors are trying to avoid a head-to-head battle between business and IT by making their product offerings more “compliant” with existing IT standards and integration stacks. Here are some examples:

• Traditional RDBMS or Analytical players integrating NoSQL (read unstructured) data access into their existing SQL structures to utilize existing connectivity infrastructures
• Rising importance of ACID database characteristics
• Development of management consoles to remove the “manual” aspects of NoSQL management. The interfaces on these management consoles are looking more like what existing DBAs expect from traditional RDBMS management features.

Are NoSQL platforms losing their “developer” identity and becoming traditional RDBMS but with a different data storage layer?

Or

Are NoSQL platforms maturing to meet the needs of IT department data management standards?

Post your comments below or ping me via twitter ( @JohnLMyers44 ) me directly.

Related EMA Content

Non-Sequitor in NoSQL
NoSQL Implementation Drivers